Integration and querying of heterogeneous, autonomous, distributed database systems

Today, databases have become an integral part of information systems. In the past two decades, we have seen different database systems being developed independently and used in different applications domains. Today's interconnected networks and advanced applications, such as data warehousing, data mining & knowledge discovery and intelligent data access to information on the Web, have created a need for integrated access to such heterogeneous, autonomous, distributed database systems. Heterogeneous/multidatabase research has focused on this issue resulting in many different approaches. However, a single, generally accepted methodology in academia or industry has not emerged providing ubiquitous intelligent data access from heterogeneous, autonomous, distributed information sources. ^ This thesis describes a heterogeneous database system being developed at High-performance Database Research Center (HPDRC). A major impediment to ubiquitous deployment of multidatabase technology is the difficulty in resolving semantic heterogeneity. That is, identifying related information sources for integration and querying purposes. Our approach considers the semantics of the meta-data constructs in resolving this issue. The major contributions of the thesis work include: (i) providing a scalable, easy-to-implement architecture for developing a heterogeneous multidatabase system, utilizing Semantic Binary Object-oriented Data Model (Sem-ODM) and Semantic SQL query language to capture the semantics of the data sources being integrated and to provide an easy-to-use query facility; (ii) a methodology for semantic heterogeneity resolution by investigating into the extents of the meta-data constructs of component schemas. This methodology is shown to be correct, complete and unambiguous; (iii) a semi-automated technique for identifying semantic relations, which is the basis of semantic knowledge for integration and querying, using shared ontologies for context-mediation; (iv) resolutions for schematic conflicts and a language for defining global views from a set of component Sem-ODM schemas; (v) design of a knowledge base for storing and manipulating meta-data and knowledge acquired during the integration process. This knowledge base acts as the interface between integration and query processing modules; (vi) techniques for Semantic SQL query processing and optimization based on semantic knowledge in a heterogeneous database environment; and (vii) a framework for intelligent computing and communication on the Internet applying the concepts of our work. ^

Self-other rating discrepancy and personality antecedents

This study explored individual difference factors to help explain the discrepancy that has been found to exist between self and other ratings in prior research. Particularly, personality characteristics of the self-rater were researched in the current study as a potential antecedent for self-other rating agreement. Self, peer, and supervisor ratings were provided for global performance as well as five competencies specific to the organization being examined. Four rating tendency categories, over-raters, under-raters, in-agreement (good), and in-agreement (poor), established in research by Atwater and Yammarino were used as the basis of the current research. The sample for rating comparisons within the current study consisted of 283 self and supervisor dyads and 275 for self and peer dyads from a large financial organization. Measures included a custom multi-rater performance instrument and the personality survey instrument, ASSESS, which measures 20 specific personality characteristics. MANCOVAs were then performed on this data to examine if specific personality characteristics significantly distinguished the four rating tendency groups. Examination of all personality dimensions and overall performance uncovered significant findings among rating groups for self-supervisor rating comparisons but not for self-peer rating comparisons. Examination of specific personality dimensions for self-supervisory ratings group comparisons and overall performance showed Detail Interest to be an important characteristic among the hypothesized variables. For self-supervisor rating comparisons and specific competencies, support was found for the hypothesized personality dimension of Fact-based Thinking which distinguished the four rating groups for the competency, Builds Relationships. For both self-supervisor and self-peer rating comparisons, the competencies, Builds Relationships and Leads in a Learning Environment, were found to have significant relationship with several personality characteristics, however, these relationships were not consistent with the hypotheses in the current study. Several unhypothesized personality dimensions were also found to distinguish rating groups for both self-supervisor and self-peer comparisons on overall performance and various competencies. Results of the current study hold implications for the training and development session that occur after a 360-degree evaluation process. Particularly, it is suggested that feedback sessions may be designed according to particular rating tendencies to maximize the interpretation, acceptance and use of evaluation information. ^

Integration and querying of heterogeneous, autonomous, distributed database systems

Today, databases have become an integral part of information systems. In the past two decades, we have seen different database systems being developed independently and used in different applications domains. Today's interconnected networks and advanced applications, such as data warehousing, data mining & knowledge discovery and intelligent data access to information on the Web, have created a need for integrated access to such heterogeneous, autonomous, distributed database systems. Heterogeneous/multidatabase research has focused on this issue resulting in many different approaches. However, a single, generally accepted methodology in academia or industry has not emerged providing ubiquitous intelligent data access from heterogeneous, autonomous, distributed information sources. This thesis describes a heterogeneous database system being developed at Highperformance Database Research Center (HPDRC). A major impediment to ubiquitous deployment of multidatabase technology is the difficulty in resolving semantic heterogeneity. That is, identifying related information sources for integration and querying purposes. Our approach considers the semantics of the meta-data constructs in resolving this issue. The major contributions of the thesis work include: (i.) providing a scalable, easy-to-implement architecture for developing a heterogeneous multidatabase system, utilizing Semantic Binary Object-oriented Data Model (Sem-ODM) and Semantic SQL query language to capture the semantics of the data sources being integrated and to provide an easy-to-use query facility; (ii.) a methodology for semantic heterogeneity resolution by investigating into the extents of the meta-data constructs of component schemas. This methodology is shown to be correct, complete and unambiguous; (iii.) a semi-automated technique for identifying semantic relations, which is the basis of semantic knowledge for integration and querying, using shared ontologies for context-mediation; (iv.) resolutions for schematic conflicts and a language for defining global views from a set of component Sem-ODM schemas; (v.) design of a knowledge base for storing and manipulating meta-data and knowledge acquired during the integration process. This knowledge base acts as the interface between integration and query processing modules; (vi.) techniques for Semantic SQL query processing and optimization based on semantic knowledge in a heterogeneous database environment; and (vii.) a framework for intelligent computing and communication on the Internet applying the concepts of our work.