Tag: analysis

Choosing R for business – What to consider?

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Additional features in R over other analytical packages-

1) Source Code is given to ensure complete custom solution and embedding for a particular application. Open source code has an advantage that is extensively peer- reviewed in Journals and Scientific Literature.  This means bugs will found, shared and corrected transparently.

2) Wide literature of training material in the form of books is available for the R analytical platform.

3) Extensively the best data visualization tools in analytical software (apart from Tableau Software ‘s latest version). The extensive data visualization available in R is of the form a variety of customizable graphs, as well as animation. The principal reason third-party software initially started creating interfaces to R is because the graphical library of packages in R is more advanced as well as rapidly getting more features by the day.

4) Free in upfront license cost for academics and thus budget friendly for small and large analytical teams.

5) Flexible programming for your data environment. This includes having packages that ensure compatibility with Java, Python and C++.

 

6) Easy migration from other analytical platforms to R Platform. It is relatively easy for a non R platform user to migrate to R platform and there is no danger of vendor lock-in due to the GPL nature of source code and open community.

Statistics are numbers that tell (descriptive), advise ( prescriptive) or forecast (predictive). Analytics is a decision-making help tool. Analytics on which no decision is to be made or is being considered can be classified as purely statistical and non analytical. Thus ease of making a correct decision separates a good analytical platform from a not so good analytical platform. The distinction is likely to be disputed by people of either background- and business analysis requires more emphasis on how practical or actionable the results are and less emphasis on the statistical metrics in a particular data analysis task. I believe one clear reason between business analytics is different from statistical analysis is the cost of perfect information (data costs in real world) and the opportunity cost of delayed and distorted decision-making.

Specific to the following domains R has the following costs and benefits

  • Business Analytics
    • R is free per license and for download
    • It is one of the few analytical platforms that work on Mac OS
    • It’s results are credibly established in both journals like Journal of Statistical Software and in the work at LinkedIn, Google and Facebook’s analytical teams.
    • It has open source code for customization as per GPL
    • It also has a flexible option for commercial vendors like Revolution Analytics (who support 64 bit windows) as well as bigger datasets
    • It has interfaces from almost all other analytical software including SAS,SPSS, JMP, Oracle Data Mining, Rapid Miner. Existing license holders can thus invoke and use R from within these software
    • Huge library of packages for regression, time series, finance and modeling
    • High quality data visualization packages
    • Data Mining
      • R as a computing platform is better suited to the needs of data mining as it has a vast array of packages covering standard regression, decision trees, association rules, cluster analysis, machine learning, neural networks as well as exotic specialized algorithms like those based on chaos models.
      • Flexibility in tweaking a standard algorithm by seeing the source code
      • The RATTLE GUI remains the standard GUI for Data Miners using R. It was created and developed in Australia.
      • Business Dashboards and Reporting
      • Business Dashboards and Reporting are an essential piece of Business Intelligence and Decision making systems in organizations. R offers data visualization through GGPLOT, and GUI like Deducer and Red-R can help even non R users create a metrics dashboard
        • For online Dashboards- R has packages like RWeb, RServe and R Apache- which in combination with data visualization packages offer powerful dashboard capabilities.
        • R can be combined with MS Excel using the R Excel package – to enable R capabilities to be imported within Excel. Thus a MS Excel user with no knowledge of R can use the GUI within the R Excel plug-in to use powerful graphical and statistical capabilities.

Additional factors to consider in your R installation-

There are some more choices awaiting you now-
1) Licensing Choices-Academic Version or Free Version or Enterprise Version of R

2) Operating System Choices-Which Operating System to choose from? Unix, Windows or Mac OS.

3) Operating system sub choice- 32- bit or 64 bit.

4) Hardware choices-Cost -benefit trade-offs for additional hardware for R. Choices between local ,cluster and cloud computing.

5) Interface choices-Command Line versus GUI? Which GUI to choose as the default start-up option?

6) Software component choice- Which packages to install? There are almost 3000 packages, some of them are complimentary, some are dependent on each other, and almost all are free.

7) Additional Software choices- Which additional software do you need to achieve maximum accuracy, robustness and speed of computing- and how to use existing legacy software and hardware for best complementary results with R.

1) Licensing Choices-
You can choose between two kinds of R installations – one is free and open source from http://r-project.org The other R installation is commercial and is offered by many vendors including Revolution Analytics. However there are other commercial vendors too.

Commercial Vendors of R Language Products-
1) Revolution Analytics http://www.revolutionanalytics.com/
2) XL Solutions- http://www.experience-rplus.com/
3) Information Builder – Webfocus RStat -Rattle GUI http://www.informationbuilders.com/products/webfocus/PredictiveModeling.html
4) Blue Reference- Inference for R http://inferenceforr.com/default.aspx

  1. Choosing Operating System
      1. Windows

 

Windows remains the most widely used operating system on this planet. If you are experienced in Windows based computing and are active on analytical projects- it would not make sense for you to move to other operating systems. This is also based on the fact that compatibility problems are minimum for Microsoft Windows and the help is extensively documented. However there may be some R packages that would not function well under Windows- if that happens a multiple operating system is your next option.

        1. Enterprise R from Revolution Analytics- Enterprise R from Revolution Analytics has a complete R Development environment for Windows including the use of code snippets to make programming faster. Revolution is also expected to make a GUI available by 2011. Revolution Analytics claims several enhancements for it’s version of R including the use of optimized libraries for faster performance.
      2. MacOS

 

Reasons for choosing MacOS remains its considerable appeal in aesthetically designed software- but MacOS is not a standard Operating system for enterprise systems as well as statistical computing. However open source R claims to be quite optimized and it can be used for existing Mac users. However there seem to be no commercially available versions of R available as of now for this operating system.

      1. Linux

 

        1. Ubuntu
        2. Red Hat Enterprise Linux
        3. Other versions of Linux

 

Linux is considered a preferred operating system by R users due to it having the same open source credentials-much better fit for all R packages and it’s customizability for big data analytics.

Ubuntu Linux is recommended for people making the transition to Linux for the first time. Ubuntu Linux had an marketing agreement with revolution Analytics for an earlier version of Ubuntu- and many R packages can  installed in a straightforward way as Ubuntu/Debian packages are available. Red Hat Enterprise Linux is officially supported by Revolution Analytics for it’s enterprise module. Other versions of Linux popular are Open SUSE.

      1. Multiple operating systems-
        1. Virtualization vs Dual Boot-

 

You can also choose between having a VMware VM Player for a virtual partition on your computers that is dedicated to R based computing or having operating system choice at the startup or booting of your computer. A software program called wubi helps with the dual installation of Linux and Windows.

  1. 64 bit vs 32 bit – Given a choice between 32 bit versus 64 bit versions of the same operating system like Linux Ubuntu, the 64 bit version would speed up processing by an approximate factor of 2. However you need to check whether your current hardware can support 64 bit operating systems and if so- you may want to ask your Information Technology manager to upgrade atleast some operating systems in your analytics work environment to 64 bit operating systems.

 

  1. Hardware choices- At the time of writing this book, the dominant computing paradigm is workstation computing followed by server-client computing. However with the introduction of cloud computing, netbooks, tablet PCs, hardware choices are much more flexible in 2011 than just a couple of years back.

Hardware costs are a significant cost to an analytics environment and are also  remarkably depreciated over a short period of time. You may thus examine your legacy hardware, and your future analytical computing needs- and accordingly decide between the various hardware options available for R.
Unlike other analytical software which can charge by number of processors, or server pricing being higher than workstation pricing and grid computing pricing extremely high if available- R is well suited for all kinds of hardware environment with flexible costs. Given the fact that R is memory intensive (it limits the size of data analyzed to the RAM size of the machine unless special formats and /or chunking is used)- it depends on size of datasets used and number of concurrent users analyzing the dataset. Thus the defining issue is not R but size of the data being analyzed.

    1. Local Computing- This is meant to denote when the software is installed locally. For big data the data to be analyzed would be stored in the form of databases.
      1. Server version- Revolution Analytics has differential pricing for server -client versions but for the open source version it is free and the same for Server or Workstation versions.
      2. Workstation
    2. Cloud Computing- Cloud computing is defined as the delivery of data, processing, systems via remote computers. It is similar to server-client computing but the remote server (also called cloud) has flexible computing in terms of number of processors, memory, and data storage. Cloud computing in the form of public cloud enables people to do analytical tasks on massive datasets without investing in permanent hardware or software as most public clouds are priced on pay per usage. The biggest cloud computing provider is Amazon and many other vendors provide services on top of it. Google is also coming for data storage in the form of clouds (Google Storage), as well as using machine learning in the form of API (Google Prediction API)
      1. Amazon
      2. Google
      3. Cluster-Grid Computing/Parallel processing- In order to build a cluster, you would need the RMpi and the SNOW packages, among other packages that help with parallel processing.
    3. How much resources
      1. RAM-Hard Disk-Processors- for workstation computing
      2. Instances or API calls for cloud computing
  2. Interface Choices
    1. Command Line
    2. GUI
    3. Web Interfaces
  3. Software Component Choices
    1. R dependencies
    2. Packages to install
    3. Recommended Packages
  4. Additional software choices
    1. Additional legacy software
    2. Optimizing your R based computing
    3. Code Editors
      1. Code Analyzers
      2. Libraries to speed up R

citation-  R Development Core Team (2010). R: A language and environment for statistical computing. R Foundation for Statistical Computing,Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.

(Note- this is a draft in progress)

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2011 Forecast-ying

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I had recently asked some friends from my Twitter lists for their take on 2011, atleast 3 of them responded back with the answer, 1 said they were still on it, and 1 claimed a recent office event.

Anyways- I take note of the view of forecasting from

http://www.uiah.fi/projekti/metodi/190.htm

The most primitive method of forecasting is guessing. The result may be rated acceptable if the person making the guess is an expert in the matter.

Ajay- people will forecast in end 2010 and 2011. many of them will get forecasts wrong, some very wrong, but by Dec 2011 most of them would be writing forecasts on 2012. almost no one will get called on by irate users-readers- (hey you got 4 out of 7 wrong last years forecast!) just wont happen. people thrive on hope. so does marketing. in 2011- and before

and some forecasts from Tom Davenport’s The International Institute for Analytics (IIA) at

http://iianalytics.com/2010/12/2011-predictions-for-the-analytics-industry/

Regulatory and privacy constraints will continue to hamper growth of marketing analytics.

(I wonder how privacy and analytics can co exist in peace forever- one view is that model building can use anonymized data suppose your IP address was anonymized using a standard secret Coco-Cola formula- then whatever model does get built would not be of concern to you individually as your privacy is protected by the anonymization formula)

Anyway- back to the question I asked-

What are the top 5 events in your industry (events as in things that occured not conferences) and what are the top 3 trends in 2011.

I define my industry as being online technology writing- research (with a heavy skew on stat computing)

My top 5 events for 2010 were-

1) Consolidation- Big 5 software providers in BI and Analytics bought more, sued more, and consolidated more.  The valuations rose. and rose. leading to even more smaller players entering. Thus consolidation proved an oxy moron as total number of influential AND disruptive players grew.

 

2) Cloudy Computing- Computing shifted from the desktop but to the mobile and more to the tablet than to the cloud. Ipad front end with Amazon Ec2 backend- yup it happened.

3) Open Source grew louder- yes it got more clients. and more revenue. did it get more market share. depends on if you define market share by revenues or by users.

Both Open Source and Closed Source had a good year- the pie grew faster and bigger so no one minded as long their slices grew bigger.

4) We didnt see that coming –

Technology continued to surprise with events (thats what we love! the surprises)

Revolution Analytics broke through R’s Big Data Barrier, Tableau Software created a big Buzz,  Wikileaks and Chinese FireWalls gave technology an entire new dimension (though not universally popular one).

people fought wars on emails and servers and social media- unfortunately the ones fighting real wars in 2009 continued to fight them in 2010 too

5) Money-

SAP,SAS,IBM,Oracle,Google,Microsoft made more money than ever before. Only Facebook got a movie named on itself. Venture Capitalists pumped in money in promising startups- really as if in a hurry to park money before tax cuts expired in some countries.

 

2011 Top Three Forecasts

1) Surprises- Expect to get surprised atleast 10 % of the time in business events. As internet grows the communication cycle shortens, the hype cycle amplifies buzz-

more unstructured data  is created (esp for marketing analytics) leading to enhanced volatility

2) Growth- Yes we predict technology will grow faster than the automobile industry. Game changers may happen in the form of Chrome OS- really its Linux guys-and customer adaptability to new USER INTERFACES. Design will matter much more in technology on your phone, on your desktop and on your internet. Packaging sells.

False Top Trend 3) I will write a book on business analytics in 2011. yes it is true and I am working with A publisher. No it is not really going to be a top 3 event for anyone except me,publisher and lucky guys who read it.

3) Creating technology and technically enabling creativity will converge at an accelerated rate. use of widgets, guis, snippets, ide will ensure creative left brains can code easier. and right brains can design faster and better due to a global supply chain of techie and artsy professionals.

 

 

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PAWCON Bay Area March

The biggest Predictive Analytics Conference comes back to the SF Bay in March next year.

From

http://www.predictiveanalyticsworld.com/sanfrancisco/2011/

Predictive Analytics World March 2011 in San Francisco is packed with the top predictive analytics experts, practitioners, authors and business thought leaders, including keynote speakers:


Sugato Basu, Ph.D.
Senior Research Scientist
Google
Lessons Learned in Predictive Modeling 
for Ad Targeting
Eric Siegel, Ph.D.
Conference Chair
Predictive Analytics World
Five Ways Predictive Analytics
Cuts Enterprise Risk



Plus special plenary sessions from industry heavy-weights:


Andreas S. Weigend, Ph.D.
weigend.com
Former Chief Scientist, Amazon.com
The State of the Social Data Revoltion
John F. Elder, Ph.D.
CEO and Founder
Elder Research
Data Mining Lessons Learned



Predictive Analytics World focuses on concrete examples of deployed predictive analytics. Hear from the horse’s mouth precisely how Fortune 500 analytics competitors and other top practitioners deploy predictive modeling, and what kind of business impact it delivers. Click here to view the agenda at-a-glance.

PAW SF 2011 will feature speakers with case studies from leading enterprises. such as:

PAW’s March agenda covers hot topics and advanced methods such as uplift (net lift) modeling, ensemble models, social data, search marketing, crowdsourcing, blackbox trading, fraud detection, risk management, survey analysis and otherinnovative applications that benefit organizations in new and creative ways.

Join PAW and access the best keynotes, sessions, workshops, exposition, expert panel, live demos, networking coffee breaks, reception, birds-of-a-feather lunches, brand-name enterprise leaders, and industry heavyweights in the business.

 

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Using SAS/IML with R

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SAS just released an updated documentation to SAS/IML language with a special chapter devoted to using R

Here is an example-

CALL EXPORTMATRIXTOR( IMLMatrix, RMatrix ) ;

CALL IMPORTMATRIXFROMR( IMLMatrix, RExpr ) ;

If you have existing SAS licences and existing hardware and loots of data -this may be the best of both worlds- without getting into the mess of technically learning MKL threads/BLAS/Premium Packages/Cloud

Another thought- its a good professional looking help book, which is what more R packages can do (work on improving ease of their help/update vignettes)

 

Link-http://support.sas.com/documentation/cdl/en/imlug/63541/HTML/default/viewer.htm#r_toc.htm

 

Calling Functions in the R Language

[continuerule]

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Brief Interview Timo Elliott

Here is a brief interview with Timo Elliott.Timo Elliott is a 19-year veteran of SAP Business Objects.

Ajay- What are the top 5 events in Business Integration and Data Visualization services you saw in 2010 and what are the top three trends you see in these in 2011.


Timo-

Top five events in 2010:

(1) Back to strong market growth. IT spending plummeted last year (BI continued to grow, but more slowly than previous years). This year, organizations reopened their wallets and funded new analytics initiatives — all the signs indicate that BI market growth will be double that of 2009.

(2) The launch of the iPad. Mobile BI has been around for years, but the iPad opened the floodgates of organizations taking a serious look at mobile analytics — and the easy-to-use, executive-friendly iPad dashboards have considerably raised the profile of analytics projects inside organizations.

(3) Data warehousing got exciting again. Decades of incremental improvements (column databases, massively parallel processing, appliances, in-memory processing…) all came together with robust commercial offers that challenged existing data storage and calculation methods. And new “NoSQL” approaches, designed for the new problems of massive amounts of less-structured web data, started moving into the mainstream.

(4) The end of Google Wave, the start of social BI.Google Wave was launched as a rethink of how we could bring together email, instant messaging, and social networks. While Google decided to close down the technology this year, it has left its mark, notably by influencing the future of “social BI”, with several major vendors bringing out commercial products this year.

(5) The start of the big BI merge. While several small independent BI vendors reported strong growth, the major trend of the year was consolidation and integration: the BI megavendors (SAP, Oracle, IBM, Microsoft) increased their market share (sometimes by acquiring smaller vendors, e.g. IBM/SPSS and SAP/Sybase) and integrated analytics with their existing products, blurring the line between BI and other technology areas.

Top three trends next year:

(1) Analytics, reinvented. New DW techniques make it possible to do sub-second, interactive analytics directly against row-level operational data. Now BI processes and interfaces need to be rethought and redesigned to make best use of this — notably by blurring the distinctions between the “design” and “consumption” phases of BI.

(2) Corporate and personal BI come together. The ability to mix corporate and personal data for quick, pragmatic analysis is a common business need. The typical solution to the problem — extracting and combining the data into a local data store (either Excel or a departmental data mart) — pleases users, but introduces duplication and extra costs and makes a mockery of information governance. 2011 will see the rise of systems that let individuals and departments load their data into personal spaces in the corporate environment, allowing pragmatic analytic flexibility without compromising security and governance.

(3) The next generation of business applications. Where are the business applications designed to support what people really do all day, such as implementing this year’s strategy, launching new products, or acquiring another company? 2011 will see the first prototypes of people-focused, flexible, information-centric, and collaborative applications, bringing together the best of business intelligence, “enterprise 2.0”, and existing operational applications.

And one that should happen, but probably won’t:

(4) Intelligence = Information + PEOPLE. Successful analytics isn’t about technology — it’s about people, process, and culture. The biggest trend in 2011 should be organizations spending the majority of their efforts on user adoption rather than technical implementation.                 About- http://timoelliott.com/blog/about

Timo Elliott is a 19-year veteran of SAP BusinessObjects, and has spent the last twenty years working with customers around the world on information strategy.

He works closely with SAP research and innovation centers around the world to evangelize new technology prototypes.

His popular Business Analytics and SAPWeb20 blogs track innovation in analytics and social media, including topics such as augmented corporate reality, collaborative decision-making, and social network analysis.

His PowerPoint Twitter Tools lets presenters see and react to tweets in real time, embedded directly within their slides.

A popular and engaging speaker, Elliott presents regularly to IT and business audiences at international conferences, on subjects such as why BI projects fail and what to do about it, and the intersection of BI and enterprise 2.0.

Prior to Business Objects, Elliott was a computer consultant in Hong Kong and led analytics projects for Shell in New Zealand. He holds a first-class honors degree in Economics with Statistics from Bristol University, England. He blogs on http://timoelliott.com/blog/ (one of the best designed blogs in BI) . You can see more about him personal web site here and photo/sketch blog here. You should follow Timo at http://twitter.com/timoelliott

Art Credit- Timo Elliott

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Short Interview Jill Dyche

Here is brief one question interview with Jill Dyche , founder Baseline Consulting.

 

In 2010.

 

  • It was more about consciousness-raising in the executive suite—
  • getting C-level managers to understand the ongoing value proposition of BI,
  • why MDM isn’t their father’s database, and
  • how data governance can pay for itself over time.
  • Some companies succeeded with these consciousness-raising efforts. Some didn’t.

 

But three big ones in 2011 would be:

  1. Predictive analytics in the cloud. The technology is now ready, and so is the market—and that includes SMB companies.
  2. Enterprise search being baked into (commoditized) BI software tools. (The proliferation of static reports is SO 2006!)
  3. Data governance will begin paying dividends. Until now it was all about common policies for data. In 2011, it will be about ROI.

I do a “Predictions for the coming year” article every January for TDWI,

Note- Jill ‘s January TDWI article seems worth waiting for in this case.

About-

Source-http://www.baseline-consulting.com/pages/page.asp?page_id=49125

Partner and Co-Founder

Jill Dyché is a partner and co-founder of Baseline Consulting.  She is responsible for key client strategies and market analysis in the areas of data governance, business intelligence, master data management, and customer relationship management. 

Jill counsels boards of directors on the strategic importance of their information investments.

Author

Jill is the author of three books on the business value of IT. Jill’s first book, e-Data (Addison Wesley, 2000) has been published in eight languages. She is a contributor to Impossible Data Warehouse Situations: Solutions from the Experts (Addison Wesley, 2002), and her book, The CRM Handbook (Addison Wesley, 2002), is the bestseller on the topic. 

Jill’s work has been featured in major publications such as Computerworld, Information Week, CIO Magazine, the Wall Street Journal, the Chicago Tribune and Newsweek.com. Jill’s latest book, Customer Data Integration (John Wiley and Sons, 2006) was co-authored with Baseline partner Evan Levy, and shows the business breakthroughs achieved with integrated customer data.

Industry Expert

Jill is a featured speaker at industry conferences, university programs, and vendor events. She serves as a judge for several IT best practice awards. She is a member of the Society of Information Managementand Women in Technology, a faculty member of TDWI, and serves as a co-chair for the MDM Insight conference. Jill is a columnist for DM Review, and a blogger for BeyeNETWORK and Baseline Consulting.

 

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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)

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