A Clustering Algorithm for Planning the Integration Process of a Large Number of Conceptual Schemas

When tens and even hundreds of schemas are involved in the integration process, criteria are needed for choosing clusters of schemas to be integrated, so as to deal with the integration problem through an efficient iterative process. Schemas in clusters should be chosen according to cohesion and coupling criteria that are based on similarities and dissimilarities among schemas. In this paper, we propose an algorithm for a novel variant of the correlation clustering approach that addresses the problem of assisting a designer in integrating a large number of conceptual schemas. The novel variant introduces upper and lower bounds to the number of schemas in each cluster, in order to avoid too complex and too simple integration contexts respectively. We give a heuristic for solving the problem, being an NP hard combinatorial problem. An experimental activity demonstrates an appreciable increment in the effectiveness of the schema integration process when clusters are computed by means of the proposed algorithm w.r.t, the ones manually defined by an expert.

Use of Component Integration Services in Multidatabase Systems: A Feasible Solution for Integrating Academic Institutions or Commercial Industries

The book chapter is an extended version of the research paper entitled “Use of Component Integration Services in Multidatabase Systems”, which is presented and published by the 13th ISITA, the National Conference of Recent Trends in Mathematical and Computer Sciences, T.M.B. University, Bhagalpur, India, January 3-4, 2015. Information is widely distributed across many remote, distributed, and autonomous databases (local component databases) in heterogeneous formats. The integration of heterogeneous remote databases is a difficult task, and it has already been addressed by several projects to certain extents. In this chapter, we have discussed how to integrate heterogeneous distributed local relational databases because of their simplicity, excellent security, performance, power, flexibility, data independence, support for new hardware technologies, and spread across the globe. We have also discussed how to constitute a global conceptual schema in the multidatabase system using Sybase Adaptive Server Enterprise’s Component Integration Services (CIS) and OmniConnect. This is feasible for higher education institutions and commercial industries as well. Considering the higher educational institutions, the CIS will improve IT integration for educational institutions with their subsidiaries or with other institutions within the country and abroad in terms of educational management, teaching, learning, and research, including promoting international students’ academic integration, collaboration, and governance. This will prove an innovative strategy to support the modernization and large expansion of academic institutions. This will be considered IT-institutional alignment within a higher education context. This will also support achieving one of the sustainable development goals set by the United Nations: “Goal 4: ensure inclusive and quality education for all and promote lifelong learning”. However, the process of IT integration into higher educational institutions must be thoroughly evaluated, identifying the vital data access points. In this chapter, Section 1 provides an introduction, including the evolution of various database systems, data models, and the emergence of multidatabase systems and their importance. Section 2 discusses component integration services (CIS), OmniConnect and considering heterogeneous relational distributed local databases from the perspective of academics, Section 3 discusses the Sybase Adaptive Server Enterprise (ASE), Section 4 discusses the role of component integration services and OmniConnect of Sybase ASE under the Multidatabase System, Section 5 shows the database architectural framework, Section 6 provides an implementation overview of the global conceptual schema in the multidatabase system, Section 7 discusses query processing in the CIS, and finally, Section 8 concludes the chapter. The chapter will help our students a lot, as we have discussed well the evolution of databases and data models and the emergence of multidatabases. Since some additional useful information is cited, the source of information for each citation is properly mentioned in the references column.

Chinese Question-Answering System

Traditional Chinese text retrieval systems return a ranked list of documentsin response to a user''s request. While a ranked list of documents may be an appropriate response forthe user, frequently it is not. Usually it would be better for the system to provide the answeritself instead of requiring the user to search for the answer in a set of documents. Since Chinesetext retrieval has just been developed lately, and due to various specific characteristics ofChinese language, the approaches to its retrieval are quite different from those studies andresearches proposed to deal with Western language. Thus, an architecture that augments existingsearch engines is developed to support Chinese natural language question answering. In this paper anew approach to building Chinese question-answering system is described, which is thegeneral-purpose, fully-automated Chinese quest ion-answering system available on the web. In theapproach, we attempt to represent Chinese text by its characteristics, and try to convert theChinese text into ERE (E: entity, R: relation) relation data lists, and then to answer the questionthrough ERE relation model. The system performs quite well giving the simplicity of the techniquesbeing utilized. Experimental results show that question-answering accuracy can be greatly improvedby analyzing more and more matching ERE relation data lists. Simple ERE relation data extractiontechniques work well in our system making it efficient to use with many backend retrieval engines.