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• Oracle Insurance Gets Innovative with Insurance Business Intelligence

  - by nicole.bruns(at)oracle.com

  Oracle Insurance announced yesterday the availability of Oracle Insurance Insight 7.0, an insurance-specific data warehouse and business intelligence (BI) system that transforms the traditional approach to BI by involving business users in the creation and maintenance."Rapid access to business intelligence is essential to compete and thrive in today's insurance industry," said Srini Venkatasantham, vice president, Product Strategy, Oracle Insurance. "The adaptive data modeling approach of Oracle Insurance Insight 7.0, combined with the insurance-specific data model, offers global insurance companies a faster, easier way to get the intelligence they need to make better-informed business decisions." New Features in Oracle Insurance 7.0 include:"Adaptive Data Modeling" via the new warehouse palette: Gives business users the power to configure lines of business via an easy-to-use warehouse palette tool. Oracle Insurance Insight then automatically creates data warehouse elements - such as line-specific database structures and extract-transform-load (ETL) processes -speeding up time-to-value for BI initiatives. Out-of-the-box insurance models or create-from-scratch option: Includes pre-built content and interfaces for six Property and Casualty (P&C) lines. Additionally, insurers can use the warehouse palette to deploy any and all P&C or General Insurance lines of business from scratch, helping insurers support operations in any country.Leverages Oracle technologies: In addition to Oracle Business Intelligence Enterprise Edition, the solution includes Oracle Database 11g as well as Oracle Data Integrator Enterprise Edition 11g, which delivers Extract, Load and Transform (E-L-T) architecture and eliminates the need for a separate transformation server. Additionally, the expanded Oracle technology infrastructure enables support for Oracle Exadata. Martina Conlon, a Principal with Novarica's Insurance practice, and author of Business Intelligence in Insurance: Current State, Challenges, and Expectations says, "The need for continued investment by insurers in business intelligence capabilities is widely understood, and the industry is acting. Arming the business intelligence implementation with predefined insurance specific content, and flexible and configurable technology will get these projects up and running faster."Learn moreTo see a demo of the Oracle Insurance Insight system, click hereTo read the press announcement, click here

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• make-like build tools for data?

  - by miku

  Make is a standard tools for building software. But make decides whether a target needs to be regenerated by comparing file modification times. Are there any proven, preferably small tools that handle builds not for software but for data? Something that regenerates targets not only on mod times but on certain other properties (e.g. completeness). (Or alternatively some paper that describes such a tool.) As illustration: I'd like to automate the following process: get data (e.g. a tarball) from some regularly updated source copy somewhere if it's not there (based e.g. on some filename-scheme) convert the files to different format (but only if there aren't successfully converted ones there - e.g. from a previous attempt - custom comparison routine) for each file find a certain data element and fetch some additional file from say an URL, but only if that hasn't been downloaded yet (decide on existence of file and file "freshness") finally compute something (e.g. word count for something identifiable and store it in the database, but only if the DB does not have an entry for that exact ID yet) Observations: there are different stages each stage is usually simple to compute or implement in isolation each stage may be simple, but the data volume may be large each stage may produce a few errors each stage may have different signals, on when (re)processing is needed Requirements: builds should be interruptable and idempotent (== robust) when interrupted, already processed objects should be reused to speedup the next run data paths should be easy to adjust (simple syntax, nothing new to learn, internal dsl would be ok) some form of dependency graph, that describes the process would be nice for later visualizations should leverage existing programs, if possible I've done some research on make alternatives like rake and have worked a lot with ant and maven in the past. All these tools naturally focus on code and software build, not on data builds. A system we have in place now for a task similar to the above is pretty much just shell scripts, which are compact (and are a ok glue for a variety of other programs written in other languages), so I wonder if worse is better?

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• Social Analytics in your current data

  - by Dan McGrath

  By now everyone is aware of the massive boom in social-networking (Twitter, Facebook, LinkedIn) and obviously a big part of its business model revolves around being able to mine this data to create information that can be used to make money for someone. Gartner has identified 'Social Analytics' as one of the top 10 strategic technologies for 2011. Has anyone looked at their existing data structures to determine if they could extract a social graph and then perform further data mining against this? How does it fit in with your other strategic development strategies? What information are you trying to extract from the data? Take for example, a bank. They could conceivably determine a social graph through account relationships and transactions. Obviously there would be open edges on the graph where funds enter/leave the institute, but that shouldn't detract from the usefulness of the data. I'm looking for actual examples with the answers, as well as why/how they did it. References to other sites will be greatly appreciated. Note: I'm not at all referring to mining data out of actual social networks.

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• The Oldest Big Data Problem: Parsing Human Language

  - by dan.mcclary

  There's a new whitepaper up on Oracle Technology Network which details the use of Digital Reasoning Systems' Synthesys software on Oracle Big Data Appliance.  Digital Reasoning's approach is inherently "big data friendly," as it leverages multiple components of the Hadoop ecosystem.  Moreover, the paper addresses the oldest big data problem of them all: extracting knowledge from human text.   You can find the paper here.   From the Executive Summary: There is a wealth of information to be extracted from natural language, but that extraction is challenging. The volume of human language we generate constitutes a natural Big Data problem, while its complexity and nuance requires a particular expertise to model and mine. In this paper we illustrate the impressive combination of Oracle Big Data Appliance and Digital Reasoning Synthesys software. The combination of Synthesys and Big Data Appliance makes it possible to analyze tens of millions of documents in a matter of hours. Moreover, this powerful combination achieves four times greater throughput than conducting the equivalent analysis on a much larger cloud-deployed Hadoop cluster.

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• Uses of persistent data structures in non-functional languages

  - by Ray Toal

  Languages that are purely functional or near-purely functional benefit from persistent data structures because they are immutable and fit well with the stateless style of functional programming. But from time to time we see libraries of persistent data structures for (state-based, OOP) languages like Java. A claim often heard in favor of persistent data structures is that because they are immutable, they are thread-safe. However, the reason that persistent data structures are thread-safe is that if one thread were to "add" an element to a persistent collection, the operation returns a new collection like the original but with the element added. Other threads therefore see the original collection. The two collections share a lot of internal state, of course -- that's why these persistent structures are efficient. But since different threads see different states of data, it would seem that persistent data structures are not in themselves sufficient to handle scenarios where one thread makes a change that is visible to other threads. For this, it seems we must use devices such as atoms, references, software transactional memory, or even classic locks and synchronization mechanisms. Why then, is the immutability of PDSs touted as something beneficial for "thread safety"? Are there any real examples where PDSs help in synchronization, or solving concurrency problems? Or are PDSs simply a way to provide a stateless interface to an object in support of a functional programming style?

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• Translate report data export from RUEI into HTML for import into OpenOffice Calc Spreadsheets

  - by [email protected]

  A common question of users is, How to import the data from the automated data export of Real User Experience Insight (RUEI) into tools for archiving, dashboarding or combination with other sets of data.XML is well-suited for such a translation via the companion Extensible Stylesheet Language Transformations (XSLT). Basically XSLT utilizes XSL, a template on what to read from your input XML data file and where to place it into the target document. The target document can be anything you like, i.e. XHTML, CSV, or even a OpenOffice Spreadsheet, etc. as long as it is a plain text format.XML 2 OpenOffice.org SpreadsheetFor the XSLT to work as an OpenOffice.org Calc Import Filter:How to add an XML Import Filter to OpenOffice CalcStart OpenOffice.org Calc andselect Tools > XML Filter SettingsNew...Fill in the details as follows:Filter name: RUEI Import filterApplication: OpenOffice.org Calc (.ods)Name of file type: Oracle Real User Experience InsightFile extension: xmlSwitch to the transformation tab and enter/select the following leaving the rest untouchedXSLT for import: ruei_report_data_import_filter.xslPlease see at the end of this blog post for a download of the referenced file.Select RUEI Import filter from list and Test XSLTClick on Browse to selectTransform file: export.php.xmlOpenOffice.org Calc will transform and load the XML file you retrieved from RUEI in a human-readable format.You can now select File > Open... and change the filetype to open your RUEI exports directly in OpenOffice.org Calc, just like any other a native Spreadsheet format.Files of type: Oracle Real User Experience Insight (*.xml)File name: export.php.xml XML 2 XHTMLMost XML-powered browsers provides for inherent XSL Transformation capabilities, you only have to reference the XSLT Stylesheet in the head of your XML file. Then open the file in your favourite Web Browser, Firefox, Opera, Safari or Internet Explorer alike.<?xml version="1.0" encoding="ISO-8859-1"?><!-- inserted line below --> <?xml-stylesheet type="text/xsl" href="ruei_report_data_export_2_xhtml.xsl"?><!-- inserted line above --><report>You can find a patched example export from RUEI plus the above referenced XSL-Stylesheets here: export.php.xml - Example report data export from RUEI ruei_report_data_export_2_xhtml.xsl - RUEI to XHTML XSL Transformation Stylesheetruei_report_data_import_filter.xsl - OpenOffice.org XML import filter for RUEI report export data If you would like to do things like this on the command line you can use either Xalan or xsltproc.The basic command syntax for xsltproc is very simple:xsltproc -o output.file stylesheet.xslt inputfile.xmlYou can use this with the above two stylesheets to translate RUEI Data Exports into XHTML and/or OpenOffice.org Calc ODS-Format. Or you could write your own XSLT to transform into Comma separated Value lists.Please let me know what you think or do with this information in the comments below.Kind regards,Stefan ThiemeReferences used:OpenOffice XML Filter - Create XSLT filters for import and export - http://user.services.openoffice.org/en/forum/viewtopic.php?f=45&t=3490SUN OpenOffice.org XML File Format 1.0 - http://xml.openoffice.org/xml_specification.pdf

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• Data Auditor by Example

  - by Jinjin.Wang

  OWB has a node Data Auditors under Oracle Module in Projects Navigator. What is data auditor and how to use it? I will give an introduction to data auditor and show its usage by examples. Data auditor is an important tool in ensuring that data quality levels meet business requirements. Data auditor validates data against a set of data rules to determine which records comply and which do not. It gathers statistical metrics on how well the data in a system complies with a rule by auditing and marking how many errors are occurring against the audited table. Data auditors are typically scheduled for regular execution as part of a process flow, to monitor the quality of the data in an operational environment such as a data warehouse or ERP system, either immediately after updates like data loads, or at regular intervals. How to use data auditor to monitor data quality? Only objects with data rules can be monitored, so the first step is to define data rules according to business requirements and apply them to the objects you want to monitor. The objects can be tables, views, materialized views, and external tables. Secondly create a data auditor containing the objects. You can configure the data auditor and set physical deployment parameters for it as optional, which will be used while running the data auditor. Then deploy and run the data auditor either manually or as part of the process flow. After execution, the data auditor sets several output values, and records that are identified as not complying with the defined data rules contained in the data auditor are written to error tables. Here is an example. We have two tables DEPARTMENTS and EMPLOYEES (see pic-1 and pic-2. Click here for DDL and data) imported into OWB. We want to gather statistical metrics on how well data in these two tables satisfies the following requirements: a. Values of the EMPLOYEES.EMPLOYEE_ID attribute are three-digit numbers. b. Valid values for EMPLOYEES.JOB_ID are IT_PROG, SA_REP, SH_CLERK, PU_CLERK, and ST_CLERK. c. EMPLOYEES.EMPLOYEE_ID is related to DEPARTMENTS.MANAGER_ID. Pic-1 EMPLOYEES Pic-2 DEPARTMENTS 1. To determine legal data within EMPLOYEES or legal relationships between data in different columns of the two tables, firstly we define data rules based on the three requirements and apply them to tables. a. The first requirement is about patterns that an attribute is allowed to conform to. We create a Domain Pattern List data rule EMPLOYEE_PATTERN_RULE here. The pattern is defined in the Oracle Database regular expression syntax as ^([0-9]{3})$ Apply data rule EMPLOYEE_PATTERN_RULE to table EMPLOYEES.

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• Big Data – Buzz Words: What is Hadoop – Day 6 of 21

  - by Pinal Dave

  In yesterday’s blog post we learned what is NoSQL. In this article we will take a quick look at one of the four most important buzz words which goes around Big Data – Hadoop. What is Hadoop? Apache Hadoop is an open-source, free and Java based software framework offers a powerful distributed platform to store and manage Big Data. It is licensed under an Apache V2 license. It runs applications on large clusters of commodity hardware and it processes thousands of terabytes of data on thousands of the nodes. Hadoop is inspired from Google’s MapReduce and Google File System (GFS) papers. The major advantage of Hadoop framework is that it provides reliability and high availability. What are the core components of Hadoop? There are two major components of the Hadoop framework and both fo them does two of the important task for it. Hadoop MapReduce is the method to split a larger data problem into smaller chunk and distribute it to many different commodity servers. Each server have their own set of resources and they have processed them locally. Once the commodity server has processed the data they send it back collectively to main server. This is effectively a process where we process large data effectively and efficiently. (We will understand this in tomorrow’s blog post). Hadoop Distributed File System (HDFS) is a virtual file system. There is a big difference between any other file system and Hadoop. When we move a file on HDFS, it is automatically split into many small pieces. These small chunks of the file are replicated and stored on other servers (usually 3) for the fault tolerance or high availability. (We will understand this in the day after tomorrow’s blog post). Besides above two core components Hadoop project also contains following modules as well. Hadoop Common: Common utilities for the other Hadoop modules Hadoop Yarn: A framework for job scheduling and cluster resource management There are a few other projects (like Pig, Hive) related to above Hadoop as well which we will gradually explore in later blog posts. A Multi-node Hadoop Cluster Architecture Now let us quickly see the architecture of the a multi-node Hadoop cluster. A small Hadoop cluster includes a single master node and multiple worker or slave node. As discussed earlier, the entire cluster contains two layers. One of the layer of MapReduce Layer and another is of HDFC Layer. Each of these layer have its own relevant component. The master node consists of a JobTracker, TaskTracker, NameNode and DataNode. A slave or worker node consists of a DataNode and TaskTracker. It is also possible that slave node or worker node is only data or compute node. The matter of the fact that is the key feature of the Hadoop. In this introductory blog post we will stop here while describing the architecture of Hadoop. In a future blog post of this 31 day series we will explore various components of Hadoop Architecture in Detail. Why Use Hadoop? There are many advantages of using Hadoop. Let me quickly list them over here: Robust and Scalable – We can add new nodes as needed as well modify them. Affordable and Cost Effective – We do not need any special hardware for running Hadoop. We can just use commodity server. Adaptive and Flexible – Hadoop is built keeping in mind that it will handle structured and unstructured data. Highly Available and Fault Tolerant – When a node fails, the Hadoop framework automatically fails over to another node. Why Hadoop is named as Hadoop? In year 2005 Hadoop was created by Doug Cutting and Mike Cafarella while working at Yahoo. Doug Cutting named Hadoop after his son’s toy elephant. Tomorrow In tomorrow’s blog post we will discuss Buzz Word – MapReduce. 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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• Master Data Management for Location Data - Oracle Site Hub

  - by david.butler(at)oracle.com

  Most MDM discussions cover key domains such as customer, supplier, product, service, and reference data. It is usually understood that these domains have complex structures and hundreds if not thousands of attributes that need governing. Location, on the other hand, strikes most people as address data. How hard can that be? But for many industries, locations are complex, and site information is critical to efficient operations and relevant analytics. Retail stores and malls, bank branches, construction sites come to mind. But one of the best industries for illustrating the power of a site mastering application is Oil & Gas.   Oracle's Master Data Management solution for location data is the Oracle Site Hub. It is a location mastering solution that enables organizations to centralize site and location specific information from heterogeneous systems, creating a single view of site information that can be leveraged across all functional departments and analytical systems.   Let's take a look at the location entities the Oracle Site Hub can manage for the Oil & Gas industry: organizations, property, land, buildings, roads, oilfield, service center, inventory site, real estate, facilities, refineries, storage tanks, vendor locations, businesses, assets; project site, area, well, basin, pipelines, critical infrastructure, offshore platform, compressor station, gas station, etc. Any site can be classified into multiple hierarchies, like organizational hierarchy, operational hierarchy, geographic hierarchy, divisional hierarchies and so on. Any site can also be associated to multiple clusters, i.e. collections of sites, and these can be used as a foundation for driving reporting, analysis, organize daily work, etc. Hierarchies can also be used to model entities which are structured or non-structured collections of nodes, like for example routes, pipelines and more. The User Defined Attribute Framework provides the needed infrastructure to add single row attributes groups like well base attributes (well IDs, well type, well structure and key characterizing measures, and more) and well geometry, and multi row attribute groups like well applications, permits, production data, activities, operations, logs, treatments, tests, drills, treatments, and KPIs. Site Hub can also model areas, lands, fields, basins, pools, platforms, eco-zones, and stratigraphic layers as specific sites, tracking their base attributes, aliases, descriptions, subcomponents and more. Midstream entities (pipelines, logistic sites, pump stations) and downstream entities (cylinders, tanks, inventories, meters, partner's sites, routes, facilities, gas stations, and competitor sites) can also be easily modeled, together with their specific attributes and relationships. Site Hub can store any type of unstructured data associated to a site. This could be stored directly or on an external content management solution, like Oracle Universal Content Management. Considering a well, for example, Site Hub can store any relevant associated multimedia file such as: CAD drawings of the well profile, structure and/or parts, engineering documents, contracts, applications, permits, logs, pictures, photos, videos and more. For any site entity, Site Hub can associate all the related assets and equipments at the site, as well as all relationships between sites, between a site and multiple parties, and between a site and any purchasable or sellable item, over time. Items can be equipment, instruments, facilities, services, products, production entities, production facilities (pipelines, batteries, compressor stations, gas plants, meters, separators, etc.), support facilities (rigs, roads, transmission or radio towers, airstrips, etc.), supplier products and services, catalogs, and more. Items can just be associated to sites using standard Site Hub features, or they can be fully mastered by implementing Oracle Product Hub. Site locations (addresses or geographical coordinates) are also managed with out-of-the-box address geo-coding capabilities coupled with Google Maps integration to deliver powerful mapping capabilities and spatial data analysis. Locations can be shared between different sites. Centered on the site location, any site can also have associated areas. Site Hub can master any site location specific information, like for example cadastral, ownership, jurisdictional, geological, seismic and more, and any site-centric area specific information, like for example economical, political, risk, weather, logistic, traffic information and more. Now if anyone ever asks you why locations need MDM, think about how all these Oil & Gas entities and attributes would translate into your business locations. To learn more about Oracle's full MDM solution for the digital oil field, here is a link to Roberto Negro's outstanding whitepaper: Oracle Site Master Data Management for mastering wells and other PPDM entities in a digital oilfield context  

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• Optimal Data Structure for our own API

  - by vermiculus

  I'm in the early stages of writing an Emacs major mode for the Stack Exchange network; if you use Emacs regularly, this will benefit you in the end. In order to minimize the number of calls made to Stack Exchange's API (capped at 10000 per IP per day) and to just be a generally responsible citizen, I want to cache the information I receive from the network and store it in memory, waiting to be accessed again. I'm really stuck as to what data structure to store this information in. Obviously, it is going to be a list. However, as with any data structure, the choice must be determined by what data is being stored and what how it will be accessed. What, I would like to be able to store all of this information in a single symbol such as stack-api/cache. So, without further ado, stack-api/cache is a list of conses keyed by last update: `(<csite> <csite> <csite>) where <csite> would be (1362501715 . <site>) At this point, all we've done is define a simple association list. Of course, we must go deeper. Each <site> is a list of the API parameter (unique) followed by a list questions: `("codereview" <cquestion> <cquestion> <cquestion>) Each <cquestion> is, you guessed it, a cons of questions with their last update time: `(1362501715 <question>) (1362501720 . <question>) <question> is a cons of a question structure and a list of answers (again, consed with their last update time): `(<question-structure> <canswer> <canswer> <canswer> and ` `(1362501715 . <answer-structure>) This data structure is likely most accurately described as a tree, but I don't know if there's a better way to do this considering the language, Emacs Lisp (which isn't all that different from the Lisp you know and love at all). The explicit conses are likely unnecessary, but it helps my brain wrap around it better. I'm pretty sure a <csite>, for example, would just turn into (<epoch-time> <api-param> <cquestion> <cquestion> ...) Concerns: Does storing data in a potentially huge structure like this have any performance trade-offs for the system? I would like to avoid storing extraneous data, but I've done what I could and I don't think the dataset is that large in the first place (for normal use) since it's all just human-readable text in reasonable proportion. (I'm planning on culling old data using the times at the head of the list; each inherits its last-update time from its children and so-on down the tree. To what extent this cull should take place: I'm not sure.) Does storing data like this have any performance trade-offs for that which must use it? That is, will set and retrieve operations suffer from the size of the list? Do you have any other suggestions as to what a better structure might look like?

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• How do you handle the fetchxml result data?

  - by Luke Baulch

  I have avoided working with fetchxml as I have been unsure the best way to handle the result data after calling crmService.Fetch(fetchXml). In a couple of situations, I have used an XDocument with LINQ to retrieve the data from this data structure, such as: XDocument resultset = XDocument.Parse(_service.Fetch(fetchXml)); if (resultset.Root == null || !resultset.Root.Elements("result").Any()) { return; } foreach (var displayItem in resultset.Root.Elements("result").Select(item => item.Element(displayAttributeName)).Distinct()) { if (displayItem!= null && displayItem.Value != null) { dropDownList.Items.Add(displayItem.Value); } } What is the best way to handle fetchxml result data, so that it can be easily used. Applications such as passing these records into an ASP.NET datagrid would be quite useful.

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• Generate Entity Data Model from Data Contract

  - by CSmooth.net

  I would like to find a fast way to convert a Data Contract to a Entity Data Model. Consider the following Data Contract: [DataContract] class PigeonHouse { [DataMember] public string housename; [DataMember] public List<Pigeon> pigeons; } [DataContract] class Pigeon { [DataMember] public string name; [DataMember] public int numberOfWings; [DataMember] public int age; } Is there an easy way to create an ADO.NET Entity Data Model from this code?

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• Detecting a Lightweight Core Data Migration

  - by hadronzoo

  I'm using Core Data's automatic lightweight migration successfully. However, when a particular entity gets created during a migration, I'd like to populate it with some data. Of course I could check if the entity is empty every time the application starts, but this seems inefficient when Core Data has a migration framework. Is it possible to detect when a lightweight migration occurs (possibly using KVO or notifications), or does this require implementing standard migrations? I've tried using the NSPersistentStoreCoordinatorStoresDidChangeNotification, but it doesn't fire when migrations occur.

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• Getting started with massive data

  - by Max

  I'm a math guy and occasionally do some statistics/machine learning analysis consulting projects on the side. The data I have access to are usually on the smaller side, at most a couple hundred of megabytes (and almost always far less), but I want to learn more about handling and analyzing data on the gigabyte/terabyte scale. What do I need to know and what are some good resources to learn from? Hadoop/MapReduce is one obvious start. Is there a particular programming language I should pick up? (I primarily work now in Python, Ruby, R, and occasionally Java, but it seems like C and Clojure are often used for large-scale data analysis?) I'm not really familiar with the whole NoSQL movement, except that it's associated with big data. What's a good place to learn about it, and is there a particular implementation (Cassandra, CouchDB, etc.) I should get familiar with? Where can I learn about applying machine learning algorithms to huge amounts of data? My math background is mostly on the theory side, definitely not on the numerical or approximation side, and I'm guessing most of the standard ML algorithms don't really scale. Any other suggestions on things to learn would be great!

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• what is best way to store long term data in iphone Core Data or SQLLite?

  - by AmitSri

  Hi all, I am working on i-Phone app targeting 3.1.3 and later SDK. I want to know the best way to store user's long term data on i-phone without losing performance, consistency and security. I know, that i can use Core Data, PList and SQL-Lite for storing user specific data in custom formats.But, want to know which one is good to use without compromising app performance and scalability in near future. Thanks

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• Prove that the set of regular languages is a proper subset of the set of the context-free languages

  - by David Relihan

  I was brushing up (not homework)on some computation-theory and came accross this problem: How can you prove that the set of regular languages is a proper subset of the set of the context-free languages. Now I know a language is regular iff it is accepted by a finite automaton. And I know a language is context-free iff it is accepted by a pushdown automaton. But I'm not sure of what solution is.

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• How to view existing data in Core Data?

  - by mshsayem

  Well, may be this question is silly, but I couldn't find a way (except programmatically). I built a project (for iPhone OS 3.0) which uses Core Data. The xcdatamodel file shows the schema description, but I want to see the data in tabular form (like the management studio for mssql server or phpmyadmin for mysql). Is there any way (except coding)? What is that? Also, which file (in disk/device) those data are stored into? [ I built the tutorial (from apple) on Core Data, named Locations. They used this line somewhere in the code: NSURL *storeUrl = [NSURL fileURLWithPath: [[self applicationDocumentsDirectory] stringByAppendingPathComponent: @"Locations.sqlite"]]; But, I did not see any "xxxxx.sqlite" file in project location (nor in the disk).]

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• Efficient alternatives to merge for larger data.frames R

  - by Etienne Low-Décarie

  I am looking for an efficient (both computer resource wise and learning/implementation wise) method to merge two larger (size1 million / 300 KB RData file) data frames. "merge" in base R and "join" in plyr appear to use up all my memory effectively crashing my system. Example load test data frame and try test.merged<-merge(test, test) or test.merged<-join(test, test, type="all") - The following post provides a list of merge and alternatives: How to join data frames in R (inner, outer, left, right)? The following allows object size inspection: https://heuristically.wordpress.com/2010/01/04/r-memory-usage-statistics-variable/ Data produced by anonym

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• find the top K most frequent numbers in a data stream

  - by Jin

  This is more of a data structure question rather than a coding question. If I am fetching a data stream, i.e, I keep receiving float numbers once at a time, how should I keep track of the top K frequent numbers? Here my memory is 4G and I prefer to have less communication with hard drive unless necessary. I think heap is good for updating the max and min. How should I design the data structure? Thanks

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• How to obtain a random sub-datatable from another data table

  - by developerit

  Introduction In this article, I’ll show how to get a random subset of data from a DataTable. This is useful when you already have queries that are filtered correctly but returns all the rows. Analysis I came across this situation when I wanted to display a random tag cloud. I already had the query to get the keywords ordered by number of clicks and I wanted to created a tag cloud. Tags that are the most popular should have more chance to get picked and should be displayed larger than less popular ones. Implementation In this code snippet, there is everything you need. ' Min size, in pixel for the tag Private Const MIN_FONT_SIZE As Integer = 9 ' Max size, in pixel for the tag Private Const MAX_FONT_SIZE As Integer = 14 ' Basic function that retreives Tags from a DataBase Public Shared Function GetTags() As MediasTagsDataTable ' Simple call to the TableAdapter, to get the Tags ordered by number of clicks Dim dt As MediasTagsDataTable = taMediasTags.GetDataValide ' If the query returned no result, return an empty DataTable If dt Is Nothing OrElse dt.Rows.Count < 1 Then Return New MediasTagsDataTable End If ' Set the font-size of the group of data ' We are dividing our results into sub set, according to their number of clicks ' Example: 10 results -> [0,2] will get font size 9, [3,5] will get font size 10, [6,8] wil get 11, ... ' This is the number of elements in one group Dim groupLenth As Integer = CType(Math.Floor(dt.Rows.Count / (MAX_FONT_SIZE - MIN_FONT_SIZE)), Integer) ' Counter of elements in the same group Dim counter As Integer = 0 ' Counter of groups Dim groupCounter As Integer = 0 ' Loop througt the list For Each row As MediasTagsRow In dt ' Set the font-size in a custom column row.c_FontSize = MIN_FONT_SIZE + groupCounter ' Increment the counter counter += 1 ' If the group counter is less than the counter If groupLenth <= counter Then ' Start a new group counter = 0 groupCounter += 1 End If Next ' Return the new DataTable with font-size Return dt End Function ' Function that generate the random sub set Public Shared Function GetRandomSampleTags(ByVal KeyCount As Integer) As MediasTagsDataTable ' Get the data Dim dt As MediasTagsDataTable = GetTags() ' Create a new DataTable that will contains the random set Dim rep As MediasTagsDataTable = New MediasTagsDataTable ' Count the number of row in the new DataTable Dim count As Integer = 0 ' Random number generator Dim rand As New Random() While count < KeyCount Randomize() ' Pick a random row Dim r As Integer = rand.Next(0, dt.Rows.Count - 1) Dim tmpRow As MediasTagsRow = dt(r) ' Import it into the new DataTable rep.ImportRow(tmpRow) ' Remove it from the old one, to be sure not to pick it again dt.Rows.RemoveAt(r) ' Increment the counter count += 1 End While ' Return the new sub set Return rep End Function Pro’s This method is good because it doesn’t require much work to get it work fast. It is a good concept when you are working with small tables, let says less than 100 records. Con’s If you have more than 100 records, out of memory exception may occur since we are coping and duplicating rows. I would consider using a stored procedure instead.

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• Accessing and Updating Data in ASP.NET: Filtering Data Using a CheckBoxList

  This article series, by Scott Mitchell, examines how to work with data using ASP.NET's data source controls. This particular article looks at how to filter data based on a user's selections from a CheckBoxList.

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• Integrating Data Mining into your BI Solution (Presentation)

  I recently gave a live meeting presentation to the UK User Group on Integrating Data Mining into your BI Solution.  In it I talk about and demo ways of using your data mining models inside Integration Services, Analysis Services and Reporting Services.  This is the first in a series of presentations I will be doing for the UG as I try to get the word out that Data Mining can be for the masses. You can download my deck and my line meeting recording from here.

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• Inside Sweden’s Nuclear Bunker Turned Data Center

  - by Jason Fitzpatrick

  A data center inside a decommissioned nuclear bunker is interesting enough, but one that looks as futuristic and awesome as the center under Stockholm begs to be seen. A hundred feet under the city of Stockholm is a decommissioned nuclear bunker that the government had previously leased out intermittently for various events, but it was never put to serious or extended use. Not until, that is,  Jon Karlung discovered the location and brought his vision of an ultra-modern, stylish, and secure data center to life. The passage from Wired’s write up of their photo tour that best encapsulates the feel of the bunker is: Most often data centers are built in boxy warehouses, so Bahnhof stands out as perhaps the world’s most stylish. In fact, it inspired Cisco IT Architect Douglas Alger to write a book on the world’s best-looking data centers. ”The idea that people were sitting in a design meeting and said, ‘what we need for our data center is waterfalls,’ that must have been a very fascinating discussion,” Alger says. Hit up the link below for the full photo tour. Deep Inside the James Bond Villain Lair That Actually Exists [Wired] Why Does 64-Bit Windows Need a Separate “Program Files (x86)” Folder? Why Your Android Phone Isn’t Getting Operating System Updates and What You Can Do About It How To Delete, Move, or Rename Locked Files in Windows

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• Is there a way to track data structure dependencies from the database, through the tiers, all the way out to a web page?

  - by Sean Mickey

  When we design applications, we generally end up with the same tiered sets of data structures: A persistent data structure that is described using DDL and implemented as RDBMS tables and columns. A set of domain objects that consist primarily of data structures, usually combined with business-rule level logic, that are implemented in a programming language such as Java. A set of service layer interfaces that directly support use case implementations (which use the domain data structures as parameters), implemented as EJBs or something equivalent in another programming language. UI screens that allow users to C reate, R etrieve, U pdate, and (maybe) D elete all manner of data structures and graphs of data structures, with numerous screens and with multiple UI widgets, all structured to support the same data structures. But if you want to change the data structures in any of these tiers, it always seems extremely difficult to assess the impact(s) the change will have across the application. UML can help, but tracing through diagram after diagram is not a real solution to this problem. The best I have ever seen was a homespun data tracking spreadsheet document that listed all of the data structures and walked the relationships from tier-to-tier. Is there a tool or accepted approach that makes it easy to identify a data structure in any tier and easily obtain a list of all dependent: database table and column data structures domain object data structures service layer interface methods and parameter data structures screen & UI component data structures

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• Big Data Accelerator

  - by Jean-Pierre Dijcks

  For everyone who does not regularly listen to earnings calls, Oracle's Q4 call was interesting (as it mostly is). One of the announcements in the call was the Big Data Accelerator from Oracle (Seeking Alpha link here - slightly tweaked for correctness shown below):  "The big data accelerator includes some of the standard open source software, HDFS, the file system and a number of other pieces, but also some Oracle components that we think can dramatically speed up the entire map-reduce process. And will be particularly attractive to Java programmers [...]. There are some interesting applications they do, ETL is one. Log processing is another. We're going to have a lot of those features, functions and pre-built applications in our big data accelerator."  Not much else we can say right now, more on this (and Big Data in general) at Openworld!

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