Data and Analytics form 2 of the 4 key pieces in internet of things

Posted by Bala Deshpande on Tue, Sep 23, 2014 @ 06:33 AM

This article was contributed by Vaibhav Waghmare.

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Tags: technology reviews, internet of things, big data

Collaborative filtering using RapidMiner: user vs. item recommenders

Posted by Bala Deshpande on Tue, Sep 16, 2014 @ 04:59 PM

In a previous article we discussed building user based recommenders in RapidMiner. Before we jump into the next type: item based recommenders, let us try to understand the key differences between the two types. From a descriptive standpoint, a user based recommender measures similarities between different users and uses the rating from the top k closest (in similarity) users to a given user to arrive at a rating prediction or recommendation for the given user.  An item based recommender measures similarities between different items and picks the top k closest (in similarity) items to a given item to arrive at a rating prediction or recommendation for a given user for a given item.

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Tags: collaborative filtering, user based recommender system, item based recommender system

Future of big data analytics: machine data and connected vehicles

Posted by Bala Deshpande on Tue, Sep 09, 2014 @ 08:35 AM

If you simply collecting a lot of data, you do not have a big data use case. However you do have a big data use case, if you need to process and analyze the collected data in order to generate greater business value. A common example cited is that if you are storing millions of records from your customers personal info in a database that is not a big data use case. However if you are collecting web logs from millions of hits to your site from online transactions, the that probably is.

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Tags: technology reviews, manufacturing analytics, big data

Collaborative filtering using RapidMiner: user based recommenders

Posted by Bala Deshpande on Wed, Sep 03, 2014 @ 09:11 AM

In an earlier article we discussed the overall intuition behind collaborative filtering based recommender systems. In this article we will describe the details behind building one using a popular GUI based tool such as RapidMiner. As mentioned in the introduction to recommender systems, the key piece of data that you will need to start with is the utility matrix. However, for practical implementation the utility matrix has to be de-pivoted. Recall that a utility matrix in this context consists of rows which represent different users and columns which represent different items that have been rated. This is sometimes called a cross-tabulation. But for reading into a tool like RapidMiner, we will have to de-pivot this into a table which has only 3 columns: a userID, an itemID and a rating. Thus each row contains a unique piece of data: the rating provided by a particular user for a particular item. See image below.

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Tags: recommender systems, collaborative filtering, user based recommender system

Predictive analytics using image analysis can de-commoditize business

Posted by Bala Deshpande on Thu, Aug 28, 2014 @ 11:09 AM

You know that predictive analytics has really arrived when there are two articles about it in a single issue of Time magazine (Aug 18, 2014 issue). While it is a rapidly maturing field (according to Gartner's hype cycle, it is already at its plateau of productivity), there are still some areas where improvements are being worked on.

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Tags: user based recommender system, item based recommender system, image analytics

The intuition behind recommender systems: collaborative filtering

Posted by Bala Deshpande on Wed, Aug 13, 2014 @ 08:15 AM

Thanks to Amazon, Netflix and the like, recommender engines have become synonymous with predictive analytics and in many ways are the benchmark indicators of predictive analytics maturity in an organization, particularly in retail and ecommerce. Its adoption is going to increase to more non-tech type companies and even smaller businesses, thanks to the open source movement in analytics and big data infrastructure. At the end of the day, all content providers (including this website, via Add This!) will make use of recommender engines.

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Tags: recommender systems, collaborative filtering, user based recommender system, item based recommender system

7 applications of IoT-connected vehicles which may soon be common

Posted by Bala Deshpande on Wed, Jul 23, 2014 @ 07:53 AM

Recently I came across this bit of interesting factoid which went mostly unnoticed by media, because for most people it is not earth-shattering news- "... the operating cost of some robots is now less than the salary of an average Chinese worker". This has tremendous implications for manufacturing in the near future (10 years at the maximum) where most mundane and low level jobs will be taken over by machines - especially connected machines. Typical among this list of jobs which will move away from humans and towards machines are assembly line work (will be handled by robots, mostly) and complex manufacturing (3d printers). However in the non-manufacturing world too, the impacts of connected machines will be clear: taxi drivers and chauffeurs (replaced by self driving cars), delivery men (by drones), pharmacists and even personal physicians (by smart Watson-type programs) and so on. This is as game-changing as the internet was barely 20 years ago.

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Tags: manufacturing analytics, machine data analytics, internet of things

3 ways predictive maintenance can increase performance and efficiency

Posted by Bala Deshpande on Thu, Jul 17, 2014 @ 07:38 AM

This article was contributed by Vaibhav Waghmare.

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Tags: manufacturing analytics, machine data analytics

Predictive analytics: cornerstone of new customer service paradigm

Posted by Bala Deshpande on Tue, Jul 08, 2014 @ 07:25 AM

Portions of this article were contributed by Vaibhav Waghmare.

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Tags: predictive analytics, advanced business analytics, sentiment analysis

How to automate bid response systems using predictive analytics

Posted by Bala Deshpande on Wed, Jun 11, 2014 @ 08:00 AM

Supplier companies which manufacture commoditized products face constant cost pressures. As a way to separate themselves from the herd, many savvy suppliers are realizing that what may set them apart is the ability to help their customers' buyers make an informed decision. A manufacturer of paper designs which supplies national retailers has realized that in addition to providing the buyer with designs and sourcing information, they can also act as trusted advisors to their customers by identifying and recommending trendy ones. This is something predictive analytics can provide, starting with some basic time series forecasting.

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Tags: manufacturing analytics, advanced business analytics