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  • Which prediction model for web page recommendation?

    - by Nilesh
    I am trying to implement a web page recommendation wherein registered users will be given a recommendation of which page to visit depending upon the previous data.So with initial study I decided to go on with clustering the data with rough sets and then will move forward to find out the sequential patters with the use of prefix span algorithm.So now I want to have a better prediction model in place which can predict the access frequency of pages.I have figured out with Markov model but still some more suggestions will be valuable.Also please help me with some references of the models too.Is it possible to directly predict the next page access with the result of PrefixSpan.If so how?

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  • How important is index size when searching?

    - by Michael K
    My company has recently began using Apache Solr to search its data. As we learn how to use it we have gone down the path of indexing multiple fields to get the results we need. Most of these are either N-Grammed or Edge-N-Grammed. Gramming by nature takes up a lot of space, which takes more time to search. Space is cheap, but time is less so. Index time is not too important, since a delta-import (only get the changes since last index) is extremely quick and you only pay a penalty on the first import. What we've not been able to determine is what effect the index size has on query times. Obviously a larger index takes longer to search, but the time added by n-gramming a field is difficult to predict. How do you determine whether a field is worth gramming? Can you predict how much longer a query will take when you gram a field?

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  • Using MOA to classify new examples?

    - by Sam Zetoloth
    I'm trying to use the java machine learning library MOA to train on a training data stream, then predict classes for a test data stream. The first part works fine, using (for example) java -cp .:moa.jar:weka.jar -javaagent:sizeofag.jar moa.DoTask "LearnModel -l MajorityClass -s (ArffFileStream -f atrain.arff -c -1) -O amodel.moa" But then I cannot figure out how to use the trained model (amodel.moa) on another stream (atest.arff) to predict the classes. Has anyone done this before?

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  • Curve-fitting in PHP

    - by Francesc
    Hi, I have a MySql table called today_stats. It has got Id, date and clicks. I'm trying to create a script to get the values and try to predict the next 7 days clicks. How I can predict it in PHP?

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  • Facebook graph API post to user's wall

    - by Lance
    I'm using the FB graph api to post content to the user's wall. I orginally tried using this method: $wall_post = array(array('message' => 'predicted the', 'name' => 'predicted the'), array('message' => $winning_team, 'name' => $winning_team, 'link' => 'http://www.sportannica.com/teams.php?team='.$winning_team.'&amp;year=2012'), array('message' => 'to beat the', 'name' => 'to beat the',), array('message' => $losing_team, 'name' => $losing_team, 'link' => 'http://www.sportannica.com/teams.php?team='.$losing_team.'&amp;year=2012'), array('message' => 'on '.$game_date.'', 'name' => 'on '.$game_date.''), array('picture' => 'http://www.sportannica.com/img/team_icons/current_season_logos/large/'.$winning_team.'.png')); $res = $facebook->api('/me/feed/', 'post', '$wall_post'); But, much to my surprise, you can't post multiple links to a users wall. So, now I'm using the graph api to post content to a user's wall much like the way spotify does. So, now I've figured out that I need to create custom actions and objects with the open graph dashboard. So, I've created the "predict" action and gave it permission to edit the object "game." So, now I have the code: $facebook = new Facebook(array( 'appId' => 'appID', 'secret' => 'SECRET', 'cookie' => true )); $access_token = $facebook->getAccessToken(); $user = $facebook->getUser(); if($user != 0) { curl -F 'access_token='$.access_token.'' \ -F 'away_team=New York Yankees' \ -F 'home_team=New York Mets' \ -F 'match=http://samples.ogp.me/413385652011237' \ 'https://graph.facebook.com/me/predict-edit-add:predict' } I keep getting an error reading: Parse error: syntax error, unexpected T_CONSTANT_ENCAPSED_STRING Any ideas?

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  • NEW 2-Day Instructor Led Course on Oracle Data Mining Now Available!

    - by chberger
    A NEW 2-Day Instructor Led Course on Oracle Data Mining has been developed for customers and anyone wanting to learn more about data mining, predictive analytics and knowledge discovery inside the Oracle Database.  Course Objectives: Explain basic data mining concepts and describe the benefits of predictive analysis Understand primary data mining tasks, and describe the key steps of a data mining process Use the Oracle Data Miner to build,evaluate, and apply multiple data mining models Use Oracle Data Mining's predictions and insights to address many kinds of business problems, including: Predict individual behavior, Predict values, Find co-occurring events Learn how to deploy data mining results for real-time access by end-users Five reasons why you should attend this 2 day Oracle Data Mining Oracle University course. With Oracle Data Mining, a component of the Oracle Advanced Analytics Option, you will learn to gain insight and foresight to: Go beyond simple BI and dashboards about the past. This course will teach you about "data mining" and "predictive analytics", analytical techniques that can provide huge competitive advantage Take advantage of your data and investment in Oracle technology Leverage all the data in your data warehouse, customer data, service data, sales data, customer comments and other unstructured data, point of sale (POS) data, to build and deploy predictive models throughout the enterprise. Learn how to explore and understand your data and find patterns and relationships that were previously hidden Focus on solving strategic challenges to the business, for example, targeting "best customers" with the right offer, identifying product bundles, detecting anomalies and potential fraud, finding natural customer segments and gaining customer insight.

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  • SQL SERVER – Get Free Books on While Learning SQL Server 2012 Error Handling

    - by pinaldave
    Fans of this blog are aware that I have recently released my new books SQL Server Functions and SQL Server 2012 Queries. The books are available in market in limited edition but you can avail them for free on Wednesday Nov 14, 2012. Not only they are free but you can additionally learn SQL Server 2012 Error Handling as well. My book’s co-author Rick Morelan is presenting a webinar tomorrow on SQL Server 2012 Error Handling. Here is the brief abstract of the webinar: People are often shocked when they see the demo in this talk where the first statement fails and all other statements still commit. For example, did you know that BEGIN TRAN…COMMIT TRAN is not enough to make everything work together? These mistakes can still happen to you in SQL Server 2012 if you are not aware of the options. Rick Morelan, creator of Joes2Pros, will teach you how to predict the Error Action and control it with & without structured error handling. Register for the webinar now to learn: How to predict the Error Action and control it Nuances between successful and failing SQL statements Essential SQL Server 2012 configuration options Register for the Webinar and be present during the webinar. My co-author will announce a winner (may be more than 1 winner) during the session. If you are present during the session – you are eligible to win the book. The webinar is scheduled for 2 different times to accommodate various time zones. 1) 10am ET/7am PT 2) 1pm ET/11am PT. Each webinar will have their own winner. You can increase your chances by attending both the webinars. Do not miss this opportunity and register for the webinar right now. The recordings of the webinar may not be available. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Joes 2 Pros, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQLAuthority News, SQLServer, T SQL, Technology

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  • The year ahead, 2011.

    - by andrewstopford
    When I look back at last years look at 2010 my blogging rate has not changed much (I suspect this is largely down to using Twitter a lot) but my interests this year have developed a lot further. My view on 2010 would be that Microsoft would commit more to OSS, while I wanted to see more hires from that audience and more projects on Outercurve foundation instead there has been support for JQuery and Gems (aka NuGet). I would love to see more from Microsoft on the OSS front in 2011, Outercurve could become like the Apache foundation with enough support. Staying on the Microsoft front I predict that 2011 will bring the following. C# 5.0 will go RTM (still no MOP though) The next release of VS will go alpha or early beta MS MVC 4.0 (I think by Mix time) and maybe this release will get a command line. I also suspect that Microsoft will want to target the tablet market with WP7 in 2011 (Mix 2011 maybe...). I also predict the following Java will fork with Apache\Google. Oracle will then take them to court and the whole thing will boil right through 2011 (Java have had enough court cases, come on guys). Java and the JVM will sadly not move forward at all in 2011. Android will cause Apple a serious headache, both the smartphone and tablet market will see figures cut from Apple share. By the end of 2011 the current 70% apple market share will be 40-50%. As the features, performance and price of Android devices gets ever better Apple will be left out in the open. Lastly after 7 years I intend to move this blog away from weblogs. In 2011 I will be exploring Java, Ruby\Rails and Android and such subjects don't make sense to talk about it here. See you in 2011.

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  • Calculating spam probability in python

    - by Hobhouse
    I am building a website in python/django and want to predict wether a user submission is valid or wether it is spam. Users have an accept rate on their submissions, like this website has. Users can moderate other users' submissions; and these moderations are later metamoderated by an admin. Given this: user A with an submission accept rate of 60% submits something. user B moderates A's post as a valid submission. However, his moderations are often wrong, and his moderations' accept rate is a mere 30%. user C moderates A's post as spam. User C is usually right. His moderations' accept rate is 80%. How can I predict the chance of A's post being spam?

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  • Storing n-grams in database in < n number of tables.

    - by kurige
    If I was writing a piece of software that attempted to predict what word a user was going to type next using the two previous words the user had typed, I would create two tables. Like so: == 1-gram table == Token | NextWord | Frequency ------+----------+----------- "I" | "like" | 15 "I" | "hate" | 20 == 2-gram table == Token | NextWord | Frequency ---------+------------+----------- "I like" | "apples" | 8 "I like" | "tomatoes" | 12 "I hate" | "tomatoes" | 20 "I hate" | "apples" | 2 Following this example implimentation the user types "I" and the software, using the above database, predicts that the next word the user is going to type is "hate". If the user does type "hate" then the software will then predict that the next word the user is going to type is "tomatoes". However, this implimentation would require a table for each additional n-gram that I choose to take into account. If I decided that I wanted to take the 5 or 6 preceding words into account when predicting the next word, then I would need 5-6 tables, and an exponentially increase in space per n-gram. What would be the best way to represent this in only one or two tables, that has no upper-limit on the number of n-grams I can support?

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  • Calculating spam probability

    - by Hobhouse
    I am building a website in python/django and want to predict wether a user submission is valid or wether it is spam. Users have an accept rate on their submissions, like this website has. Users can moderate other users' submissions; and these moderations are later metamoderated by an admin. Given this: user A with an submission accept rate of 60% submits something. user B moderates A's post as a valid submission. However, his moderations are often wrong, and his moderations' accept rate is a mere 30%. user C moderates A's post as spam. User C is usually right. His moderations' accept rate is 80%. How can I predict the chance of A's post being spam?

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  • NoSQL replacement for memcache

    - by Juan Antonio Gomez Moriano
    We are having a situation in which the values we store on memcache are bigger than 1MB. It is not possible to make such values smaller, and even if there was a way, we need to persist them to disk. One solution would be to recompile the memcache server to allow say 2MB values, but this is either not clean nor a complete solution (again, we need to persist the values). Good news is that We can predict quite acurately how many key/values pair we are going to have We can also predict the total size we will need. A key feature for us is the speed of memcache. So question is: is there any noSQL replacement for memcache which will allow us to have values longer than 1MB AND store them in disk without loss of speed? In the past I have used tokyotyrant/cabinet but seems to be deprecated now. Any idea?

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  • What is the actual difference between Computer Programmers and Software Engineers? Is this description accurate?

    - by Ari
    According to the Bureau of Labor Statistics, this is the difference: Computer programmers write programs. After computer software engineers and systems analysts design software programs, the programmer converts that design into a logical series of instructions that the computer can follow They predict employment to increase for software engineers by 34% but to decline for programmers. Is there actually any such real distinction between the 2 jobs? How can one get a job designing programs (to be implemented by others)?

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  • Predicting Likelihood of Click with Multiple Presentations

    - by Michel Adar
    When using predictive models to predict the likelihood of an ad or a banner to be clicked on it is common to ignore the fact that the same content may have been presented in the past to the same visitor. While the error may be small if the visitors do not often see repeated content, it may be very significant for sites where visitors come repeatedly. This is a well recognized problem that usually gets handled with presentation thresholds – do not present the same content more than 6 times. Observations and measurements of visitor behavior provide evidence that something better is needed. Observations For a specific visitor, during a single session, for a banner in a not too prominent space, the second presentation of the same content is more likely to be clicked on than the first presentation. The difference can be 30% to 100% higher likelihood for the second presentation when compared to the first. That is, for example, if the first presentation has an average click rate of 1%, the second presentation may have an average CTR of between 1.3% and 2%. After the second presentation the CTR stays more or less the same for a few more presentations. The number of presentations in this plateau seems to vary by the location of the content in the page and by the visual attraction of the content. After these few presentations the CTR starts decaying with a curve that is very well approximated by an exponential decay. For example, the 13th presentation may have 90% the likelihood of the 12th, and the 14th has 90% the likelihood of the 13th. The decay constant seems also to depend on the visibility of the content. Modeling Options Now that we know the empirical data, we can propose modeling techniques that will correctly predict the likelihood of a click. Use presentation number as an input to the predictive model Probably the most straight forward approach is to add the presentation number as an input to the predictive model. While this is certainly a simple solution, it carries with it several problems, among them: If the model learns on each case, repeated non-clicks for the same content will reinforce the belief of the model on the non-clicker disproportionately. That is, the weight of a person that does not click for 200 presentations of an offer may be the same as 100 other people that on average click on the second presentation. The effect of the presentation number is not a customer characteristic or a piece of contextual data about the interaction with the customer, but it is contextual data about the content presented. Models tend to underestimate the effect of the presentation number. For these reasons it is not advisable to use this approach when the average number of presentations of the same content to the same person is above 3, or when there are cases of having the presentation number be very large, in the tens or hundreds. Use presentation number as a partitioning attribute to the predictive model In this approach we essentially build a separate predictive model for each presentation number. This approach overcomes all of the problems in the previous approach, nevertheless, it can be applied only when the volume of data is large enough to have these very specific sub-models converge.

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  • The Google Prediction API

    The Google Prediction API The Prediction API enables you to make your smart apps even smarter. The API accesses Google's machine learning algorithms to analyze your historic data and predict likely future outcomes. Using the Google Prediction API, you can build the following intelligence into your applications. Read more at code.google.com From: GoogleDevelopers Views: 15834 113 ratings Time: 01:37 More in Science & Technology

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  • On-Demand Webcast: Managing Oracle Exadata with Oracle Enterprise Manager 11g

    - by Scott McNeil
    Watch this on-demand webcast and discover how Oracle Enterprise Manager 11g's unique management capabilities allow you to efficiently manage all stages of Oracle Exadata's lifecycle, from testing applications on Exadata to deployment. You'll learn how to: Maximize and predict database performance Drive down IT operational costs through automation Ensure service quality with proactive management Register today and unlock the potential of Oracle Exadata for your enterprise. Register Now!

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  • Avg. Visit Duration 00:00:00 conclusion

    - by user1592845
    What can I predict when I see in Google Analytics that total visits by search for some day are 93 visits while 70 visits of them have the value 00:00:00 for Avg. Visit Duration? Did those visits made by robots? or How could they regarded as visits while they don't spend any time on the website? Or this is dysfunction of the Google's Analytics script by which it does not able to count the visit time?

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  • Harnessing Business Events for Predictive Decision Making - part 1 / 3

    - by Sanjeev Sharma
    Businesses have long relied on data mining to elicit patterns and forecast future demand and supply trends. Improvements in computing hardware, specifically storage and compute capacity, have significantly enhanced the ability to store and analyze mountains of data in ever shrinking time-frames. Nevertheless, the reality is that data growth is outpacing storage capacity by a factor of two and computing power is still very much bounded by Moore's Law, doubling only every 18 months.Faced with this data explosion, businesses are exploring means to develop human brain-like capabilities in their decision systems (including BI and Analytics) to make sense of the data storm, in other words business events, in real-time and respond pro-actively rather than re-actively. It is more like having a little bit of the right information just a little bit before hand than having all of the right information after the fact. To appreciate this thought better let's first understand the workings of the human brain.Neuroscience research has revealed that the human brain is predictive in nature and that talent is nothing more than exceptional predictive ability. The cerebral-cortex, part of the human brain responsible for cognition, thought, language etc., comprises of five layers. The lowest layer in the hierarchy is responsible for sensory perception i.e. discrete, detail-oriented tasks whereas each of the above layers increasingly focused on assembling higher-order conceptual models. Information flows both up and down the layered memory hierarchy. This allows the conceptual mental-models to be refined over-time through experience and repetition. Secondly, and more importantly, the top-layers are able to prime the lower layers to anticipate certain events based on the existing mental-models thereby giving the brain a predictive ability. In a way the human brain develops a "memory of the future", some sort of an anticipatory thinking which let's it predict based on occurrence of events in real-time. A higher order of predictive ability stems from being able to recognize the lack of certain events. For instance, it is one thing to recognize the beats in a music track and another to detect beats that were missed, which involves a higher order predictive ability.Existing decision systems analyze historical data to identify patterns and use statistical forecasting techniques to drive planning. They are similar to the human-brain in that they employ business rules very much like mental-models to chunk and classify information. However unlike the human brain existing decision systems are unable to evolve these rules automatically (AI still best suited for highly specific tasks) and  predict the future based on real-time business events. Mistake me not,  existing decision systems remain vital to driving long-term and broader business planning. For instance, a telco will still rely on BI and Analytics software to plan promotions and optimize inventory but tap into business events enabled predictive insight to identify specifically which customers are likely to churn and engage with them pro-actively. In the next post, i will depict the technology components that enable businesses to harness real-time events and drive predictive decision making.

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  • Planning Your Online Marketing Budget For 2010

    Although Social Media is everywhere, you'll find that Maryland businesses and companies everywhere are still planning a balanced online attack for 2010, with search engine optimization and online advertising as front runners, as they've provided proven success on the web. Despite a recession, Search Engine Optimization and search engine marketing practices are still growing each year. Experts predict that Search Engine marketing activities in the US will have doubled from $13 billion dollars spent in 2009 to $26 billion by the year 2014.

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  • Why Use PHP on Your Website?

    PHP or the lengthy term known as Hypertext Preprocessor is a programming language used for creating or enhancing webpages. Most common use of PHP is with databases, but it does have many more uses. If you have a website and predict in the future multiple pages being added, PHP may be for you.

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  • Introduction to SMART

    <b>Linux Magazine:</b> "Did you know your drive was SMART? Actually: Self-Monitoring, Analysis, and Reporting Technology. It can be used to gather information about your hard drives and offers some additional information about the status of your storage devices. It can also be used with other tools to help predict drive failure. "

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  • Server cost for smartphone app with web service

    - by FrankieA
    Hello, I am working on a smartphone application that will require a backend web service - but I have absolutely clueless to how much it will cost. Web Service will handle: - login of users - cataloging of our user base - holding minimal profile information for users (the only binary data is a display picture which will be < 20k each) - performing some very minor calculation/algorithm before return results - All the above will be communicated to server from a smartphone (iPhone/BlackBerry/Android) Bandwidth Requirements: - We want to handle up to 10k users throughout the day. - I predict 10k * 50 HTTP requests a day = 500,000 requests a day * 30 = 15 million requests a month Space Requirements: - Data will be in SQL database. - I predict 1MB/user * 10k = 10GB + overhead. In other words - space is not a big issue. Software Requirements: (unless someone knows an alternative) - Windows Server 2008 + IIS - MSFT SQL Server Note: This is 100% new to me, so please hit me with all you got. Do I need Windows Server or are there alternative? Is it better to get multiple cheap servers to distribute load? Will Amazon S3 work for me? How about Windows Azure? Thank you!!

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