David S. Read

At this year’s Semantic Technology and Business Conference in San Francisco, Mike Delaney and I presented a session discussing Semantic Technology adoption in the enterprise entitled, When to Consider Semantic Technology for Your Enterprise. Our focus in the talk was centered on 3 key messages: 1) describe semantic technology as it relates to enterprise data and applications; 2) discuss where semantic technology augments current data persistence and access technologies; and 3) highlight situations that should lead an enterprise to begin using semantic technology as part of their enterprise architecture.

In order to allow a broader audience to benefit from our session we are creating a set of videos based on our original presentation. These are being released as part of Blue Slate Solutions’ Experts Exchange Series.  Each video will be 5 to 10 minutes in length and will focus on one of the sub-topics from the presentation.

Here is the overall agenda for the video series:

We’ll release a couple of videos every other week so be on the lookout during July and August for this series to be completed. We would appreciate your feedback on the information as well as hearing about your experiences deploying semantic technology as part of an enterprise’s application architecture.

The playlist for the series is located at: http://www.youtube.com/playlist?list=PLyQYGnkKpiugIl0Tz0_ZlmeFhbWQ4XE1I The playlist will be updated with the new videos as they are released.

When depicting the Cognitive Corporation™ as a graphic, the use of semantic technology is not highlighted.  Semantic technology serves two key roles in the Cognitive Corporation™ – data storage (part of Know) and data integration, which connects all of the concepts.  I’ll explore the integration role since it is a vital part of supporting a learning organization.

In my last post I talked about the fact that integration between components has to be based on the meaning of the data, not simply passing compatible data types between systems.  Semantic technology supports this need through its design.  What key capabilities does semantic technology offer in support of integration?  Here I’ll highlight a few.

Logical and Physical Structures are (largely) Separate

Semantic technology reduces the tie between the logical and physical structures of the data versus a relational database.  In a relational database it is the physical structure (columns and tables) along with the foreign keys that maintain the relationships in the data.  Just think back to relational database design class, in a normalized database all of the column values are related to the table’s key.

This tight tie between data relationships (logical) and data structure (physical) imposes a steep cost if a different set of logical data relationships is desired.  Traditionally, we create data marts and data warehouses to allow us to represent multiple logical data relationships.  These are copies of the data with differing physical structures and foreign key relationships.  We may need these new structures to allow us to report differently on our data or to integrate with different systems which need the altered logical representations.

With semantic data we can take a physical representation of the data (our triples) and apply different logical representations in the form of ontologies.  To be fair, the physical structure (subject->predicate->object) forces certain constrains on the ontology but a logical transformation is far simpler than a physical one even with such constraints.

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I am excited to share the news that Blue Slate Solutions has kicked off a formal innovation program, creating a lab environment which will leverage the Cognitive Corporation™ framework and apply it to a suite of processes, tools and techniques.  The lab will use a broad set of enterprise technologies, applying the learning organization concepts implicit in the Cognitive Corporation’s™ feedback loop.

I’ve blogged a couple of times (see references at the end of this blog entry) about the Cognitive Corporation™.  The depiction has changed slightly but the fundamentals of the framework are unchanged.

The focus is to create a learning enterprise, where the learning is built into the system integrations and interactions. Enterprises have been investing in these individual components for several years; however they have not truly been integrating them in a way to promote learning.

By “integrating” I mean allowing the system to understand the meaning of the data being passed between them.  Creating a screen in a workflow (BPM) system that presents data from a database to a user is not “integration” in my opinion.  It is simply passing data around.  This prevents the enterprise ecosystem (all the components) from working together and collectively learning.

I liken such connections to my taking a hand-written note in a foreign language, which I don’t understand, and typing the text into an email for someone who does understand the original language.  Sure, the recipient can read it, but I, representing the workflow tool passing the information from database (note) to screen (email) in this case, have no idea what the data means and cannot possibly participate in learning from it.  Integration requires understanding.  Understanding requires defined and agreed-upon semantics.

This is just one of the Cognitive Corporation™ concepts that we will be exploring in the lab environment.  We will also be looking at the value of these technologies within different horizontal and vertical domains.  Given our expertise in healthcare, finance and insurance, our team is well positioned to use the lab to explore the use of learning BPM in many contexts.

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I had the pleasure of attending the Semantic Technology and Business Conference in Washington, DC last week.  I have a strong interest in semantic technology and its capabilities to enhance the way in which we leverage information systems.  There was a good selection of topics discussed by people with a variety of  backgrounds working in different verticals.

To begin the conference I attended the half day “Ontology 101” presented by Elisa Kendall and Deborah McGuinness.  They indicated that this presentation has been given at each semantic technology conference and the interest is still strong.  The implication being that new people continue to want to understand this art.

Their material was very useful and if you are someone looking to get a grounding in ontologies (what are they?  how do you go about creating them?) I recommend attending this session the next time it is offered.  Both leaders clearly have deep experience and expertise in this field.  Also, the discussion was not tied to a technology (e.g. RDF) so it was applicable regardless of underlying implementation details.

I wrapped up the first day with Richard Ordowich who discussed the process of reverse engineering semantics (meaning) from legacy data.  The goal of such projects being to achieve a data harmonization of information across the enterprise.

A point he stressed was that a business really needs to be ready to start such a journey.  This type of work is very hard and very time consuming.  It requires an enterprise wide discipline.  He suggests that before working with a company on such an initiative one should ask for examples of prior enterprise program success (e.g. something like BPM, SDLC).

Fundamentally, a project that seeks to harmonize the meaning of data across an enterprise requires organization readiness to go beyond project execution.  The enterprise must put effective governance in place to operate and maintain the resulting ontologies, taxonomies and metadata.

The full conference kicked off the following day.  One aspect that jumped out for me was that a lot of the presentations dealt with government-related projects.  This could have been a side-effect of the conference being held in Washington, DC but I think it is more indicative that spending in this technology is more heavily weighted to public rather than private industry.

Being government-centric I found any claims of “value” suspect.  A project can be valuable, or show value, without being cost effective.  Commercial businesses have gone bankrupt even though they delivered value to their customers.  More exposure of positive-ROI commercial projects will be important to help accelerate the adoption of these technologies.

Other than the financial aspect, the presentations were incredibly valuable in terms of presenting lessons learned, best practices and in-depth tool discussions.  I’ll highlight a few of the sessions and key thoughts that I believe will assist as we continue to apply semantic technology to business system challenges.

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The Cognitive Corporation™ is a framework introduced in an earlier posting.  The framework is meant to outline a set of general capabilities that work together in order to support a growing and thinking organization.  For this post I will drill into one of the least mature of those capabilities in terms of enterprise solution adoption – Learn.

Business rules, decision engines, BPM, complex event processing (CEP), these all invoke images of computers making speedy decisions to the benefit of our businesses.  The infrastructure, technologies and software that provide these solutions (SOA, XML schemas, rule engines, workflow engines, etc.) support the decision automation process.  However, they don’t know what decisions to make.

The BPM-related components we acquire provide the how of decision making (send an email, route a claim, suggest an offer).  Learning, supported by data analytics, provides a powerful path to the what and why of automated decisions (send this email to that person because they are at risk of defecting, route this claim to that underwriter because it looks suspicious, suggest this product to that customer because they appear to be buying these types of items).

I’ll start by outlining the high level journey from data to rules and the cyclic nature of that journey.  Data leads to rules, rules beget responses, responses manifest as more data, new data leads to new rules, and so on.  Therefore, the journey does not end with the definition of a set of processes and rules.  This link between updated data and the determination of new processes and rules is the essence of any learning process, providing a key function for the cognitive corporation.

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My last two days at JavaOne 2010 included some interesting sessions as well as spending some time in the pavilion.  I’ll mention a few of the session topics that I found interesting as well as some of the products that I intend to check out.

I attended a session on creating a web architecture focused on high-performance with low-bandwidth.  The speaker was tasked with designing a web-based framework for the government of Ethiopia.  He discussed the challenges that are presented by that country’s infrastructure – consider network speed on the order of 5Kbps between sites.  He also had to work with an IT group that, although educated and intelligent, did not have a lot of depth beyond working with an Oracle database’s features.

His solution allows developers to create fully functional web applications that keep exchanged payloads under 10K.  Although I understand the logic of the approach in this case, I’m not sure the technique would be practical in situations without such severe bandwidth and skill set limitations.

A basic theme during his talk was to keep the data and logic tightly co-located.  In his case it is all located in the database (PL/SQL) but he agreed that it could all be in the application tier (e.g. NoSQL).  I’m not convinced that this is a good approach to creating maintainable high-volume applications.  It could be that the domain of business applications and business verticals in which I often find myself differ from the use cases that are common to developers promoting the removal of tiers from the stack (whether removing the DB server or the mid-tier logic server).

One part of his approach with which I absolutely concur is to push processing onto the client. The use of the client’s CPU seems common sense to me.  The work is around balancing that with security and bandwidth.  However, it can be done and I believe we will continue to find more effective ways to leverage all that computer power.

I also enjoyed a presentation on moving data between a data center and the cloud to perform heavy and intermittent processing.  The presenters did a great job of describing their trials and successes with leveraging the cloud to perform computationally expensive processing on transient data (e.g. they copy the data up each time they run the process rather than pay to store their data).  They also provided a lot of interesting information regarding options, advantages and challenges when leveraging the cloud (Amazon EC2 in this case).

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I’ve been at Oracle’s combined JavaOne and OpenWorld events for two days.  I am here as both an attendee, learning from a variety of experts, and as a speaker.  Of course this is the first JavaOne since Oracle acquired Sun.  I have been to several JavaOne conferences over the years so I was curious how the event might be different.

One of the first changes that I’ve noticed is that due to the co-location of these two large conferences the venue is very different than when Sun ran JavaOne as a standalone event.  The time between sessions is a full half hour, probably due to the fact that you may find yourself going between venues that are several blocks apart.  I used to think that having getting from Moscone North the Moscone South took a while.   Now I’m walking from the Moscone center to a variety of hotels and back again.  Perhaps this is actually a health regime for programmers!

The new session pre-registration system is interesting. I don’t know if this system has been routine with Oracle’s other conferences but it is new to JavaOne.  Attendees go on-line and pre-register for the sessions they want to attend.  When you show up at the session your badge is scanned.  If you had registered you are allowed in.  If you didn’t preregister and the session is full you have to wait outside the room to see if anyone who registered fails to show up.

I think I like the system, with the assumption that they would stop people from entering when the room was full.  At previous conferences it seemed like popular sessions would just be standing room only, but that was probably a violation of fire codes.  The big advantage of this approach is that it reduces the likelihood of your investing the time to walk to the venue only to find out you can’t get in.  As long as you arranged your schedule on-line and you show up on-time, you’re guaranteed a seat.

Enough about new processes.  After all, I came here to co-present a session and to learn from a variety of others.

Paul Evans and I spoke on the topic of web services and their use with a rules engine. Specifically we were using JAX-WS and Drools.  We also threw in jUDDI to show the value of service location decoupling.  The session was well attended (essentially the room was full) and seemed to keep the attendees’ attention.  We had some good follow-up conversations regarding aspects of the presentation that caught people’s interest, which is always rewarding. The source code for the demonstration program is located at http://bit.ly/blueslate-javaone2010.

Since I am a speaker I have access to both JavaOne and OpenWorld sessions.  I took advantage of that by attending several OpenWorld sessions in addition to a bunch of JavaOne talks.

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In an earlier blog entry I discussed the potential reduction in refactoring effort if our data is represented as RDF triples rather than relational structures.  As a way to give myself easy access to RDF data and to work more with semantic web tool features I have created a program to export relational data to RDF.

The program is really a proof-of-concept.  It takes a SQL query and converts the resulting rows into assertions of triples.  The approach is simple: given a SQL statement and a chosen primary key column (PK) to represent the instance for the exported data, assert triples with the primary key column value as the subject, the column names as the predicates and the non-PK column values as the objects.

Here is a brief sample taken from the documentation accompanying the code.

• Given a table named people with the following columns and rows:

       id    name    age
       --    ----    ---
       1     Fred    20
       2     Martha  25

• And a query of:  select id, name, age from people
• And the primary key column set to: id
• Then the asserted triples (shown using Turtle and skipping prefixes) will be:

       dsr:PK_1
          a       owl:Thing , dsr:RdbData ;
          rdfs:label "1" ;
          dsr:name "Fred" ;
          dsr:age "20" .

       dsr:PK_2
          a       owl:Thing , dsr:RdbData ;
          rdfs:label "2" ;
          dsr:name "Martha" ;
          dsr:age "25" .

You can see that the approach represents a quick way to convert the data.

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Privacy is an issue which is consistently in the news.  Large amounts of data are stored by retailers, governments, health care providers, employers and so forth. Much of this data contains personal information.  Keeping that data private has proven itself to be a difficult task.

We have seen numerous examples of unintended data loss (unintended by the company whose systems are stolen or attacked).

We hear about thefts of laptops containing personal information for hundreds of thousands of people.  Internet-based attacks that allow attackers access to financial transaction data and even rogue credit card swiping equipment hidden in gas pumps have become background noise in a sea of leaked data.  This is an area that gets the lion’s share of attention in the media and by security professionals.

Worse than these types of personal data loss, because they are completely preventable, are those that are predicated on a company consciously releasing their customer data.  Such companies always assume that they are not introducing risk, but often they are.  In all cases, if the owner of the data had simply held it internally no privacy loss would have occurred.

There have been cases of personal data loss due to mistakes in judgment.

AOL released a large collection of search data to researchers.  The people releasing the data didn’t consider this a risk to privacy.  How could the search terms entered by anonymous people present a risk to privacy?

Of course we now now know that within the data were people’s social security numbers (SSN), phone numbers, credit card numbers and so forth.  Why?  Well, it turns out that some people will search for those things, quite possibly to prove to themselves that their data is safe.  What better way to see if your SSN or credit card number is published on the Internet than by typing it into a search engine?  No matches, great!

Personal data has even been lost by companies releasing data after attempting to mask or anonymize it.

The intent of masking is to remove enough information, the personally identifying information (PII), so that the data cannot be associated with real people. Of course this has to be done without losing the important details that allow patterns and relationships in the data to be found.

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