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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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  • Getting started with character and text processing (encoding, regular expressions)

    - by TK
    I'd like to learn foundations of encodings, characters and text. Understanding these is important for dealing with a large set of text whether that are log files or text source for building algorithms for collective intelligence. My current knowledge is pretty basic: something like "As long as I use UTF-8, I'm okay." I don't say I need to learn about advanced topics right away. But I need to know: Bit and bytes level knowledge of encodings. Characters and alphabets not used in English. Multi-byte encodings. (I understand some Chinese and Japanese. And parsing them is important.) Regular expressions. Algorithm for text processing. Parsing natural languages. I also need an understanding of mathematics and corpus linguistics. The current and future web (semantic, intelligent, real-time web) needs processing, parsing and analyzing large text. I'm looking for some resources (maybe books?) that get me started with some of the bullets. (I find many helpful discussion on regular expressions here on Stack Overflow. So, you don't need to suggest resources on that topic.)

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  • Performing text processing on flatpage content to include handling of custom tag

    - by Dzejkob
    Hi. I'm using flatpages app in my project to manage some html content. That content will include images, so I've made a ContentImage model allowing user to upload images using admin panel. The user should then be able to include those images in content of the flatpages. He can of course do that by manually typing image url into <img> tag, but that's not what I'm looking for. To make including images more convenient, I'm thinking about something like this: User edits an additional, let's say pre_content field of CustomFlatPage model (I'm using custom flatpage model already) instead of defining <img> tags directly, he uses a custom tag, something like [img=...] where ... is name of the ContentImage instance now the hardest part: before CustomFlatPage is saved, pre_content field is checked for all [img=...] occurences and they are processed like this: ContentImage model is searched if there's image instance with given name and if so, [img=...] is replaced with proper <img> tag. flatpage actual content is filled with processed pre_content and then flatpage is saved (pre_content is leaved unchanged, as edited by user) The part that I can't cope with is text processing. Should I use regular expressions? Apparently they can be slow for large strings. And how to organize logic? I assume it's rather algorithmic question, but I'm not familliar with text processing in Python enough, to do it myself. Can somebody give me any clues?

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  • Shape object in Processing, translate individual shapes.

    - by Zain
    I am relatively new to Processing but have been working in Java for about 2 years now. I am facing difficulty though with the translate() function for objects as well as objects in general in processing. I went through the examples and tried to replicate the manners by which they instantiated the objects but cannot seem to even get the shapes to appear on the screen no less move them. I instantiate the objects into an array using a nested for loop and expect a grid of the objects to be rendered. However, nothing at all is rendered. My nested for loop structure to instantiate the tiles: for(int i=0; i<102; i++){ for(int j=0; j<102; j++){ tiles[i][j]=new tile(i,0,j); tiles[i][j].display(); } } And the constructors for the tile class: tile(int x, int y, int z){ this.x=x; this.y=y; this.z=z; beginShape(); vertex(x,y,z); vertex(x+1,y,z); vertex(x+1,y,z-1); vertex(x,y,z-1); endShape(); } Nothing is rendered at all when this runs. Furthermore, if this is of any concern, my translations(movements) are done in a method I wrote for the tile class called move which simply calls translate. Is this the correct way? How should one approach this? I can't seem to understand at all how to render/create/translate individual objects/shapes. Thanks for any help any of you are able to provide!

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  • Creative Gesture Camera in Processing

    - by user2892963
    I'm trying to use the creative gesture camera in Processing. I started with the Intel Perceptual Computing SDK, and ran into an issue. I want to get the hand openness, and I am running into some issues - no matter what, the hand.openness returns 0. It otherwise runs quite well... Some Sample code I'm trying to get to work: If you open your hand it starts printing to the console, close it and it stops. import intel.pcsdk.*; PXCUPipeline session; PXCMGesture.GeoNode hand = new PXCMGesture.GeoNode(); void setup() { session = new PXCUPipeline(this); if(!session.Init(PXCUPipeline.GESTURE)) exit(); } void draw() { background(0); if(session.AcquireFrame(false)) { if(session.QueryGeoNode(PXCMGesture.GeoNode.LABEL_BODY_HAND_PRIMARY|PXCMGesture.GeoNode.LABEL_OPEN, hand)) //Only when primary hand is open { rect(0, 0, 10, 10); println(hand.openness + " : " + frameCount); //Openness should be from 0 to 100 } session.ReleaseFrame(); } } Using the current version of Processing (2.0.3), Perceptual Computing SDK Version 7383.

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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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  • .NET photo processing component

    - by John Williams
    Hi folks! I'm looking for a .NET image processing component or an open source alternative to automate the following tasks: Photo capture (webcams and photo cameras) Photo printing (grid/strip modes) Applying photo effects Saving photos AtalaSoft DotImage is quite expensive, any other suggestions are welcome. Thanks J

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  • Parallel processing in R 2.11 Windows 64-bit using SNOW not quite working

    - by Abhijit
    I'm running R 2.11 64-bit on a WinXP64 machine with 8 processors. With R 2.10.1 the following code spawned 6 R processes for parallel processing: require(foreach) require(doSNOW) cl = makeCluster(6, type='SOCK') registerDoSNOW(cl) bl2 = foreach(i=icount(length(unqmrno))) %dopar% { (Some code here) } stopCluster(cl) When I run the same code in R 2.11 Win64, the 6 R processes are not spawning, and the code hangs. I'm wondering if this is a problem with the port of SNOW to 2.11-64bit, or if any additional code is required on my part. Thanks

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