The Experts below are selected from a list of 21 Experts worldwide ranked by ideXlab platform
Kuck, Sharon Mccure - One of the best experts on this subject based on the ideXlab platform.
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A Design Methodology for A Universal Relation Scheme Implementation via Codasyl
1Co-Authors: Kuck, Sharon MccureAbstract:143 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1982.We develop a design methodology for a relational database system that uses the network model for implementation purposes. Recent advances in design theory for relational databases are incorporated into the design. Two major topics in the translation of one data model into another, addressed in this thesis, are schema translation and query translation. Schema translation begins with a well-designed relational schema. We use calc and search Keys to efficiently enforce all Key Constraints in the network database (thereby enforcing all functional dependencies embodied in the relational schema). The resulting network schema handles incomplete information, since we use only the modified Foreign-Key Constraint and not the universal instance assumption. The representative instance, a correct representation of information stored in the relational database, can be stored into the network database and later retrieved. We include a schema translation algorithm for a relational schema whose corresponding database has the property that each relation can be updated independently of the other relations. We give two algorithms for translating a relational query to a network application program. Suppose that the relational query refers to the set of attributes X. The network query computes the X-total projection of the representative instance which is the correct answer to a query. The first algorithm is confined to relational queries that express selections and projections over the universal relation scheme. The second algorithm includes all relational queries over select, project and (natural) join. Optimization techniques are discussed for both types of translation along with a global optimization algorithm.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD
Dongxu Shao - One of the best experts on this subject based on the ideXlab platform.
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DASFAA Workshops - Conditioning Probabilistic Relational Data with Referential Constraints
Database Systems for Advanced Applications, 2014Co-Authors: Ruiming Tang, Dongxu ShaoAbstract:A probabilistic relational database is a compact form of a set of deterministic relational databases (namely, possible worlds), each of which has a probability. In our framework, the existence of tuples is determined by associated Boolean formulae based on elementary events. An estimation, within such a setting, of the probabilities of possible worlds uses a prior probability distribution specified over the elementary events. Direct observations and general knowledge, in the form of Constraints, help refining these probabilities, possibly ruling out some possible worlds. More precisely, new Constraints can translate the observation of the existence or non-existence of a tuple, the knowledge of a well-defined rule, such as primary Key Constraint, Foreign Key Constraint, referential Constraint, etc. Informally, the process of enforcing knowledge on a probabilistic database, which consists of computing a new subset of valid possible worlds together with their new (conditional) probabilities, is called conditioning. In this paper, we are interested in finding a new probabilistic relational database after conditioning with referential Constraints involved. In the most general case, conditioning is intractable. As a result, we restricted our study to probabilistic relational databases in which formulae of tuples are independent events in order to achieve some tractability results. We devise and present polynomial algorithms for conditioning probabilistic relational databases with referential Constraints.
V. Tietz - One of the best experts on this subject based on the ideXlab platform.
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ICDE - Efficiently Detecting Inclusion Dependencies
2007 IEEE 23rd International Conference on Data Engineering, 2007Co-Authors: Jana Bauckmann, Ulf Leser, Felix Naumann, V. TietzAbstract:Data sources for data integration often come with spurious schema definitions such as undefined Foreign Key Constraints. Such metadata are important for querying the database and for database integration. We present our algorithm SPIDER (single pass inclusion dependency recognition) for detecting inclusion dependencies, as these are the automatically testable part of a Foreign Key Constraint. For IND detection all pairs of attributes must be tested. SPIDER solves this task very efficiently by testing all attribute pairs in parallel. It analyzes a 2 GB database in ~ 20 min and a 21 GB database in ~ 4 h.
Li Ming - One of the best experts on this subject based on the ideXlab platform.
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Design and Implementation of the Database in Timetable Planning System
Computer and Modernization, 2003Co-Authors: Li MingAbstract:Discusses the designs of concept models,logic models,physical models and standardization of the database in timetable planning system. In order to hold back the input or output of misdata which is unfit for the semantics,the database design uses the primary Key Constraint,the uniqueness Constraint,the Foreign Key Constraint,defaults,rules and triggers to keep the integrality of database.
Ruiming Tang - One of the best experts on this subject based on the ideXlab platform.
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DASFAA Workshops - Conditioning Probabilistic Relational Data with Referential Constraints
Database Systems for Advanced Applications, 2014Co-Authors: Ruiming Tang, Dongxu ShaoAbstract:A probabilistic relational database is a compact form of a set of deterministic relational databases (namely, possible worlds), each of which has a probability. In our framework, the existence of tuples is determined by associated Boolean formulae based on elementary events. An estimation, within such a setting, of the probabilities of possible worlds uses a prior probability distribution specified over the elementary events. Direct observations and general knowledge, in the form of Constraints, help refining these probabilities, possibly ruling out some possible worlds. More precisely, new Constraints can translate the observation of the existence or non-existence of a tuple, the knowledge of a well-defined rule, such as primary Key Constraint, Foreign Key Constraint, referential Constraint, etc. Informally, the process of enforcing knowledge on a probabilistic database, which consists of computing a new subset of valid possible worlds together with their new (conditional) probabilities, is called conditioning. In this paper, we are interested in finding a new probabilistic relational database after conditioning with referential Constraints involved. In the most general case, conditioning is intractable. As a result, we restricted our study to probabilistic relational databases in which formulae of tuples are independent events in order to achieve some tractability results. We devise and present polynomial algorithms for conditioning probabilistic relational databases with referential Constraints.