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The design of faster relational database query processors to improve the data retrieval capability of a database was the goal of this research. The emphasis was on evaluating the potential of parallel implementations to allow use of multiprocessing. First, the theoretical properties of applying relational operations to distributed data were considered to provide an underlying data distribution and parallel processing environment model. Next, analytical models were constructed to evaluate various implementations of the select, project, and join relational operations and the update operations of addition, deletion, and modification for a range of data structures and architectural configurations. To simulate the performance of the query processor for all cases, the individual operator models needed to be extended for complex queries consisting of several relational operations. A solution to modeling multi-step queries was the use of a general normal form to express a query. This normal form query tree used combined operations to express relational algebra equivalent queries in a standard form. This standard tree form was then used to construct analytical models for multi-step queries. These models provide the capability to simulate the potential of different forms of parallelism in solving complex queries. The analysis of results of the analytical models presents a logical design for a multiprocessor query processor. This logical query processor using multiple processors and employing parallelism illustrated the potential for an improved query processor when the analytical model results of complex queries were compared to a benchmark of some current database systems. Keywords: Parallel processing; Theses.

This paper introduces the intuitionistic rough set and intuitionistic rough relational and rough object oriented database models. Rough set, fuzzy set, and intuitionistic set uncertainty management are discussed and compared, and the model based on intuitionistic and rough sets developed here is applied to databases. The intuitionistic rough set database models draw benefits from both the rough set and intuitionistic techniques, providing greater management of uncertainty for databases applications in a less than certain world.

This paper introduces the intuitionistic rough set and intuitionistic rough relational and rough object oriented database models. Rough set, fuzzy set, and intuitionistic set uncertainty management are discussed and compared, and the model based on intuitionistic and rough sets developed here is applied to databases. The intuitionistic rough set database models draw benefits from both the rough set and intuitionistic techniques, providing greater management of uncertainty for databases applications in a less than certain world.

This paper introduces the intuitionistic rough set and intuitionistic rough relational and rough object oriented database models. Rough set, fuzzy set, and intuitionistic set uncertainty management are discussed and compared, and the model based on intuitionistic and rough sets developed here is applied to databases. The intuitionistic rough set database models draw benefits from both the rough set and intuitionistic techniques, providing greater management of uncertainty for databases applications in a less than certain world.

This paper introduces the intuitionistic rough set and intuitionistic rough relational and rough object oriented database models. Rough set, fuzzy set, and intuitionistic set uncertainty management are discussed and compared, and the model based on intuitionistic and rough sets developed here is applied to databases. The intuitionistic rough set database models draw benefits from both the rough set and intuitionistic techniques, providing greater management of uncertainty for databases applications in a less than certain world.

Work for this grant enabled the advancement of the state of research for areas of fuzzy spatial data modeling, uncertainty and conflation. Specific published work supported by this grant included a range of topics, including: fuzzy spatial relationship refinements, spatial query interfaces, uncertainty in distributed spatial information systems, spatial relationship querying, distributed spatial object-based systems, spatial indexing, distributed conflation model and issues, and an integrated image change detection/conflation algorithm. Special journal issues co-edited by the grant recipient on Spatial Data Management, Uncertainty in Geographic Information Systems and Spatial Data and Distributed Object-Oriented Systems resulted in newly published works from some of the foremost researchers in the area, including Michael Goodchild, Peter Fisher, Hans Guesgen, Douglas Schmidt and others.