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  • Better data structure for a game like Bubble Witch

    - by CrociDB
    I'm implementing a bubble-witch-like game (http://www.king.com/games/puzzle-games/bubble-witch/), and I was thinking on what's the better way to store the "bubbles" and to work with. I thought of using graphs, but that might be too complex for a trivial thing. Thought of a matrix, just like a tile map, but that might get too 'workaroundy'. I don't know. I'll be doing in Flash/AS3, though. Thanks. :)

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  • map data structure in pacman

    - by Sam Fisher
    i am trying to make a pacman game in c# using GDI+, i have done some basic work and i have previously replicated games like copter-it and minesweeper. but i am confused about how do i implement the map in pacman, i mean which datastructure to use, so i can use it for moving AI controlled objects and check collisions with walls. i thought of a 2d array of ints but that didnt make sense to me. looking for some help. thanks.

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  • How to retrieve data from a corruped volume

    - by explorex
    Hi, My Ubuntu 10.10 just crashed(probably due to hardware error and in the end I was getting error like Unknown filesystem ..... grub> .. GRUB console before i could take some action) and i reinstalled the same version form USB stick. I had ubuntu installed in ext4 file system and I am also having the same filesystem in the same hard disk on different drive. When I try to access my previous filesystem, i get error Error mounting: mount: wrong fs type, bad option, bad superblock on /dev/sda6, missing codepage or helper program, or other error In some cases useful info is found in syslog - try dmesg | tail or so I had some important files in the previous volume, I don't know how to retrieve them. And what are the chances that I would get the same outcome (hardware error)? Please help me!

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  • How Big Data and Social Won the Election

    - by Mike Stiles
    The story of big data’s influence on the outcome of the US Presidential election is worth a good look, because a) it’s a harbinger of things to come, and b) it’s an example of similar successes available to any enterprise seriously resourcing integrated big data, modeling, and data-driven execution on all assets, including social. Obama campaign manager Jim Messina fielded a data and analytics brain trust 5 times larger than 2008. At that time, there were numerous databases from various sources, few of them talking to each other. This time, the mission was to be metrics-centered and measure everything measurable, and in context with all the other data. Big data showed them exactly what they needed to know and told them what to do about it. It showed them women 40-49 on the west coast would donate big money if they got to eat with George Clooney. Women on the east coast would pony up to hang out with Sarah Jessica Parker. Extensive daily modeling showed them what kinds of email appeals, from who, and to whom, would prove most successful in raising cash, recruiting volunteers, and getting out the vote. Swing state voters were profiled and approached with more customized targeting that at any time in history. Ads were purchased on specific shows watched by the targets, increasing efficiency 14% over traditional media buys. For all the criticism of the candidate’s focus on appearing on comedy and entertainment shows, and local radio morning shows, that’s where the data sent them to reach the voters most likely to turn out for them. And then there was social. Again, more than in any other election, Facebook was used for virtual, highly efficient door-to-door canvasing. Facebook fans got pictures of friends in swing states and were asked to encourage them to act. Using that approach, 1 in 5 peer-to-peer appeals led to the desired action. Assumptions, gut, intuition, campaign experience, all took a backseat to strategy shifts solidly backed up by data. Zeroing in on demographics likely to back the President and tracking their mood daily literally changed the voter landscape. The Romney team watched Obama voters appear seemingly out of thin air. One Obama campaign aide said, “We ran the election 66,000 times every night.” Which brings us to your organization. If you’re starting to feel like the battle-cry of “but this is the way we’ve always done it” is starting to put you in an extremely vulnerable position, you’re right. Social has become a key communication tool of the 21st century. Failing to use it, or failing to invest in a deep understanding of who your customers and prospects are so the content you post there will achieve desired actions and results, will leave you waking up one morning wondering, “What happened?”@mikestilesPhoto stock.xchng

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  • Wiped data, and duplicated folders into files.

    - by Kaustubh P
    Something weird happened today, and I dont know how. Within a folder, all folders have a file by the same name, with a colon appended to it. And all the files from the most inner-most directory in my home, have been dumped to ~, with a size of 0 bytes. I have not executed any scripts or anything. I was just checking out some easter eggs, namely the gegls from outer space and free the fish and was away from the computer and was logged because of the screensaver. I couldnt log-back in with my password, so I just reset the PC, and while booting, the PC went into a drive check. BUT, IIRC, i saw the duplicate "folder files" before I had logged out, so thats not the reason! All the files have a timestamp of 14 Jan. Also, the contents of my eclipse folder have been dumped into ~. Right down to the jars and ini files. HELP!

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  • recover data in linux removed with rm

    - by user3717896
    Today i deleted my home directory (in wrong action) with: sudo rm -rf * And when i use extundelete i get this message: root@ubuntu:~# sudo extundelete --restore-directory /home/hamed/ /dev/sda2WARNING: Extended attributes are not restored. Loading filesystem metadata ... 746 groups loaded. Loading journal descriptors ... 29931 descriptors loaded. Searching for recoverable inodes in directory /home/hamed/ ... 498 recoverable inodes found. Looking through the directory structure for deleted files ... 498 recoverable inodes still lost. No files were undeleted. why it can't recover? Anyone can help me to return my Desktop, Documents and etc? I have ubuntu 14.04.

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  • Create association between informations

    - by Andrea Girardi
    I deployed a project some days ago that allow to extract some medical articles using the results of a questionnaire completed by a user. For instance, if I reply on questionnaire I'm affected by Diabetes type 2 and I'm a smoker, my algorithm extracts all articles related to diabetes bubbling up all articles contains information about Diabetes type 2 and smoking. Basically we created a list of topic and, for every topic we define a kind of "guideline" that allows to extract and order informations for a user. I'm quite sure there are some better way to put on relationship two content but I was not able to find them on network. Could you suggest my a model, algorithm or paper to better understand this kind of problem and that helps me to find a faster, and more accurate way to extract information for an user?

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  • Trying to recover deleted Ubuntu partition

    - by user110984
    I made a mistake in logging into my 200 GB Ubuntu partition. I could not access Grub after that. Using a live CD I then ran Boot_Repair and apparently deleted the partition, I guess because I ran it from my 70 GB Windows partition. I can send the results of boot_info before that and of Boot_Repair. Then I ran TestDisk, which apparently found only dev/sda/ -320GB / 298 / GiB - WDC - WD3200BEVT-22A23T0 (Was there any more I could have done with TestDisk? I looked at the TestDisk_Step_By_Step example and found no way forward given that no other partitions turned up) I have run gpart and found this: /sda1 - 15 GB /sda2 - system reserved /sda3 - 70.15 GB /sda4 - extended 212.84 unallocated - 209.10 /sda5 - unknown 3.74 . I have been told I can recover the partition using gparted's Rescue start end command, but I don't know what to enter for start and end. [--EDIT: TestDisk Deeper Search stated that "the following partitions can't be recovered" and listed a 220-GB Linux partition 6 times. Then it stated that "The current number of heads per cylinder is 255 but the correct value may be 128" and I could try to change it in the Geometry menu (because apparently these are overlapping partitions) So should I do that?--]

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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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  • 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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  • How to retrieve data from a corrupted volume

    - by explorex
    Hi, My Ubuntu 10.10 just crashed, probably due to hardware error (and in the end I was getting errors like Unknown filesystem ..... grub> .., and it went to the GRUB console before I could take any other action). I reinstalled the same version from a USB stick. I had Ubuntu installed with the ext4 file system and I also have the same filesystem in the same hard disk on a different drive. When I try to access my previous filesystem, I get errors: Error mounting: mount: wrong fs type, bad option, bad superblock on /dev/sda6, missing codepage or helper program, or other error In some cases useful info is found in syslog - try dmesg | tail or so I had some important files in the previous volume ; I don't know how to retrieve them. And what are the chances that I would get the same outcome (hardware error)? Please help me!

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  • Oracle Social Analytics with the Big Data Appliance

    - by thegreeneman
    Found an awesome demo put together by one of the Oracle NoSQL Database partners, eDBA, on using the Big Data Appliance to do social analytics. In this video, James Anthony is showing off the BDA, Hadoop, the Oracle Big Data Connectors and how they can be used and integrated with the Oracle Database to do an end-to-end sentiment analysis leveraging twitter data.   A really great demo worth the view. 

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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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  • SQL Server 2012 disponible en version finale : AlwaysOn, Big Data, Power View, Microsoft tient ses promesses

    SQL Server 2012 disponible en version finale AlwaysOn, Big Data, Power View, la plateforme de gestion et d'analyse d'information de Microsoft tient ses promesses Mise à jour du 03/04/2012 Comme l'avait promis Microsoft, la version finale de SQL Server 2012 est disponible depuis le 1er avril, mais a été annoncée officiellement hier. La plateforme de gestion et d'analyse d'information de Microsoft a été conçue pour être l'environnement de référence des applications critiques d'entreprise, offrir une solution décisionnelle plus complète intégrant le Big Data et permettre une meilleure connexion avec le Cloud. ...

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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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  • RPi and Java Embedded GPIO: Big Data and Java Technology

    - by hinkmond
    Java Embedded and Big Data go hand-in-hand, especially as demonstrated by prototyping on a Raspberry Pi to show how well the Java Embedded platform can perform on a small embedded device which then becomes the proof-of-concept for industrial controllers, medical equipment, networking gear or any type of sensor-connected device generating large amounts of data. The key is a fast and reliable way to access that data using Java technology. In the previous blog posts you've seen the integration of a static electricity sensor and the Raspberry Pi through the GPIO port, then accessing that data through Java Embedded code. It's important to point out how this works and why it works well with Java code. First, the version of Linux (Debian Wheezy/Raspian) that is found on the RPi has a very convenient way to access the GPIO ports through the use of Linux OS managed file handles. This is key in avoiding terrible and complex coding using register manipulation in C code, or having to program in a less elegant and clumsy procedural scripting language such as python. Instead, using Java Embedded, allows a fast way to access those GPIO ports through those same Linux file handles. Java already has a very easy to program way to access file handles with a high degree of performance that matches direct access of those file handles with the Linux OS. Using the Java API java.io.FileWriter lets us open the same file handles that the Linux OS has for accessing the GPIO ports. Then, by first resetting the ports using the unexport and export file handles, we can initialize them for easy use in a Java app. // Open file handles to GPIO port unexport and export controls FileWriter unexportFile = new FileWriter("/sys/class/gpio/unexport"); FileWriter exportFile = new FileWriter("/sys/class/gpio/export"); ... // Reset the port unexportFile.write(gpioChannel); unexportFile.flush(); // Set the port for use exportFile.write(gpioChannel); exportFile.flush(); Then, another set of file handles can be used by the Java app to control the direction of the GPIO port by writing either "in" or "out" to the direction file handle. // Open file handle to input/output direction control of port FileWriter directionFile = new FileWriter("/sys/class/gpio/gpio" + gpioChannel + "/direction"); // Set port for input directionFile.write("in"); // Or, use "out" for output directionFile.flush(); And, finally, a RandomAccessFile handle can be used with a high degree of performance on par with native C code (only milliseconds to read in data and write out data) with low overhead (unlike python) to manipulate the data going in and out on the GPIO port, while the object-oriented nature of Java programming allows for an easy way to construct complex analytic software around that data access functionality to the external world. RandomAccessFile[] raf = new RandomAccessFile[GpioChannels.length]; ... // Reset file seek pointer to read latest value of GPIO port raf[channum].seek(0); raf[channum].read(inBytes); inLine = new String(inBytes); It's Big Data from sensors and industrial/medical/networking equipment meeting complex analytical software on a small constraint device (like a Linux/ARM RPi) where Java Embedded allows you to shine as an Embedded Device Software Designer. Hinkmond

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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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  • NRF Big Show 2011 -- Part 3

    - by David Dorf
    I'm back from the NRF show having been one of the lucky people who's flight was not canceled. The show was very crowded with a reported 20% increase in attendance and everyone seemed in high spirits. After two years of sluggish retail sales, things are really picking up and it was reflected in everyone's mood. The pop-up Disney Store in the Oracle booth was great and attracted lots of interest in their mobile POS. I know many attendees visited the Disney Store in Times Square to see the entire operation. It's an impressive two-story store that keeps kids engaged. The POS demonstration station, where most of our innovations were demoed, was always crowded. Unfortunately most of the demos used WiFi and the signals from other booths prevented anything from working reliably. Nevertheless, the demo team did an excellent job walking people through the scenarios and explaining how shopping is being impacted by mobile, analytics, and RFID. Big Show Links Disney uncovers its store magic Top 10 Things You Missed at the NRF Big Show 2011 Oracle Retail Stores Innovation Station at NRF Big Show 2011 (video) The buzz of the show was again around mobile solutions. Several companies are creating mobile POS using the iPod Touch, including integrations to Oracle POS for the following retailers: Disney Stores with InfoGain Victoria's Secret with InfoGain Urban Outfitters with Starmount The Gap with Global Bay Keeping with the mobile theme, the NRF release a revised version of their Mobile Blueprint at NRF. It will be posted to the NRF site very soon. The alternate payments section had a major rewrite that provides a great overview and proximity and remote payment technologies. NRF Mobile Blueprint Links New mobile blueprint provides fresh insights NRF Mobile Blueprint 2011 (slides) I hope to do some posts on some of the interesting companies I spoke with in the coming weeks.

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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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  • 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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