Extending the JColibri Open Source Architecture for Managing High-Dimensional Data and Large Case Bases

CBR systems designers and developers' research can benefit from the availability of existing platforms, able to provide software design and implementation assistance. The JColibri platform, realized and maintained by the University of Madrid, is one of the most well known among such tools. In t...

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Bibliographic Details
Main Authors: Bottrighi, A., Leonardi, G., Montani, S., Portinale, L.
Format: Conference Proceeding
Language:English
Subjects:
Application software
Artificial intelligence
Case-based reasoning
Computer architecture
Conference management
Design optimization
high-dimensional data
Information retrieval
Jcolibri framework
large case bases
Open source software
Pivoting-based retrieval
Software design
Software development management
Testing
Time series
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Summary:CBR systems designers and developers' research can benefit from the availability of existing platforms, able to provide software design and implementation assistance. The JColibri platform, realized and maintained by the University of Madrid, is one of the most well known among such tools. In this work, we describe a couple of extensions we have provided to the core JColibri open source software. In particular, our extensions are meant to optimize case retrieval performances, in data-rich applications. Specifically, we focused our attention on treating (i) large case bases, in which retrieval time may become unacceptable, and (ii) cases with high-dimensional features - namely time series features - on which proper case representation and retrieval solutions need to be studied. The implemented code has been preliminarly tested, and it is now ready to be integrated with the JColibri code, and made available to the CBR research community. Additional extensions, always dealing with retrieval optimization, are foreseen as our future work.
ISSN:1082-3409
2375-0197
DOI:10.1109/ICTAI.2009.57