PMML Plugin for Greenplum now available

Predictive Model Markup Language
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From a press release from Zementis.

 

, the Universal PMML Plug-in for in-database scoring. Available now for the EMC Greenplum Database, a high-performance massively parallel processing (MPP) database, the plug-in leverages the Predictive Model Markup Language (PMML) to execute predictive models directly within EMC Greenplum, for highly optimized in-database scoring.

Universal PMML Plug-in

Developed by the Data Mining Group (DMG), PMML is supported by all major data mining vendors, e.g., IBM SPSS, SAS, Teradata, FICO, STASTICA, Microstrategy, TIBCO and Revolution Analytics as well as open source tools like R, KNIME and RapidMiner. With PMML, models built in any of these data mining tools can now instantly be deployed in the EMC Greenplum database. The net result is the ability to leverage the power of standards-based predictive analytics on a massive scale, right where the data resides.

“By partnering with Zementis, a true PMML innovator, we are able to offer a vendor-agnostic solution for moving enterprise-level predictive analytics into the database execution environment,” said Dr. Steven Hillion, Vice President of Analytics at EMC Greenplum. “With Zementis and PMML, the de-facto standard for representing data mining models, we are eliminating the need to recode predictive analytic models in order to deploy them within our database. In turn, this enables an analyst to reduce the time to insight required in most businesses today.”

Want to learn more?
 

To learn more about how the EMC Greenplum Database and the Universal PMML Plug-in work together, feel free to:

  1. Visit the PMML Plug-in product page
  2. Download the white paper

The Universal PMML Plug-in for the EMC Greenplum Database is available now. Contact us today for more information.

Michael Zeller, CEO, Zementis

 

 

Complex Event Processing- SASE Language

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Complex Event Processing (CEP- not to be confused by Circular Probability Error) is defined processing many events happening across all the layers of an organization, identifying the most meaningful events within the event cloud, analyzing their impact, and taking subsequent action in real time.

Software supporting CEP are-

Oracle http://www.oracle.com/us/technologies/soa/service-oriented-architecture-066455.html

Oracle CEP is a Java application server for the development and deployment of high-performance event driven applications. It can detect patterns in the flow of events and message payloads, often based on filtering, correlation, and aggregation across event sources, and includes industry leading temporal and ordering capabilities. It supports ultra-high throughput (1 million/sec++) and microsecond latency.

Tibco is also trying to get into this market (it claims to have a 40 % market share in the public CEP market 😉 though probably they have not measured the DoE and DoD as worthy of market share yet

– see webcast by TIBCO ‘s head here http://www.tibco.com/products/business-optimization/complex-event-processing/default.jsp

and product info here-http://www.tibco.com/products/business-optimization/complex-event-processing/businessevents/default.jsp

TIBCO is the undisputed leader in complex event processing (CEP) software with over 40 percent market share, according to a recent IDC Study.

A good explanation of how social media itself can be used as an analogy for CEP is given in this SAS Global Paper

http://support.sas.com/resources/papers/proceedings10/040-2010.pdf

You can see a report on Predictive Analytics and Data Mining  in q1 2010 also from SAS’s website  at –http://www.sas.com/news/analysts/forresterwave-predictive-analytics-dm-104388-0210.pdf

A very good explanation on architecture involved is given by SAS CTO Keith Collins here on SAS’s Knowledge Exchange site,

http://www.sas.com/knowledge-exchange/risk/four-ways-divide-conquer.html

What it is: Methods 1 through 3 look at historical data and traditional architectures with information stored in the warehouse. In this environment, it often takes months of data cleansing and preparation to get the data ready to analyze. Now, what if you want to make a decision or determine the effect of an action in real time, as a sale is made, for instance, or at a specific step in the manufacturing process. With streaming data architectures, you can look at data in the present and make immediate decisions. The larger flood of data coming from smart phones, online transactions and smart-grid houses will continue to increase the amount of data that you might want to analyze but not keep. Real-time streaming, complex event processing (CEP) and analytics will all come together here to let you decide on the fly which data is worth keeping and which data to analyze in real time and then discard.

When you use it: Radio-frequency identification (RFID) offers a good user case for this type of architecture. RFID tags provide a lot of information, but unless the state of the item changes, you don’t need to keep warehousing the data about that object every day. You only keep data when it moves through the door and out of the warehouse.

The same concept applies to a customer who does the same thing over and over. You don’t need to keep storing data for analysis on a regular pattern, but if they change that pattern, you might want to start paying attention.

Figure  4: Traditional architecture vs. streaming architecture

Figure 4: Traditional architecture vs. streaming architecture

 

In academia  here is something called SASE Language

  • A rich declarative event language
  • Formal semantics of the event language
  • Theorectical underpinnings of CEP
  • An efficient automata-based implementation

http://sase.cs.umass.edu/

and

http://avid.cs.umass.edu/sase/index.php?page=navleft_1col

Financial Services

The query below retrieves the total trading volume of Google stocks in the 4 hour period after some bad news occurred.

PATTERN SEQ(News a, Stock+ b[ ])WHERE   [symbol]    AND	a.type = 'bad'    AND	b[i].symbol = 'GOOG' WITHIN  4 hoursHAVING  b[b.LEN].volume < 80%*b[1].volumeRETURN  sum(b[ ].volume)

The next query reports a one-hour period in which the price of a stock increased from 10 to 20 and its trading volume stayed relatively stable.

PATTERN	SEQ(Stock+ a[])WHERE 	 [symbol]   AND	  a[1].price = 10   AND	  a[i].price > a[i-1].price   AND	  a[a.LEN].price = 20            WITHIN  1 hourHAVING	avg(a[].volume) ≥ a[1].volumeRETURN	a[1].symbol, a[].price

The third query detects a more complex trend: in an hour, the volume of a stock started high, but after a period of price increasing or staying relatively stable, the volume plummeted.

PATTERN SEQ(Stock+ a[], Stock b)WHERE 	 [symbol]   AND	  a[1].volume > 1000   AND	  a[i].price > avg(a[…i-1].price))   AND	  b.volume < 80% * a[a.LEN].volume           WITHIN  1 hourRETURN	a[1].symbol, a[].(price,volume), b.(price,volume)

(note from Ajay-

 

I was not really happy about the depth of resources on CEP available online- there seem to be missing bits and pieces in both open source, academic and corporate information- one reason for this is the obvious military dual use of this technology- like feeds from Satellite, Audio Scans, etc)

New Deal in Statistical Training

The United States Government is planning a new initiative at providing employable skills to people, to cope with unemployment.
One skill perpetually in shortage is analytics training along with skills in statistics.

It is time that corporates like IBM SPSS, SAS Institute and Revolution Analytics as well as offshore companies in India or Asia can ramp up their on demand trainings, certification as well as academic partnership bundles. Indeed offshroing companies can earn revenue as well as goodwill if they help in with trainers available via video- conferencing. The new Deal initiative would require creative thinking as well as direct top management support to focus their best internal brains at developing this new revenue stream. Again the company that trains the most users (be it Revolution for R, IBM for SPSS-Cognos, SAS Institute for Base SAS-JMP, WPS for SAS language) is going to get a bigger chunk of new users and analysts.

Analytics skills are hot. There is big new demand for hot new skills by millions of unemployed Americans and Asians. How do you think this services market will play out?

If the US government could pump 800 Billion for bailouts, how much is your opinion it should spend on training programs to help citizens compete globally?

From http://www.nytimes.com/2010/10/03/business/economy/03skills.html?hpw

The national program is a response to frustrations from both workers and employers who complain that public retraining programs frequently do not provide students with employable skills. This new initiative is intended to help better align community college curriculums with the demands of local companies.

SAS recognizes the market –

see http://www.sas.com/news/preleases/aba-tech-engage.html

In tough economic times, it is more important than ever that companies be able to make better decisions using analytics. SAS is involved in two programs this summer that offer MBAs and unemployed technology workers the opportunity to learn and enhance analytics skills, and increase their marketability.

SAS is a partner in TechEngage, a week-long program of training classes that offer unemployed technology professionals new skills at a low cost to help them compete effectively in the marketplace.”

So does IBM-

http://www-03.ibm.com/press/us/en/pressrelease/28994.wss

. “Fordham has a long history of collaboration with IBM that has brought innovative new skills to our curriculum to prepare students for future jobs. With this effort, Fordham is preparing students with marketable skills for a coming wave of jobs in healthcare, sustainability, and social services where analytics can be applied to everyday challenges.”

and R

Well TIBCO and Revolution ….hmmm…mmmm

I am not sure there is even a R Analytics Certification program at the least.

Interview Dean Abbott Abbott Analytics

Here is an interview with noted Analytics Consultant and trainer Dean Abbott. Dean is scheduled to take a workshop on Predictive Analytics at PAW (Predictive Analytics World Conference)  Oct 18 , 2010 in Washington D.C

Ajay-  Describe your upcoming hands on workshop at Predictive Analytics World and how it can help people learn more predictive modeling.

Refer- http://www.predictiveanalyticsworld.com/dc/2010/handson_predictive_analytics.php

Dean- The hands-on workshop is geared toward individuals who know something about predictive analytics but would like to experience the process. It will help people in two regards. First, by going through the data assessment, preparation, modeling and model assessment stages in one day, the attendees will see how predictive analytics works in reality, including some of the pain associated with false starts and mistakes. At the same time, they will experience success with building reasonable models to solve a problem in a single day. I have found that for many, having to actually build the predictive analytics solution if an eye-opener. Seeing demonstrations show the capabilities of a tool, but greater value for an end-user is the development of intuition of what to do at each each stage of the process that makes the theory of predictive analytics real.

Second, they will gain experience using a top-tier predictive analytics software tool, Enterprise Miner (EM). This is especially helpful for those who are considering purchasing EM, but also for those who have used open source tools and have never experienced the additional power and efficiencies that come with a tool that is well thought out from a business solutions standpoint (as opposed to an algorithm workbench).

Ajay-  You are an instructor with software ranging from SPSS, S Plus, SAS Enterprise Miner, Statistica and CART. What features of each software do you like best and are more suited for application in data cases.

Dean- I’ll add Tibco Spotfire Miner, Polyanalyst and Unica’s Predictive Insight to the list of tools I’ve taught “hands-on” courses around, and there are at least a half dozen more I demonstrate in lecture courses (JMP, Matlab, Wizwhy, R, Ggobi, RapidMiner, Orange, Weka, RandomForests and TreeNet to name a few). The development of software is a fascinating undertaking, and each tools has its own strengths and weaknesses.

I personally gravitate toward tools with data flow / icon interface because I think more that way, and I’ve tired of learning more programming languages.

Since the predictive analytics algorithms are roughly the same (backdrop is backdrop no matter which tool you use), the key differentiators are

(1) how data can be loaded in and how tightly integrated can the tool be with the database,

(2) how well big data can be handled,

(3) how extensive are the data manipulation options,

(4) how flexible are the model reporting options, and

(5) how can you get the models and/or predictions out.

There are vast differences in the tools on these matters, so when I recommend tools for customers, I usually interview them quite extensively to understand better how they use data and how the models will be integrated into their business practice.

A final consideration is related to the efficiency of using the tool: how much automation can one introduce so that user-interaction is minimized once the analytics process has been defined. While I don’t like new programming languages, scripting and programming often helps here, though some tools have a way to run the visual programming data diagram itself without converting it to code.

Ajay- What are your views on the increasing trend of consolidation and mergers and acquisitions in the predictive analytics space. Does this increase the need for vendor neutral analysts and consultants as well as conferences.

Dean- When companies buy a predictive analytics software package, it’s a mixed bag. SPSS purchasing of Clementine was ultimately good for the predictive analytics, though it took several years for SPSS to figure out what they wanted to do with it. Darwin ultimately disappeared after being purchased by Oracle, but the newer Oracle data mining tool, ODM, integrates better with the database than Darwin did or even would have been able to.

The biggest trend and pressure for the commercial vendors is the improvements in the Open Source and GNU tools. These are becoming more viable for enterprise-level customers with big data, though from what I’ve seen, they haven’t caught up with the big commercial players yet. There is great value in bringing both commercial and open source tools to the attention of end-users in the context of solutions (rather than sales) in a conference setting, which is I think an advantage that Predictive Analytics World has.

As a vendor-neutral consultant, flux is always a good thing because I have to be proficient in a variety of tools, and it is the breadth that brings value for customers entering into the predictive analytics space. But it is very difficult to keep up with the rapidly-changing market and that is something I am weighing myself: how many tools should I keep in my active toolbox.

Ajay-  Describe your career and how you came into the Predictive Analytics space. What are your views on various MS Analytics offered by Universities.

Dean- After getting a masters degree in Applied Mathematics, my first job was at a small aerospace engineering company in Charlottesville, VA called Barron Associates, Inc. (BAI); it is still in existence and doing quite well! I was working on optimal guidance algorithms for some developmental missile systems, and statistical learning was a key part of the process, so I but my teeth on pattern recognition techniques there, and frankly, that was the most interesting part of the job. In fact, most of us agreed that this was the most interesting part: John Elder (Elder Research) was the first employee at BAI, and was there at that time. Gerry Montgomery and Paul Hess were there as well and left to form a data mining company called AbTech and are still in analytics space.

After working at BAI, I had short stints at Martin Marietta Corp. and PAR Government Systems were I worked on analytics solutions in DoD, primarily radar and sonar applications. It was while at Elder Research in the 90s that began working in the commercial space more in financial and risk modeling, and then in 1999 I began working as an independent consultant.

One thing I love about this field is that the same techniques can be applied broadly, and therefore I can work on CRM, web analytics, tax and financial risk, credit scoring, survey analysis, and many more application, and cross-fertilize ideas from one domain into other domains.

Regarding MS degrees, let me first write that I am very encouraged that data mining and predictive analytics are being taught in specific class and programs rather than as just an add-on to an advanced statistics or business class. That stated, I have mixed feelings about analytics offerings at Universities.

I find that most provide a good theoretical foundation in the algorithms, but are weak in describing the entire process in a business context. For those building predictive models, the model-building stage nearly always takes much less time than getting the data ready for modeling and reporting results. These are cross-discipline tasks, requiring some understanding of the database world and the business world for us to define the target variable(s) properly and clean up the data so that the predictive analytics algorithms to work well.

The programs that have a practicum of some kind are the most useful, in my opinion. There are some certificate programs out there that have more of a business-oriented framework, and the NC State program builds an internship into the degree itself. These are positive steps in the field that I’m sure will continue as predictive analytics graduates become more in demand.

Biography-

DEAN ABBOTT is President of Abbott Analytics in San Diego, California. Mr. Abbott has over 21 years of experience applying advanced data mining, data preparation, and data visualization methods in real-world data intensive problems, including fraud detection, response modeling, survey analysis, planned giving, predictive toxicology, signal process, and missile guidance. In addition, he has developed and evaluated algorithms for use in commercial data mining and pattern recognition products, including polynomial networks, neural networks, radial basis functions, and clustering algorithms, and has consulted with data mining software companies to provide critiques and assessments of their current features and future enhancements.

Mr. Abbott is a seasoned instructor, having taught a wide range of data mining tutorials and seminars for a decade to audiences of up to 400, including DAMA, KDD, AAAI, and IEEE conferences. He is the instructor of well-regarded data mining courses, explaining concepts in language readily understood by a wide range of audiences, including analytics novices, data analysts, statisticians, and business professionals. Mr. Abbott also has taught both applied and hands-on data mining courses for major software vendors, including Clementine (SPSS, an IBM Company), Affinium Model (Unica Corporation), Statistica (StatSoft, Inc.), S-Plus and Insightful Miner (Insightful Corporation), Enterprise Miner (SAS), Tibco Spitfire Miner (Tibco), and CART (Salford Systems).

Trrrouble in land of R…and Open Source Suggestions

Recently some comments by Ross Ihake , founder of R Statistical Software on Revolution Analytics, leading commercial vendor of R….. came to my attention-

http://www.stat.auckland.ac.nz/mail/archive/r-downunder/2010-May/000529.html

[R-downunder] Article on Revolution Analytics

Ross Ihaka ihaka at stat.auckland.ac.nz
Mon May 10 14:27:42 NZST 2010


On 09/05/10 09:52, Murray Jorgensen wrote:
> Perhaps of interest:
>
> http://www.theregister.co.uk/2010/05/06/revolution_commercial_r/

Please note that R is "free software" not "open source".  These guys
are selling a GPLed work without disclosing the source to their part
of the work. I have complained to them and so far they have given me
the brush off. I am now considering my options.

Don't support these guys by buying their product. The are not feeding
back to the rights holders (the University of Auckland and I are rights
holders and they didn't even have the courtesy to contact us).

--
Ross Ihaka                         Email:  ihaka at stat.auckland.ac.nz
Department of Statistics           Phone:  (64-9) 373-7599 x 85054
University of Auckland             Fax:    (64-9) 373-7018
Private Bag 92019, Auckland
New Zealand
and from http://www.theregister.co.uk/2010/05/06/revolution_commercial_r/
Open source purists probably won't be all too happy to learn that Revolution is going to be employing an "open core" strategy, which means the core R programs will remain open source and be given tech support under a license model, but the key add-ons that make R more scalable will be closed source and sold under a separate license fee. Because most of those 2,500 add-ons for R were built by academics and Revolution wants to supplant SPSS and SAS as the tools used by students, Revolution will be giving the full single-user version of the R Enterprise stack away for free to academics. 
Conclusion-
So one co-founder of R is advocating not to buy from Revolution Analytics , which has the other co-founder of R, Gentleman on its board. 
Source- http://www.revolutionanalytics.com/aboutus/leadership.php

2) If Revolution Analytics is using 2500 packages for free but insisting on getting paid AND closing source of it’s packages (which is a technical point- how exactly can you prevent source code of a R package from being seen)

Maybe there can be a PACKAGE marketplace just like Android Apps, Facebook Apps, and Salesforce.com Apps – so atleast some of the thousands of R package developers can earn – sorry but email lists do not pay mortgages and no one is disputing the NEED for commercializing R or rewarding developers.

Though Barr created SAS, he gave up control to Goodnight and Sall https://decisionstats.wordpress.com/2010/06/02/sas-early-days/

and Goodnight and Sall do pay their developers well- to the envy of not so well paid counterparts.

3) I really liked the innovation of Revolution Analytics RevoScalar, and I wish that the default R dataset be converted to XDF dataset so that it basically kills

off the R criticism of being slow on bigger datasets. But I also realize the need for creating an analytics marketplace for R developers and R students- so academic version of R being free and Revolution R being paid seems like a trade off.

Note- You can still get a job faster as a stats student if you mention SAS and not R as a statistical skill- not all stats students go into academics.

4) There can be more elegant ways of handling this than calling for ignoring each other as REVOLUTION and Ihake seem to be doing to each other.

I can almost hear people in Cary, NC chuckling at Norman Nie, long time SPSS opponent and now REVOLUTION CEO, and his antagonizing R’s academicians within 1 year of taking over- so I hope this ends well for all. The road to hell is paved with good intentions- so if REVOLUTION can share some source code with say R Core members (even Microsoft shares source code with partners)- and R Core and Revolution agree on a licensing royalty from each other, they can actually speed up R package creation rather than allow this 2 decade effort to end up like S and S plus and TIBCO did.

Maybe Richard Stallman can help-or maybe Ihaka has a better sense of where things will go down in a couple of years-he must know something-he invented it, didnt he

On 09/05/10 09:52, Murray Jorgensen wrote:
> Perhaps of interest:
>
> http://www.theregister.co.uk/2010/05/06/revolution_commercial_r/

Please note that R is "free software" not "open source".  These guys
are selling a GPLed work without disclosing the source to their part
of the work. I have complained to them and so far they have given me
the brush off. I am now considering my options.

Don't support these guys by buying their product. The are not feeding
back to the rights holders (the University of Auckland and I are rights
holders and they didn't even have the courtesy to contact us).

--
Ross Ihaka                         Email:  ihaka at stat.auckland.ac.nz
Department of Statistics           Phone:  (64-9) 373-7599 x 85054
University of Auckland             Fax:    (64-9) 373-7018
Private Bag 92019, Auckland
New Zealand

So what happened to S Plus

Splus – The corporate version of S ( the predecessor of R) is still being marketed by Tibco corporation- again rumoured to be an acquisition target of  (???)

  • SAS ( who have desired R like capabilties especially in their IML  product to be released soon
  • SAP who lost out to IBM in the SPSS acquisition
  • Oracle
  • Microsoft
  • Rogue Wave (acquirer of Visual Numerics)
  • etc etc.

Anyways S Plus is still alive and kicking-

“The S language and the S+ application have been critical to our ability to manage big data objects intrinsic to wind analytics and wind energy development,” said Brad Horn, Director of Wind Analytics at NextEra Energy.  “We credit our long-term interface and Spotfire consulting with unlocking new ideas and sources of value.  Joint dialogue on configuration alternatives and our recent efforts to restructure legacy code is allowing us to transition from simple interactive use of S+ to a customized S+ configuration with integrated batch processing, server load balancing, and parallel processing.  S+ has a central role in supporting internal decisions and our group emphasis on scale, speed, and quality.”

http://spotfire.tibco.com/news/press-releases/2009/2_17_2009.aspx

  • Wavelets, Spatial Stats, EnvironmentalStats: Apply statistics for advanced analysis of signal and image data, spatially correlated data, and environmental data.
  • Resampling: Apply resampling techniques, such as bootstrap and permutation tests, to enable the use of standard statistics on smaller data sets.
  • Association Rules: Uncover relationships between variables in large data sets, most commonly to detect purchase patterns (Market Basket Analysis), or in many other areas like web site usage analysis.
  • Recode Values: Easily handle and prepare data from multiple sources by changing the values in a column to a new value.
  • Deployment and Integration:

    • Spotfire Integration: Read and write Spotfire Text Data files, and leverage examples of using Spotfire Professional to visualize, explore and share model results.
    • Custom Java & C++ nodes: Extend Spotfire Miner by writing custom nodes in Java and C++.
    • Remote Script Execution: Execute S+ scripts remotely on S+ Server to offload and distribute intensive jobs.
    • Global Worksheet Parameters: Make workflows more flexible and reusable to interactive and batch applications.
    • FlexBayes: Create more realistic models, provide a natural way to address missing data, and take advantage of prior analysis.

    Data Access and Preparation:

    • New Data File Types: Unlock more data sources by reading new formats including Spotfire Text Data, Microsoft Excel 2007, Microsoft Access 2007, and Matlab 7.
    • JDBC Access: Access new data sources for analysis with data import and export via the sjdbc library in Spotfire S+ 8.1.

    Citation:

    http://spotfire.tibco.com/Products/S-Plus-Overview.aspx

    http://spotfire.tibco.com/Products/Whatsnew-Splus.aspx