Traditional research on preserving privacy in data mining focuses on time-invariant privacy issues. With the emergence of time series data mining, traditional snapshot-based privacy issues need to be extended to be multi-dimensional with the addition

Content-based retrieval in image databases requires appropriate features of images to be derived and used in the indexing and searching process. Fast and accurate retrieval is crucial from a user point of view. The kinds of features used, their organization in suitable data structures, and the similarity search scheme, directly affect the speed and quality of content-based retrieval. In this paper, we evaluate the relative significance of three different image features - geometry, moments, and Fourier descriptors in the context of content-based retrieval, and present experimental results. The evaluation metrics are retrieval quality and search time. These could be used to tune the search process which speeds up retrieval without significantly affecting retrieval quality.

In this paper, an approach for reorganizing Web sites based on user access patterns is proposed. The approach consists of three steps: preprocessing, page classification, and site reorganization. In preprocessing, pages on a Web site are processed to create an internal representation of the site, and page access information of its users is extracted from its server log. In page classification, the Web pages on the site are classified into two categories, index pages and content pages, based on the page access information. After the pages are classified, in site reorganization, the Web site is examined to find better ways to organize and arrange the pages on the site. Our experiments on a large real data set show that the approach is efficient and practical for adaptive Web sites.

A top-down progressive deepening method is developed for efficient mining of multiple-level association rules from large transaction databases based on the a priori principle. A group of variant algorithms is proposed based on the ways of sharing intermediate results, with the relative performance tested and analyzed. The enforcement of different interestingness measurements to find more interesting rules, and the relaxation of rule conditions for finding "level-crossing" association rules, are also investigated. The study shows that efficient algorithms can be developed from large databases for the discovery of interesting and strong multiple-level association rules.

A novel indexing structure-the join index hierarchy-is proposed to handle the "gotos on disk" problem in object-oriented query processing. The method constructs a hierarchy of join indices and transforms a sequence of pointer-chasing operations into a simple search in an appropriate join index file, and thus accelerates navigation in object-oriented databases. The method extends the join index structure studied in relational and spatial databases, supports both forward and backward navigation among objects and classes, and localizes update propagations in the hierarchy. Our performance study shows that a partial join index hierarchy outperforms several other indexing mechanisms in object-oriented query processing.

Based on our years-of-research, a data mining system, DB-Miner, has been developed for interactive mining of multiple-level knowledge in large relational databases. The system implements a wide spectrum of data mining functions, including generalization, characterization, association, classification, and prediction. By incorporation of several interesting data mining techniques, including attribute-oriented induction, progressive deepening for mining multiple-level rules, and meta-rule guided knowledge mining, the system provides a user-friendly, interactive data mining environment with good performance.

Many sequential algorithms have been proposed for the mining of association rules. However, very little work has been done in mining association rules in distributed databases. A direct application of sequential algorithms to distributed databases is not effective, because it requires a large amount of communication overhead. In this study, an efficient algorithm called DMA (Distributed Mining of Association rules), is proposed. It generates a small number of candidate sets and requires only O(n) messages for support-count exchange for each candidate set, where n is the number of sites in a distributed database. The algorithm has been implemented on an experimental testbed, and its performance is studied. The results show that DMA has superior performance, when compared with the direct application of a popular sequential algorithm, in distributed databases.

Knowledge discovery facilitates querying database knowledge and intelligent query answering in database systems. We investigate the application of discovered knowledge, concept hierarchies, and knowledge discovery tools for intelligent query answering in database systems. A knowledge-rich data model is constructed to incorporate discovered knowledge and knowledge discovery tools. Queries are classified into data queries and knowledge queries. Both types of queries can be answered directly by simple retrieval or intelligently by analyzing the intent of query and providing generalized, neighborhood or associated information using stored or discovered knowledge. Techniques have been developed for intelligent query answering using discovered knowledge and/or knowledge discovery tools, which includes generalization, data summarization, concept clustering, rule discovery, query rewriting, deduction, lazy evaluation, application of multiple-layered databases, etc. Our study shows that knowledge discovery substantially broadens the spectrum of intelligent query answering and may have deep implications on query answering in data- and knowledge-base systems.

A prototyped data mining system, DBLearn, has been developed, which efficiently and effectively extracts different kinds of knowledge rules from relational databases. It has the following features: high level learning interfaces, tightly integrated with commercial relational database systems, automatic refinement of concept hierarchies, efficient discovery algorithms and good performance. Substantial extensions of its knowledge discovery power towards knowledge mining in object-oriented, deductive and spatial databases are under research and development.