Within this period I
managed to serialize the whole drools rules file as a Knowledge Base Object,
and observed a significant improvement (approximately 10s) but still far less
than the execution time of the current RelEx2Frame. Also that serialization
needed JVM stack size to be increased to 2MB. Still we felt that the
performance is not up to the requirement so we decided to split the drools
rules file according to 100 rules per file basis and Nisansa did that task.
Danaja came up with a design which is focused on applying concurrency and
parallelism for the RelEx2Frame system, and was accepted by all the members of
the team as the basic design which will be altered and improved after further
analysis.
In the current RelEx2Frame
there is a significant limitation of the concepts or the words that are
detected. Statistical learning methods can be used to reduce this limitation.
One approach is to use an existing application and the other would be to
implement statistical learner. Google Sets [1] is one of the existing applications
that we are considering. During this period I have implemented an application
which accesses Google Sets and generate new set of words for a given
combination of words (<4). I have used an existing library called ‘XGoogle’
[2] written in Python programming language which provides an interface to
access Google Sets. Since I was not familiar with Python, I had to learn Python
and which I successfully managed to do. We will keep the results came out from
this application and will compare with the results from our statistical learner
to choose the most appropriate set of words.
Preparing the design
document was the major work that we had done during this period, since it was
due on 3rd December. All of us contributed to the design document in
several ways and I contributed by writing design constraints, design decisions
and designing rule learning component. Design constraints part involved
basically three sub sections, namely Operating Environment, End-user
Environment and Performance Requirements. Design decisions consisted with major
decisions, some of which were already taken and others yet to be taken. Programming
language selection, rule engine selection, caching knowledge bases, statistical
learning of concepts and selecting the best suited data mining algorithm were
the main design considerations discussed there.
Designing the rule learner
was the most challenging task to me. I read many documents [3-5] on existing
rule learners, existing rule induction algorithms, data mining techniques etc. After considerable amount of literature survey
I came up with the architecture for the statistical rule learner using data
mining techniques, which will be altered and improved as it requires. Chamilka
reviewed it and made few suggestions.
We were successfully
managed to submit the design document on 3rd of December.
[1] “Google sets labs,”
[Online]. Available: http://labs.google.com/sets
[2] “XGoogle,” [Online].
Available: http://www.catonmat.net/blog/python-library-for-google-sets
[3] K. Mhashilkar. “Data
Mining Technology,” [Online]. Available: http://www.executionmih.com/data-mining/technology-architecture-application-frontend.php
[4] J. Grzymala-Busse, “Three strategies to rule
induction from data with numerical attributes,” presented at the International
Workshop on Rough Sets in Knowledge Discovery (RSKD 2003), associated with the
European Joint Conferences on Theory and Practice of Software 2003, Warsaw,
Poland, April 5–13, 2003.
[5]
“Rule Learner,” [Online]. Available: http://openrules.com/RuleLearner.htm
