Interview: Hjálmar Gíslason, CEO of DataMarket.com

Here is an interview with Hjálmar Gíslason, CEO of Datamarket.com  . DataMarket is an active marketplace for structured data and statistics. Through powerful search and visual data exploration, DataMarket connects data seekers with data providers.

 

Ajay-  Describe your journey as an entrepreneur and techie in Iceland. What are the 10 things that surprised you most as a tech entrepreneur.

HG- DataMarket is my fourth tech start-up since at age 20 in 1996. The previous ones have been in gaming, mobile and web search. I come from a technical background but have been moving more and more to the business side over the years. I can still prototype, but I hope there isn’t a single line of my code in production!

Funny you should ask about the 10 things that have surprised me the most on this journey, as I gave a presentation – literally yesterday – titled: “9 things nobody told me about the start-up business”

They are:
* Do NOT generalize – especially not to begin with
* Prioritize – and find a work-flow that works for you
* Meet people – face to face
* You are a sales person – whether you like it or not
* Technology is not a product – it’s the entire experience
* Sell the current version – no matter how amazing the next one is
* Learn from mistakes – preferably others’
* Pick the right people – good people is not enough
* Tell a good story – but don’t make them up

I obviously elaborate on each of these points in the talk, but the points illustrate roughly some of the things I believe I’ve learned … so far 😉

9 things nobody told me about the start-up business

Ajay-

Both Amazon  and Google  have entered the public datasets space. Infochimps  has 14,000+ public datasets. The US has http://www.data.gov/

So clearly the space is both competitive and yet the demand for public data repositories is clearly under served still. 

How does DataMarket intend to address this market in a unique way to differentiate itself from others.

HG- DataMarket is about delivering business data to decision makers. We help data seekers find the data they need for planning and informed decision making, and data publishers reaching this audience. DataMarket.com is the meeting point, where data seekers can come to find the best available data, and data publishers can make their data available whether for free or for a fee. We’ve populated the site with a wealth of data from public sources such as the UN, Eurostat, World Bank, IMF and others, but there is also premium data that is only available to those that subscribe to and pay for the access. For example we resell the entire data offering from the EIU (Economist Intelligence Unit) (link: http://datamarket.com/data/list/?q=provider:eiu)

DataMarket.com allows all this data to be searched, visualized, compared and downloaded in a single place in a standard, unified manner.

We see many of these efforts not as competition, but as valuable potential sources of data for our offering, while others may be competing with parts of our proposition, such as easy access to the public data sets.

 

Ajay- What are your views on data confidentiality and access to data owned by Governments funded by tax payer money.

HG- My views are very simple: Any data that is gathered or created for taxpayers’ money should be open and free of charge unless higher priorities such as privacy or national security indicate otherwise.

Reflecting that, any data that is originally open and free of charge is still open and free of charge on DataMarket.com, just easier to find and work with.

Ajay-  How is the technology entrepreneurship and venture capital scene in Iceland. What things work and what things can be improved?

HG- The scene is quite vibrant, given the small community. Good teams with promising concepts have been able to get the funding they need to get started and test their footing internationally. When the rapid growth phase is reached outside funding may still be needed.

There are positive and negative things about any location. Among the good things about Iceland from the stand point of a technology start-up are highly skilled tech people and a relatively simple corporate environment. Among the bad things are a tiny local market, lack of skills in international sales and marketing and capital controls that were put in place after the crash of the Icelandic economy in 2008.

I’ve jokingly said that if a company is hot in the eyes of VCs it would get funding even if it was located in the jungles of Congo, while if they’re only lukewarm towards you, they will be looking for any excuse not to invest. Location can certainly be one of them, and in that case being close to the investor communities – physically – can be very important.

We’re opening up our sales and marketing offices in Boston as we speak. Not to be close to investors though, but to be close to our market and current customers.

Ajay- Describe your hobbies when you are not founding amazing tech startups.

HG- Most of my time is spent working – which happens to by my number one hobby.

It is still important to step away from it all every now and then to see things in perspective and come back with a clear mind.

I *love* traveling to exotic places. Me and my wife have done quite a lot of traveling in Africa and S-America: safari, scuba diving, skiing, enjoying nature. When at home I try to do some sports activities 3-4 times a week at least, and – recently – play with my now 8 month old son as much as I can.

About-

http://datamarket.com/p/about/team/

Management

Hjalmar GislasonHjálmar Gíslason, Founder and CEO: Hjalmar is a successful entrepreneur, founder of three startups in the gaming, mobile and web sectors since 1996. Prior to launching DataMarket, Hjalmar worked on new media and business development for companies in the Skipti Group (owners of Iceland Telecom) after their acquisition of his search startup – Spurl. Hjalmar offers a mix of business, strategy and technical expertise. DataMarket is based largely on his vision of the need for a global exchange for structured data.

hjalmar.gislason@datamarket.com

To know more, have a quick  look at  http://datamarket.com/

Cloud Computing – can be evil

Cloud Computing can be evil because-

1) Most browsers are owned by for profit corporations . Corporations can be evil, sometimes

And corporations can go bankrupt. You can back up data locally, but try backing up a corporation.

2) The content on your web page can be changed using translator extensions . This has interesting ramifications as in George Orwell. You may not be even aware of subtle changes introduced in your browser in the way it renders the html or some words using keywords from a browser extension app.

Imagine a new form of language called Politically Correct Truthspeak, and that can be in English but using machine learning learn to substitute politically sensitive words with Govt sanctioned words.

3) Your DNS and IP settings can be redirected using extensions. This means if a Govt passes a law- you can be denied the websites using just the browser not even the ISP.

Thats an extreme scenario for a authoritative govt creating its own version of Mafiaafire Redirector.

So how to keep the cloud computer honest?Move some stuff to the desktop

How to keep desktop computing efficient?Use some more cloud computing

It is not an OR but an AND function in which some computing can be local, some shared and some in the cloud.

Si?

Predictive Models Ain’t Easy to Deploy

 

This is a guest blog post by Carole Ann Matignon of Sparkling Logic. You can see more on Sparkling Logic at http://my.sparklinglogic.com/

Decision Management is about combining predictive models and business rules to automate decisions for your business. Insurance underwriting, loan origination or workout, claims processing are all very good use cases for that discipline… But there is a hiccup… It ain’t as easy you would expect…

What’s easy?

If you have a neat model, then most tools would allow you to export it as a PMML model – PMML stands for Predictive Model Markup Language and is a standard XML representation for predictive model formulas. Many model development tools let you export it without much effort. Many BRMS – Business rules Management Systems – let you import it. Tada… The model is ready for deployment.

What’s hard?

The problem that we keep seeing over and over in the industry is the issue around variables.

Those neat predictive models are formulas based on variables that may or may not exist as is in your object model. When the variable is itself a formula based on the object model, like the min, max or sum of Dollar amount spent in Groceries in the past 3 months, and the object model comes with transaction details, such that you can compute it by iterating through those transactions, then the problem is not “that” big. PMML 4 introduced some support for those variables.

The issue that is not easy to fix, and yet quite frequent, is when the model development data model does not resemble the operational one. Your Data Warehouse very likely flattened the object model, and pre-computed some aggregations that make the mapping very hard to restore.

It is clearly not an impossible project as many organizations do that today. It comes with a significant overhead though that forces modelers to involve IT resources to extract the right data for the model to be operationalized. It is a heavy process that is well justified for heavy-duty models that were developed over a period of time, with a significant ROI.

This is a show-stopper though for other initiatives which do not have the same ROI, or would require too frequent model refresh to be viable. Here, I refer to “real” model refresh that involves a model reengineering, not just a re-weighting of the same variables.

For those initiatives where time is of the essence, the challenge will be to bring closer those two worlds, the modelers and the business rules experts, in order to streamline the development AND deployment of analytics beyond the model formula. The great opportunity I see is the potential for a better and coordinated tuning of the cut-off rules in the context of the model refinement. In other words: the opportunity to refine the strategy as a whole. Very ambitious? I don’t think so.

About Carole Ann Matignon

http://my.sparklinglogic.com/index.php/company/management-team

Carole-Ann Matignon Print E-mail

Carole-Ann MatignonCarole-Ann Matignon – Co-Founder, President & Chief Executive Officer

She is a renowned guru in the Decision Management space. She created the vision for Decision Management that is widely adopted now in the industry.  Her claim to fame is managing the strategy and direction of Blaze Advisor, the leading BRMS product, while she also managed all the Decision Management tools at FICO (business rules, predictive analytics and optimization). She has a vision for Decision Management both as a technology and a discipline that can revolutionize the way corporations do business, and will never get tired of painting that vision for her audience.  She speaks often at Industry conferences and has conducted university classes in France and Washington DC.

She started her career building advanced systems using all kinds of technologies — expert systems, rules, optimization, dashboarding and cubes, web search, and beta version of database replication. At Cleversys (acquired by Kurt Salmon & Associates), she also conducted strategic consulting gigs around change management.

While playing with advanced software components, she found a passion for technology and joined ILOG (acquired by IBM). She developed a growing interest in Optimization as well as Business Rules. At ILOG, she coined the term BRMS while brainstorming with her Sales counterpart. She led the Presales organization for Telecom in the Americas up until 2000 when she joined Blaze Software (acquired by Brokat Technologies, HNC Software and finally FICO).

Her 360-degree experience allowed her to gain appreciation for all aspects of a software company, giving her a unique perspective on the business. Her technical background kept her very much in touch with technology as she advanced.

Oracle launches its version of R #rstats

From-

http://www.oracle.com/us/corporate/press/1515738

Integrates R Statistical Programming Language into Oracle Database 11g

News Facts

Oracle today announced the availability of Oracle Advanced Analytics, a new option for Oracle Database 11g that bundles Oracle R Enterprise together with Oracle Data Mining.
Oracle R Enterprise delivers enterprise class performance for users of the R statistical programming language, increasing the scale of data that can be analyzed by orders of magnitude using Oracle Database 11g.
R has attracted over two million users since its introduction in 1995, and Oracle R Enterprise dramatically advances capability for R users. Their existing R development skills, tools, and scripts can now also run transparently, and scale against data stored in Oracle Database 11g.
Customer testing of Oracle R Enterprise for Big Data analytics on Oracle Exadata has shown up to 100x increase in performance in comparison to their current environment.
Oracle Data Mining, now part of Oracle Advanced Analytics, helps enable customers to easily build and deploy predictive analytic applications that help deliver new insights into business performance.
Oracle Advanced Analytics, in conjunction with Oracle Big Data ApplianceOracle Exadata Database Machine and Oracle Exalytics In-Memory Machine, delivers the industry’s most integrated and comprehensive platform for Big Data analytics.

Comprehensive In-Database Platform for Advanced Analytics

Oracle Advanced Analytics brings analytic algorithms to data stored in Oracle Database 11g and Oracle Exadata as opposed to the traditional approach of extracting data to laptops or specialized servers.
With Oracle Advanced Analytics, customers have a comprehensive platform for real-time analytic applications that deliver insight into key business subjects such as churn prediction, product recommendations, and fraud alerting.
By providing direct and controlled access to data stored in Oracle Database 11g, customers can accelerate data analyst productivity while maintaining data security throughout the enterprise.
Powered by decades of Oracle Database innovation, Oracle R Enterprise helps enable analysts to run a variety of sophisticated numerical techniques on billion row data sets in a matter of seconds making iterative, speed of thought, and high-quality numerical analysis on Big Data practical.
Oracle R Enterprise drastically reduces the time to deploy models by eliminating the need to translate the models to other languages before they can be deployed in production.
Oracle R Enterprise integrates the extensive set of Oracle Database data mining algorithms, analytics, and access to Oracle OLAP cubes into the R language for transparent use by R users.
Oracle Data Mining provides an extensive set of in-database data mining algorithms that solve a wide range of business problems. These predictive models can be deployed in Oracle Database 11g and use Oracle Exadata Smart Scan to rapidly score huge volumes of data.
The tight integration between R, Oracle Database 11g, and Hadoop enables R users to write one R script that can run in three different environments: a laptop running open source R, Hadoop running with Oracle Big Data Connectors, and Oracle Database 11g.
Oracle provides single vendor support for the entire Big Data platform spanning the hardware stack, operating system, open source R, Oracle R Enterprise and Oracle Database 11g.
To enable easy enterprise-wide Big Data analysis, results from Oracle Advanced Analytics can be viewed from Oracle Business Intelligence Foundation Suite and Oracle Exalytics In-Memory Machine.

Supporting Quotes

“Oracle is committed to meeting the challenges of Big Data analytics. By building upon the analytical depth of Oracle SQL, Oracle Data Mining and the R environment, Oracle is delivering a scalable and secure Big Data platform to help our customers solve the toughest analytics problems,” said Andrew Mendelsohn, senior vice president, Oracle Server Technologies.
“We work with leading edge customers who rely on us to deliver better BI from their Oracle Databases. The new Oracle R Enterprise functionality allows us to perform deep analytics on Big Data stored in Oracle Databases. By leveraging R and its library of open source contributed CRAN packages combined with the power and scalability of Oracle Database 11g, we can now do that,” said Mark Rittman, co-founder, Rittman Mead.
Oracle Advanced Analytics — an option to Oracle Database 11g Enterprise Edition – extends the database into a comprehensive advanced analytics platform through two major components: Oracle R Enterprise and Oracle Data Mining. With Oracle Advanced Analytics, customers have a comprehensive platform for real-time analytic applications that deliver insight into key business subjects such as churn prediction, product recommendations, and fraud alerting.

Oracle R Enterprise tightly integrates the open source R programming language with the database to further extend the database with Rs library of statistical functionality, and pushes down computations to the database. Oracle R Enterprise dramatically advances the capability for R users, and allows them to use their existing R development skills and tools, and scripts can now also run transparently and scale against data stored in Oracle Database 11g.

Oracle Data Mining provides powerful data mining algorithms that run as native SQL functions for in-database model building and model deployment. It can be accessed through the SQL Developer extension Oracle Data Miner to build, evaluate, share and deploy predictive analytics methodologies. At the same time the high-performance Oracle-specific data mining algorithms are accessible from R.

BENEFITS

  • Scalability—Allows customers to easily scale analytics as data volume increases by bringing the algorithms to where the data resides – in the database
  • Performance—With analytical operations performed in the database, R users can take advantage of the extreme performance of Oracle Exadata
  • Security—Provides data analysts with direct but controlled access to data in Oracle Database 11g, accelerating data analyst productivity while maintaining data security
  • Save Time and Money—Lowers overall TCO for data analysis by eliminating data movement and shortening the time it takes to transform “raw data” into “actionable information”
Oracle R Hadoop Connector Gives R users high performance native access to Hadoop Distributed File System (HDFS) and MapReduce programming framework.
This is a  R package
From the datasheet at

Radoop 0.3 launched- Open Source Graphical Analytics meets Big Data

What is Radoop? Quite possibly an exciting mix of analytics and big data computing

 

http://blog.radoop.eu/?p=12

What is Radoop?

Hadoop is an excellent tool for analyzing large data sets, but it lacks an easy-to-use graphical interface. RapidMiner is an excellent tool for data analytics, but its data size is limited by the memory available, and a single machine is often not enough to run the analyses on time. In this project, we combine the strengths of both projects and provide a RapidMiner extension for editing and running ETL, data analytics and machine learning processes over Hadoop.

We have closely integrated the highly optimized data analytics capabilities of Hive and Mahout, and the user-friendly interface of RapidMiner to form a powerful and easy-to-use data analytics solution for Hadoop.

 

and what’s new

http://blog.radoop.eu/?p=198

Radoop 0.3 released – fully graphical big data analytics

Today, Radoop had a major step forward with its 0.3 release. The new version of the visual big data analytics package adds full support for all major Hadoop distributions used these days: Apache Hadoop 0.20.2, 0.20.203, 1.0 and Cloudera’s Distribution including Apache Hadoop 3 (CDH3). It also adds support for large clusters by allowing the namenode, the jobtracker and the Hive server to reside on different nodes.

As Radoop’s promise is to make big data analytics easier, the 0.3 release is also focused on improving the user interface. It has an enhanced breakpointing system which allows to investigate intermediate results, and it adds dozens of quick fixes, so common process design mistakes get much easier to solve.

There are many further improvements and fixes, so please consult the release notes for more details. Radoop is in private beta mode, but heading towards a public release in Q2 2012. If you would like to get early access, then please apply at the signup page or describe your use case in email (beta at radoop.eu).

Radoop 0.3 (15 February 2012)

  • Support for Apache Hadoop 0.20.2, 0.20.203, 1.0 and Cloudera’s Distribution Including Apache Hadoop 3 (CDH3) in a single release
  • Support for clusters with separate master nodes (namenode, jobtracker, Hive server)
  • Enhanced breakpointing to evaluate intermediate results
  • Dozens of quick fixes for the most common process design errors
  • Improved process design and error reporting
  • New welcome perspective to help in the first steps
  • Many bugfixes and performance improvements

Radoop 0.2.2 (6 December 2011)

  • More Aggregate functions and distinct option
  • Generate ID operator for convenience
  • Numerous bug fixes and improvements
  • Improved user interface

Radoop 0.2.1 (16 September 2011)

  • Set Role and Data Multiplier operators
  • Management panel for testing Hadoop connections
  • Stability improvements for Hive access
  • Further small bugfixes and improvements

Radoop 0.2 (26 July 2011)

  • Three new algoritms: Fuzzy K-Means, Canopy, and Dirichlet clustering
  • Three new data preprocessing operators: Normalize, Replace, and Replace Missing Values
  • Significant speed improvements in data transmission and interactive analytics
  • Increased stability and speedup for K-Means
  • More flexible settings for Join operations
  • More meaningful error messages
  • Other small bugfixes and improvements

Radoop 0.1 (14 June 2011)

Initial release with 26 operators for data transmission, data preprocessing, and one clustering algorithm.

Note that Rapid Miner also has a great R extension so you can use R, a graphical interface and big data analytics is now easier and more powerful than ever.


Internet Encryption Algols are flawed- too little too late!

Some news from a paper I am reading- not surprised that RSA has a problem .

http://eprint.iacr.org/2012/064.pdf

Abstract. We performed a sanity check of public keys collected on the web. Our main goal was to test the validity of the assumption that di erent random choices are made each time keys are generated.We found that the vast majority of public keys work as intended. A more disconcerting fi nding is that two out of every one thousand RSA moduli that we collected off er no security.

 

Our conclusion is that the validity of the assumption is questionable and that generating keys in the real world for multiple-secrets” cryptosystems such as RSA is signi cantly riskier than for single-secret” ones such as ElGamal or (EC)DSA which are based on Die-Hellman.

Keywords: Sanity check, RSA, 99.8% security, ElGamal, DSA, ECDSA, (batch) factoring, discrete logarithm, Euclidean algorithm, seeding random number generators, K9.

and

 

99.8% Security. More seriously, we stumbled upon 12720 di erent 1024-bit RSA moduli that o ffer no security. Their secret keys are accessible to anyone who takes the trouble to redo our work. Assuming access to the public key collection, this is straightforward compared to more

traditional ways to retrieve RSA secret keys (cf. [5,15]). Information on the a ected X.509 certi cates and PGP keys is given in the full version of this paper, cf. below. Overall, over the data we collected 1024-bit RSA provides 99.8% security at best (but see Appendix A).

 

However no algol is perfect and even Elliptic Based Crypto ( see http://en.wikipedia.org/wiki/Elliptic_curve_cryptography#Fast_reduction_.28NIST_curves.29 )has a flaw called Shor http://en.wikipedia.org/wiki/Shor%27s_algorithm

Funny thing is ECC is now used for Open DNS


http://dnscurve.org/crypto.html

The DNSCurve project adds link-level public-key protection to DNS packets. This page discusses the cryptographic tools used in DNSCurve.

ELLIPTIC-CURVE CRYPTOGRAPHY

DNSCurve uses elliptic-curve cryptography, not RSA.

RSA is somewhat older than elliptic-curve cryptography: RSA was introduced in 1977, while elliptic-curve cryptography was introduced in 1985. However, RSA has shown many more weaknesses than elliptic-curve cryptography. RSA’s effective security level was dramatically reduced by the linear sieve in the late 1970s, by the quadratic sieve and ECM in the 1980s, and by the number-field sieve in the 1990s. For comparison, a few attacks have been developed against some rare elliptic curves having special algebraic structures, and the amount of computer power available to attackers has predictably increased, but typical elliptic curves require just as much computer power to break today as they required twenty years ago.

IEEE P1363 standardized elliptic-curve cryptography in the late 1990s, including a stringent list of security criteria for elliptic curves. NIST used the IEEE P1363 criteria to select fifteen specific elliptic curves at five different security levels. In 2005, NSA issued a new “Suite B” standard, recommending the NIST elliptic curves (at two specific security levels) for all public-key cryptography and withdrawing previous recommendations of RSA.

Some specific types of elliptic-curve cryptography are patented, but DNSCurve does not use any of those types of elliptic-curve cryptography.

No wonder college kids are hacking defense databases easily nowadays!!