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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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  • Document generation template engine for production usage NVelocity vs StringTemplate

    - by Chris Marisic
    For building a document generation engine what would be the primary .NET framework to be used in production. The 2 main ones I see are NVelocity and StringTemplate. NVelocity in all forks to be almost unsupported at this point where as ST been active atleast as of this year. Are either or both of these stable for use in production (if nv which fork)? Has anyone had any particularly good success with or failures using either of those frameworks?

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  • C# & ASP.Net - determine linq query generation time

    - by Chris Klepeis
    I'd like to detemine the amount of time it takes for my ASP.Net program to generate certain sql queries using linq.... note - I want the query generation time, not the query execution time. Is this possible, or even feasable (if its usually fast)? My website has some heavy traffic and I want to cover all of my bases.

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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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  • 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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  • Transformation of Product Management in Telecommunications for Rapid Launch of Next Generation Products

    - by raul.goycoolea
    @font-face { font-family: "Arial"; }@font-face { font-family: "Courier New"; }@font-face { font-family: "Wingdings"; }@font-face { font-family: "Cambria"; }p.MsoNormal, li.MsoNormal, div.MsoNormal { margin: 0cm 0cm 0.0001pt; font-size: 12pt; font-family: "Times New Roman"; }a:link, span.MsoHyperlink { color: blue; text-decoration: underline; }a:visited, span.MsoHyperlinkFollowed { color: purple; text-decoration: underline; }p.MsoListParagraph, li.MsoListParagraph, div.MsoListParagraph { margin: 0cm 0cm 0.0001pt 36pt; font-size: 12pt; font-family: "Times New Roman"; }p.MsoListParagraphCxSpFirst, li.MsoListParagraphCxSpFirst, div.MsoListParagraphCxSpFirst { margin: 0cm 0cm 0.0001pt 36pt; font-size: 12pt; font-family: "Times New Roman"; }p.MsoListParagraphCxSpMiddle, li.MsoListParagraphCxSpMiddle, div.MsoListParagraphCxSpMiddle { margin: 0cm 0cm 0.0001pt 36pt; font-size: 12pt; font-family: "Times New Roman"; }p.MsoListParagraphCxSpLast, li.MsoListParagraphCxSpLast, div.MsoListParagraphCxSpLast { margin: 0cm 0cm 0.0001pt 36pt; font-size: 12pt; font-family: "Times New Roman"; }div.Section1 { page: Section1; }ol { margin-bottom: 0cm; }ul { margin-bottom: 0cm; } The Telecom industry continues to evolve through disruptive products, uncertain markets, shorter product lifecycles and convergence of technologies. Today’s market has moved from network centric to consumer centric and focuses primarily on the customer experience. It has resulted in several product management challenges such as an increased complexity and volume of offerings, creating product variants, accelerating time-to-market, ability to provide multiple product views for varied stakeholders, leveraging OSS intelligence to BSS layer, product co-creation and increasing audit and security concerns for service providers. The document discusses how enterprise product management enabled by PLM-based product catalogue solutions helps to launch next generation products rapidly in the context of the Telecommunication Industry.   1.0.       Introduction   Figure 1: Business Scenario   Modern business demands the launch of complex products in a very short timeframe and effecting changes in the price plan faster without IT intervention. One of the key transformation initiatives companies are focusing on is in the area of product management transformation and operational efficiency improvement. As part of these initiatives, companies are investing in best- in-class COTs-based Product Management solutions developed on industry-wide standards.   The new COTs packages are planned to integrate with existing or new B/OSS systems to provide a strategic end-to-end agile solution for reduced time-to-market and order journey time. In addition, system rationalization is being undertaken to phase out legacy systems and migrate to strategic systems.   2.0.       An Overview of Product Management in Telecom   Product data in telecom is multi- dimensional and difficult to manage. It increased significantly due to the complexity of the product, product offerings on the converged network, increased volume of offerings, bundled offering structures and ever increasing regulatory requirements.   In addition, the shrinking product lifecycle in telecom makes it difficult to manage the dynamic product data. Mergers and acquisitions coupled with organic growth pose major challenges in product portfolio management. It is a roadblock in the journey towards becoming an agile organization.       Figure 2: Complexity in Product Management   Network Technology’ is the new dimension in telecom product management where the same products are realized through different networks i.e., Soiled network to Converged network. Consequently, the product solution is different.     Figure 3: Current Scenario - Pain Points in Product Management   The major business implications arising out of the current scenario are slow time-to-market and an inefficient process that affects innovation.   3.0. Transformation of Next Generation Product Management   Companies must focus on their Product Management Transformation Journey in the areas of:   ·       Management of single truth of product information across the organization/geographies which is currently managed in heterogeneous systems   ·       Management of the Intellectual Property (IP) on the product concept and partnership in the design of discrete components to integrate into the system   ·       Leveraging structured and unstructured product data within the extended enterprise to extract consumer insights and drive innovation   ·       Management of effective operational separation to comply with regulatory bodies   ·       Reuse of existing designs and add relevant features such as value-added services to enable effective product bundling     Figure 4: Next generation needs   PLM-based Enterprise Product Catalogue solutions efficiently address the above requirements and act as an enabler towards product management transformation and rapid product launch.   4.0. PLM-based Enterprise Product Management     Figure 5: PLM-based Enterprise Product Mastering   Enterprise Product Management (EPM) enables the business to manage complex product attributes of data in complex environments. Product Mastering helps create a 'single view' of the product by creating a business-driven, IT-supported environment where a global 'single truth record' is created, managed and reused.   4.1 The Business Case for Telco PLM-based solutions for Enterprise Product Management   ·       Telco PLM-based Product Mastering solutions provide a centralized authoring environment for product definition and control of all product data and rules   ·       PLM packages are designed to support multiple perspectives of product data (ordering perspective, billing perspective, provisioning perspective)   ·       Maintains relationships/links between different elements of the entire product definition   ·       Telco PLM packages are specialized in next generation lifecycle management requirements of products such as revision and state management, test and release management, role management and impact analysis)   ·       Takes into consideration all aspects of OSS product requirements compared to CRM product catalogue solutions where the product data managed is mostly order oriented and transactional     ·       New breed of Telco PLM packages are designed with 'open' standards such as SID and eTOM. They are interoperable, support integration frameworks such as subscription and notification.   ·       Telco PLM packages have developed good collaboration frameworks to integrate suppliers and partners into the product development value chain   4.2 Various Architectures/Approaches for Product Mastering using Telco PLM systems   4. 2.a Single Central Product Management (Mastering) Approach   Figure 6: Single Central Product Management (Master) Approach       This approach is implemented across verticals such as aerospace and automotive. It focuses on a physically centralized product master to which other sources are dependent on. The product definition data (Product bundles, service bundles, price plans, offers and discounts, product configuration rules and market campaigns) is created and maintained physically in a centralized environment. In addition, the product definition/authoring environment is centralized. The existing legacy product definition data available in CRM product catalogue, billing catalogue and the legacy product catalogue is migrated to the centralized PLM-based Enterprise Product Management solution.   Architectural changes must be made in the existing business landscape of applications to create and revise data because the applications have to refer to the central repository for approvals and validation of product configurations. It is achieved by modifying how the applications write data or how the applications can be adapted to use the rules to be managed and published.   Complete product configuration validation will be done in enterprise / central product catalogue and final configuration will be sent to the B/OSS system through the SOA compliant product distribution architecture. The approach/architecture enables greater control in terms of product data management and product data governance.   4.2.b Federated Product Management (Mastering) Architecture     Figure 7: Federated Product Management (Mastering) Architecture   In the federated product mastering approach, the basic unique product definition data (product id, description product hierarchy, basic price plans and simple product design rules) will be centrally created and will be maintained. And, the advanced product definition (Product bundling, promotions, offers & discount plans) will be created in respective down stream OSS systems. The advanced product definition (Product bundling, promotions, offers and discount plans) will be created in respective downstream OSS systems.   For example, basic product definitions such as attributes, product hierarchy and basic price plans will be created and maintained in Enterprise/Central product reference catalogue and distributed to downstream OSS systems. Respective downstream OSS systems build product bundles, promotions, advanced price plans over the basic product definition and master the advanced product definition. Central reference database accesses the respective other source product master data and assembles a point-in-time consolidated view of the product. The approach is typically adapted in some merger and acquisition scenarios where there is a low probability of a central physical authority managing the data. In addition, the migration effort in this case is minimal and there are no big architectural changes to the organization application landscape. However, this approach will not result in better product data management and data governance.   5.0 Customer Scenario – Before EPC deployment   A leading global telecommunications service provider wanted to launch a quad play and triple play service offering in the shortest possible lead time. The service provider was offering Broadband and VoIP services to customers. The company wanted to reuse a majority of the Broadband services and price plans and bundle them with new wireless and IPTV services for quad play and triple play. The challenges in launching the new service offerings were:       Figure 8: Triple Play Plan   ·       Broadband product data was stored in multiple product catalogues (CRM catalogue, Billing catalogue, spread sheets)   ·       Product managers spent a lot of time performing tasks involving duplication or re-keying of data. Manual effort caused errors, cost and time over-runs.   ·       No effective product and price data governance mechanism. Price change issues arising from the lack of data consistency across systems resulted in leakage of customer value and revenue.   ·       Product data had re-usability issues and was not in a structured format. It resulted in uncontrolled product portfolio creation and product management issues.   ·       Lack of enterprise product model resulted into product distribution challenges and thus delays in product launch.   ·       Designers are constrained by existing legacy product management solutions to model product/service requirements and product configuration rules such as upgrading, downgrading and cross selling.    5.1 Customer Scenario - After EPC deployment     Figure 9: SOA-based end-to-end EPC Solution   The company deployed PLM-based Enterprise Product Catalogue solutions to launch quad play service after evaluating various product catalogues. The broadband product offering, service and price data were migrated to the new system, and the product and price plan hierarchy for new offerings were created using the entities defined in the Enterprise Product Model. Supplier product catalogue data such as routers and set up boxes were loaded onto the new solution through SOA-based web service. Price plans and configuration rules were built in the new system. The validated final product configurations were extracted from the product catalogue in a SID format and were distributed to the downstream B/OSS systems through exposed SOA-based web services. The transformations required for the B/OSS system were handled using the transformation layer as part of the solution.   6.0 How PLM enabled Product Management Transformation         Figure 10: Product Management Transformation     PLM-based Product Catalogue Solution helped the customer reduce the product launch cycle time by 30% and enable transformation of Product Management for next generation services.   7.0 Conclusion   On the one hand, the telecom industry is undergoing changes due to disruptions, uncertain product markets and increased complexity of products. On the other hand, the ARPU is decreasing year-on-year. Communications Service Providers are embarking on convergence, bundled service offerings, flexibility to cross-sell and up-sell, introduce new value-added services, leverage Web 2.0 concepts and network capabilities. Consequently, large scale IT transformation initiatives to improve their ARPU supporting network and business transformations are a business imperative. Product Management has become a focus area. Companies are investing in best-in- class COTS solutions to reduce time-to-market, ensure rapid service delivery and improve operational efficiency. An efficient PLM-based enterprise product mastering solution plays a key role in achieving zero touch automation and rapid product launch.   References:   1.     Preston G.Smith, Donald G.Reineristsem, Van Nostrand Reinhold “Developing Products in Half the time”.   2.     John G. Innes, "Achieving Successful Product Change", Pitman Publishing.   3.     D T Pham and R M Setchi (16th Jan, 2001) "Authoring environment for documentation development" University of Wales Cardiff, U.K., Proceedings on Institution of Mechanical Engineers, Vol. 215, Part B.   4.     Oracle Product Hub for Communications:   http://www.oracle.com/us/products/applications/master-data-management/product-hub-082059.html  

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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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  • 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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