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  • What is meant by a primitive data type?

    - by Appy
    My understanding of a primitive datatype is that It is a datatype provided by a language implicitly (Others are user defined classes) So different languages have different sets of datatypes which are considered primitive for that particular language. Is that right? And what is the difference between a "basic datatype" and "built-in datatype". Wikipedia says a primitive datatype is either of the two. PS - Why is "string" type considered as a primitive type in SNOBOL4 and not in Java ?

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  • Master Data Management for Product Data

    In this AppsCast, Hardeep Gulati, VP PLM and PIM Product Strategy discusses the benefits companies are getting from Product MDM, more details about Oracle Product Hub solution and the progress, and where we are going from here.

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  • In Search Data Structure And Algorithm Project Title Based on Topic

    - by Salehin Suhaimi
    As the title says, my lecturer gave me a project that i needed to finish in 3 weeks before final semester exams. So i thought i will start now. The requirement is to "build a simple program that has GUI based on all the chapter that we've learned." But i got stuck on WHAT program should i build. Any idea a program that is related to this chapter i've learned? Any input will help. list, array list, linked list, vectors, stacks, Queues, ADT, Hashing, Binary Search Tree, AVL Tree, That's about all i can remember. Any idea where can i start looking?

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  • Am I sending large amounts of data sensibly?

    - by Sofus Albertsen
    I am about to design a video conversion service, that is scalable on the conversion side. The architecture is as follows: Webpage for video upload When done, a message gets sent out to one of several resizing servers The server locates the video, saves it on disk, and converts it to several formats and resolutions The resizing server uploads the output to a content server, and messages back that the conversion is done. Messaging is something I have covered, but right now I am transferring via FTP, and wonder if there is a better way? is there something faster, or more reliable? All the servers will be sitting in the same gigabit switch or neighboring switch, so fast transfer is expected.

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  • Distributed Computing - Hybrid Systems Considerations

    When the Cloud was new, it was often presented as an 'all or nothing' solution. Nowadays, the canny Systems Architect will exploit the best advantages of 'cloud' distributed computing in the right place, and use in-house services where most appropriate. So what are the issues that govern these architectural decisions? What can SQL Monitor 3.2 monitor?Whatever you think is most important. Use custom metrics to monitor and alert on data that's most important for your environment. Find out more.

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  • Using replacement to get possible outcomes to then search through HUGE amount of data

    - by Samuel Cambridge
    I have a database table holding 40 million records (table A). Each record has a string a user can search for. I also have a table with a list of character replacements (table B) i.e. i = Y, I = 1 etc. I need to be able to take the string a user is searching for, iterate through each letter and create an array of every possible outcome (the users string, then each outcome with alternative letters used). I need to check for alternatives on both lower and uppercase letters in the word A search string can be no longer than 10 characters long. I'm using PHP and a MySQL database. Does anyone have any thoughts / articles / guidance on doing this in an efficient way?

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  • Data Mining open source tools

    - by Andriyev
    Hi I'm due to take up a project which is into data mining. Before I jump in I wanted to probe around for different data mining tools (preferably open source) which allows web based reporting. In my scenario the all the data would be provided to me, so I'm not supposed to crawl for it. In n nutshell, am looking for a tool which does - Data Analysis, Web based Reporting, provides some kind of a dashboard and mining features. I have worked on the Microsoft Analysis Services and BOXI and off late I have been looking at Pentaho, which seems to be a good option. Please share your experiences on any such tool which you know of. cheers

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  • DMX Analysis Services question

    - by user282382
    Hi, I am have two mining models, both are time series. One is [Company_Inputs] and the other is [Booking_Projections]. What I want to do is use EXTEND_MODEL_CASES to join the results of [Company_Inputs] as the extended cases. So basically something like: Select Flattened PredictTimeSeries([Bookings], 1, 6, EXTEND_MODEL_CASES) FROM [Booking_Projections] Natural Prediction Join (Select Flattened PredictTimeSeries([Metric1], 1, 6) From [Company_Inputs]) AS T This code of course doesn't work, but the idea is to use the predictions made from [Company_Inputs] as cases for predicting future values of [Booking_Projections] If anyone has an idea of how I can accomplish this I would appreciate it very much.

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  • Is A Web App Feasible For A Heavy Use Data Entry System?

    - by Rob
    Looking for opinions on this, we're working on a project that is essentially a data entry system for a production line. Heavy data input by users who normally work in Excel or other thick client data systems. We've been told (as a consequence) that we have to develop this as a thick client using .NET. Our argument was to develop as a web app, as it resolves a lot of issues and would be easier to write and maintain. Their argument against the web is that (supposedly) the web is not ready yet for a heavy duty data entry system, and that the web in a browser does not offer the speed, responsiveness, and fluid experience for the end-user that a thick client can (citing things such as drag and drop, rapid auto-entry and data navigation, etc.) Personally, I think that with good form design and JQuery/AJAX, a web app could do everything a thick client does just as well, and they just don't know what they're talking about. The irony is that a thick client has to go to a lot more effort to manage the deployment and connectivity back to the central data server than a web app would need to do, so in terms of speed I would expect a web app to be faster. What are the thoughts of those out there? Are there any technologies currently in production use that modern data entry systems are being developed as web apps in? Appreciate any feedback. Regards, Rob.

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  • Pulling Data out of an object in Javascript

    - by PerryCS
    I am having a problem retreiving data out of an object passed back from PHP. I've tried many different ways to access this data and none work. In Firebug I see the following... (it looks nicer in Firebug) - I tried to make this look as close to Firebug as possible results Object { data="{"formName":"form3","formData":"data goes here"}", phpLiveDebug="<...s: 198.91.215.227"} data "{"formName":"form3","formData":"data goes here"}" phpLiveDebug "<...s: 198.91.215.227" I can access phpLiveDebug no problem, but the data portion is an object. I have tried the following... success: function(results) { //$("#formName").val(results.data.formName); //$("#formName").val(results.data[0].formName); //$("#formName").val(results.data[0]); //$("#formName").val(results.data[1]); //$("#formName").val(results.data[0]["formName"]); var tmp = results.data[formName]; alert("!" + tmp + "!"); $("#formName").val(tmp); $("#jqueryPHPDebug").val(results.phpLiveDebug); } This line works in the example above... $("#jqueryPHPDebug").val(results.phpLiveDebug); but... I can't figure out how to get at the data inside the results.data portion... as you can see above, I have been trying different things and more not even listed there. I was really hoping this line would work :) var tmp = results.data[formName]; But it doesn't. So, after many days of reading, tinkering, my solution was to re-write it to return data similar to the phpLiveDebug but then I thought... it's gotta be something simple I'm overlooking... Thank you for your time. Please try and explain why my logic (my horrible attempts at trying to figure out the proper method) above is wrong if you can?

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  • CG miner "configure: error: No mining configured in "

    - by Jorma
    Nvidia Gt 630 cuda 5.5 running CGminer not. Cuda examples fine. Should CGminer work or is there limitations to it? sudo ./autogen.sh --disable-cpumining --enable-opencl && make Configuration Options Summary: libcurl(GBT+getwork).: Enabled: -lcurl curses.TUI...........: FOUND: -lncurses Avalon.ASICs.........: Disabled BlackArrow.ASICs.....: Disabled BFL.ASICs............: Disabled BitForce.FPGAs.......: Disabled BitFury.ASICs........: Disabled Hashfast.ASICs.......: Disabled Icarus.ASICs/FPGAs...: Disabled Klondike.ASICs.......: Disabled KnC.ASICs............: Disabled ModMiner.FPGAs.......: Disabled configure: error: No mining configured in

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  • Why is GPU used for mining bitcoins?

    - by starcorn
    Something that I have not really grasped is the idea of bitcoins. Especially since everybody can mine for it using a powerful GPU. I wonder why is GPU used for this purpose? Is the work done by GPU used by some huge organization or is it just wasted resource that goes into simulated mining? I mean for example SETI uses your GPU for the purpose of finding aliens, but what I can see of bitmining it seems for no actual purpose than wasted resource.

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  • Building dynamic OLAP data marts on-the-fly

    - by DrJohn
    At the forthcoming SQLBits conference, I will be presenting a session on how to dynamically build an OLAP data mart on-the-fly. This blog entry is intended to clarify exactly what I mean by an OLAP data mart, why you may need to build them on-the-fly and finally outline the steps needed to build them dynamically. In subsequent blog entries, I will present exactly how to implement some of the techniques involved. What is an OLAP data mart? In data warehousing parlance, a data mart is a subset of the overall corporate data provided to business users to meet specific business needs. Of course, the term does not specify the technology involved, so I coined the term "OLAP data mart" to identify a subset of data which is delivered in the form of an OLAP cube which may be accompanied by the relational database upon which it was built. To clarify, the relational database is specifically create and loaded with the subset of data and then the OLAP cube is built and processed to make the data available to the end-users via standard OLAP client tools. Why build OLAP data marts? Market research companies sell data to their clients to make money. To gain competitive advantage, market research providers like to "add value" to their data by providing systems that enhance analytics, thereby allowing clients to make best use of the data. As such, OLAP cubes have become a standard way of delivering added value to clients. They can be built on-the-fly to hold specific data sets and meet particular needs and then hosted on a secure intranet site for remote access, or shipped to clients' own infrastructure for hosting. Even better, they support a wide range of different tools for analytical purposes, including the ever popular Microsoft Excel. Extension Attributes: The Challenge One of the key challenges in building multiple OLAP data marts based on the same 'template' is handling extension attributes. These are attributes that meet the client's specific reporting needs, but do not form part of the standard template. Now clearly, these extension attributes have to come into the system via additional files and ultimately be added to relational tables so they can end up in the OLAP cube. However, processing these files and filling dynamically altered tables with SSIS is a challenge as SSIS packages tend to break as soon as the database schema changes. There are two approaches to this: (1) dynamically build an SSIS package in memory to match the new database schema using C#, or (2) have the extension attributes provided as name/value pairs so the file's schema does not change and can easily be loaded using SSIS. The problem with the first approach is the complexity of writing an awful lot of complex C# code. The problem of the second approach is that name/value pairs are useless to an OLAP cube; so they have to be pivoted back into a proper relational table somewhere in the data load process WITHOUT breaking SSIS. How this can be done will be part of future blog entry. What is involved in building an OLAP data mart? There are a great many steps involved in building OLAP data marts on-the-fly. The key point is that all the steps must be automated to allow for the production of multiple OLAP data marts per day (i.e. many thousands, each with its own specific data set and attributes). Now most of these steps have a great deal in common with standard data warehouse practices. The key difference is that the databases are all built to order. The only permanent database is the metadata database (shown in orange) which holds all the metadata needed to build everything else (i.e. client orders, configuration information, connection strings, client specific requirements and attributes etc.). The staging database (shown in red) has a short life: it is built, populated and then ripped down as soon as the OLAP Data Mart has been populated. In the diagram below, the OLAP data mart comprises the two blue components: the Data Mart which is a relational database and the OLAP Cube which is an OLAP database implemented using Microsoft Analysis Services (SSAS). The client may receive just the OLAP cube or both components together depending on their reporting requirements.  So, in broad terms the steps required to fulfil a client order are as follows: Step 1: Prepare metadata Create a set of database names unique to the client's order Modify all package connection strings to be used by SSIS to point to new databases and file locations. Step 2: Create relational databases Create the staging and data mart relational databases using dynamic SQL and set the database recovery mode to SIMPLE as we do not need the overhead of logging anything Execute SQL scripts to build all database objects (tables, views, functions and stored procedures) in the two databases Step 3: Load staging database Use SSIS to load all data files into the staging database in a parallel operation Load extension files containing name/value pairs. These will provide client-specific attributes in the OLAP cube. Step 4: Load data mart relational database Load the data from staging into the data mart relational database, again in parallel where possible Allocate surrogate keys and use SSIS to perform surrogate key lookup during the load of fact tables Step 5: Load extension tables & attributes Pivot the extension attributes from their native name/value pairs into proper relational tables Add the extension attributes to the views used by OLAP cube Step 6: Deploy & Process OLAP cube Deploy the OLAP database directly to the server using a C# script task in SSIS Modify the connection string used by the OLAP cube to point to the data mart relational database Modify the cube structure to add the extension attributes to both the data source view and the relevant dimensions Remove any standard attributes that not required Process the OLAP cube Step 7: Backup and drop databases Drop staging database as it is no longer required Backup data mart relational and OLAP database and ship these to the client's infrastructure Drop data mart relational and OLAP database from the build server Mark order complete Start processing the next order, ad infinitum. So my future blog posts and my forthcoming session at the SQLBits conference will all focus on some of the more interesting aspects of building OLAP data marts on-the-fly such as handling the load of extension attributes and how to dynamically alter the structure of an OLAP cube using C#.

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  • Distributed computing for a company? Is there such a 'free' thing?

    - by Jakub
    I am new to the whole distributed computing / cloud thing. But I had an idea at work for our multimedia stuff like movie encoding / cpu intensive things tasks (which sometimes take a few hours). Is there a 'free' (linux?) way to go about using a Windows machine, and offsetting those cpu cycles for that task to say 10 servers that are generally idle (cpu wise)? I'm just curious if there is a way to do this or am I just grasping at straws here. My thought is that a 'cloud' setup would achieve this, however like I stated initially, I am a total newbie when it comes to it. This is just an idea, looking for some thoughts? Anyone achieve this?

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  • The Best Data Integration for Exadata Comes from Oracle

    - by maria costanzo
    Oracle Data Integrator and Oracle GoldenGate offer unique and optimized data integration solutions for Oracle Exadata. For example, customers that choose to feed their data warehouse or reporting database with near real-time throughout the day, can do so without decreasing  performance or availability of source and target systems. And if you ask why real-time, the short answer is: in today’s fast-paced, always-on world, business decisions need to use more relevant, timely data to be able to act fast and seize opportunities. A longer response to "why real-time" question can be found in a related blog post. If we look at the solution architecture, as shown on the diagram below,  Oracle Data Integrator and Oracle GoldenGate are both uniquely designed to take full advantage of the power of the database and to eliminate unnecessary middle-tier components. Oracle Data Integrator (ODI) is the best bulk data loading solution for Exadata. ODI is the only ETL platform that can leverage the full power of Exadata, integrate directly on the Exadata machine without any additional hardware, and by far provides the simplest setup and fastest overall performance on an Exadata system. We regularly see customers achieving a 5-10 times boost when they move their ETL to ODI on Exadata. For  some companies the performance gain is even much higher. For example a large insurance company did a proof of concept comparing ODI vs a traditional ETL tool (one of the market leaders) on Exadata. The same process that was taking 5hrs and 11 minutes to complete using the competing ETL product took 7 minutes and 20 seconds with ODI. Oracle Data Integrator was 42 times faster than the conventional ETL when running on Exadata.This shows that Oracle's own data integration offering helps you to gain the most out of your Exadata investment with a truly optimized solution. GoldenGate is the best solution for streaming data from heterogeneous sources into Exadata in real time. Oracle GoldenGate can also be used together with Data Integrator for hybrid use cases that also demand non-invasive capture, high-speed real time replication. Oracle GoldenGate enables real-time data feeds from heterogeneous sources non-invasively, and delivers to the staging area on the target Exadata system. ODI runs directly on Exadata to use the database engine power to perform in-database transformations. Enterprise Data Quality is integrated with Oracle Data integrator and enables ODI to load trusted data into the data warehouse tables. Only Oracle can offer all these technical benefits wrapped into a single intelligence data warehouse solution that runs on Exadata. Compared to traditional ETL with add-on CDC this solution offers: §  Non-invasive data capture from heterogeneous sources and avoids any performance impact on source §  No mid-tier; set based transformations use database power §  Mini-batches throughout the day –or- bulk processing nightly which means maximum availability for the DW §  Integrated solution with Enterprise Data Quality enables leveraging trusted data in the data warehouse In addition to Starwood Hotels and Resorts, Morrison Supermarkets, United Kingdom’s fourth-largest food retailer, has seen the power of this solution for their new BI platform and shared their story with us. Morrisons needed to analyze data across a large number of manufacturing, warehousing, retail, and financial applications with the goal to achieve single view into operations for improved customer service. The retailer deployed Oracle GoldenGate and Oracle Data Integrator to bring new data into Oracle Exadata in near real-time and replicate the data into reporting structures within the data warehouse—extending visibility into operations. Using Oracle's data integration offering for Exadata, Morrisons produced financial reports in seconds, rather than minutes, and improved staff productivity and agility. You can read more about Morrison’s success story here and hear from Starwood here. From an Irem Radzik article.

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  • How are Reads Distributed in a Workload

    - by Bill Graziano
    People have uploaded nearly one millions rows of trace data to TraceTune.  That’s enough data to start to look at the results in aggregate.  The first thing I want to look at is logical reads.  This is the easiest metric to identify and fix. When you upload a trace, I rank each statement based on the total number of logical reads.  I also calculate each statement’s percentage of the total logical reads.  I do the same thing for CPU, duration and logical writes.  When you view a statement you can see all the details like this: This single statement consumed 61.4% of the total logical reads on the system while we were tracing it.  I also wanted to see the distribution of reads across statements.  That graph looks like this: On average, the highest ranked statement consumed just under 50% of the reads on the system.  When I tune a system, I’m usually starting in one of two modes: this “piece” is slow or the whole system is slow.  If a given piece (screen, report, query, etc.) is slow you can usually find the specific statements behind it and tune it.  You can make that individual piece faster but you may not affect the whole system. When you’re trying to speed up an entire server you need to identity those queries that are using the most disk resources in aggregate.  Fixing those will make them faster and it will leave more disk throughput for the rest of the queries. Here are some of the things I’ve learned querying this data: The highest ranked query averages just under 50% of the total reads on the system. The top 3 ranked queries average 73% of the total reads on the system. The top 10 ranked queries average 91% of the total reads on the system. Remember these are averages across all the traces that have been uploaded.  And I’m guessing that people mainly upload traces where there are performance problems so your mileage may vary. I also learned that slow queries aren’t the problem.  Before I wrote ClearTrace I used to identify queries by filtering on high logical reads using Profiler.  That picked out individual queries but those rarely ran often enough to put a large load on the system. If you look at the execution count by rank you’d see that the highest ranked queries also have the highest execution counts.  The graph would look very similar to the one above but flatter.  These queries don’t look that bad individually but run so often that they hog the disk capacity. The take away from all this is that you really should be tuning the top 10 queries if you want to make your system faster.  Tuning individually slow queries will help those specific queries but won’t have much impact on the system as a whole.

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  • Jini : single server with multiple clients

    - by user200340
    Hi all, I have a question about how to make multiple clients can access a single file located on server side and keep the file consistent. I have a simple PhoneBook server-client Jini program running at the moment, and server only provides some getter functions to clients, such as getName(String number), getNumber(String name) from a PhoneBook class(serializable), phonebook data are stored in a text file (phonebook.txt) at the moment. I have tried to implement some functions allowing to write a new records into the phonebook.txt file. If the writing record (name) is existing, an integer number will be added into the writing record. for example the existing phonebook.txt is .... John 01-01010101 .... if the writing record is "John 01-12345678",then "John_1 01-12345678" will be writen into phonebook.txt However, if i start with two clients A and B (on the same machine using localhost), and A tries to write "John 01-11111111", B tries to write "John 01-22222222". The early record will be overwritten later record. So, there must be something i did complete wrong. My client and server code are just like Jini HelloWorld example. My server side code is . 1. LookupDiscovery with parameter new String[]{""}; 2. DiscoveryListener for LookupDiscovery 3. registrations are saved into a HashTable 4. for every discovered lookup service, i use registrar to register the ServiceItem, ServiceItem contains a null attributeSets, a null serviceId, and a service. The client code has: 1. LookupDiscovery with parameter new String[]{""}; 2. DiscoveryListener for LookupDiscovery 3. a ServiceTemplate with null attributeSets, a null serviceId and a type, the type is the interface class. 4. for each found ServiceRegistrar, if it can find the looking for ServiceTemplate, the returned Object is cast into the type of the interface class. I have tried to google more details, and i found JavaSpace could be the one i missed. But i am still not sure about it (i only start Jini for a very short time). So any help would be greatly appreciated.

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  • Data access pattern

    - by andlju
    I need some advice on what kind of pattern(s) I should use for pushing/pulling data into my application. I'm writing a rule-engine that needs to hold quite a large amount of data in-memory in order to be efficient enough. I have some rather conflicting requirements; It is not acceptable for the engine to always have to wait for a full pre-load of all data before it is functional. Only fetching and caching data on-demand will lead to the engine taking too long before it is running quickly enough. An external event can trigger the need for specific parts of the data to be reloaded. Basically, I think I need a combination of pushing and pulling data into the application. A simplified version of my current "pattern" looks like this (in psuedo-C# written in notepad): // This interface is implemented by all classes that needs the data interface IDataSubscriber { void RegisterData(Entity data); } // This interface is implemented by the data access class interface IDataProvider { void EnsureLoaded(Key dataKey); void RegisterSubscriber(IDataSubscriber subscriber); } class MyClassThatNeedsData : IDataSubscriber { IDataProvider _provider; MyClassThatNeedsData(IDataProvider provider) { _provider = provider; _provider.RegisterSubscriber(this); } public void RegisterData(Entity data) { // Save data for later StoreDataInCache(data); } void UseData(Key key) { // Make sure that the data has been stored in cache _provider.EnsureLoaded(key); Entity data = GetDataFromCache(key); } } class MyDataProvider : IDataProvider { List<IDataSubscriber> _subscribers; // Make sure that the data for key has been loaded to all subscribers public void EnsureLoaded(Key key) { if (HasKeyBeenMarkedAsLoaded(key)) return; PublishDataToSubscribers(key); MarkKeyAsLoaded(key); } // Force all subscribers to get a new version of the data for key public void ForceReload(Key key) { PublishDataToSubscribers(key); MarkKeyAsLoaded(key); } void PublishDataToSubscribers(Key key) { Entity data = FetchDataFromStore(key); foreach(var subscriber in _subscribers) { subscriber.RegisterData(data); } } } // This class will be spun off on startup and should make sure that all data is // preloaded as quickly as possible class MyPreloadingThread { IDataProvider _provider; MyPreloadingThread(IDataProvider provider) { _provider = provider; } void RunInBackground() { IEnumerable<Key> allKeys = GetAllKeys(); foreach(var key in allKeys) { _provider.EnsureLoaded(key); } } } I have a feeling though that this is not necessarily the best way of doing this.. Just the fact that explaining it seems to take two pages feels like an indication.. Any ideas? Any patterns out there I should have a look at?

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  • Data access pattern, combining push and pull?

    - by andlju
    I need some advice on what kind of pattern(s) I should use for pushing/pulling data into my application. I'm writing a rule-engine that needs to hold quite a large amount of data in-memory in order to be efficient enough. I have some rather conflicting requirements; It is not acceptable for the engine to always have to wait for a full pre-load of all data before it is functional. Only fetching and caching data on-demand will lead to the engine taking too long before it is running quickly enough. An external event can trigger the need for specific parts of the data to be reloaded. Basically, I think I need a combination of pushing and pulling data into the application. A simplified version of my current "pattern" looks like this (in psuedo-C# written in notepad): // This interface is implemented by all classes that needs the data interface IDataSubscriber { void RegisterData(Entity data); } // This interface is implemented by the data access class interface IDataProvider { void EnsureLoaded(Key dataKey); void RegisterSubscriber(IDataSubscriber subscriber); } class MyClassThatNeedsData : IDataSubscriber { IDataProvider _provider; MyClassThatNeedsData(IDataProvider provider) { _provider = provider; _provider.RegisterSubscriber(this); } public void RegisterData(Entity data) { // Save data for later StoreDataInCache(data); } void UseData(Key key) { // Make sure that the data has been stored in cache _provider.EnsureLoaded(key); Entity data = GetDataFromCache(key); } } class MyDataProvider : IDataProvider { List<IDataSubscriber> _subscribers; // Make sure that the data for key has been loaded to all subscribers public void EnsureLoaded(Key key) { if (HasKeyBeenMarkedAsLoaded(key)) return; PublishDataToSubscribers(key); MarkKeyAsLoaded(key); } // Force all subscribers to get a new version of the data for key public void ForceReload(Key key) { PublishDataToSubscribers(key); MarkKeyAsLoaded(key); } void PublishDataToSubscribers(Key key) { Entity data = FetchDataFromStore(key); foreach(var subscriber in _subscribers) { subscriber.RegisterData(data); } } } // This class will be spun off on startup and should make sure that all data is // preloaded as quickly as possible class MyPreloadingThread { IDataProvider _provider; MyPreloadingThread(IDataProvider provider) { _provider = provider; } void RunInBackground() { IEnumerable<Key> allKeys = GetAllKeys(); foreach(var key in allKeys) { _provider.EnsureLoaded(key); } } } I have a feeling though that this is not necessarily the best way of doing this.. Just the fact that explaining it seems to take two pages feels like an indication.. Any ideas? Any patterns out there I should have a look at?

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  • Import csv data (SDK iphone)

    - by Ni
    I am new to cocoa. I have been working on these stuff for a few days. For the following code, i can read all the data in the string, and successfully get the data for plot. NSMutableArray *contentArray = [NSMutableArray array]; NSString *filePath = @"995,995,995,995,995,995,995,995,1000,997,995,994,992,993,992,989,988,987,990,993,989"; NSArray *myText = [filePath componentsSeparatedByString:@","]; NSInteger idx; for (idx = 0; idx < myText.count; idx++) { NSString *data =[myText objectAtIndex:idx]; NSLog(@"%@", data); id x = [NSNumber numberWithFloat:0+idx*0.002777778]; id y = [NSDecimalNumber decimalNumberWithString:data]; [contentArray addObject: [NSMutableDictionary dictionaryWithObjectsAndKeys:x, @"x", y, @"y", nil]]; } self.dataForPlot = contentArray; then, i try to load the data from csv file. the data in Data.csv file has the same value and the same format as 995,995,995,995,995,995,995,995,1000,997,995,994,992,993,992,989,988,987,990,993,989. I run the code, it is supposed to give the same graph output. however, it seems that the data is not loaded from csv file successfully. i can not figure out what's wrong with my code. NSMutableArray *contentArray = [NSMutableArray array]; NSString *filePath = [[NSBundle mainBundle] pathForResource:@"Data" ofType:@"csv"]; NSString *Data = [NSString stringWithContentsOfFile:filePath encoding:NSUTF8StringEncoding error:nil ]; if (Data) { NSArray *myText = [Data componentsSeparatedByString:@","]; NSInteger idx; for (idx = 0; idx < myText.count; idx++) { NSString *data =[myText objectAtIndex:idx]; NSLog(@"%@", data); id x = [NSNumber numberWithFloat:0+idx*0.002777778]; id y = [NSDecimalNumber decimalNumberWithString:data]; [contentArray addObject: [NSMutableDictionary dictionaryWithObjectsAndKeys:x, @"x", y, @"y",nil]]; } self.dataForPlot = contentArray; } The only difference is NSString *filePath = [[NSBundle mainBundle] pathForResource:@"Data" ofType:@"csv"]; NSString *Data = [NSString stringWithContentsOfFile:filePath encoding:NSUTF8StringEncoding error:nil ]; if (data){ } did i do anything wrong here?? Thanks for your help!!!!

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  • POST data not being received

    - by Alexander
    I've got an iPhone App that is supposed to send POST data to my server to register the device in a MySQL database so we can send notifications etc... to it. It sends it's unique identifier, device name, token, and a few other small things like passwords and usernames as a POST request to our server. The problem is that sometimes the server doesn't receive the data. And by this I mean, its not just receiving blank values for the POST inputs but, its not receiving ANY post data at all. I am logging all POST inputs to my server into some log files and when the script that relies on the POST data from the device fails (detects no data) I notice that its because NO POST data was sent. Is this a problem on the server, like refusing data or something or does this have to be on the client's side? What could be causing this?

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  • Oracle Big Data Learning Library - Click on LEARN BY PRODUCT to Open Page

    - by chberger
    Oracle Big Data Learning Library... Learn about Oracle Big Data, Data Science, Learning Analytics, Oracle NoSQL Database, and more! Oracle Big Data Essentials Attend this Oracle University Course! Using Oracle NoSQL Database Attend this Oracle University class! Oracle and Big Data on OTN See the latest resource on OTN. Search Welcome Get Started Learn by Role Learn by Product Latest Additions Additional Resources Oracle Big Data Appliance Oracle Big Data and Data Science Basics Meeting the Challenge of Big Data Oracle Big Data Tutorial Video Series Oracle MoviePlex - a Big Data End-to-End Series of Demonstrations Oracle Big Data Overview Oracle Big Data Essentials Data Mining Oracle NoSQL Database Tutorial Videos Oracle NoSQL Database Tutorial Series Oracle NoSQL Database Release 2 New Features Using Oracle NoSQL Database Exalytics Enterprise Manager 12c R3: Manage Exalytics Setting Up and Running Summary Advisor on an E s Oracle R Enterprise Oracle R Enterprise Tutorial Series Oracle Big Data Connectors Integrate All Your Data with Oracle Big Data Connectors Using Oracle Direct Connector for HDFS to Read the Data from HDSF Using Oracle R Connector for Hadoop to Analyze Data Oracle NoSQL Database Oracle NoSQL Database Tutorial Videos Oracle NoSQL Database Tutorial Series Oracle NoSQL Database Release 2 New Features  Using Oracle NoSQL Database eries Oracle Business Intelligence Enterprise Edition Oracle Business Intelligence Oracle BI 11g R1: Create Analyses and Dashboards - 4 day class Oracle BI Publisher 11g R1: Fundamentals - 3 day class Oracle BI 11g R1: Build Repositories - 5 day class

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  • Let's introduce the Oracle Enterprise Data Quality family!

    - by Sarah Zanchetti
    The Oracle Enterprise Data Quality family of products helps you to achieve maximum value from their business applications by delivering fit-­for-­purpose data. OEDQ is a state-of-the-art collaborative data quality profiling, analysis, parsing, standardization, matching and merging product, designed to help you understand, improve, protect and govern the quality of the information your business uses, all from a single integrated environment. Oracle Enterprise Data Quality products are: Oracle Enterprise Data Quality Profile and Audit Oracle Enterprise Data Quality Parsing and Standardization Oracle Enterprise Data Quality Match and Merge Oracle Enterprise Data Quality Address Verification Server Oracle Enterprise Data Quality Product Data Parsing and Standardization Oracle Enterprise Data Quality Product Data Match and Merge Also, the following are some of the key features of OEDQ: Integrated data profiling, auditing, cleansing and matching Browser-based client access Ability to handle all types of data – for example customer, product, asset, financial, operational Connection to any JDBC-compliant data sources and targets Multi-user project support (role-based access, issue tracking, process annotation, and version control) Services Oriented Architecture (SOA) - support for designing processes that may be exposed to external applications as a service Designed to process large data volumes A single repository to hold data along with gathered statistics and project tracking information, with shared access Intuitive graphical user interface designed to help you solve real-world information quality issues quickly Easy, data-led creation and extension of validation and transformation rules Fully extensible architecture allowing the insertion of any required custom processing  If you need to learn more about EDQ, or get assistance for any kind of issue, the Oracle Technology Network offers a huge range of resources on Oracle software. Discuss technical problems and solutions on the Discussion Forums. Get hands-on step-by-step tutorials with Oracle By Example. Download Sample Code. Get the latest news and information on any Oracle product. You can also get further help and information with Oracle software from: My Oracle Support Oracle Support Services An Information Center is available, where you can find technical information and fast solutions to the most common already solved issues: Information Center: Oracle Enterprise Data Quality [ID 1555073.2]

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  • 'Similarity' in Data Mining

    - by Shailesh Tainwala
    In the field of Data Mining, is there a specific sub-discipline called 'Similarity'? If yes, what does it deal with. Any examples, links, references will be helpful. Also, being new to the field, I would like the community opinion on how closely related Data Mining and Artificial Intelligence are. Are they synonyms, is one the subset of the other? Thanks in advance for sharing your knowledge.

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  • Algorithm detect repeating/similiar strings in a corpus of data -- say email subjects, in Python

    - by RizwanK
    I'm downloading a long list of my email subject lines , with the intent of finding email lists that I was a member of years ago, and would want to purge them from my Gmail account (which is getting pretty slow.) I'm specifically thinking of newsletters that often come from the same address, and repeat the product/service/group's name in the subject. I'm aware that I could search/sort by the common occurrence of items from a particular email address (and I intend to), but I'd like to correlate that data with repeating subject lines.... Now, many subject lines would fail a string match, but "Google Friends : Our latest news" "Google Friends : What we're doing today" are more similar to each other than a random subject line, as is: "Virgin Airlines has a great sale today" "Take a flight with Virgin Airlines" So -- how can I start to automagically extract trends/examples of strings that may be more similar. Approaches I've considered and discarded ('because there must be some better way'): Extracting all the possible substrings and ordering them by how often they show up, and manually selecting relevant ones Stripping off the first word or two and then count the occurrence of each sub string Comparing Levenshtein distance between entries Some sort of string similarity index ... Most of these were rejected for massive inefficiency or likelyhood of a vast amount of manual intervention required. I guess I need some sort of fuzzy string matching..? In the end, I can think of kludgy ways of doing this, but I'm looking for something more generic so I've added to my set of tools rather than special casing for this data set. After this, I'd be matching the occurring of particular subject strings with 'From' addresses - I'm not sure if there's a good way of building a data structure that represents how likely/not two messages are part of the 'same email list' or by filtering all my email subjects/from addresses into pools of likely 'related' emails and not -- but that's a problem to solve after this one. Any guidance would be appreciated.

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