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Full-Text Articles in Science and Technology Studies

Logical Information Modeling Of Web-Accessible Heterogeneous Digital Assets, Kshitij Shah, Amit P. Sheth Apr 1998

Logical Information Modeling Of Web-Accessible Heterogeneous Digital Assets, Kshitij Shah, Amit P. Sheth

Kno.e.sis Publications

This paper introduces the MREF framework for representing and correlating information at a higher semantic level than is possible with Web-based information systems today. The role that metadata plays in this framework is described, together with a metadata based infrastructure to support our media independent information correlation paradigm. To keep it consistent with evolving standards, broader acceptance and ease of implementation, MREF abstraction is structured on top of RDF and XML. Its central role in the context of the InfoQuilt system, for exploiting heterogeneous digital media using a federated and scalable architecture, is briefly described.


Zebra Image Access System, Srilekha Mudumbai, Kshitij Shah, Amit P. Sheth, Krishnan Parasuraman, Clemens Bertram Feb 1998

Zebra Image Access System, Srilekha Mudumbai, Kshitij Shah, Amit P. Sheth, Krishnan Parasuraman, Clemens Bertram

Kno.e.sis Publications

The ZEBRA system, which is part of the VisualHarness platform for managing heterogeneous data, supports three types of access to distributed image repositories: keyword based, attribute based, and image content based. A user can assign different weights (relative importance) to each of the three types, and within the last type of access, to each of the image properties. The image based access component (IBAC) supports access based on computable image properties such as those based on spatial domain, frequency domain or statistical and structural analysis. However, it uses a novel black box approach of utilizing a Visual Information Retrieval (VIR ...


Interestingness Of Discovered Association Rules In Terms Of Neighborhood-Based Unexpectedness, Guozhu Dong, Jinyan Li Jan 1998

Interestingness Of Discovered Association Rules In Terms Of Neighborhood-Based Unexpectedness, Guozhu Dong, Jinyan Li

Kno.e.sis Publications

One of the central problems in knowledge discovery is the development of good measures of interestingness of discovered patterns. With such measures, a user needs to manually examine only the more interesting rules, instead of each of a large number of mined rules. Previous proposals of such measures include rule templates, minimal rule cover, actionability, and unexpectedness in the statistical sense or against user beliefs.

In this paper we will introduce neighborhood-based interestingness by considering unexpectedness in terms of neighborhood-based parameters. We first present some novel notions of distance between rules and of neighborhood of rules. The neighborhood-based interestingness of ...