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• Big Data – Basics of Big Data Analytics – Day 18 of 21

  - by Pinal Dave

  In yesterday’s blog post we learned the importance of the various components in Big Data Story. In this article we will understand what are the various analytics tasks we try to achieve with the Big Data and the list of the important tools in Big Data Story. When you have plenty of the data around you what is the first thing which comes to your mind? “What do all these data means?” Exactly – the same thought comes to my mind as well. I always wanted to know what all the data means and what meaningful information I can receive out of it. Most of the Big Data projects are built to retrieve various intelligence all this data contains within it. Let us take example of Facebook. When I look at my friends list of Facebook, I always want to ask many questions such as - On which date my maximum friends have a birthday? What is the most favorite film of my most of the friends so I can talk about it and engage them? What is the most liked placed to travel my friends? Which is the most disliked cousin for my friends in India and USA so when they travel, I do not take them there. There are many more questions I can think of. This illustrates that how important it is to have analysis of Big Data. Here are few of the kind of analysis listed which you can use with Big Data. Slicing and Dicing: This means breaking down your data into smaller set and understanding them one set at a time. This also helps to present various information in a variety of different user digestible ways. For example if you have data related to movies, you can use different slide and dice data in various formats like actors, movie length etc. Real Time Monitoring: This is very crucial in social media when there are any events happening and you wanted to measure the impact at the time when the event is happening. For example, if you are using twitter when there is a football match, you can watch what fans are talking about football match on twitter when the event is happening. Anomaly Predication and Modeling: If the business is running normal it is alright but if there are signs of trouble, everyone wants to know them early on the hand. Big Data analysis of various patterns can be very much helpful to predict future. Though it may not be always accurate but certain hints and signals can be very helpful. For example, lots of data can help conclude that if there is lots of rain it can increase the sell of umbrella. Text and Unstructured Data Analysis: unstructured data are now getting norm in the new world and they are a big part of the Big Data revolution. It is very important that we Extract, Transform and Load the unstructured data and make meaningful data out of it. For example, analysis of lots of images, one can predict that people like to use certain colors in certain months in their cloths. Big Data Analytics Solutions There are many different Big Data Analystics Solutions out in the market. It is impossible to list all of them so I will list a few of them over here. Tableau – This has to be one of the most popular visualization tools out in the big data market. SAS – A high performance analytics and infrastructure company IBM and Oracle – They have a range of tools for Big Data Analysis Tomorrow In tomorrow’s blog post we will discuss about very important components of the Big Data Ecosystem – Data Scientist. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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• Big Data – What is Big Data – 3 Vs of Big Data – Volume, Velocity and Variety – Day 2 of 21

  - by Pinal Dave

  Data is forever. Think about it – it is indeed true. Are you using any application as it is which was built 10 years ago? Are you using any piece of hardware which was built 10 years ago? The answer is most certainly No. However, if I ask you – are you using any data which were captured 50 years ago, the answer is most certainly Yes. For example, look at the history of our nation. I am from India and we have documented history which goes back as over 1000s of year. Well, just look at our birthday data – atleast we are using it till today. Data never gets old and it is going to stay there forever.  Application which interprets and analysis data got changed but the data remained in its purest format in most cases. As organizations have grown the data associated with them also grew exponentially and today there are lots of complexity to their data. Most of the big organizations have data in multiple applications and in different formats. The data is also spread out so much that it is hard to categorize with a single algorithm or logic. The mobile revolution which we are experimenting right now has completely changed how we capture the data and build intelligent systems.  Big organizations are indeed facing challenges to keep all the data on a platform which give them a  single consistent view of their data. This unique challenge to make sense of all the data coming in from different sources and deriving the useful actionable information out of is the revolution Big Data world is facing. Defining Big Data The 3Vs that define Big Data are Variety, Velocity and Volume. Volume We currently see the exponential growth in the data storage as the data is now more than text data. We can find data in the format of videos, musics and large images on our social media channels. It is very common to have Terabytes and Petabytes of the storage system for enterprises. As the database grows the applications and architecture built to support the data needs to be reevaluated quite often. Sometimes the same data is re-evaluated with multiple angles and even though the original data is the same the new found intelligence creates explosion of the data. The big volume indeed represents Big Data. Velocity The data growth and social media explosion have changed how we look at the data. There was a time when we used to believe that data of yesterday is recent. The matter of the fact newspapers is still following that logic. However, news channels and radios have changed how fast we receive the news. Today, people reply on social media to update them with the latest happening. On social media sometimes a few seconds old messages (a tweet, status updates etc.) is not something interests users. They often discard old messages and pay attention to recent updates. The data movement is now almost real time and the update window has reduced to fractions of the seconds. This high velocity data represent Big Data. Variety Data can be stored in multiple format. For example database, excel, csv, access or for the matter of the fact, it can be stored in a simple text file. Sometimes the data is not even in the traditional format as we assume, it may be in the form of video, SMS, pdf or something we might have not thought about it. It is the need of the organization to arrange it and make it meaningful. It will be easy to do so if we have data in the same format, however it is not the case most of the time. The real world have data in many different formats and that is the challenge we need to overcome with the Big Data. This variety of the data represent  represent Big Data. Big Data in Simple Words Big Data is not just about lots of data, it is actually a concept providing an opportunity to find new insight into your existing data as well guidelines to capture and analysis your future data. It makes any business more agile and robust so it can adapt and overcome business challenges. Tomorrow In tomorrow’s blog post we will try to answer discuss Evolution of Big Data. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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• The Data Scientist

  - by BuckWoody

  A new term - well, perhaps not that new - has come up and I’m actually very excited about it. The term is Data Scientist, and since it’s new, it’s fairly undefined. I’ll explain what I think it means, and why I’m excited about it. In general, I’ve found the term deals at its most basic with analyzing data. Of course, we all do that, and the term itself in that definition is redundant. There is no science that I know of that does not work with analyzing lots of data. But the term seems to refer to more than the common practices of looking at data visually, putting it in a spreadsheet or report, or even using simple coding to examine data sets. The term Data Scientist (as far as I can make out this early in it’s use) is someone who has a strong understanding of data sources, relevance (statistical and otherwise) and processing methods as well as front-end displays of large sets of complicated data. Some - but not all - Business Intelligence professionals have these skills. In other cases, senior developers, database architects or others fill these needs, but in my experience, many lack the strong mathematical skills needed to make these choices properly. I’ve divided the knowledge base for someone that would wear this title into three large segments. It remains to be seen if a given Data Scientist would be responsible for knowing all these areas or would specialize. There are pretty high requirements on the math side, specifically in graduate-degree level statistics, but in my experience a company will only have a few of these folks, so they are expected to know quite a bit in each of these areas. Persistence The first area is finding, cleaning and storing the data. In some cases, no cleaning is done prior to storage - it’s just identified and the cleansing is done in a later step. This area is where the professional would be able to tell if a particular data set should be stored in a Relational Database Management System (RDBMS), across a set of key/value pair storage (NoSQL) or in a file system like HDFS (part of the Hadoop landscape) or other methods. Or do you examine the stream of data without storing it in another system at all? This is an important decision - it’s a foundation choice that deals not only with a lot of expense of purchasing systems or even using Cloud Computing (PaaS, SaaS or IaaS) to source it, but also the skillsets and other resources needed to care and feed the system for a long time. The Data Scientist sets something into motion that will probably outlast his or her career at a company or organization. Often these choices are made by senior developers, database administrators or architects in a company. But sometimes each of these has a certain bias towards making a decision one way or another. The Data Scientist would examine these choices in light of the data itself, starting perhaps even before the business requirements are created. The business may not even be aware of all the strategic and tactical data sources that they have access to. Processing Once the decision is made to store the data, the next set of decisions are based around how to process the data. An RDBMS scales well to a certain level, and provides a high degree of ACID compliance as well as offering a well-known set-based language to work with this data. In other cases, scale should be spread among multiple nodes (as in the case of Hadoop landscapes or NoSQL offerings) or even across a Cloud provider like Windows Azure Table Storage. In fact, in many cases - most of the ones I’m dealing with lately - the data should be split among multiple types of processing environments. This is a newer idea. Many data professionals simply pick a methodology (RDBMS with Star Schemas, NoSQL, etc.) and put all data there, regardless of its shape, processing needs and so on. A Data Scientist is familiar not only with the various processing methods, but how they work, so that they can choose the right one for a given need. This is a huge time commitment, hence the need for a dedicated title like this one. Presentation This is where the need for a Data Scientist is most often already being filled, sometimes with more or less success. The latest Business Intelligence systems are quite good at allowing you to create amazing graphics - but it’s the data behind the graphics that are the most important component of truly effective displays. This is where the mathematics requirement of the Data Scientist title is the most unforgiving. In fact, someone without a good foundation in statistics is not a good candidate for creating reports. Even a basic level of statistics can be dangerous. Anyone who works in analyzing data will tell you that there are multiple errors possible when data just seems right - and basic statistics bears out that you’re on the right track - that are only solvable when you understanding why the statistical formula works the way it does. And there are lots of ways of presenting data. Sometimes all you need is a “yes” or “no” answer that can only come after heavy analysis work. In that case, a simple e-mail might be all the reporting you need. In others, complex relationships and multiple components require a deep understanding of the various graphical methods of presenting data. Knowing which kind of chart, color, graphic or shape conveys a particular datum best is essential knowledge for the Data Scientist. Why I’m excited I love this area of study. I like math, stats, and computing technologies, but it goes beyond that. I love what data can do - how it can help an organization. I’ve been fortunate enough in my professional career these past two decades to work with lots of folks who perform this role at companies from aerospace to medical firms, from manufacturing to retail. Interestingly, the size of the company really isn’t germane here. I worked with one very small bio-tech (cryogenics) company that worked deeply with analysis of complex interrelated data. So  watch this space. No, I’m not leaving Azure or distributed computing or Microsoft. In fact, I think I’m perfectly situated to investigate this role further. We have a huge set of tools, from RDBMS to Hadoop to allow me to explore. And I’m happy to share what I learn along the way.

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• Fraud Detection with the SQL Server Suite Part 2

  - by Dejan Sarka

  This is the second part of the fraud detection whitepaper. You can find the first part in my previous blog post about this topic. My Approach to Data Mining Projects It is impossible to evaluate the time and money needed for a complete fraud detection infrastructure in advance. Personally, I do not know the customer’s data in advance. I don’t know whether there is already an existing infrastructure, like a data warehouse, in place, or whether we would need to build one from scratch. Therefore, I always suggest to start with a proof-of-concept (POC) project. A POC takes something between 5 and 10 working days, and involves personnel from the customer’s site – either employees or outsourced consultants. The team should include a subject matter expert (SME) and at least one information technology (IT) expert. The SME must be familiar with both the domain in question as well as the meaning of data at hand, while the IT expert should be familiar with the structure of data, how to access it, and have some programming (preferably Transact-SQL) knowledge. With more than one IT expert the most time consuming work, namely data preparation and overview, can be completed sooner. I assume that the relevant data is already extracted and available at the very beginning of the POC project. If a customer wants to have their people involved in the project directly and requests the transfer of knowledge, the project begins with training. I strongly advise this approach as it offers the establishment of a common background for all people involved, the understanding of how the algorithms work and the understanding of how the results should be interpreted, a way of becoming familiar with the SQL Server suite, and more. Once the data has been extracted, the customer’s SME (i.e. the analyst), and the IT expert assigned to the project will learn how to prepare the data in an efficient manner. Together with me, knowledge and expertise allow us to focus immediately on the most interesting attributes and identify any additional, calculated, ones soon after. By employing our programming knowledge, we can, for example, prepare tens of derived variables, detect outliers, identify the relationships between pairs of input variables, and more, in only two or three days, depending on the quantity and the quality of input data. I favor the customer’s decision of assigning additional personnel to the project. For example, I actually prefer to work with two teams simultaneously. I demonstrate and explain the subject matter by applying techniques directly on the data managed by each team, and then both teams continue to work on the data overview and data preparation under our supervision. I explain to the teams what kind of results we expect, the reasons why they are needed, and how to achieve them. Afterwards we review and explain the results, and continue with new instructions, until we resolve all known problems. Simultaneously with the data preparation the data overview is performed. The logic behind this task is the same – again I show to the teams involved the expected results, how to achieve them and what they mean. This is also done in multiple cycles as is the case with data preparation, because, quite frankly, both tasks are completely interleaved. A specific objective of the data overview is of principal importance – it is represented by a simple star schema and a simple OLAP cube that will first of all simplify data discovery and interpretation of the results, and will also prove useful in the following tasks. The presence of the customer’s SME is the key to resolving possible issues with the actual meaning of the data. We can always replace the IT part of the team with another database developer; however, we cannot conduct this kind of a project without the customer’s SME. After the data preparation and when the data overview is available, we begin the scientific part of the project. I assist the team in developing a variety of models, and in interpreting the results. The results are presented graphically, in an intuitive way. While it is possible to interpret the results on the fly, a much more appropriate alternative is possible if the initial training was also performed, because it allows the customer’s personnel to interpret the results by themselves, with only some guidance from me. The models are evaluated immediately by using several different techniques. One of the techniques includes evaluation over time, where we use an OLAP cube. After evaluating the models, we select the most appropriate model to be deployed for a production test; this allows the team to understand the deployment process. There are many possibilities of deploying data mining models into production; at the POC stage, we select the one that can be completed quickly. Typically, this means that we add the mining model as an additional dimension to an existing DW or OLAP cube, or to the OLAP cube developed during the data overview phase. Finally, we spend some time presenting the results of the POC project to the stakeholders and managers. Even from a POC, the customer will receive lots of benefits, all at the sole risk of spending money and time for a single 5 to 10 day project: The customer learns the basic patterns of frauds and fraud detection The customer learns how to do the entire cycle with their own people, only relying on me for the most complex problems The customer’s analysts learn how to perform much more in-depth analyses than they ever thought possible The customer’s IT experts learn how to perform data extraction and preparation much more efficiently than they did before All of the attendees of this training learn how to use their own creativity to implement further improvements of the process and procedures, even after the solution has been deployed to production The POC output for a smaller company or for a subsidiary of a larger company can actually be considered a finished, production-ready solution It is possible to utilize the results of the POC project at subsidiary level, as a finished POC project for the entire enterprise Typically, the project results in several important “side effects” Improved data quality Improved employee job satisfaction, as they are able to proactively contribute to the central knowledge about fraud patterns in the organization Because eventually more minds get to be involved in the enterprise, the company should expect more and better fraud detection patterns After the POC project is completed as described above, the actual project would not need months of engagement from my side. This is possible due to our preference to transfer the knowledge onto the customer’s employees: typically, the customer will use the results of the POC project for some time, and only engage me again to complete the project, or to ask for additional expertise if the complexity of the problem increases significantly. I usually expect to perform the following tasks: Establish the final infrastructure to measure the efficiency of the deployed models Deploy the models in additional scenarios Through reports By including Data Mining Extensions (DMX) queries in OLTP applications to support real-time early warnings Include data mining models as dimensions in OLAP cubes, if this was not done already during the POC project Create smart ETL applications that divert suspicious data for immediate or later inspection I would also offer to investigate how the outcome could be transferred automatically to the central system; for instance, if the POC project was performed in a subsidiary whereas a central system is available as well Of course, for the actual project, I would repeat the data and model preparation as needed It is virtually impossible to tell in advance how much time the deployment would take, before we decide together with customer what exactly the deployment process should cover. Without considering the deployment part, and with the POC project conducted as suggested above (including the transfer of knowledge), the actual project should still only take additional 5 to 10 days. The approximate timeline for the POC project is, as follows: 1-2 days of training 2-3 days for data preparation and data overview 2 days for creating and evaluating the models 1 day for initial preparation of the continuous learning infrastructure 1 day for presentation of the results and discussion of further actions Quite frequently I receive the following question: are we going to find the best possible model during the POC project, or during the actual project? My answer is always quite simple: I do not know. Maybe, if we would spend just one hour more for data preparation, or create just one more model, we could get better patterns and predictions. However, we simply must stop somewhere, and the best possible way to do this, according to my experience, is to restrict the time spent on the project in advance, after an agreement with the customer. You must also never forget that, because we build the complete learning infrastructure and transfer the knowledge, the customer will be capable of doing further investigations independently and improve the models and predictions over time without the need for a constant engagement with me.

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• Big Data – Beginning Big Data Series Next Month in 21 Parts

  - by Pinal Dave

  Big Data is the next big thing. There was a time when we used to talk in terms of MB and GB of the data. However, the industry is changing and we are now moving to a conversation where we discuss about data in Petabyte, Exabyte and Zettabyte. It seems that the world is now talking about increased Volume of the data. In simple world we all think that Big Data is nothing but plenty of volume. In reality Big Data is much more than just a huge volume of the data. When talking about the data we need to understand about variety and volume along with volume. Though Big data look like a simple concept, it is extremely complex subject when we attempt to start learning the same. My Journey I have recently presented on Big Data in quite a few organizations and I have received quite a few questions during this roadshow event. I have collected all the questions which I have received and decided to post about them on the blog. In the month of October 2013, on every weekday we will be learning something new about Big Data. Every day I will share a concept/question and in the same blog post we will learn the answer of the same. Big Data – Plenty of Questions I received quite a few questions during my road trip. Here are few of the questions. I want to learn Big Data – where should I start? Do I need to know SQL to learn Big Data? What is Hadoop? There are so many organizations talking about Big Data, and every one has a different approach. How to start with big Data? Do I need to know Java to learn about Big Data? What is different between various NoSQL languages. I will attempt to answer most of the questions during the month long series in the next month. Big Data – Big Subject Big Data is a very big subject and I no way claim that I will be covering every single big data concept in this series. However, I promise that I will be indeed sharing lots of basic concepts which are revolving around Big Data. We will discuss from fundamentals about Big Data and continue further learning about it. I will attempt to cover the concept so simple that many of you might have wondered about it but afraid to ask. Your Role! During this series next month, I need your one help. Please keep on posting questions you might have related to big data as blog post comments and on Facebook Page. I will monitor them closely and will try to answer them as well during this series. Now make sure that you do not miss any single blog post in this series as every blog post will be linked to each other. You can subscribe to my feed or like my Facebook page or subscribe via email (by entering email in the blog post). Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Big Data, PostADay, SQL, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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• Big Data – Role of Cloud Computing in Big Data – Day 11 of 21

  - by Pinal Dave

  In yesterday’s blog post we learned the importance of the NewSQL. In this article we will understand the role of Cloud in Big Data Story What is Cloud? Cloud is the biggest buzzword around from last few years. Everyone knows about the Cloud and it is extremely well defined online. In this article we will discuss cloud in the context of the Big Data. Cloud computing is a method of providing a shared computing resources to the application which requires dynamic resources. These resources include applications, computing, storage, networking, development and various deployment platforms. The fundamentals of the cloud computing are that it shares pretty much share all the resources and deliver to end users as a service.  Examples of the Cloud Computing and Big Data are Google and Amazon.com. Both have fantastic Big Data offering with the help of the cloud. We will discuss this later in this blog post. There are two different Cloud Deployment Models: 1) The Public Cloud and 2) The Private Cloud Public Cloud Public Cloud is the cloud infrastructure build by commercial providers (Amazon, Rackspace etc.) creates a highly scalable data center that hides the complex infrastructure from the consumer and provides various services. Private Cloud Private Cloud is the cloud infrastructure build by a single organization where they are managing highly scalable data center internally. Here is the quick comparison between Public Cloud and Private Cloud from Wikipedia:   Public Cloud Private Cloud Initial cost Typically zero Typically high Running cost Unpredictable Unpredictable Customization Impossible Possible Privacy No (Host has access to the data Yes Single sign-on Impossible Possible Scaling up Easy while within defined limits Laborious but no limits Hybrid Cloud Hybrid Cloud is the cloud infrastructure build with the composition of two or more clouds like public and private cloud. Hybrid cloud gives best of the both the world as it combines multiple cloud deployment models together. Cloud and Big Data – Common Characteristics There are many characteristics of the Cloud Architecture and Cloud Computing which are also essentially important for Big Data as well. They highly overlap and at many places it just makes sense to use the power of both the architecture and build a highly scalable framework. Here is the list of all the characteristics of cloud computing important in Big Data Scalability Elasticity Ad-hoc Resource Pooling Low Cost to Setup Infastructure Pay on Use or Pay as you Go Highly Available Leading Big Data Cloud Providers There are many players in Big Data Cloud but we will list a few of the known players in this list. Amazon Amazon is arguably the most popular Infrastructure as a Service (IaaS) provider. The history of how Amazon started in this business is very interesting. They started out with a massive infrastructure to support their own business. Gradually they figured out that their own resources are underutilized most of the time. They decided to get the maximum out of the resources they have and hence  they launched their Amazon Elastic Compute Cloud (Amazon EC2) service in 2006. Their products have evolved a lot recently and now it is one of their primary business besides their retail selling. Amazon also offers Big Data services understand Amazon Web Services. Here is the list of the included services: Amazon Elastic MapReduce – It processes very high volumes of data Amazon DynammoDB – It is fully managed NoSQL (Not Only SQL) database service Amazon Simple Storage Services (S3) – A web-scale service designed to store and accommodate any amount of data Amazon High Performance Computing – It provides low-tenancy tuned high performance computing cluster Amazon RedShift – It is petabyte scale data warehousing service Google Though Google is known for Search Engine, we all know that it is much more than that. Google Compute Engine – It offers secure, flexible computing from energy efficient data centers Google Big Query – It allows SQL-like queries to run against large datasets Google Prediction API – It is a cloud based machine learning tool Other Players Besides Amazon and Google we also have other players in the Big Data market as well. Microsoft is also attempting Big Data with the Cloud with Microsoft Azure. Additionally Rackspace and NASA together have initiated OpenStack. The goal of Openstack is to provide a massively scaled, multitenant cloud that can run on any hardware. Thing to Watch The cloud based solutions provides a great integration with the Big Data’s story as well it is very economical to implement as well. However, there are few things one should be very careful when deploying Big Data on cloud solutions. Here is a list of a few things to watch: Data Integrity Initial Cost Recurring Cost Performance Data Access Security Location Compliance Every company have different approaches to Big Data and have different rules and regulations. Based on various factors, one can implement their own custom Big Data solution on a cloud. Tomorrow In tomorrow’s blog post we will discuss about various Operational Databases supporting Big Data. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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• Search multiple datepicker on same grid

  - by DHF

  I'm using multiple datepicker on same grid and I face the problem to get a proper result. I used 3 datepicker in 1 grid. Only the first datepicker (Order Date)is able to output proper result while the other 2 datepicker (Start Date & End Date) are not able to generate proper result. There is no problem with the query, so could you find out what's going on here? Thanks in advance! php wrapper <?php ob_start(); require_once 'config.php'; // include the jqGrid Class require_once "php/jqGrid.php"; // include the PDO driver class require_once "php/jqGridPdo.php"; // include the datepicker require_once "php/jqCalendar.php"; // Connection to the server $conn = new PDO(DB_DSN,DB_USER,DB_PASSWORD); // Tell the db that we use utf-8 $conn->query("SET NAMES utf8"); // Create the jqGrid instance $grid = new jqGridRender($conn); // Write the SQL Query $grid->SelectCommand = "SELECT c.CompanyID, c.CompanyCode, c.CompanyName, c.Area, o.OrderCode, o.Date, m.maID ,m.System, m.Status, m.StartDate, m.EndDate, m.Type FROM company c, orders o, maintenance_agreement m WHERE c.CompanyID = o.CompanyID AND o.OrderID = m.OrderID "; // Set the table to where you update the data $grid->table = 'maintenance_agreement'; // set the ouput format to json $grid->dataType = 'json'; // Let the grid create the model $grid->setPrimaryKeyId('maID'); // Let the grid create the model $grid->setColModel(); // Set the url from where we obtain the data $grid->setUrl('grouping_ma_details.php'); // Set grid caption using the option caption $grid->setGridOptions(array( "sortable"=>true, "rownumbers"=>true, "caption"=>"Group by Maintenance Agreement", "rowNum"=>20, "height"=>'auto', "width"=>1300, "sortname"=>"maID", "hoverrows"=>true, "rowList"=>array(10,20,50), "footerrow"=>false, "userDataOnFooter"=>false, "grouping"=>true, "groupingView"=>array( "groupField" => array('CompanyName'), "groupColumnShow" => array(true), //show or hide area column "groupText" =>array('<b> Company Name: {0}</b>',), "groupDataSorted" => true, "groupSummary" => array(true) ) )); if(isset($_SESSION['login_admin'])) { $grid->addCol(array( "name"=>"Action", "formatter"=>"actions", "editable"=>false, "sortable"=>false, "resizable"=>false, "fixed"=>true, "width"=>60, "formatoptions"=>array("keys"=>true), "search"=>false ), "first"); } // Change some property of the field(s) $grid->setColProperty("CompanyID", array("label"=>"ID","hidden"=>true,"width"=>30,"editable"=>false,"editoptions"=>array("readonly"=>"readonly"))); $grid->setColProperty("CompanyName", array("label"=>"Company Name","hidden"=>true,"editable"=>false,"width"=>150,"align"=>"center","fixed"=>true)); $grid->setColProperty("CompanyCode", array("label"=>"Company Code","hidden"=>true,"width"=>50,"align"=>"center")); $grid->setColProperty("OrderCode", array("label"=>"Order Code","width"=>110,"editable"=>false,"align"=>"center","fixed"=>true)); $grid->setColProperty("maID", array("hidden"=>true)); $grid->setColProperty("System", array("width"=>150,"fixed"=>true,"align"=>"center")); $grid->setColProperty("Type", array("width"=>280,"fixed"=>true)); $grid->setColProperty("Status", array("width"=>70,"align"=>"center","edittype"=>"select","editoptions"=>array("value"=>"Yes:Yes;No:No"),"fixed"=>true)); $grid->setSelect('System', "SELECT DISTINCT System, System AS System FROM master_ma_system ORDER BY System", false, true, true, array(""=>"All")); $grid->setSelect('Type', "SELECT DISTINCT Type, Type AS Type FROM master_ma_type ORDER BY Type", false, true, true, array(""=>"All")); $grid->setColProperty("StartDate", array("label"=>"Start Date","width"=>120,"align"=>"center","fixed"=>true, "formatter"=>"date", "formatoptions"=>array("srcformat"=>"Y-m-d H:i:s","newformat"=>"d M Y") )); // this is only in this case since the orderdate is set as date time $grid->setUserTime("d M Y"); $grid->setUserDate("d M Y"); $grid->setDatepicker("StartDate",array("buttonOnly"=>false)); $grid->datearray = array('StartDate'); $grid->setColProperty("EndDate", array("label"=>"End Date","width"=>120,"align"=>"center","fixed"=>true, "formatter"=>"date", "formatoptions"=>array("srcformat"=>"Y-m-d H:i:s","newformat"=>"d M Y") )); // this is only in this case since the orderdate is set as date time $grid->setUserTime("d M Y"); $grid->setUserDate("d M Y"); $grid->setDatepicker("EndDate",array("buttonOnly"=>false)); $grid->datearray = array('EndDate'); $grid->setColProperty("Date", array("label"=>"Order Date","width"=>100,"editable"=>false,"align"=>"center","fixed"=>true, "formatter"=>"date", "formatoptions"=>array("srcformat"=>"Y-m-d H:i:s","newformat"=>"d M Y") )); // this is only in this case since the orderdate is set as date time $grid->setUserTime("d M Y"); $grid->setUserDate("d M Y"); $grid->setDatepicker("Date",array("buttonOnly"=>false)); $grid->datearray = array('Date'); // This command is executed after edit $maID = jqGridUtils::GetParam('maID'); $Status = jqGridUtils::GetParam('Status'); $StartDate = jqGridUtils::GetParam('StartDate'); $EndDate = jqGridUtils::GetParam('EndDate'); $Type = jqGridUtils::GetParam('Type'); // This command is executed immediatley after edit occur. $grid->setAfterCrudAction('edit', "UPDATE maintenance_agreement SET m.Status=?, m.StartDate=?, m.EndDate=?, m.Type=? WHERE m.maID=?", array($Status,$StartDate,$EndDate,$Type,$maID)); $selectorder = <<<ORDER function(rowid, selected) { if(rowid != null) { jQuery("#detail").jqGrid('setGridParam',{postData:{CompanyID:rowid}}); jQuery("#detail").trigger("reloadGrid"); // Enable CRUD buttons in navigator when a row is selected jQuery("#add_detail").removeClass("ui-state-disabled"); jQuery("#edit_detail").removeClass("ui-state-disabled"); jQuery("#del_detail").removeClass("ui-state-disabled"); } } ORDER; // We should clear the grid data on second grid on sorting, paging, etc. $cleargrid = <<<CLEAR function(rowid, selected) { // clear the grid data and footer data jQuery("#detail").jqGrid('clearGridData',true); // Disable CRUD buttons in navigator when a row is not selected jQuery("#add_detail").addClass("ui-state-disabled"); jQuery("#edit_detail").addClass("ui-state-disabled"); jQuery("#del_detail").addClass("ui-state-disabled"); } CLEAR; $grid->setGridEvent('onSelectRow', $selectorder); $grid->setGridEvent('onSortCol', $cleargrid); $grid->setGridEvent('onPaging', $cleargrid); $grid->setColProperty("Area", array("width"=>100,"hidden"=>false,"editable"=>false,"fixed"=>true)); $grid->setColProperty("HeadCount", array("label"=>"Head Count","align"=>"center", "width"=>100,"hidden"=>false,"fixed"=>true)); $grid->setSelect('Area', "SELECT DISTINCT AreaName, AreaName AS Area FROM master_area ORDER BY AreaName", false, true, true, array(""=>"All")); $grid->setSelect('CompanyName', "SELECT DISTINCT CompanyName, CompanyName AS CompanyName FROM company ORDER BY CompanyName", false, true, true, array(""=>"All")); $custom = <<<CUSTOM jQuery("#getselected").click(function(){ var selr = jQuery('#grid').jqGrid('getGridParam','selrow'); if(selr) { window.open('http://www.smartouch-cdms.com/order.php?CompanyID='+selr); } else alert("No selected row"); return false; }); CUSTOM; $grid->setJSCode($custom); // Enable toolbar searching $grid->toolbarfilter = true; $grid->setFilterOptions(array("stringResult"=>true,"searchOnEnter"=>false,"defaultSearch"=>"cn")); // Enable navigator $grid->navigator = true; // disable the delete operation programatically for that table $grid->del = false; // we need to write some custom code when we are in delete mode. // get the grid operation parameter to see if we are in delete mode // jqGrid sends the "oper" parameter to identify the needed action $deloper = $_POST['oper']; // det the company id $cid = $_POST['CompanyID']; // if the operation is del and the companyid is set if($deloper == 'del' && isset($cid) ) { // the two tables are linked via CompanyID, so let try to delete the records in both tables try { jqGridDB::beginTransaction($conn); $comp = jqGridDB::prepare($conn, "DELETE FROM company WHERE CompanyID= ?", array($cid)); $cont = jqGridDB::prepare($conn,"DELETE FROM contact WHERE CompanyID = ?", array($cid)); jqGridDB::execute($comp); jqGridDB::execute($cont); jqGridDB::commit($conn); } catch(Exception $e) { jqGridDB::rollBack($conn); echo $e->getMessage(); } } // Enable only deleting if(isset($_SESSION['login_admin'])) { $grid->setNavOptions('navigator', array("pdf"=>true, "excel"=>true,"add"=>false,"edit"=>true,"del"=>false,"view"=>true, "search"=>true)); } else $grid->setNavOptions('navigator', array("pdf"=>true, "excel"=>true,"add"=>false,"edit"=>false,"del"=>false,"view"=>true, "search"=>true)); // In order to enable the more complex search we should set multipleGroup option // Also we need show query roo $grid->setNavOptions('search', array( "multipleGroup"=>false, "showQuery"=>true )); // Set different filename $grid->exportfile = 'Company.xls'; // Close the dialog after editing $grid->setNavOptions('edit',array("closeAfterEdit"=>true,"editCaption"=>"Update Company","bSubmit"=>"Update","dataheight"=>"auto")); $grid->setNavOptions('add',array("closeAfterAdd"=>true,"addCaption"=>"Add New Company","bSubmit"=>"Update","dataheight"=>"auto")); $grid->setNavOptions('view',array("Caption"=>"View Company","dataheight"=>"auto","width"=>"1100")); ob_end_clean(); //solve TCPDF error // Enjoy $grid->renderGrid('#grid','#pager',true, null, null, true,true); $conn = null; ?> javascript code jQuery(document).ready(function ($) { jQuery('#grid').jqGrid({ "width": 1300, "hoverrows": true, "viewrecords": true, "jsonReader": { "repeatitems": false, "subgrid": { "repeatitems": false } }, "xmlReader": { "repeatitems": false, "subgrid": { "repeatitems": false } }, "gridview": true, "url": "session_ma_details.php", "editurl": "session_ma_details.php", "cellurl": "session_ma_details.php", "sortable": true, "rownumbers": true, "caption": "Group by Maintenance Agreement", "rowNum": 20, "height": "auto", "sortname": "maID", "rowList": [10, 20, 50], "footerrow": false, "userDataOnFooter": false, "grouping": true, "groupingView": { "groupField": ["CompanyName"], "groupColumnShow": [false], "groupText": ["<b> Company Name: {0}</b>"], "groupDataSorted": true, "groupSummary": [true] }, "onSelectRow": function (rowid, selected) { if (rowid != null) { jQuery("#detail").jqGrid('setGridParam', { postData: { CompanyID: rowid } }); jQuery("#detail").trigger("reloadGrid"); // Enable CRUD buttons in navigator when a row is selected jQuery("#add_detail").removeClass("ui-state-disabled"); jQuery("#edit_detail").removeClass("ui-state-disabled"); jQuery("#del_detail").removeClass("ui-state-disabled"); } }, "onSortCol": function (rowid, selected) { // clear the grid data and footer data jQuery("#detail").jqGrid('clearGridData', true); // Disable CRUD buttons in navigator when a row is not selected jQuery("#add_detail").addClass("ui-state-disabled"); jQuery("#edit_detail").addClass("ui-state-disabled"); jQuery("#del_detail").addClass("ui-state-disabled"); }, "onPaging": function (rowid, selected) { // clear the grid data and footer data jQuery("#detail").jqGrid('clearGridData', true); // Disable CRUD buttons in navigator when a row is not selected jQuery("#add_detail").addClass("ui-state-disabled"); jQuery("#edit_detail").addClass("ui-state-disabled"); jQuery("#del_detail").addClass("ui-state-disabled"); }, "datatype": "json", "colModel": [ { "name": "Action", "formatter": "actions", "editable": false, "sortable": false, "resizable": false, "fixed": true, "width": 60, "formatoptions": { "keys": true }, "search": false }, { "name": "CompanyID", "index": "CompanyID", "sorttype": "int", "label": "ID", "hidden": true, "width": 30, "editable": false, "editoptions": { "readonly": "readonly" } }, { "name": "CompanyCode", "index": "CompanyCode", "sorttype": "string", "label": "Company Code", "hidden": true, "width": 50, "align": "center", "editable": true }, { "name": "CompanyName", "index": "CompanyName", "sorttype": "string", "label": "Company Name", "hidden": true, "editable": false, "width": 150, "align": "center", "fixed": true, "edittype": "select", "editoptions": { "value": "Aquatex Industries:Aquatex Industries;Benithem Sdn Bhd:Benithem Sdn Bhd;Daily Bakery Sdn Bhd:Daily Bakery Sdn Bhd;Eurocor Asia Sdn Bhd:Eurocor Asia Sdn Bhd;Evergrown Technology:Evergrown Technology;Goldpar Precision:Goldpar Precision;MicroSun Technologies Asia:MicroSun Technologies Asia;NCI Industries Sdn Bhd:NCI Industries Sdn Bhd;PHHP Marketing:PHHP Marketing;Smart Touch Technology:Smart Touch Technology;THOSCO Treatech:THOSCO Treatech;YHL Trading (Johor) Sdn Bhd:YHL Trading (Johor) Sdn Bhd;Zenxin Agri-Organic Food:Zenxin Agri-Organic Food", "separator": ":", "delimiter": ";" }, "stype": "select", "searchoptions": { "value": ":All;Aquatex Industries:Aquatex Industries;Benithem Sdn Bhd:Benithem Sdn Bhd;Daily Bakery Sdn Bhd:Daily Bakery Sdn Bhd;Eurocor Asia Sdn Bhd:Eurocor Asia Sdn Bhd;Evergrown Technology:Evergrown Technology;Goldpar Precision:Goldpar Precision;MicroSun Technologies Asia:MicroSun Technologies Asia;NCI Industries Sdn Bhd:NCI Industries Sdn Bhd;PHHP Marketing:PHHP Marketing;Smart Touch Technology:Smart Touch Technology;THOSCO Treatech:THOSCO Treatech;YHL Trading (Johor) Sdn Bhd:YHL Trading (Johor) Sdn Bhd;Zenxin Agri-Organic Food:Zenxin Agri-Organic Food", "separator": ":", "delimiter": ";" } }, { "name": "Area", "index": "Area", "sorttype": "string", "width": 100, "hidden": true, "editable": false, "fixed": true, "edittype": "select", "editoptions": { "value": "Cemerlang:Cemerlang;Danga Bay:Danga Bay;Kulai:Kulai;Larkin:Larkin;Masai:Masai;Nusa Cemerlang:Nusa Cemerlang;Nusajaya:Nusajaya;Pasir Gudang:Pasir Gudang;Pekan Nenas:Pekan Nenas;Permas Jaya:Permas Jaya;Pontian:Pontian;Pulai:Pulai;Senai:Senai;Skudai:Skudai;Taman Gaya:Taman Gaya;Taman Johor Jaya:Taman Johor Jaya;Taman Molek:Taman Molek;Taman Pelangi:Taman Pelangi;Taman Sentosa:Taman Sentosa;Tebrau 4:Tebrau 4;Ulu Tiram:Ulu Tiram", "separator": ":", "delimiter": ";" }, "stype": "select", "searchoptions": { "value": ":All;Cemerlang:Cemerlang;Danga Bay:Danga Bay;Kulai:Kulai;Larkin:Larkin;Masai:Masai;Nusa Cemerlang:Nusa Cemerlang;Nusajaya:Nusajaya;Pasir Gudang:Pasir Gudang;Pekan Nenas:Pekan Nenas;Permas Jaya:Permas Jaya;Pontian:Pontian;Pulai:Pulai;Senai:Senai;Skudai:Skudai;Taman Gaya:Taman Gaya;Taman Johor Jaya:Taman Johor Jaya;Taman Molek:Taman Molek;Taman Pelangi:Taman Pelangi;Taman Sentosa:Taman Sentosa;Tebrau 4:Tebrau 4;Ulu Tiram:Ulu Tiram", "separator": ":", "delimiter": ";" } }, { "name": "OrderCode", "index": "OrderCode", "sorttype": "string", "label": "Order No.", "width": 110, "editable": false, "align": "center", "fixed": true }, { "name": "Date", "index": "Date", "sorttype": "date", "label": "Order Date", "width": 100, "editable": false, "align": "center", "fixed": true, "formatter": "date", "formatoptions": { "srcformat": "Y-m-d H:i:s", "newformat": "d M Y" }, "editoptions": { "dataInit": function(el) { setTimeout(function() { if (jQuery.ui) { if (jQuery.ui.datepicker) { jQuery(el).datepicker({ "disabled": false, "dateFormat": "dd M yy" }); jQuery('.ui-datepicker').css({ 'font-size': '75%' }); } } }, 100); } }, "searchoptions": { "dataInit": function(el) { setTimeout(function() { if (jQuery.ui) { if (jQuery.ui.datepicker) { jQuery(el).datepicker({ "disabled": false, "dateFormat": "dd M yy" }); jQuery('.ui-datepicker').css({ 'font-size': '75%' }); } } }, 100); } } }, { "name": "maID", "index": "maID", "sorttype": "int", "key": true, "hidden": true, "editable": true }, { "name": "System", "index": "System", "sorttype": "string", "width": 150, "fixed": true, "align": "center", "edittype": "select", "editoptions": { "value": "Payroll:Payroll;TMS:TMS;TMS & Payroll:TMS & Payroll", "separator": ":", "delimiter": ";" }, "stype": "select", "searchoptions": { "value": ":All;Payroll:Payroll;TMS:TMS;TMS & Payroll:TMS & Payroll", "separator": ":", "delimiter": ";" }, "editable": true }, { "name": "Status", "index": "Status", "sorttype": "string", "width": 70, "align": "center", "edittype": "select", "editoptions": { "value": "Yes:Yes;No:No" }, "fixed": true, "editable": true }, { "name": "StartDate", "index": "StartDate", "sorttype": "date", "label": "Start Date", "width": 120, "align": "center", "fixed": true, "formatter": "date", "formatoptions": { "srcformat": "Y-m-d H:i:s", "newformat": "d M Y" }, "editoptions": { "dataInit": function(el) { setTimeout(function() { if (jQuery.ui) { if (jQuery.ui.datepicker) { jQuery(el).datepicker({ "disabled": false, "dateFormat": "dd M yy" }); jQuery('.ui-datepicker').css({ 'font-size': '75%' }); } } }, 100); } }, "searchoptions": { "dataInit": function(el) { setTimeout(function() { if (jQuery.ui) { if (jQuery.ui.datepicker) { jQuery(el).datepicker({ "disabled": false, "dateFormat": "dd M yy" }); jQuery('.ui-datepicker').css({ 'font-size': '75%' }); } } }, 100); } }, "editable": true }, { "name": "EndDate", "index": "EndDate", "sorttype": "date", "label": "End Date", "width": 120, "align": "center", "fixed": true, "formatter": "date", "formatoptions": { "srcformat": "Y-m-d H:i:s", "newformat": "d M Y" }, "editoptions": { "dataInit": function(el) { setTimeout(function() { if (jQuery.ui) { if (jQuery.ui.datepicker) { jQuery(el).datepicker({ "disabled": false, "dateFormat": "dd M yy" }); jQuery('.ui-datepicker').css({ 'font-size': '75%' }); } } }, 100); } }, "searchoptions": { "dataInit": function(el) { setTimeout(function() { if (jQuery.ui) { if (jQuery.ui.datepicker) { jQuery(el).datepicker({ "disabled": false, "dateFormat": "dd M yy" }); jQuery('.ui-datepicker').css({ 'font-size': '75%' }); } } }, 100); } }, "editable": true }, { "name": "Type", "index": "Type", "sorttype": "string", "width": 530, "fixed": true, "edittype": "select", "editoptions": { "value": "Comprehensive MA:Comprehensive MA;FOC service, 20% spare part discount:FOC service, 20% spare part discount;Standard Package, FOC 1 time service, 20% spare part discount:Standard Package, FOC 1 time service, 20% spare part discount;Standard Package, FOC 2 time service, 20% spare part discount:Standard Package, FOC 2 time service, 20% spare part discount;Standard Package, FOC 3 time service, 20% spare part discount:Standard Package, FOC 3 time service, 20% spare part discount;Standard Package, FOC 4 time service, 20% spare part discount:Standard Package, FOC 4 time service, 20% spare part discount;Standard Package, FOC 6 time service, 20% spare part discount:Standard Package, FOC 6 time service, 20% spare part discount;Standard Package, no free:Standard Package, no free", "separator": ":", "delimiter": ";" }, "stype": "select", "searchoptions": { "value": ":All;Comprehensive MA:Comprehensive MA;FOC service, 20% spare part discount:FOC service, 20% spare part discount;Standard Package, FOC 1 time service, 20% spare part discount:Standard Package, FOC 1 time service, 20% spare part discount;Standard Package, FOC 2 time service, 20% spare part discount:Standard Package, FOC 2 time service, 20% spare part discount;Standard Package, FOC 3 time service, 20% spare part discount:Standard Package, FOC 3 time service, 20% spare part discount;Standard Package, FOC 4 time service, 20% spare part discount:Standard Package, FOC 4 time service, 20% spare part discount;Standard Package, FOC 6 time service, 20% spare part discount:Standard Package, FOC 6 time service, 20% spare part discount;Standard Package, no free:Standard Package, no free", "separator": ":", "delimiter": ";" }, "editable": true } ], "postData": { "oper": "grid" }, "prmNames": { "page": "page", "rows": "rows", "sort": "sidx", "order": "sord", "search": "_search", "nd": "nd", "id": "maID", "filter": "filters", "searchField": "searchField", "searchOper": "searchOper", "searchString": "searchString", "oper": "oper", "query": "grid", "addoper": "add", "editoper": "edit", "deloper": "del", "excel": "excel", "subgrid": "subgrid", "totalrows": "totalrows", "autocomplete": "autocmpl" }, "loadError": function(xhr, status, err) { try { jQuery.jgrid.info_dialog(jQuery.jgrid.errors.errcap, '<div class="ui-state-error">' + xhr.responseText + '</div>', jQuery.jgrid.edit.bClose, { buttonalign: 'right' } ); } catch(e) { alert(xhr.responseText); } }, "pager": "#pager" }); jQuery('#grid').jqGrid('navGrid', '#pager', { "edit": true, "add": false, "del": false, "search": true, "refresh": true, "view": true, "excel": true, "pdf": true, "csv": false, "columns": false }, { "drag": true, "resize": true, "closeOnEscape": true, "dataheight": "auto", "errorTextFormat": function (r) { return r.responseText; }, "closeAfterEdit": true, "editCaption": "Update Company", "bSubmit": "Update" }, { "drag": true, "resize": true, "closeOnEscape": true, "dataheight": "auto", "errorTextFormat": function (r) { return r.responseText; }, "closeAfterAdd": true, "addCaption": "Add New Company", "bSubmit": "Update" }, { "errorTextFormat": function (r) { return r.responseText; } }, { "drag": true, "closeAfterSearch": true, "multipleSearch": true }, { "drag": true, "resize": true, "closeOnEscape": true, "dataheight": "auto", "Caption": "View Company", "width": "1100" } ); jQuery('#grid').jqGrid('navButtonAdd', '#pager', { id: 'pager_excel', caption: '', title: 'Export To Excel', onClickButton: function (e) { try { jQuery("#grid").jqGrid('excelExport', { tag: 'excel', url: 'session_ma_details.php' }); } catch (e) { window.location = 'session_ma_details.php?oper=excel'; } }, buttonicon: 'ui-icon-newwin' }); jQuery('#grid').jqGrid('navButtonAdd', '#pager', { id: 'pager_pdf', caption: '', title: 'Export To Pdf', onClickButton: function (e) { try { jQuery("#grid").jqGrid('excelExport', { tag: 'pdf', url: 'session_ma_details.php' }); } catch (e) { window.location = 'session_ma_details.php?oper=pdf'; } }, buttonicon: 'ui-icon-print' }); jQuery('#grid').jqGrid('filterToolbar', { "stringResult": true, "searchOnEnter": false, "defaultSearch": "cn" }); jQuery("#getselected").click(function () { var selr = jQuery('#grid').jqGrid('getGridParam', 'selrow'); if (selr) { window.open('http://www.smartouch-cdms.com/order.php?CompanyID=' + selr); } else alert("No selected row"); return false; }); });

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• My grid based collision detection is slow

  - by Fibericon

  Something about my implementation of a basic 2x4 grid for collision detection is slow - so slow in fact, that it's actually faster to simply check every bullet from every enemy to see if the BoundingSphere intersects with that of my ship. It becomes noticeably slow when I have approximately 1000 bullets on the screen (36 enemies shooting 3 bullets every .5 seconds). By commenting it out bit by bit, I've determined that the code used to add them to the grid is what's slowest. Here's how I add them to the grid: for (int i = 0; i < enemy[x].gun.NumBullets; i++) { if (enemy[x].gun.bulletList[i].isActive) { enemy[x].gun.bulletList[i].Update(timeDelta); int bulletPosition = 0; if (enemy[x].gun.bulletList[i].position.Y < 0) { bulletPosition = (int)Math.Floor((enemy[x].gun.bulletList[i].position.X + 900) / 450); } else { bulletPosition = (int)Math.Floor((enemy[x].gun.bulletList[i].position.X + 900) / 450) + 4; } GridItem bulletItem = new GridItem(); bulletItem.index = i; bulletItem.type = 5; bulletItem.parentIndex = x; if (bulletPosition > -1 && bulletPosition < 8) { if (!grid[bulletPosition].Contains(bulletItem)) { for (int j = 0; j < grid.Length; j++) { grid[j].Remove(bulletItem); } grid[bulletPosition].Add(bulletItem); } } } } And here's how I check if it collides with the ship: if (ship.isActive && !ship.invincible) { BoundingSphere shipSphere = new BoundingSphere( ship.Position, ship.Model.Meshes[0].BoundingSphere.Radius * 9.0f); for (int i = 0; i < grid.Length; i++) { if (grid[i].Contains(shipItem)) { for (int j = 0; j < grid[i].Count; j++) { //Other collision types omitted else if (grid[i][j].type == 5) { if (enemy[grid[i][j].parentIndex].gun.bulletList[grid[i][j].index].isActive) { BoundingSphere bulletSphere = new BoundingSphere(enemy[grid[i][j].parentIndex].gun.bulletList[grid[i][j].index].position, enemy[grid[i][j].parentIndex].gun.bulletModel.Meshes[0].BoundingSphere.Radius); if (shipSphere.Intersects(bulletSphere)) { ship.health -= enemy[grid[i][j].parentIndex].gun.damage; enemy[grid[i][j].parentIndex].gun.bulletList[grid[i][j].index].isActive = false; grid[i].RemoveAt(j); break; //no need to check other bullets } } else { grid[i].RemoveAt(j); } } What am I doing wrong here? I thought a grid implementation would be faster than checking each one.

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• Alternatives to NSMutableArray for storing 2D grid - iOS Cocos2d

  - by SundayMonday

  I'm creating a grid-based iOS game using Cocos2d. Currently the grid is stored in an NSMutableArray that contains other NSMutableArrays (the latter are rows in the grid). This works ok and performance so far is pretty good. However the syntax feels bulky and the indexing isn't very elegant (using CGPoints, would prefer integer indices). I'm looking for an alternative. What are some alternatives data structures for 2D arrays in this situation? In my game it's very common to add and remove rows from the bottom of the grid. So the grid might start off 10x10, grow to 17x10, shrink to 8x10 and then finally end with 2x10. Note the column count is constant. I've consider using a vector<vector<Object*>>. Also I'm vaguely aware of some type of "fast array" or similar offered by Cocos2d. I'd just like to learn about best practices from other developers!

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• Big Data – Buzz Words: Importance of Relational Database in Big Data World – Day 9 of 21

  - by Pinal Dave

  In yesterday’s blog post we learned what is HDFS. In this article we will take a quick look at the importance of the Relational Database in Big Data world. A Big Question? Here are a few questions I often received since the beginning of the Big Data Series - Does the relational database have no space in the story of the Big Data? Does relational database is no longer relevant as Big Data is evolving? Is relational database not capable to handle Big Data? Is it true that one no longer has to learn about relational data if Big Data is the final destination? Well, every single time when I hear that one person wants to learn about Big Data and is no longer interested in learning about relational database, I find it as a bit far stretched. I am not here to give ambiguous answers of It Depends. I am personally very clear that one who is aspiring to become Big Data Scientist or Big Data Expert they should learn about relational database. NoSQL Movement The reason for the NoSQL Movement in recent time was because of the two important advantages of the NoSQL databases. Performance Flexible Schema In personal experience I have found that when I use NoSQL I have found both of the above listed advantages when I use NoSQL database. There are instances when I found relational database too much restrictive when my data is unstructured as well as they have in the datatype which my Relational Database does not support. It is the same case when I have found that NoSQL solution performing much better than relational databases. I must say that I am a big fan of NoSQL solutions in the recent times but I have also seen occasions and situations where relational database is still perfect fit even though the database is growing increasingly as well have all the symptoms of the big data. Situations in Relational Database Outperforms Adhoc reporting is the one of the most common scenarios where NoSQL is does not have optimal solution. For example reporting queries often needs to aggregate based on the columns which are not indexed as well are built while the report is running, in this kind of scenario NoSQL databases (document database stores, distributed key value stores) database often does not perform well. In the case of the ad-hoc reporting I have often found it is much easier to work with relational databases. SQL is the most popular computer language of all the time. I have been using it for almost over 10 years and I feel that I will be using it for a long time in future. There are plenty of the tools, connectors and awareness of the SQL language in the industry. Pretty much every programming language has a written drivers for the SQL language and most of the developers have learned this language during their school/college time. In many cases, writing query based on SQL is much easier than writing queries in NoSQL supported languages. I believe this is the current situation but in the future this situation can reverse when No SQL query languages are equally popular. ACID (Atomicity Consistency Isolation Durability) – Not all the NoSQL solutions offers ACID compliant language. There are always situations (for example banking transactions, eCommerce shopping carts etc.) where if there is no ACID the operations can be invalid as well database integrity can be at risk. Even though the data volume indeed qualify as a Big Data there are always operations in the application which absolutely needs ACID compliance matured language. The Mixed Bag I have often heard argument that all the big social media sites now a days have moved away from Relational Database. Actually this is not entirely true. While researching about Big Data and Relational Database, I have found that many of the popular social media sites uses Big Data solutions along with Relational Database. Many are using relational databases to deliver the results to end user on the run time and many still uses a relational database as their major backbone. Here are a few examples: Facebook uses MySQL to display the timeline. (Reference Link) Twitter uses MySQL. (Reference Link) Tumblr uses Sharded MySQL (Reference Link) Wikipedia uses MySQL for data storage. (Reference Link) There are many for prominent organizations which are running large scale applications uses relational database along with various Big Data frameworks to satisfy their various business needs. Summary I believe that RDBMS is like a vanilla ice cream. Everybody loves it and everybody has it. NoSQL and other solutions are like chocolate ice cream or custom ice cream – there is a huge base which loves them and wants them but not every ice cream maker can make it just right  for everyone’s taste. No matter how fancy an ice cream store is there is always plain vanilla ice cream available there. Just like the same, there are always cases and situations in the Big Data’s story where traditional relational database is the part of the whole story. In the real world scenarios there will be always the case when there will be need of the relational database concepts and its ideology. It is extremely important to accept relational database as one of the key components of the Big Data instead of treating it as a substandard technology. Ray of Hope – NewSQL In this module we discussed that there are places where we need ACID compliance from our Big Data application and NoSQL will not support that out of box. There is a new termed coined for the application/tool which supports most of the properties of the traditional RDBMS and supports Big Data infrastructure – NewSQL. Tomorrow In tomorrow’s blog post we will discuss about NewSQL. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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• To sample or not to sample...

  - by [email protected]

  Ideally, we would know the exact answer to every question. How many people support presidential candidate A vs. B? How many people suffer from H1N1 in a given state? Does this batch of manufactured widgets have any defective parts? Knowing exact answers is expensive in terms of time and money and, in most cases, is impractical if not impossible. Consider asking every person in a region for their candidate preference, testing every person with flu symptoms for H1N1 (assuming every person reported when they had flu symptoms), or destructively testing widgets to determine if they are "good" (leaving no product to sell). Knowing exact answers, fortunately, isn't necessary or even useful in many situations. Understanding the direction of a trend or statistically significant results may be sufficient to answer the underlying question: who is likely to win the election, have we likely reached a critical threshold for flu, or is this batch of widgets good enough to ship? Statistics help us to answer these questions with a certain degree of confidence. This focuses on how we collect data. In data mining, we focus on the use of data, that is data that has already been collected. In some cases, we may have all the data (all purchases made by all customers), in others the data may have been collected using sampling (voters, their demographics and candidate choice). Building data mining models on all of your data can be expensive in terms of time and hardware resources. Consider a company with 40 million customers. Do we need to mine all 40 million customers to get useful data mining models? The quality of models built on all data may be no better than models built on a relatively small sample. Determining how much is a reasonable amount of data involves experimentation. When starting the model building process on large datasets, it is often more efficient to begin with a small sample, perhaps 1000 - 10,000 cases (records) depending on the algorithm, source data, and hardware. This allows you to see quickly what issues might arise with choice of algorithm, algorithm settings, data quality, and need for further data preparation. Instead of waiting for a model on a large dataset to build only to find that the results don't meet expectations, once you are satisfied with the results on the initial sample, you can  take a larger sample to see if model quality improves, and to get a sense of how the algorithm scales to the particular dataset. If model accuracy or quality continues to improve, consider increasing the sample size. Sampling in data mining is also used to produce a held-aside or test dataset for assessing classification and regression model accuracy. Here, we reserve some of the build data (data that includes known target values) to be used for an honest estimate of model error using data the model has not seen before. This sampling transformation is often called a split because the build data is split into two randomly selected sets, often with 60% of the records being used for model building and 40% for testing. Sampling must be performed with care, as it can adversely affect model quality and usability. Even a truly random sample doesn't guarantee that all values are represented in a given attribute. This is particularly troublesome when the attribute with omitted values is the target. A predictive model that has not seen any examples for a particular target value can never predict that target value! For other attributes, values may consist of a single value (a constant attribute) or all unique values (an identifier attribute), each of which may be excluded during mining. Values from categorical predictor attributes that didn't appear in the training data are not used when testing or scoring datasets. In subsequent posts, we'll talk about three sampling techniques using Oracle Database: simple random sampling without replacement, stratified sampling, and simple random sampling with replacement.

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• Kipróbálható az ingyenes új Oracle Data Miner 11gR2 grafikus workflow-val

  - by Fekete Zoltán

  Oracle Data Mining technológiai információs oldal. Oracle Data Miner 11g Release 2 - Early Adopter oldal. Megjelent, letöltheto és kipróbálható az Oracle Data Mining, az Oracle adatbányászat új grafikus felülete, az Oracle Data Miner 11gR2. Az Oracle Data Minerhez egyszeruen az SQL Developer-t kell letöltenünk, mivel az adatbányászati felület abból indítható. Az Oracle Data Mining az Oracle adatbáziskezelobe ágyazott adatbányászati motor, ami az Oracle Database Enterprise Edition opciója. Az adatbányászat az adattárházak elemzésének kifinomult eszköze és folyamata. Az Oracle Data Mining in-database-mining elonyeit felvonultatja: - nincs felesleges adatmozgatás, a teljes adatbányászati folyamatban az adatbázisban maradnak az adatok - az adatbányászati modellek is az Oracle adatbázisban vannak - az adatbányászati eredmények, cluster adatok, döntések, valószínuségek, stb. szintén az adatbázisban keletkeznek, és ott közvetlenül elemezhetoek Az új ingyenes Data Miner felület "hatalmas gazdagodáson" ment keresztül az elozo verzióhoz képest. - grafikus adatbányászati workflow szerkesztés és futtatás jelent meg! - továbbra is ingyenes - kibovült a felület - új elemzési lehetoségekkel bovült - az SQL Developer 3.0 felületrol indítható, ez megkönnyíti az adatbányászati funkciók meghívását az adatbázisból, ha épp nem a grafikus felületetet szeretnénk erre használni Az ingyenes Data Miner felület az Oracle SQL Developer kiterjesztéseként érheto el, így az elemzok közvetlenül dolgozhatnak az adatokkal az adatbázisban és a Data Miner grafikus felülettel is, építhetnek és kiértékelhetnek, futtathatnak modelleket, predikciókat tehetnek és elemezhetnek, támogatást kapva az adatbányászati módszertan megvalósítására. A korábbi Oracle Data Miner felület a Data Miner Classic néven fut és továbbra is letöltheto az OTN-rol. Az új Data Miner GUI-ból egy képernyokép: Milyen feladatokra ad megoldási lehetoséget az Oracle Data Mining: - ügyfél viselkedés megjövendölése, prediktálása - a "legjobb" ügyfelek eredményes megcélzása - ügyfél megtartás, elvándorlás kezelés (churn) - ügyfél szegmensek, klaszterek, profilok keresése és vizsgálata - anomáliák, visszaélések felderítése - stb.

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• Big Data – Operational Databases Supporting Big Data – RDBMS and NoSQL – Day 12 of 21

  - by Pinal Dave

  In yesterday’s blog post we learned the importance of the Cloud in the Big Data Story. In this article we will understand the role of Operational Databases Supporting Big Data Story. Even though we keep on talking about Big Data architecture, it is extremely crucial to understand that Big Data system can’t just exist in the isolation of itself. There are many needs of the business can only be fully filled with the help of the operational databases. Just having a system which can analysis big data may not solve every single data problem. Real World Example Think about this way, you are using Facebook and you have just updated your information about the current relationship status. In the next few seconds the same information is also reflected in the timeline of your partner as well as a few of the immediate friends. After a while you will notice that the same information is now also available to your remote friends. Later on when someone searches for all the relationship changes with their friends your change of the relationship will also show up in the same list. Now here is the question – do you think Big Data architecture is doing every single of these changes? Do you think that the immediate reflection of your relationship changes with your family member is also because of the technology used in Big Data. Actually the answer is Facebook uses MySQL to do various updates in the timeline as well as various events we do on their homepage. It is really difficult to part from the operational databases in any real world business. Now we will see a few of the examples of the operational databases. Relational Databases (This blog post) NoSQL Databases (This blog post) Key-Value Pair Databases (Tomorrow’s post) Document Databases (Tomorrow’s post) Columnar Databases (The Day After’s post) Graph Databases (The Day After’s post) Spatial Databases (The Day After’s post) Relational Databases We have earlier discussed about the RDBMS role in the Big Data’s story in detail so we will not cover it extensively over here. Relational Database is pretty much everywhere in most of the businesses which are here for many years. The importance and existence of the relational database are always going to be there as long as there are meaningful structured data around. There are many different kinds of relational databases for example Oracle, SQL Server, MySQL and many others. If you are looking for Open Source and widely accepted database, I suggest to try MySQL as that has been very popular in the last few years. I also suggest you to try out PostgreSQL as well. Besides many other essential qualities PostgreeSQL have very interesting licensing policies. PostgreSQL licenses allow modifications and distribution of the application in open or closed (source) form. One can make any modifications and can keep it private as well as well contribute to the community. I believe this one quality makes it much more interesting to use as well it will play very important role in future. Nonrelational Databases (NOSQL) We have also covered Nonrelational Dabases in earlier blog posts. NoSQL actually stands for Not Only SQL Databases. There are plenty of NoSQL databases out in the market and selecting the right one is always very challenging. Here are few of the properties which are very essential to consider when selecting the right NoSQL database for operational purpose. Data and Query Model Persistence of Data and Design Eventual Consistency Scalability Though above all of the properties are interesting to have in any NoSQL database but the one which most attracts to me is Eventual Consistency. Eventual Consistency RDBMS uses ACID (Atomicity, Consistency, Isolation, Durability) as a key mechanism for ensuring the data consistency, whereas NonRelational DBMS uses BASE for the same purpose. Base stands for Basically Available, Soft state and Eventual consistency. Eventual consistency is widely deployed in distributed systems. It is a consistency model used in distributed computing which expects unexpected often. In large distributed system, there are always various nodes joining and various nodes being removed as they are often using commodity servers. This happens either intentionally or accidentally. Even though one or more nodes are down, it is expected that entire system still functions normally. Applications should be able to do various updates as well as retrieval of the data successfully without any issue. Additionally, this also means that system is expected to return the same updated data anytime from all the functioning nodes. Irrespective of when any node is joining the system, if it is marked to hold some data it should contain the same updated data eventually. As per Wikipedia - Eventual consistency is a consistency model used in distributed computing that informally guarantees that, if no new updates are made to a given data item, eventually all accesses to that item will return the last updated value. In other words -  Informally, if no additional updates are made to a given data item, all reads to that item will eventually return the same value. Tomorrow In tomorrow’s blog post we will discuss about various other Operational Databases supporting Big Data. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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• timetable in a jTable

  - by chandra

  I want to create a timetable in a jTable. For the top row it will display from monday to sunday and the left colume will display the time of the day with 2h interval e.g 1st colume (0000 - 0200), 2nd colume (0200 - 0400) .... And if i click a button the timing will change from 2h interval to 1h interval. I do not want to hardcode it because i need to do for 2h, 1h, 30min , 15min, 1min, 30sec and 1 sec interval and it will take too long for me to hardcode. Can anyone show me an example or help me create an example for the 2h to 1h interval so that i know what to do? The data array is for me to store data and are there any other easier or shortcuts for me to store them because if it is in 1 sec interval i got thousands of array i need to type it out. private void oneHour() //1 interval functions { if(!once) { initialize(); once = true; } jTable.setModel(new javax.swing.table.DefaultTableModel( new Object [][] { {"0000 - 0100", data[0][0], data[0][1], data[0][2], data[0][3], data[0][4], data[0][5], data[0][6]}, {"0100 - 0200", data[2][0], data[2][1], data[2][2], data[2][3], data[2][4], data[2][5], data[2][6]}, {"0200 - 0300", data[4][0], data[4][1], data[4][2], data[4][3], data[4][4], data[4][5], data[4][6]}, {"0300 - 0400", data[6][0], data[6][1], data[6][2], data[6][3], data[6][4], data[6][5], data[6][6]}, {"0400 - 0600", data[8][0], data[8][1], data[8][2], data[8][3], data[8][4], data[8][5], data[8][6]}, {"0600 - 0700", data[10][0], data[4][1], data[10][2], data[10][3], data[10][4], data[10][5], data[10][6]}, {"0700 - 0800", data[12][0], data[12][1], data[12][2], data[12][3], data[12][4], data[12][5], data[12][6]}, {"0800 - 0900", data[14][0], data[14][1], data[14][2], data[14][3], data[14][4], data[14][5], data[14][6]}, {"0900 - 1000", data[16][0], data[16][1], data[16][2], data[16][3], data[16][4], data[16][5], data[16][6]}, {"1000 - 1100", data[18][0], data[18][1], data[18][2], data[18][3], data[18][4], data[18][5], data[18][6]}, {"1100 - 1200", data[20][0], data[20][1], data[20][2], data[20][3], data[20][4], data[20][5], data[20][6]}, {"1200 - 1300", data[22][0], data[22][1], data[22][2], data[22][3], data[22][4], data[22][5], data[22][6]}, {"1300 - 1400", data[24][0], data[24][1], data[24][2], data[24][3], data[24][4], data[24][5], data[24][6]}, {"1400 - 1500", data[26][0], data[26][1], data[26][2], data[26][3], data[26][4], data[26][5], data[26][6]}, {"1500 - 1600", data[28][0], data[28][1], data[28][2], data[28][3], data[28][4], data[28][5], data[28][6]}, {"1600 - 1700", data[30][0], data[30][1], data[30][2], data[30][3], data[30][4], data[30][5], data[30][6]}, {"1700 - 1800", data[32][0], data[32][1], data[32][2], data[32][3], data[32][4], data[32][5], data[32][6]}, {"1800 - 1900", data[34][0], data[34][1], data[34][2], data[34][3], data[34][4], data[34][5], data[34][6]}, {"1900 - 2000", data[36][0], data[36][1], data[36][2], data[36][3], data[36][4], data[36][5], data[36][6]}, {"2000 - 2100", data[38][0], data[38][1], data[38][2], data[38][3], data[38][4], data[38][5], data[38][6]}, {"2100 - 2200", data[40][0], data[40][1], data[40][2], data[40][3], data[40][4], data[40][5], data[40][6]}, {"2200 - 2300", data[42][0], data[42][1], data[42][2], data[42][3], data[42][4], data[42][5], data[42][6]}, {"2300 - 2400", data[44][0], data[44][1], data[44][2], data[44][3], data[44][4], data[44][5], data[44][6]}, {"2400 - 0000", data[46][0], data[46][1], data[46][2], data[46][3], data[46][4], data[46][5], data[46][6]}, }, new String [] { "Time/Day", "(Mon)", "(Tue)", "(Wed)", "(Thurs)", "(Fri)", "(Sat)", "(Sun)" } )); } private void twoHour() //2 hour interval functions { if(!once) { initialize(); once = true; } jTable.setModel(new javax.swing.table.DefaultTableModel( new Object [][] { {"0000 - 0200", data[0][0], data[0][1], data[0][2], data[0][3], data[0][4], data[0][5], data[0][6]}, {"0200 - 0400", data[4][0], data[4][1], data[4][2], data[4][3], data[4][4], data[4][5], data[4][6]}, {"0400 - 0600", data[8][0], data[8][1], data[8][2], data[8][3], data[8][4], data[8][5], data[8][6]}, {"0600 - 0800", data[12][0], data[12][1], data[12][2], data[12][3], data[12][4], data[12][5], data[12][6]}, {"0800 - 1000", data[16][0], data[16][1], data[16][2], data[16][3], data[16][4], data[16][5], data[16][6]}, {"1000 - 1200", data[20][0], data[20][1], data[20][2], data[20][3], data[20][4], data[20][5], data[20][6]}, {"1200 - 1400", data[24][0], data[24][1], data[24][2], data[24][3], data[24][4], data[24][5], data[24][6]}, {"1400 - 1600", data[28][0], data[28][1], data[28][2], data[28][3], data[28][4], data[28][5], data[28][6]}, {"1600 - 1800", data[32][0], data[32][1], data[32][2], data[32][3], data[32][4], data[32][5], data[32][6]}, {"1800 - 2000", data[36][0], data[36][1], data[36][2], data[36][3], data[36][4], data[36][5], data[36][6]}, {"2000 - 2200", data[40][0], data[40][1], data[40][2], data[40][3], data[40][4], data[40][5], data[40][6]}, {"2200 - 2400",data[44][0], data[44][1], data[44][2], data[44][3], data[44][4], data[44][5], data[44][6]} },

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• How to implement a birds eye view of 2D Grid Map using Android

  - by IM_Adan

  I'm a true beginner with using the android platform and I'm having difficulties on implementing a 2D grid system for a tower defense type game. Where I can place towers on a specific tile and enemies will be able to traverse through tiles etc. What I would like is a practical explanation of how I could tackle this. A step by step guide for dummies. This is what I believe are the necessary steps to take, I think I might be wrong but I hope someone could help me out. Calculate the Width and Height of the view I'm working with. Based on that, determine the number of tiles required and their dimensions, (Still not sure how I would do this) Create each tile as a Rectangle object and draw these rectangle on a canvas I would really be grateful if someone could steer me in the right direction on how to implement a 2D Grid Map using android. I hope the answer to this questions helps the TRUE beginners out there like me. I have looked at the following links below yet I still feel that I don't trully understand what's going on. For XNA: 2D Grid based game - how should I draw grid lines? How to Create a Grid for a 2D Game? Also a quick note: All my previous game development has been in Java, mostly using Java SE and Swing. I also have good understanding of the game development process, it is only android thats confusing me :S

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• Inconsistent movement / line-of-sight around obstacles on a hexagonal grid

  - by Darq

  In a roguelike game I've been working on, one of my core design goals has been to allow the player to "Play the game, not the grid." In essence, I want the player's positioning to be tactical because of elements in the game world, not simply because some grid tiles are more advantageous than others, in relation to enemies. I am fine with world geometry not being realistic, but it needs to be consistent. In this process I have ran into most of the common problems (Square tiles? Diagonal movement, LOS, corner cases, etc.) and have moved to a hexagonal tile grid. For the most part this has been great, and I've not had too many inconsistencies. Recently however I have been stumped by the following: Points A and B are both distance 4 from the player (red lines). Line-of-sight to both are blocked by walls (black tiles). However, due to the hexagonal grid, A can be reached in 4 moves, whereas B requires 5 moves (blue lines). On a hex grid, "shortest path" seems divorced from "direct path", there may be multiple shortest paths to any point, but there is only one direct path (or two in some situations). This is fine, geometry need not be realistic. However this also seems inconsistent, similar obstacles are more effective in some positions than in others. A player running away from an enemy should be able to run in any direction, increasing the distance between the two actors. However when placing obstacles or traps between themselves and enemies, the player is best served by running in one of the six directions that don't have multiple shortest paths. Is there a way to rationalise this? Am I missing something that makes this behaviour consistent? Or is there a way to make this behaviour consistent? I am most certainly over-thinking this, but as it is one of my goals, I should do it due diligence.

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• WPF Beginner - A simple XAML layout not working as expected

  - by OrWhen

  Hi, I've just started learning WPF, and followed a book to make this sample calculator application in XAML. The XAML code is attached below. I don't have any UI specific code in the xaml.cs file. However, I'm seeing a difference between design time and runtime. As you can see in the attached screenshot, the upper left button of the calculator is bigger than the rest. Even more confusingly, the designer when I edit the XAML shows the button correctly. I've tried to determine why is that, and I'm stumped. Can anyone help? I'm using VS2008, targeting framework 3.5, if it's any help. Here's the XAML: <TextBlock Grid.Row="0" Grid.Column="0" Grid.ColumnSpan="4" FontSize="24" Name="Header" VerticalAlignment="Center" HorizontalAlignment="Center">Calculator</TextBlock> <TextBox Grid.ColumnSpan="4" Grid.Column="0" Grid.Row="1" Name="Display" HorizontalContentAlignment="Left" Margin="5" /> <Button Grid.Row="2" Grid.Column="0" Click="Button_Click">7</Button> <Button Grid.Row="2" Grid.Column="1" Click="Button_Click">8</Button> <Button Grid.Row="2" Grid.Column="2" Click="Button_Click">9</Button> <Button Grid.Row="3" Grid.Column="0" Click="Button_Click">4</Button> <Button Grid.Row="3" Grid.Column="1" Click="Button_Click">5</Button> <Button Grid.Column="2" Grid.Row="3" Click="Button_Click">6</Button> <Button Grid.Row="4" Grid.Column="0" Click="Button_Click">1</Button> <Button Grid.Row="4" Grid.Column="1" Click="Button_Click">2</Button> <Button Grid.Row="4" Grid.Column="2" Click="Button_Click">3</Button> <Button Grid.Row="5" Grid.Column="0" Click="Button_Click">0</Button> <Button Grid.Row="5" Grid.Column="3" Tag="{x:Static local:Operation.PLUS}" Click="Op_Click">+</Button> <Button Grid.Row="4" Grid.Column="3" Tag="{x:Static local:Operation.MINUS}" Click="Op_Click">-</Button> <Button Grid.Row="3" Grid.Column="3" Tag="{x:Static local:Operation.TIMES}" Click="Op_Click">*</Button> <Button Grid.Row="2" Grid.Column="3" Tag="{x:Static local:Operation.DIVIDE}" Click="Op_Click">/</Button> <Button Grid.Row="5" Grid.Column="1" >.</Button> <Button Grid.Row="5" Grid.Column="2" Tag="{x:Static local:Operation.EQUALS}" Click="Op_Click">=</Button> </Grid>

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• Oracle Data Mining a Star Schema: Telco Churn Case Study

  - by charlie.berger

  There is a complete and detailed Telco Churn case study "How to" Blog Series just posted by Ari Mozes, ODM Dev. Manager.  In it, Ari provides detailed guidance in how to leverage various strengths of Oracle Data Mining including the ability to: mine Star Schemas and join tables and views together to obtain a complete 360 degree view of a customer combine transactional data e.g. call record detail (CDR) data, etc. define complex data transformation, model build and model deploy analytical methodologies inside the Database  His blog is posted in a multi-part series.  Below are some opening excerpts for the first 3 blog entries.  This is an excellent resource for any novice to skilled data miner who wants to gain competitive advantage by mining their data inside the Oracle Database.  Many thanks Ari! Mining a Star Schema: Telco Churn Case Study (1 of 3) One of the strengths of Oracle Data Mining is the ability to mine star schemas with minimal effort.  Star schemas are commonly used in relational databases, and they often contain rich data with interesting patterns.  While dimension tables may contain interesting demographics, fact tables will often contain user behavior, such as phone usage or purchase patterns.  Both of these aspects - demographics and usage patterns - can provide insight into behavior.Churn is a critical problem in the telecommunications industry, and companies go to great lengths to reduce the churn of their customer base.  One case study1 describes a telecommunications scenario involving understanding, and identification of, churn, where the underlying data is present in a star schema.  That case study is a good example for demonstrating just how natural it is for Oracle Data Mining to analyze a star schema, so it will be used as the basis for this series of posts...... Mining a Star Schema: Telco Churn Case Study (2 of 3) This post will follow the transformation steps as described in the case study, but will use Oracle SQL as the means for preparing data.  Please see the previous post for background material, including links to the case study and to scripts that can be used to replicate the stages in these posts.1) Handling missing values for call data recordsThe CDR_T table records the number of phone minutes used by a customer per month and per call type (tariff).  For example, the table may contain one record corresponding to the number of peak (call type) minutes in January for a specific customer, and another record associated with international calls in March for the same customer.  This table is likely to be fairly dense (most type-month combinations for a given customer will be present) due to the coarse level of aggregation, but there may be some missing values.  Missing entries may occur for a number of reasons: the customer made no calls of a particular type in a particular month, the customer switched providers during the timeframe, or perhaps there is a data entry problem.  In the first situation, the correct interpretation of a missing entry would be to assume that the number of minutes for the type-month combination is zero.  In the other situations, it is not appropriate to assume zero, but rather derive some representative value to replace the missing entries.  The referenced case study takes the latter approach.  The data is segmented by customer and call type, and within a given customer-call type combination, an average number of minutes is computed and used as a replacement value.In SQL, we need to generate additional rows for the missing entries and populate those rows with appropriate values.  To generate the missing rows, Oracle's partition outer join feature is a perfect fit.  select cust_id, cdre.tariff, cdre.month, minsfrom cdr_t cdr partition by (cust_id) right outer join     (select distinct tariff, month from cdr_t) cdre     on (cdr.month = cdre.month and cdr.tariff = cdre.tariff);   ....... Mining a Star Schema: Telco Churn Case Study (3 of 3) Now that the "difficult" work is complete - preparing the data - we can move to building a predictive model to help identify and understand churn.The case study suggests that separate models be built for different customer segments (high, medium, low, and very low value customer groups).  To reduce the data to a single segment, a filter can be applied: create or replace view churn_data_high asselect * from churn_prep where value_band = 'HIGH'; It is simple to take a quick look at the predictive aspects of the data on a univariate basis.  While this does not capture the more complex multi-variate effects as would occur with the full-blown data mining algorithms, it can give a quick feel as to the predictive aspects of the data as well as validate the data preparation steps.  Oracle Data Mining includes a predictive analytics package which enables quick analysis. begin  dbms_predictive_analytics.explain(   'churn_data_high','churn_m6','expl_churn_tab'); end; /select * from expl_churn_tab where rank <= 5 order by rank; ATTRIBUTE_NAME       ATTRIBUTE_SUBNAME EXPLANATORY_VALUE RANK-------------------- ----------------- ----------------- ----------LOS_BAND                                      .069167052          1MINS_PER_TARIFF_MON  PEAK-5                   .034881648          2REV_PER_MON          REV-5                    .034527798          3DROPPED_CALLS                                 .028110322          4MINS_PER_TARIFF_MON  PEAK-4                   .024698149          5From the above results, it is clear that some predictors do contain information to help identify churn (explanatory value > 0).  The strongest uni-variate predictor of churn appears to be the customer's (binned) length of service.  The second strongest churn indicator appears to be the number of peak minutes used in the most recent month.  The subname column contains the interior piece of the DM_NESTED_NUMERICALS column described in the previous post.  By using the object relational approach, many related predictors are included within a single top-level column. .....   NOTE:  These are just EXCERPTS.  Click here to start reading the Oracle Data Mining a Star Schema: Telco Churn Case Study from the beginning.    

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• Extjs Dynamic Grid

  - by rkenshin

  Hi, I'm trying to create a dynamic grid using Extjs. The grid is built and displayed when a click event is fired then an ajax request is sent to the server to fetch the columns, records and records definition a.k.a Store Fields. Each node could have different grid structure and that depends on the level of the node in the tree. The only way i came up with so far is function showGrid(response, request) { var jsonData = Ext.util.JSON.decode(response.responseText); var grid = Ext.getCmp('contentGrid'+request.params.owner); if(grid) { grid.destroy(); } var store = new Ext.data.ArrayStore({ id : 'arrayStore', fields : jsonData.recordFields, autoDestroy : true }); grid = new Ext.grid.GridPanel({ defaults: {sortable:true}, id:'contentGrid'+request.params.owner, store: store, columns: jsonData.columns, //width:540, //height:200, loadMask: true }); store.loadData(jsonData.records); if(Ext.getCmp('tab-'+request.params.owner)) { Ext.getCmp('tab-'+request.params.owner).show(); } else { grid.render('grid-div'); Ext.getCmp('card-tabs-panel').add({ id:'tab-'+request.params.owner, title: request.params.text, iconCls:'silk-tab', html:Ext.getDom('grid-div').innerHTML, closable:true }).show(); } } The function above is called when a click event is fired 'click': function(node) { Ext.Ajax.request({ url: 'showCtn', success: function(response, request) { alert('Success'); showGrid(response,request); }, failure: function(results, request) { alert('Error'); }, params: Ext.urlDecode(node.attributes.options); } The problem i'm getting with this code is that a new grid is displayed each time the showGrid function is called. The end user sees the old grids and the new one. To mitigate this problem, I tried destroying the grid and also removing the grid element on each request, and that seems to solve the problem only that records never get displayed this time. if(grid) { grid.destroy(true); } The behavior i'm looking for is to display the result of a grid within a tab and if that tab exists replaced the old grid. Any help is appreciated. Thank you

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• WPF Databinding- Not your fathers databinding Part 1-3

  - by Shervin Shakibi

  As Promised here is my advanced databinding presentation from South Florida Code camp and also Orlando Code camp. you can find the demo files here. http://ssccinc.com/wpfdatabinding.zip Here is a quick description of the first demos, there will be 2 other Blogposting in the next few days getting into more advance databinding topics.   Example00 Here we have 3 textboxes, The first textbox mySourceElement Second textbox has a binding to mySourceElement and Path= Text <Binding ElementName="mySourceElement" Path="Text"  />   Third textbox is also bound to the Text property but we use inline Binding <TextBlock Text="{Binding ElementName=mySourceElement,Path=Text }" Grid.Row="2" /> Here is the entire XAML     <Grid  >           <Grid.RowDefinitions >             <RowDefinition Height="*" />             <RowDefinition Height="*" />             <RowDefinition Height="*" />         </Grid.RowDefinitions>         <TextBox Name="mySourceElement" Grid.Row="0"                  TextChanged="mySourceElement_TextChanged">Hello Orlnado</TextBox>         <TextBlock Grid.Row="1">                        <TextBlock.Text>                 <Binding ElementName="mySourceElement" Path="Text"  />             </TextBlock.Text>         </TextBlock>         <TextBlock Text="{Binding ElementName=mySourceElement,Path=Text }" Grid.Row="2" />     </Grid> </Window> Example01 we have a slider control, then we have two textboxes bound to the value property of the slider. one has its text property bound, the second has its fontsize property bound. <Grid>      <Grid.RowDefinitions >          <RowDefinition Height="40px" />          <RowDefinition Height="40px" />          <RowDefinition Height="*" />      </Grid.RowDefinitions>      <Slider Name="fontSizeSlider" Minimum="5" Maximum="100"              Value="10" Grid.Row="0" />      <TextBox Name="SizeTextBox"                    Text="{Binding ElementName=fontSizeSlider, Path=Value}" Grid.Row="1"/>      <TextBlock Text="Example 01"                 FontSize="{Binding ElementName=SizeTextBox,  Path=Text}"  Grid.Row="2"/> </Grid> Example02 very much like the previous example but it also has a font dropdown <Grid>      <Grid.RowDefinitions >          <RowDefinition Height="20px" />          <RowDefinition Height="40px" />          <RowDefinition Height="40px" />          <RowDefinition Height="*" />      </Grid.RowDefinitions>      <ComboBox Name="FontNameList" SelectedIndex="0" Grid.Row="0">          <ComboBoxItem Content="Arial" />          <ComboBoxItem Content="Calibri" />          <ComboBoxItem Content="Times New Roman" />          <ComboBoxItem Content="Verdana" />      </ComboBox>      <Slider Name="fontSizeSlider" Minimum="5" Maximum="100" Value="10" Grid.Row="1" />      <TextBox Name="SizeTextBox"      Text="{Binding ElementName=fontSizeSlider, Path=Value}" Grid.Row="2"/>      <TextBlock Text="Example 01" FontFamily="{Binding ElementName=FontNameList, Path=Text}"                 FontSize="{Binding ElementName=SizeTextBox,  Path=Text}"  Grid.Row="3"/> </Grid> Example03 In this example we bind to an object Employee.cs Notice we added a directive to our xaml which is clr-namespace and the namespace for our employee Class xmlns:local="clr-namespace:Example03" In Our windows Resources we create an instance of our object <Window.Resources>     <local:Employee x:Key="MyEmployee" EmployeeNumber="145"                     FirstName="John"                     LastName="Doe"                     Department="Product Development"                     Title="QA Manager" /> </Window.Resources> then we bind our container to the that instance of the data <Grid DataContext="{StaticResource MyEmployee}">         <Grid.RowDefinitions>             <RowDefinition Height="*" />             <RowDefinition Height="*" />             <RowDefinition Height="*" />             <RowDefinition Height="*" />             <RowDefinition Height="*" />         </Grid.RowDefinitions>         <Grid.ColumnDefinitions >             <ColumnDefinition Width="130px" />             <ColumnDefinition Width="178*" />         </Grid.ColumnDefinitions>     </Grid> and Finally we have textboxes that will bind to that textbox         <Label Grid.Row="0" Grid.Column="0">Employee Number</Label>         <TextBox Grid.Row="0" Grid.Column="1" Text="{Binding Path=EmployeeNumber}"></TextBox>         <Label Grid.Row="1" Grid.Column="0">First Name</Label>         <TextBox Grid.Row="1" Grid.Column="1" Text="{Binding Path=FirstName}"></TextBox>         <Label Grid.Row="2" Grid.Column="0">Last Name</Label>         <TextBox Grid.Row="2" Grid.Column="1" Text="{Binding Path=LastName}" />         <Label Grid.Row="3" Grid.Column="0">Title</Label>         <TextBox Grid.Row="3" Grid.Column="1" Text="{Binding Path=Title}"></TextBox>         <Label Grid.Row="4" Grid.Column="0">Department</Label>         <TextBox Grid.Row="4" Grid.Column="1" Text="{Binding Path=Department}" />

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• Kendo Grid: Foreign Key Dropdown does not update grid cell after update

  - by JookyDFW

  I have a Kendo MVC grid that contains a nullable property (short) that is bound as a foreign key and uses a dropdown list as an editor template. I am also using inline editing. When the property value is null, the dropdown list selected value does not get set into the grid cell after the update button is clicked. This works fine if incell editing is used. I am looking for a workaround that will solve my problem. I am including a stripped down version of my code below Everything works if the nullable value is set to a non-null value. GRID @(Html.Kendo().Grid<AssetViewModel>() .Name("DealAssets") .Columns(c => { c.Bound(x => x.Name); c.ForeignKey(x => x.AssetTypeID, (IEnumerable<SelectListItem>)ViewBag.AssetTypeList, "Value", "Text"); c.ForeignKey(x => x.SeniorityTypeID, seniorityTypeList, "Value", "Text").EditorTemplateName("GridNullableForeignKey"); c.ForeignKey(x => x.RateBaseID, rateBaseList, "Value", "Text").EditorTemplateName("GridNullableForeignKey"); ; c.Command(m => { m.Edit(); m.Destroy(); }); }) .ToolBar(toolbar => toolbar.Create().Text("Add New Asset")) .Editable(x => x.Mode(GridEditMode.InLine)) .DataSource(ds => ds .Ajax() .Model(model => model.Id(request => request.ID)) .Read(read => read.Action("ReadAssets", "Deal", new { id = Model.ID })) .Create(create => create.Action("CreateAsset", "Deal", new { currentDealID = Model.ID })) .Update(update => update.Action("UpdateAsset", "Deal")) .Destroy(destroy => destroy.Action("DeleteAsset", "Deal")) ) ) EDITOR TEMPLATE @model short? @{ var controlName = ViewData.TemplateInfo.GetFullHtmlFieldName(""); } @( Html.Kendo().DropDownListFor(m => m) .Name(controlName) .OptionLabel("- Please select -") .BindTo((SelectList)ViewData[ViewData.TemplateInfo.GetFullHtmlFieldName("") + "_Data"]) ) UPDATE ACTION public ActionResult UpdateAsset([DataSourceRequest] DataSourceRequest request, int ID) { var dealAsset = DataContext.DealAssets.SingleOrDefault(o => o.ID == ID); if (dealAsset != null) { if (TryUpdateModel(dealAsset.Asset, new[] {"Name","AssetTypeID","SeniorityTypeID","RateBaseID" })) { DataContext.SaveChanges(); } } return Json(new[] { new AssetViewModel(dealAsset) }.ToDataSourceResult(request, ModelState), JsonRequestBehavior.AllowGet); }

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• SQL Rally Pre-Con: Data Warehouse Modeling – Making the Right Choices

  - by Davide Mauri

  As you may have already learned from my old post or Adam’s or Kalen’s posts, there will be two SQL Rally in North Europe. In the Stockholm SQL Rally, with my friend Thomas Kejser, I’ll be delivering a pre-con on Data Warehouse Modeling: Data warehouses play a central role in any BI solution. It's the back end upon which everything in years to come will be created. For this reason, it must be rock solid and yet flexible at the same time. To develop such a data warehouse, you must have a clear idea of its architecture, a thorough understanding of the concepts of Measures and Dimensions, and a proven engineered way to build it so that quality and stability can go hand-in-hand with cost reduction and scalability. In this workshop, Thomas Kejser and Davide Mauri will share all the information they learned since they started working with data warehouses, giving you the guidance and tips you need to start your BI project in the best way possible?avoiding errors, making implementation effective and efficient, paving the way for a winning Agile approach, and helping you define how your team should work so that your BI solution will stand the test of time. You'll learn: Data warehouse architecture and justification Agile methodology Dimensional modeling, including Kimball vs. Inmon, SCD1/SCD2/SCD3, Junk and Degenerate Dimensions, and Huge Dimensions Best practices, naming conventions, and lessons learned Loading the data warehouse, including loading Dimensions, loading Facts (Full Load, Incremental Load, Partitioned Load) Data warehouses and Big Data (Hadoop) Unit testing Tracking historical changes and managing large sizes With all the Self-Service BI hype, Data Warehouse is become more and more central every day, since if everyone will be able to analyze data using self-service tools, it’s better for him/her to rely on correct, uniform and coherent data. Already 50 people registered from the workshop and seats are limited so don’t miss this unique opportunity to attend to this workshop that is really a unique combination of years and years of experience! http://www.sqlpass.org/sqlrally/2013/nordic/Agenda/PreconferenceSeminars.aspx See you there!

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• Oracle Financial Analytics for SAP Certified with Oracle Data Integrator EE

  - by denis.gray

  Two days ago Oracle announced the release of Oracle Financial Analytics for SAP.  With the amount of press this has garnered in the past two days, there's a key detail that can't be missed.  This release is certified with Oracle Data Integrator EE - now making the combination of Data Integration and Business Intelligence a force to contend with.  Within the Oracle Press Release there were two important bullets: ·         Oracle Financial Analytics for SAP includes a pre-packaged ABAP code compliant adapter and is certified with Oracle Data Integrator Enterprise Edition to integrate SAP Financial Accounting data directly with the analytic application.  ·         Helping to integrate SAP financial data and disparate third-party data sources is Oracle Data Integrator Enterprise Edition which delivers fast, efficient loading and transformation of timely data into a data warehouse environment through its high-performance Extract Load and Transform (E-LT) technology. This is very exciting news, demonstrating Oracle's overall commitment to Oracle Data Integrator EE.   This is a great way to start off the new year and we look forward to building on this momentum throughout 2011.   The following links contain additional information and media responses about the Oracle Financial Analytics for SAP release. IDG News Service (Also appeared in PC World, Computer World, CIO: "Oracle is moving further into rival SAP's turf with Oracle Financial Analytics for SAP, a new BI (business intelligence) application that can crunch ERP (enterprise resource planning) system financial data for insights." Information Week: "Oracle talks a good game about the appeal of an optimized, all-Oracle stack. But the company also recognizes that we live in a predominantly heterogeneous IT world" CRN: "While some businesses with SAP Financial Accounting already use Oracle BI, those integrations had to be custom developed. The new offering provides pre-built integration capabilities." ECRM Guide:  "Among other features, Oracle Financial Analytics for SAP helps front-line managers improve financial performance and decision-making with what the company says is comprehensive, timely and role-based information on their departments' expenses and revenue contributions."   SAP Getting Started Guide for ODI on OTN: http://www.oracle.com/technetwork/middleware/data-integrator/learnmore/index.html For more information on the ODI and its SAP connectivity please review the Oracle® Fusion Middleware Application Adapters Guide for Oracle Data Integrator11g Release 1 (11.1.1)

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• What is the definition of "Big Data"?

  - by Ben

  Is there one? All the definitions I can find describe the size, complexity / variety or velocity of the data. Wikipedia's definition is the only one I've found with an actual number Big data sizes are a constantly moving target, as of 2012 ranging from a few dozen terabytes to many petabytes of data in a single data set. However, this seemingly contradicts the MIKE2.0 definition, referenced in the next paragraph, which indicates that "big" data can be small and that 100,000 sensors on an aircraft creating only 3GB of data could be considered big. IBM despite saying that: Big data is more simply than a matter of size. have emphasised size in their definition. O'Reilly has stressed "volume, velocity and variety" as well. Though explained well, and in more depth, the definition seems to be a re-hash of the others - or vice-versa of course. I think that a Computer Weekly article title sums up a number of articles fairly well "What is big data and how can it be used to gain competitive advantage". But ZDNet wins with the following from 2012: “Big Data” is a catch phrase that has been bubbling up from the high performance computing niche of the IT market... If one sits through the presentations from ten suppliers of technology, fifteen or so different definitions are likely to come forward. Each definition, of course, tends to support the need for that supplier’s products and services. Imagine that. Basically "big data" is "big" in some way shape or form. What is "big"? Is it quantifiable at the current time? If "big" is unquantifiable is there a definition that does not rely solely on generalities?

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• Big Data – Operational Databases Supporting Big Data – Columnar, Graph and Spatial Database – Day 14 of 21

  - by Pinal Dave

  In yesterday’s blog post we learned the importance of the Key-Value Pair Databases and Document Databases in the Big Data Story. In this article we will understand the role of Columnar, Graph and Spatial Database supporting Big Data Story. Now we will see a few of the examples of the operational databases. Relational Databases (The day before yesterday’s post) NoSQL Databases (The day before yesterday’s post) Key-Value Pair Databases (Yesterday’s post) Document Databases (Yesterday’s post) Columnar Databases (Tomorrow’s post) Graph Databases (Today’s post) Spatial Databases (Today’s post) Columnar Databases  Relational Database is a row store database or a row oriented database. Columnar databases are column oriented or column store databases. As we discussed earlier in Big Data we have different kinds of data and we need to store different kinds of data in the database. When we have columnar database it is very easy to do so as we can just add a new column to the columnar database. HBase is one of the most popular columnar databases. It uses Hadoop file system and MapReduce for its core data storage. However, remember this is not a good solution for every application. This is particularly good for the database where there is high volume incremental data is gathered and processed. Graph Databases For a highly interconnected data it is suitable to use Graph Database. This database has node relationship structure. Nodes and relationships contain a Key Value Pair where data is stored. The major advantage of this database is that it supports faster navigation among various relationships. For example, Facebook uses a graph database to list and demonstrate various relationships between users. Neo4J is one of the most popular open source graph database. One of the major dis-advantage of the Graph Database is that it is not possible to self-reference (self joins in the RDBMS terms) and there might be real world scenarios where this might be required and graph database does not support it. Spatial Databases  We all use Foursquare, Google+ as well Facebook Check-ins for location aware check-ins. All the location aware applications figure out the position of the phone with the help of Global Positioning System (GPS). Think about it, so many different users at different location in the world and checking-in all together. Additionally, the applications now feature reach and users are demanding more and more information from them, for example like movies, coffee shop or places see. They are all running with the help of Spatial Databases. Spatial data are standardize by the Open Geospatial Consortium known as OGC. Spatial data helps answering many interesting questions like “Distance between two locations, area of interesting places etc.” When we think of it, it is very clear that handing spatial data and returning meaningful result is one big task when there are millions of users moving dynamically from one place to another place & requesting various spatial information. PostGIS/OpenGIS suite is very popular spatial database. It runs as a layer implementation on the RDBMS PostgreSQL. This makes it totally unique as it offers best from both the worlds. Courtesy: mushroom network Tomorrow In tomorrow’s blog post we will discuss about very important components of the Big Data Ecosystem – Hive. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL

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