Author: Ajay Ohri

http://about.me/ajayohri

Talk on Big Data Analytics

My talk on Big Data Analytics using mostly R went off peacefully.

Here are the slides

Big data Big Analytics

View more presentations from Ajay Ohri.

Churn Analytics Contest at Crowd Analytix

Crowd Analytix- the Bangalore based Indian startup is moving fast in the

data scientist contest space (so watch out Kaggle!! )

—
Churn (loss of customers to competition) is a problem for telecom companies because it is more expensive to acquire a new customer than to keep your existing one from leaving. This contest is about enabling churn reduction using analytics.

To join, go to – http://www.crowdanalytix.com/contests/why-customer-churn/

Interview James G Kobielus IBM Big Data

Here is an interview with  James G Kobielus, who is the Senior Program Director, Product Marketing, Big Data Analytics Solutions at IBM. Special thanks to Payal Patel Cudia of IBM’s communication team,for helping with the logistics for this.

Ajay -What are the specific parts of the IBM Platform that deal with the three layers of Big Data -variety, velocity and volume

James-Well first of all, let’s talk about the IBM Information Management portfolio. Our big data platform addresses the three layers of big data to varying degrees either together in a product , or two out of the three or even one of the three aspects. We don’t have separate products for the variety, velocity and volume separately.

Let us define these three layers-Volume refers to the hundreds of terabytes and petabytes of stored data inside organizations today. Velocity refers to the whole continuum from batch to real time continuous and streaming data.

Variety refers to multi-structure data from structured to unstructured files, managed and stored in a common platform analyzed through common tooling.

For Volume-IBM has a highly scalable Big Data platform. This includes Netezza and Infosphere groups of products, and Watson-like technologies that can support petabytes volume of data for analytics. But really the support of volume ranges across IBM’s Information Management portfolio both on the database side and the advanced analytics side.

For real time Velocity, we have real time data acquisition. We have a product called IBM Infosphere, part of our Big Data platform, that is specifically built for streaming real time data acquisition and delivery through complex event processing. We have a very rich range of offerings that help clients build a Hadoop environment that can scale.

Our Hadoop platform is the most real time capable of all in the industry. We are differentiated by our sheer breadth, sophistication and functional depth and tooling integrated in our Hadoop platform. We are differentiated by our streaming offering integrated into the Hadoop platform. We also offer a great range of modeling and analysis tools, pretty much more than any other offering in the Big Data space.

Attached- Jim’s slides from Hadoop World

Ibm big data hadoop summit 2012 james kobielus final 6-13-12(1)

Ajay- Any plans for Mahout for Hadoop

Jim- I cant speak about product plans. We have plans but I cant tell you anything more. We do have a feature in Big Insights called System ML, a library for machine learning.

Ajay- How integral are acquisitions for IBM in the Big Data space (Netezza,Cognos,SPSS etc). Is it true that everything that you have in Big Data is acquired or is the famous IBM R and D contributing here . (see a partial list of IBM acquisitions at at http://www.ibm.com/investor/strategy/acquisitions.wss )

Jim- We have developed a lot on our own. We have the deepest R and D of anybody in the industry in all things Big Data.

For example – Watson has Big Insights Hadoop at its core. Apache Hadoop is the heart and soul of Big Data (see http://www-01.ibm.com/software/data/infosphere/hadoop/ ). A great deal that makes Big Insights so differentiated is that not everything that has been built has been built by the Hadoop community.

We have built additions out of the necessity for security, modeling, monitoring, and governance capabilities into BigInsights to make it truly enterprise ready. That is one example of where we have leveraged open source and we have built our own tools and technologies and layered them on top of the open source code.

Yes of course we have done many strategic acquisitions over the last several years related to Big Data Management and we continue to do so. This quarter we have done 3 acquisitions with strong relevance to Big Data. One of them is Vivisimo (http://www-03.ibm.com/press/us/en/pressrelease/37491.wss ).

Vivisimo provides federated Big Data discovery, search and profiling capabilities to help you figure out what data is out there,what is relevance of that data to your data science project- to help you answer the question which data should you bring in your Hadoop Cluster.

 We also did Varicent , which is more performance management and we did TeaLeaf , which is a customer experience solution provider where customer experience management and optimization is one of the hot killer apps for Hadoop in the cloud. We have done great many acquisitions that have a clear relevance to Big Data.

Netezza already had a massively parallel analytics database product with an embedded library of models called Netezza Analytics, and in-database capabilties to massively parallelize Map Reduce and other analytics management functions inside the database. In many ways, Netezza provided capabilities similar to that IBM had provided for many years under the Smart Analytics Platform (http://www-01.ibm.com/software/data/infosphere/what-is-advanced-analytics/ ) .

There is a differential between Netezza and ISAS.

ISAS was built predominantly in-house over several years . If you go back a decade ago IBM acquired Ascential Software , a product portfolio that was the heart and soul of IBM InfoSphere Information Manager that is core to our big Data platform. In addition to Netezza, IBM bought SPSS two years back. We already had data mining tools and predictive modeling in the InfoSphere portfolio, but we realized we needed to have the best of breed, SPSS provided that and so IBM acquired them.

 Cognos– We had some BI reporting capabilities in the InfoSphere portfolio that we had built ourselves and also acquired for various degrees from prior acquisitions. But clearly Cognos was one of the best BI vendors , and we were lacking such a rich tool set in our product in visualization and cubing and so for that reason we acquired Cognos.

There is also Unica – which is a marketing campaign optimization which in many ways is a killer app for Hadoop. Projects like that are driving many enterprises.

Ajay- How would you rank order these acquisitions in terms of strategic importance rather than data of acquisition or price paid.

Jim-Think of Big Data as an ecosystem that has components that are fitted to particular functions for data analytics and data management. Is the database the core, or the modeling tool the core, or the governance tools the core, or is the hardware platform the core. Everything is critically important. We would love to hear from you what you think have been most important. Each acquisition has helped play a critical role to build the deepest and broadest solution offering in Big Data. We offer the hardware, software, professional services, the hosting service. I don’t think there is any validity to a rank order system.

Ajay-What are the initiatives regarding open source that Big Data group have done or are planning?

Jim- What we are doing now- We are very much involved with the Apache Hadoop community. We continue to evolve the open source code that everyone leverages.. We have built BigInsights on Apache Hadoop. We have the closest, most up to date in terms of version number to Apache Hadoop ( Hbase,HDFS, Pig etc) of all commercial distributions with our BigInsights 1.4 .

We have an R library integrated with BigInsights . We have a R library integrated with Netezza Analytics. There is support for R Models within the SPSS portfolio. We already have a fair amount of support for R across the portfolio.

Ajay- What are some of the concerns (privacy,security,regulation) that you think can dampen the promise of Big Data.

Jim- There are no showstoppers, there is really a strong momentum. Some of the concerns within the Hadoop space are immaturity of the technology, the immaturity of some of the commercial offerings out there that implement Hadoop, the lack of standardization for formal sense for Hadoop.

There is no Open Standards Body that declares, ratifies the latest version of Mahout, Map Reduce, HDFS etc. There is no industry consensus reference framework for layering these different sub projects. There are no open APIs. There are no certifications or interoperability standards or organizations to certify different vendors interoperability around a common API or framework.

The lack of standardization is troubling in this whole market. That creates risks for users because users are adopting multiple Hadoop products. There are lots of Hadoop deployments in the corporate world built around Apache Hadoop (purely open source). There may be no assurance that these multiple platforms will interoperate seamlessly. That’s a huge issue in terms of just magnifying the risk. And it increases the need for the end user to develop their own custom integrated code if they want to move data between platforms, or move map-reduce jobs between multiple distributions.

Also governance is a consideration. Right now Hadoop is used for high volume ETL on multi structured and unstructured data sources, or Hadoop is used for exploratory sand boxes for data scientists. These are important applications that are a majority of the Hadoop deployments . Some Hadoop deployments are stand alone unstructured data marts for specific applications like sentiment analysis like.

Hadoop is not yet ready for data warehousing. We don’t see a lot of Hadoop being used as an alternative to data warehouses for managing the single version of truth of system or record data. That day will come but there needs to be out there in the marketplace a broader range of data governance mechanisms , master data management, data profiling products that are mature that enterprises can use to make sure their data inside their Hadoop clusters is clean and is the single version of truth. That day has not arrived yet.

One of the great things about IBM’s acquisition of Vivisimo is that a piece of that overall governance picture is discovery and profiling for unstructured data , and that is done very well by Vivisimo for several years.

What we will see is vendors such as IBM will continue to evolve security features inside of our Hadoop platform. We will beef up our data governance capabilities for this new world of Hadoop as the core of Big Data, and we will continue to build up our ability to integrate multiple databases in our Hadoop platform so that customers can use data from a bit of Hadoop,some data from a bit of traditional relational data warehouse, maybe some noSQL technology for different roles within a very complex Big Data environment.

That latter hybrid deployment model is becoming standard across many enterprises for Big Data. A cause for concern is when your Big Data deployment has a bit of Hadoop, bit of noSQL, bit of EDW, bit of in-memory , there are no open standards or frameworks for putting it all together for a unified framework not just for interoperability but also for deployment.

There needs to be a virtualization or abstraction layer for unified access to all these different Big Data platforms by the users/developers writing the queries, by administrators so they can manage data and resources and jobs across all these disparate platforms in a seamless unified way with visual tooling. That grand scenario, the virtualization layer is not there yet in any standard way across the big data market. It will evolve, it may take 5-10 years to evolve but it will evolve.

So, that’s the concern that can dampen some of the enthusiasm for Big Data Analytics.

About-

You can read more about Jim at http://www.linkedin.com/pub/james-kobielus/6/ab2/8b0 or

follow him on Twitter at http://twitter.com/jameskobielus

You can read more about IBM Big Data at http://www-01.ibm.com/software/data/bigdata/

Google Visualization Tools Can Help You Build a Personal Dashboard

The Google Visualization API is a great way for people to make dashboards with slick graphics based  on data without getting into the fine print of the scripting language  itself.  It utilizes the same tools as Google itself does, and makes visualizing data using API calls to the Visualization API. Thus a real-time customizable dashboard that is publishable to the internet can be created within minutes, and more importantly insights can be much more easily drawn from graphs than from looking at rows of tables and numbers.

  1. There are 41 gadgets (including made by both Google and third-party developers ) available in the Gadget  Gallery ( https://developers.google.com/chart/interactive/docs/gadgetgallery)
  2. There are 12 kinds of charts available in the Chart Gallery (https://developers.google.com/chart/interactive/docs/gallery) .
  3. However there 26 additional charts in the charts page at https://developers.google.com/chart/interactive/docs/more_charts )

Building and embedding charts is simplified to a few steps

  • Load the AJAX API
  • Load the Visualization API and the appropriate package (like piechart or barchart from the kinds of chart)
  • Set a callback to run when the Google Visualization API is loaded
    • Within the Callback – It creates and populates a data table, instantiates the particular chart type chosen, passes in the data and draws it.
    • Create the data table with appropriately named columns and data rows.
    • Set chart options with Title, Width and Height
  • Instantiate and draw the chart, passing in some options including the name and id
  • Finally write the HTML/ Div that will hold the chart

You can simply copy and paste the code directly from https://developers.google.com/chart/interactive/docs/quick_start without getting into any details, and tweak them according to your data, chart preference and voila your web dashboard is ready!
That is the beauty of working with API- you can create and display genius ideas without messing with the scripting languages and code (too much). If you like to dive deeper into the API, you can look at the various objects at https://developers.google.com/chart/interactive/docs/reference

First launched in Mar 2008, Google Visualization API has indeed come a long way in making dashboards easier to build for people wanting to utilize advanced data visualization . It came about directly as a result of Google’s 2007 acquisition of GapMinder (of Hans Rosling fame).
As invariably and inevitably computing shifts to the cloud, visualization APIs will be very useful. Tableau Software has been a pioneer in selling data visualizing to the lucrative business intelligence and business dashboards community (you can see the Tableau Software API at http://onlinehelp.tableausoftware.com/v7.0/server/en-us/embed_api.htm ), and Google Visualization can do the same and capture business dashboard and visualization market , if there is more focus on integrating it from Google in it’s multiple and often confusing API offerings.
However as of now, this is quite simply the easiest way to create a web dashboard for your personal needs. Google guarantees 3 years of backward compatibility with this API and it is completely free.

Interview Alain Chesnais Chief Scientist Trendspottr.com

Here is a brief interview with Alain Chesnais ,Chief Scientist  Trendspottr.com. It is a big honor to interview such a legend in computer science, and I am grateful to both him and Mark Zohar for taking time to write these down.
alain_chesnais2.jpg

Ajay-  Describe your career from your student days to being the President of ACM (Association of Computing Machinery http://www.acm.org/ ). How can we increase  the interest of students in STEM education, particularly in view of the shortage of data scientists.
 
Alain- I’m trying to sum up a career of over 35 years. This may be a bit long winded…
I started my career in CS when I was in high school in the early 70’s. I was accepted in the National Science Foundation’s Science Honors Program in 9th grade and the first course I took was a Fortran programming course at Columbia University. This was on an IBM 360 using punch cards.
The next year my high school got a donation from DEC of a PDP-8E mini computer. I ended up spending a lot of time in the machine room all through high school at a time when access to computers wasn’t all that common. I went to college in Paris and ended up at l’Ecole Normale Supérieure de Cachan in the newly created Computer Science department.
My first job after finishing my graduate studies was as a research assistant at the Centre National de la Recherche Scientifique where I focused my efforts on modelling the behaviour of distributed database systems in the presence of locking. When François Mitterand was elected president of France in 1981, he invited Nicholas Negroponte and Seymour Papert to come to France to set up the Centre Mondial Informatique. I was hired as a researcher there and continued on to become director of software development until it was closed down in 1986. I then started up my own company focusing on distributed computer graphics. We sold the company to Abvent in the early 90’s.
After that, I was hired by Thomson Digital Image to lead their rendering team. We were acquired by Wavefront Technologies in 1993 then by SGI in 1995 and merged with Alias Research. In the merged company: Alias|wavefront, I was director of engineering on the Maya project. Our team received an Oscar in 2003 for the creation of the Maya software system.
Since then I’ve worked at various companies, most recently focusing on social media and Big Data issues associated with it. Mark Zohar and I worked together at SceneCaster in 2007 where we developed a Facebook app that allowed users to create their own 3D scenes and share them with friends via Facebook without requiring a proprietary plugin. In December 2007 it was the most popular app in its category on Facebook.
Recently Mark approached me with a concept related to mining the content of public tweets to determine what was trending in real time. Using math similar to what I had developed during my graduate studies to model the performance of distributed databases in the presence of locking, we built up a real time analytics engine that ranks the content of tweets as they stream in. The math is designed to scale linearly in complexity with the volume of data that we analyze. That is the basis for what we have created for TrendSpottr.
In parallel to my professional career, I have been a very active volunteer at ACM. I started out as a member of the Paris ACM SIGGRAPH chapter in 1985 and volunteered to help do our mailings (snail mail at the time). After taking on more responsibilities with the chapter, I was elected chair of the chapter in 1991. I was first appointed to the SIGGRAPH Local Groups Steering Committee, then became ACM Director for Chapters. Later I was successively elected SIGGRAPH Vice Chair, ACM SIG Governing Board (SGB) Vice Chair for Operations, SGB Chair, ACM SIGGRAPH President, ACM Secretary/Treasurer, ACM Vice President, and finally, in 2010, I was elected ACM President. My term as ACM President has just ended on July 1st. Vint Cerf is our new President. I continue to serve on the ACM Executive Committee in my role as immediate Past President.
(Note- About ACM
ACM, the Association for Computing Machinery www.acm.org, is the world’s largest educational and scientific computing society, uniting computing educators, researchers and professionals to inspire dialogue, share resources and address the field’s challenges. )
Ajay- What sets Trendspotter apart from other startups out there in terms of vision in trying to achieve a more coherent experience on the web.
 
Alain- The Basic difference with other approaches that we are aware of is that we have developed an incremental solution that calculates the results on the fly as the data streams in. Our evaluators are based on solid mathematical foundations that have proven their usefulness over time. One way to describe what we do is to think of it as signal processing where the tweets are the signal and our evaluators are like triggers that tell you what elements of the signal have the characteristics that we are filtering for (velocity and acceleration). One key result of using this approach is that our unit cost per tweet analyzed does not go up with increased volume. Using more traditional data analysis approaches involving an implicit sort would imply a complexity of N*log(N), where N is the volume of tweets being analyzed. That would imply that the cost per tweet analyzed would go up with the volume of tweets. Our approach was designed to avoid that, so that we can maintain a cap on our unit costs of analysis, no matter what volume of data we analyze.
Ajay- What do you think is the future of big data visualization going to look like? What are some of the technologies that you are currently bullish on?
Alain- I see several trends that would have deep impact on Big Data visualization. I firmly believe that with large amounts of data, visualization is key tool for understanding both the structure and the relationships that exist between data elements. Let’s focus on some of the key things that are pushing in this direction:
  • the volume of data that is available is growing at a rate we have never seen before. Cisco has measured an 8 fold increase in the volume of IP traffic over the last 5 years and predicts that we will reach the zettabyte of data over IP in 2016
  • more of the data is becoming publicly available. This isn’t only on social networks such as Facebook and twitter, but joins a more general trend involving open research initiatives and open government programs
  • the desired time to get meaningful results is going down dramatically. In the past 5 years we have seen the half life of data on Facebook, defined as the amount of time that half of the public reactions to any given post (likes, shares., comments) take place, go from about 12 hours to under 3 hours currently
  • our access to the net is always on via mobile device. You are always connected.
  • the CPU and GPU capabilities of mobile devices is huge (an iPhone has 10 times the compute power of a Cray-1 and more graphics capabilities than early SGI workstations)
Put all of these observations together and you quickly come up with a massive opportunity to analyze data visually on the go as it happens no matter where you are. We can’t afford to have to wait for results. When something of interest occurs we need to be aware of it immediately.
Ajay- What are some of the applications we could use Trendspottr. Could we predict events like Arab Spring, or even the next viral thing.
 
Alain- TrendSpottr won’t predict what will happen next. What it *will* do is alert you immediately when it happens. You can think of it like a smoke detector. It doesn’t tell that a fire will take place, but it will save your life when a fire does break out.
Typical uses for TrendSpottr are
  • thought leadership by tracking content that your readership is interested in via TrendSpottr you can be seen as a thought leader on the subject by being one of the first to share trending content on a given subject. I personally do this on my Facebook page (http://www.facebook.com/alain.chesnais) and have seen my klout score go up dramatically as a result
  • brand marketing to be able to know when something is trending about your brand and take advantage of it as it happens.
  • competitive analysis to see what is being said about two competing elements. For instance, searching TrendSpottr for “Obama OR Romney” gives you a very good understanding about how social networks are reacting to each politician. You can also do searches like “$aapl OR $msft OR $goog” to get a sense of what is the current buzz for certain hi tech stocks.
  • understanding your impact in real time to be able to see which of the content that you are posting is trending the most on social media so that you can highlight it on your main page. So if all of your content is hosted on common domain name (ourbrand.com), searching for ourbrand.com will show you the most active of your site’s content. That can easily be set up by putting a TrendSpottr widget on your front page

Ajay- What are some of the privacy guidelines that you keep in  mind- given the fact that you collect individual information but also have government agencies as potential users.

 
Alain- We take privacy very seriously and anonymize all of the data that we collect. We don’t keep explicit records of the data we collected through the various incoming streams and only store the aggregate results of our analysis.
About–
Alain Chesnais is immediate Past President of ACM, elected for the two-year term beginning July 1, 2010.Chesnais studied at l’Ecole Normale Supérieure de l’Enseignement Technique and l’Université de Paris where he earned a Maîtrise de Mathematiques, a Maitrise de Structure Mathématique de l’Informatique, and a Diplôme d’Etudes Approfondies in Compuer Science. He was a high school student at the United Nations International School in New York, where, along with preparing his International Baccalaureate with a focus on Math, Physics and Chemistry, he also studied Mandarin Chinese.Chesnais recently founded Visual Transitions, which specializes in helping companies move to HTML 5, the newest standard for structuring and presenting content on the World Wide Web. He was the CTO of SceneCaster.com from June 2007 until April 2010, and was Vice President of Product Development at Tucows Inc. from July 2005 – May 2007. He also served as director of engineering at Alias|Wavefront on the team that received an Oscar from the Academy of Motion Picture Arts and Sciences for developing the Maya 3D software package.

Prior to his election as ACM president, Chesnais was vice president from July 2008 – June 2010 as well as secretary/treasurer from July 2006 – June 2008. He also served as president of ACM SIGGRAPH from July 2002 – June 2005 and as SIG Governing Board Chair from July 2000 – June 2002.

As a French citizen now residing in Canada, he has more than 20 years of management experience in the software industry. He joined the local SIGGRAPH Chapter in Paris some 20 years ago as a volunteer and has continued his involvement with ACM in a variety of leadership capacities since then.

About Trendspottr.com

TrendSpottr is a real-time viral search and predictive analytics service that identifies the most timely and trending information for any topic or keyword. Our core technology analyzes real-time data streams and spots emerging trends at their earliest acceleration point — hours or days before they have become “popular” and reached mainstream awareness.

TrendSpottr serves as a predictive early warning system for news and media organizations, brands, government agencies and Fortune 500 companies and helps them to identify emerging news, events and issues that have high viral potential and market impact. TrendSpottr has partnered with HootSuite, DataSift and other leading social and big data companies.

Hacker Alert- Darpa project 10$ K for summer

If you bleed red,white and blue and know some geo-spatial analysis ,social network analysis and some supervised and unsupervised learning (and unlearning)- here is a chance for you to put your skills for an awesome project

 

from wired-

http://www.wired.com/dangerroom/2012/07/hackathon-guinea-pig/

 

For this challenge, Darpa will lodge a selected six to eight teams at George Mason University and provide them with an initial $10,000 for equipment and access to unclassified data sets including “ground-level video of human activity in both urban and rural environments; high-resolution wide-area LiDAR of urban and mountainous terrain, wide-area airborne full motion video; and unstructured amateur photos and videos, such as would be taken from an adversary’s cell phone.” However, participants are encouraged to use any open sourced, legal data sets they want. (In the hackathon spirit, we would encourage the consumption of massive quantities of pizza and Red Bull, too.)

 

DARPA Innovation House Project

Home | Data Access | Awards | Team Composition | Logisitics | Deliverables | Proposals | Evaluation Criteria | FAQ

PROPOSAL SUBMISSION

Proposals must be one to three pages. Team resumes of any length must be attached and do not count against the page limit. Proposals must have 1-inch margins, use a font size of at least 11, and be delivered in Microsoft Word or Adobe PDF format.

Proposals must be emailed to InnovationHouse@c4i.gmu.edu by 4:00PM ET on Tuesday, July 31, 2012.

Proposals must have a Title and contain at least the following sections with the following contents.

  1. Team Members

Each team member must be listed with name, email and phone.
The Lead Developer should be indicated.
The statement “All team members are proposed as Key Personnel.” must be included.

  1. Capability Description

The description should clearly explain what capability the software is designed to provide the user, how it is proposed to work, and what data it will process.

In addition, a clear argument should be made as to why it is a novel approach that is not incremental to existing methods in the field.

  1. Proposed Phase 1 Demonstration

This section should clearly explain what will be demonstrated at the end of Session I. The description should be expressive, and as concrete as possible about the nature of the designs and software the team intends to produce in Session I.

  1. Proposed Phase 2 Demonstration

This section should clearly explain how the final software capability will be demonstrated as quantitatively as possible (for example, positing the amount of data that will be processed during the demonstration), how much time that will take, and the nature of the results the processing aims to achieve.

In addition, the following sections are optional.

  1. Technical Approach

The technical approach section amplifies the Capability Description, explaining proposed algorithms, coding practices, architectural designs and/or other technical details.

  1. Team Qualifications

Team qualifications should be included if the team?s experience base does not make it obvious that it has the potential to do this level of software development. In that case, this section should make a credible argument as to why the team should be considered to have a reasonable chance of completing its goals, especially under the tight timelines described.

Other sections may be included at the proposers? discretion, provided the proposal does not exceed three pages.

[Top]

 

http://www.darpa.mil/NewsEvents/Releases/2012/07/10.aspx

 

 

 

Enter MARS

I guess China is so busy changing the weather on Earth, or grabbing precious rare earth ores, it will lose out on the biggest Gold Rush of all time— Mars as a resource (plus the very doable dual use technologies you just saw)