Yasuma Mori

An Intelligent System for Integrating Autonomous Nomenclature Databases in Semantic Heterogenity

We developed a new integration algorithm for autonomous nomenclature databases in the presence of semantic heterogeneity over international computer networks. First, the algorithm requires users to assign priority according to order of belief for each one of the accessible nomenclature databases. Integration is achieved by increasing the kernel database, selected by the algorithm as the one with the highest priority among the databases. Complex problems are included to recognize consistent parts that can be incrementally added to the kernel database in the remainder of the accessible nomenclature databases through the use of tree structures. The algorithm with relational operators, was proposed after considering both structural and cooperative constraints useful in managing single and multiple databases. In addition, we present a nomenclature database system including the useful algorithm. The system is also accessible from remote users through World-Wide Web and is implemented in SQL programming and CGI (Common Gateway Interface) scripts of World-Wide Web. The system can be useful in both integrating and managing biological taxonomy databases over international computer networks.

Design and Implementation of Parallel Modified PrefixSpan Method

The parallelization of a Modified PrefixSpan method is proposed in this paper. The Modified PrefixSpan method is used to extract the frequent pattern from a sequence database. This system developed by authors requires the use of multiple computers connected in local area network. This system, which has a dynamic load balancing mechanism, is achieved through communication among multiple computers using a socket and an MPI library. It also includes multi-threads to achieve communication between a master process and multiple slave processes. The master process controls both the global job pool, to manage the set of subtrees generated in the initial processing and multiple slave processes. The results obtained here indicated that 8 computers were approximately 6 times faster than 1 computer in trial implementation experiments.

A Extraction Method of Overlapping Cluster Based on Network Structure Analysis

In this paper, we propose a method of overlapping clustering based on network structure analysis that improves the crisp clustering algorithm proposed by Newman et al. In the proposed technique, we cluster the nodes using Newmans algorithm. We then make a contraction graph in which a cluster is considered as a node. In addition, we cluster the created contraction graph using Newmans clustering algorithm again and identify the overlapping nodes. Overlapping clustering is more flexible than crisp clustering. The experimental results using the real network data and trackback data represented the efficacy of the proposed technique.

Extraction of Ambiguous Sequential Patterns with Least Minimum Generalization from Mismatch Clusters

An ambiguous query in sequence databases returns a set of similar subsequences, called a mismatch cluster, to the user. The inherent problem is that it is difficult for users to identify the characteristics of very large similar subsequences in a mismatch cluster. In order to support user comprehension of mismatch clusters, it is important to extract a set of ambiguous sequence patterns with the least minimum generalization in the mismatch cluster. The extraction of the ambiguous sequential pattern set requires an enormous amount of computational time, since we have to discover generalized patterns with minimum covers for the mismatch cluster from candidate generalized patterns. The present paper is a proposal for an iterative refinement method to extract ambiguous sequence patterns with minimum cover for mismatch clusters selected from a sequence database. It includes a proposal to use the method with a domain segmentation method to achieve an efficient pattern extraction. Moreover, a prototype implementing the two proposed methods has been applied to three datasets included in PROSITE in order to evaluate their usefulness. The proposed methods resulted in a high capability to extract ambiguous sequential patterns from mismatch clusters that are provided by an ambiguous query in the sequence database.

An Integration Methodology for Autonomous Taxonomy Databases Using Priorities

In order to integrate autonomous taxonomy databases in the presence of semantic heterogeneity . we developed and evaluated a new integration algorithm over international computer networks. Whole taxonoms databases in the real applications are encoded in the relational model. First, the algorithm requires users to assign priority according to order of belief for each one of the accessible taxonom? databases. Integration is achieved by increasing the kernel database. selected by the algorithm as the one with. the high.est priority among the databases. Complex problems are included to recognize consistent parts that can be incrementally added to the kernel database in the remainder of the accessible taxonomy databases through the use of tree structures. The crZgorir/tm wirh relational operators, was proposed after considering both structural and cooperative constraints useful in managing single and multiple databases. In addition, we present a taxonomy database system including the useful algorithm. The system is also accessible from remote users through WWW (World-Wide Web) and is implemented in SQL programming and CGI (Common Gateway Inter-j&e) scripts of WWW.