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Includes bibliographies and index

Includes bibliographies and index

Includes bibliographies and index

Includes bibliographies and index

Includes bibliographies and index

12 p. 28 cm

12 p. 28 cm

Graph traversals are a basic but fundamental ingredient for a variety of graph algorithms and graph-oriented queries. To achieve the best possible query performance, they need to be implemented at the core of a database management system that aims at storing, manipulating, and querying graph data. Increasingly, modern business applications demand native graph query and processing capabilities for enterprise-critical operations on data stored in relational database management systems. In this paper we propose an extensible graph traversal framework (GRAPHITE) as a central graph processing component on a common storage engine inside a relational database management system. We study the influence of the graph topology on the execution time of graph traversals and derive two traversal algorithm implementations specialized for different graph topologies and traversal queries. We conduct extensive experiments on GRAPHITE for a large variety of real-world graph data sets and input configurations. Our experiments show that the proposed traversal algorithms differ by up to two orders of magnitude for different input configurations and therefore demonstrate the need for a versatile framework to efficiently process graph traversals on a wide range of different graph topologies and types of queries. Finally, we highlight that the query performance of our traversal implementations is competitive with those of two native graph database management systems.

Includes bibliographical references and index

Includes bibliographical references and index

Includes bibliographical references and index

Current relational Database Management Systems must be extended to function well in Engineering and Artificial Intelligence applications. Various additional functionalities have been proposed and in this thesis we study the optimization of one extended environment. Specifically, we consider the optimization of a version of the QUEL query language extended with two new features: the repetitive execution of commands; and the execution of relation fields in which collections of QUEL commands are stored. An extended query processing algorithm based on the original INGRES decomposition algorithm is first presented and then various modifications aiming to improve its performance are suggested. Caching of query results is also considered as another means to improve the performance of the processing engine. We analyze and suggest solutions to the various problems related to the design of a query result cache (replacement policies, invalidation techniques, etc). Based on the above extensions, a relation field may contain more than one QUEL commands. Accessing such a field triggers the execution of all these commands. We present a set of tactics that can be to reduce the cost of processing multiple commands using some interquery analysis. Special cases amenable to different kind of processing are also identified and studied.

In today's world, Web-Based Distance Education Systems have a great importance. Web-based Distance Education Systems are usually known as Learning Management Systems (LMS). In this article, a database design, which was developed to create an educational institution as a Learning Management System, is described. In this sense, developed Learning Management System consists of basis of Virtual Education Institutions. In this study, a fully relational database design has been realized in compliance with SCORM standards and got ready to be used as Virtual Education Institutions. This system can be used for any required education institute and it can be run within the same interface. In LMS that will be generated, a faculty or institute can be defined and academic and all administrative processes of the defined institute can be managed with the designed system. Proposed database design has been used in a LMS of Afyon Kocatepe University. In this system, many processes like indexing, uploading, downloading, production and editing of web based learning materials can also be performed easily and safely. (Contains 17 figures.)

Traditional database queries follow a simple model: they define constraints that each tuple in the result must satisfy. This model is computationally efficient, as the database system can evaluate the query conditions on each tuple individually. However, many practical, real-world problems require a collection of result tuples to satisfy constraints collectively, rather than individually. In this paper, we present package queries, a new query model that extends traditional database queries to handle complex constraints and preferences over answer sets. We develop a full-fledged package query system, implemented on top of a traditional database engine. Our work makes several contributions. First, we design PaQL, a SQL-based query language that supports the declarative specification of package queries. We prove that PaQL is as least as expressive as integer linear programming, and therefore, evaluation of package queries is in general NP-hard. Second, we present a fundamental evaluation strategy that combines the capabilities of databases and constraint optimization solvers to derive solutions to package queries. The core of our approach is a set of translation rules that transform a package query to an integer linear program. Third, we introduce an offline data partitioning strategy allowing query evaluation to scale to large data sizes. Fourth, we introduce SketchRefine, a scalable algorithm for package evaluation, with strong approximation guarantees ($(1 \pm\epsilon)^6$-factor approximation). Finally, we present extensive experiments over real-world and benchmark data. The results demonstrate that SketchRefine is effective at deriving high-quality package results, and achieves runtime performance that is an order of magnitude faster than directly using ILP solvers over large datasets.

Thesis advisor, C. Thomas Wu

Thesis advisor, C. Thomas Wu

This report contains a statement of the problems studied and the results and conclusions reached on the translation and standardization of relational and network type database management systems. (Author)

The general objective of the information management component of the New York Bight Study was to develop a District-based system (a geographic information system or GIS) to store, manipulate, and analyze the data sets associated with and developed by the New York Bight study. To accomplish this objective, the following efforts were required: database development, hardware/ software acquisition, system installation, input of database attributes and graphic elements, and training of New York District personnel. Intergraph hardware and software were chosen as the system platform. Oracle database software was selected because it is one of the more common relational database management systems that provides comprehensive relational database (RDB) functions and data storage capabilities. The selection of a base map projection and coordinate system was one of the first decisions made in the process of loading graphic data. Several files were loaded into the system including: digitized shoreline files, graphic representations of gauge locations, tide data, satellite images, model output for water surface elevation, temperature, salinity, and dissolved oxygen, wave hindcast data, and ship observation data. Appendix A presents a basic user training guide. Project training was focused to create geographic map displays from measured data and model output, and to perform analyses on these data. Training was performed in the manner that reflected the types of projects undertaken by the New York District and addressed problems or questions of interest to the New York District. Geographic information system, New York bight, Intergraph, Relational database.

The general objective of the information management component of the New York Bight Study was to develop a District-based system (a geographic information system or GIS) to store, manipulate, and analyze the data sets associated with and developed by the New York Bight study. To accomplish this objective, the following efforts were required: database development, hardware/ software acquisition, system installation, input of database attributes and graphic elements, and training of New York District personnel. Intergraph hardware and software were chosen as the system platform. Oracle database software was selected because it is one of the more common relational database management systems that provides comprehensive relational database (RDB) functions and data storage capabilities. The selection of a base map projection and coordinate system was one of the first decisions made in the process of loading graphic data. Several files were loaded into the system including: digitized shoreline files, graphic representations of gauge locations, tide data, satellite images, model output for water surface elevation, temperature, salinity, and dissolved oxygen, wave hindcast data, and ship observation data. Appendix A presents a basic user training guide. Project training was focused to create geographic map displays from measured data and model output, and to perform analyses on these data. Training was performed in the manner that reflected the types of projects undertaken by the New York District and addressed problems or questions of interest to the New York District. Geographic information system, New York bight, Intergraph, Relational database.

A more formal approach to Relational Database Management Systems, compared the way they were covered during Web Applications. Database systems are discussed from the physical layer of B-trees and file servers to the abstract layer of relational design. Also includes alternative and generic approaches to database design and database management system including relational, object-relational, and object-oriented systems, SQL standards, algebraic query languages, integrity constraints, triggers, functional dependencies, and normal forms. Other topics include tuning database transactions, security from the application perspective, and data warehousing. Text: Database Management Systems, Ramakrishnan and Gehrke. Requirements: Two exams, 10 assignments, one project. Note : The first exam and most of the assignments are missing.

The combination of artificial intelligence (AI) and database technology allows the capability of extending the usefulness of existing database applications. More specifically, the use of AI provides the means to develop an intelligent front end to allow nonintrusive assess to multiple relational databases. A knowledge source is built for each participating database application. A meta-knowledge source, constructed from all knowledge sources in the system, is consulted to determine the appropriate application to which a database request is channeled. This report introduces a proposed architecture and presents a prototype developed to accomplish this task.... Composite databases, Federated databases, Interconnectivity, Co-relationship, Heterogeneous database systems, Multidatabase systems, Distributed databases, Homogenous database systems, Relational database management systems,

To many critics and researchers, semantic meagerness is the main limitation of relational database management systems. The burden placed on users to remember attribute names and their domains is discussed and difficulties associated with the lack of set as a type for an attribute is examined. This thesis explores the implications to high-level query languages necessitated by a set-type attribute. The allowance of semantically improper joins by DBMS is studied as is the lack of strong data type checking. The semantic shortcomings of system-chosen access paths is discussed. These problems are followed by recommended solutions.

To many critics and researchers, semantic meagerness is the main limitation of relational database management systems. The burden placed on users to remember attribute names and their domains is discussed and difficulties associated with the lack of set as a type for an attribute is examined. This thesis explores the implications to high-level query languages necessitated by a set-type attribute. The allowance of semantically improper joins by DBMS is studied as is the lack of strong data type checking. The semantic shortcomings of system-chosen access paths is discussed. These problems are followed by recommended solutions.

To many critics and researchers, semantic meagerness is the main limitation of relational database management systems. The burden placed on users to remember attribute names and their domains is discussed and difficulties associated with the lack of set as a type for an attribute is examined. This thesis explores the implications to high-level query languages necessitated by a set-type attribute. The allowance of semantically improper joins by DBMS is studied as is the lack of strong data type checking. The semantic shortcomings of system-chosen access paths is discussed. These problems are followed by recommended solutions.

To many critics and researchers, semantic meagerness is the main limitation of relational database management systems. The burden placed on users to remember attribute names and their domains is discussed and difficulties associated with the lack of set as a type for an attribute is examined. This thesis explores the implications to high-level query languages necessitated by a set-type attribute. The allowance of semantically improper joins by DBMS is studied as is the lack of strong data type checking. The semantic shortcomings of system-chosen access paths is discussed. These problems are followed by recommended solutions.

To many critics and researchers, semantic meagerness is the main limitation of relational database management systems. The burden placed on users to remember attribute names and their domains is discussed and difficulties associated with the lack of set as a type for an attribute is examined. This thesis explores the implications to high-level query languages necessitated by a set-type attribute. The allowance of semantically improper joins by DBMS is studied as is the lack of strong data type checking. The semantic shortcomings of system-chosen access paths is discussed. These problems are followed by recommended solutions.

To many critics and researchers, semantic meagerness is the main limitation of relational database management systems. The burden placed on users to remember attribute names and their domains is discussed and difficulties associated with the lack of set as a type for an attribute is examined. This thesis explores the implications to high-level query languages necessitated by a set-type attribute. The allowance of semantically improper joins by DBMS is studied as is the lack of strong data type checking. The semantic shortcomings of system-chosen access paths is discussed. These problems are followed by recommended solutions.