During the last three or four years several investigators have been exploring “semantic models ” for formatted databases. The intent is to capture (in a more or less formal way) more of the meaning of the data so that database design can become more systematic and the database system itself can behave more intelligently. Two major thrusts are clear: (I) the search for meaningful units that are as small as possible--atomic semantics; (2) the search for meaningful units that are larger than the usual n-ary relation-molecular semantics. In this paper we propose extensions to the relational model to support certain atomic and molecular semantics. These extensions represent a synthesis of many ideas from the published work in semantic modeling plus the introduction of new rules for insertion, update, and deletion, as well as new algebraic operators.

The outerjoin operator is currently available in the query language of several major DBMSs, and it is included in the proposed SQL2 standard draft. However, "associativity problems" of the operator have been pointed out since its introduction. In this paper we propose a shift in the intuition behind outerjoin: Instead of computing the join while also preserving its arguments, outerjoin delivers tuples that come either from the join or from the arguments. Queries with joins and outerjoins deliver tuples that come from one out of several joins, where a single relation is a trivial join. An advantage of this view is that, in contrast to preservation, disjunction is commutative and associative, which is a significant property for intuition, formalisms, and generation of execution plans. Based on a disjunctive normal form, we show that some data merging queries cannot be evaluated by means of binary outerjoins, and give alternative procedures to evaluate those queries. We also explore several evaluation strategies for outerjoin queries, including the use of semijoin programs to reduce base relations. CR Subject Classification (1991): [H.2.4] Database Systems, Query Processing; [H.3.3] Information Search and Retrieval, Query Formulation; [G.2.2] Graph Theory; [F.2.2] Nonnumerical algorithms and problems. Keywords and Phrases: Outerjoin, Query Graph, Query Formulation and Processing. Note: The author had an ERCIM postdoctoral fellowship while conducting this work. 1

This paper describes a generalization of the relational model in order to capture and manipulate a type of probabilistic information. Probabilistic databases are formalized by means of logic theories based on a probabilistic first-order language proposed by Halpern. A sound a complete method is described for evaluating queries in probabilistic theories. The generalization proposed can be incorporated into existing relational systems with the addition of a component for manipulating propositional formulas. 1

Conventional database optimizers take full advantage of associativity and commutativity properties of join to implement efficient and powerful optimizations on select/project/join queries. However, only limited optimization is performed on other binary operators. In this paper, we present the theory and algorithms needed to generate alternative evaluation orders for the optimization of queries containing outerjoins. Our results include both a complete set of transformation rules, suitable for new-generation, transformation-based optimizers, and a bottom-up join enumeration algorithm compatible with those used by traditional optimizers.

Information quality plays a crucial role in every application that integrates data from autonomous sources. However, information quality is hard to measure and complex to consider for the tasks of information integration, even if the integrating sources cooperate. We present a systematic and formal approach to the measurement of information quality and the combination of such measurements for information integration. Our approach is based on a value model that incorporates both extensional value (coverage) and intensional value (density) of information. For both aspects we provide merge functions for adequately scoring integrated results. Also, we combine the two criteria to an overall completeness criterion that formalizes the intuitive notion of completeness of query results. This completeness measure is a valuable tool to assess source size and to predict result sizes of queries in integrated information systems. We propose this measure as an important step towards the usage of information quality for source selection, query planning, query optimization, and quality feedback to users.

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