S. Ceri, G. Gotlob, L. Tanca, \Logic Programming and Databases", Surveys in Computer Science, Springer Verlag, 1990  Home/Search   Document Not in Database   Summary   Related Articles   Check

....45] We note that there is a close connection between mediator based systems [79] and distributed database systems [53] Many mediator systems have borrowed techniques for query optimization from the world of distributed databases. There has been a great deal of published work on these techniques [11, 53, 65]. However, most of this work focuses on the efficient evaluation of Select Project Join (SPJ) queries. It does not adequately address the special needs of fusion queries. In this section we study how existing optimizers would handle fusion queries, and we explore opportunities for improvement ....

....in the number of the constituent SPJ subqueries, which in turn is exponential in the size of the original fusion query. Examples of systems in our first category are Information Manifold [41] TSIMMIS [55] HERMES [68] and Infomaster [23] Query processing in these systems is based on resolution [11, 24], which leads to the distribution of the join over the union. For example, Information Manifold would process our sample fusion query by generating nine datalog [75] subqueries, one for each of the nine constituent SPJ subqueries in Figure 4.11. It then unions the results of the nine datalog ....

S. Ceri, G. Gottlobb, L. Tanca. Logic Programming and Databases, Surveys in Computer Science. Springer-Verlag, 1990.

....we often need a third source corresponding to the knowledge that developers and users can bring to the system. Examples are type hierarchies in conceptual graphs [CM92] TBox (Terminological Box) in description logics [Nap97] conceptual scales in concept analysis [Pre97] and rules in Datalog [CGT90] This knowledge, we choose to call terminology is intermediate between logic and context as it concerns intentions, and then complete the basic logic, but is dynamic as it can be updated and extended. From this point of view, terminology can be considered as a parameter in logic. Instead of ....

Ceri (Stefano), Gottlob (Georg) et Tanca (Letizia). Logic Programming and Databases. Berlin, Heidelberg, New York, Springer-Verlag, 1990, Surveys in Computer Science.

....or either. 11 In fact, logic programs can be seen as a logical extension of relational databases, in which rules can be used to derive new data not explicitly stored in the original database. Datalog is the best known of the logic programming languages adapted for use as extended databases, Ceri et al. 1990 ] It is purely relational, i.e. it di#ers from Prolog, say, by having no functions, but extends relational databases with the use of rules. 12 Conclusion Automated deduction has grown into a broad field in which a wide variety of proof methods is used on a wide variety of logics and applied ....

Stefano Ceri, Georg Gottlob, and Leitzia Tanca. Logic Programming and Databases. Surveys in Computer Science. Springer-Verlag, Berlin, 1990.

....languages. EM85] p. 3) 1 Introduction In relational DBMSs, both the data de nition language and the query language are organized on two levels: SQL (or Datalog) operates at a higher and man oriented level, while relational algebra acts as an intermediate, mainly machine oriented, language (cf. [Ull89, CGT90]) It is conceivable that the man machine interaction in a theorem proving system be organized similarly, with a rst order (or higher order) predicate language interfacing man, and with the proof search and model building activities directly rooted on a modernized and enriched version of the ....

S. Ceri, G. Gottlob, and L. Tanca. Logic Programming and Databases. Springer-Verlag, Surveys in Computer Science, 1990.

....or either. 11 In fact, logic programs can be seen as a logical extension of relational databases, in which rules can be used to derive new data not explicitly stored in the original database. Datalog is the best known of the logic programming languages adapted for use as extended databases, Ceri et al. 1990 ] It is purely relational, i.e. it differs from Prolog, say, by having no functions, but extends relational databases with the use of rules. 12 Conclusion Automated deduction has grown into a broad field in which a wide variety of proof methods is used on a wide variety of logics and applied ....

Stefano Ceri, Georg Gottlob, and Leitzia Tanca. Logic Programming and Databases. Surveys in Computer Science. Springer-Verlag, Berlin, 1990.

....and their evaluation requires the same inference mechanisms, hence enabling more sophisticated reasoning about the database content. On the other hand, logic programming systems do not yet provide an effective technology for managing large, shared, persistent and reliable data connections [10]. Taking the advantages of both systems, some studies have been done for combining deductive capabilities of a logic programming system in formulating queries and constrains with the efficient and reliable data retrieval capabilities of database management systems. Most of these studies are based ....

....of the person is meltem turhan with employee number 501, her telephone number is known and it is 2230812, her age is between 23 and 26. Her address is bahcelievler. Height is known and it is 1.65. Distance is a fuzzy value near . employee childl e no salary childname childage 501 6. 0 fayse [5,10], ali [7,7]g 5005 high f g Prolog representation: employee(501,k(6.0) childl( ayse ,cage(5,10) child( ali ,cage(7,7) employee(5005,uk( high ) Salary of the employee with number 501 is known and it is 6.0 Million. The third attribute of this relation is a list of another relation child ....

S. Ceri, G. Gottlab, and L. Tanca. Logic Programming and Databases. Springer-Verlag, Surveys in Computer Science, 1990.

....[Bayer, 1985, Bancilhon, 1986] Bancilhon and Ramakrishnan, 1986] gives a good overview. The DBI in PROTOS L uses a mixed top down and bottom up strategy, combining ideas from [Vieille, 1988] and [Hulin, 1989] For a more complete overview on combining logic programming systems with databases see [Ceri et al. 1990]. Acknowledgements: PROTOS L was developed within the EUREKA Project PROTOS (EU 56) at IBM in Stuttgart. Many people contributed directly or indirectly to its design and implementation. As research staff members and guest scientists Udo Pletat and Kurt Rothermel contributed to a first design, Gert ....

S. Ceri, G. Gottlob, and L. Tanca. Logic Programming and Databases. Surveys in Computer Science. Springer Verlag, 1990.

....and applications. It provides all the necessary primitives to manipulate OM structures as built in methods and operators. ROLL is a pure, functionfree definite clause language, equivalent to Datalog with stratification and range restriction to support negation and operations on primitive values [9]. Like Datalog, ROLL is order independent and cannot effect state transitions. These characteristics maximize optimization opportunities and ensure that ROLL evaluation is tractable [5] and benefits from techniques adapted from a relational context into an object oriented one [10] Most of the ....

....in ROCK ROLL [10] These constraints require that ROLL methods be stratified and range restricted, and that the evaluation of ROLL methods and queries does not effect any state transition in the database. Equivalent constraints to these are widely applied to pure, deductive database languages [9]. Their purpose is to ensure that ROLL evaluation is logically sound, order independent and terminating. Their enforcement guarantees that ROLL methods and queries support negation and arithmetic, are capable of being comprehensively optimized using standard techniques and have a tractable [5] ....

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Stefano Ceri, Georg Gottlob, and Letizia Tanca. Logic Programming and Databases. Surveys in Computer Science. Springer-Verlag, Berlin, Germany., 1990. ISBN 3-540-51728-6, 0-387-51728-6.

....and applications. It provides all the necessary primitives to manipulate OM structures as built in methods and operators. ROLL is a pure, functionfree definite clause language, equivalent to Datalog with stratification and range restriction to support negation and operations on primitive values [10]. Like Datalog, ROLL is order independent and cannot effect state transitions. These characteristics maximize optimization opportunities and ensure that ROLL evaluation is tractable [5] and benefits from techniques adapted from a relational context into an object oriented one [12] Most of the ....

....whereas the signature of a ROLL method only declares the type of its arguments, because in its body only logical variables occur and there is no need for names local to the method. Figure 1(b) shows an implementation for the interface of state and illustrates method writing in ROCK ROLL. 1 See [10] for a description of variable binding in deductive languages. type state: properties: public: name : string, network : roads, counties : regions; ROCK: roadsatlevel(l: string) roads; ROLL: hasroad(road) end type # state type roads: public road ; # interface omitted end type # roads type ....

[Article contains additional citation context not shown here]

Stefano Ceri, Georg Gottlob, and Letizia Tanca. Logic Programming and Databases. Surveys in Computer Science. Springer-Verlag, Berlin, Germany., 1990. ISBN 3-540-51728-6, 0-387-51728-6.

....in a translation of disjunctive rules into Horn clauses with some list valued arguments. This shows that at least the addition of a few disjunctive rules to an otherwise Horn database does not destroy the possibility of efficient query evaluation. 1 Introduction The theory of deductive databases [Ull88, Ull89, HPRV89, CGT90] is by now well matured and has lead to quite a few prototype systems (e.g. NT89, PDR91, RSS92] Commercial systems probably will become available in the next few years. But all of these systems are restricted to Horn clauses with (more or less) stratified negation. The importance of ....

S. Ceri, G. Gottlob, L. Tanca: Logic Programming and Databases. Surveys in Computer Science. Springer-Verlag, Berlin, 1990.

....represents a cyclic graph, then this program remains modularly stratified. Indeed, evaluation of this program will still terminate, but the relation for bom will not contain tuples for those parts involved in a cycle. A. 3 Company Control In this section, we look at a problem presented in [8] and in [22] It is further investigated in [26] Say that the base relation direct(X,Y,V) is true when company X directly owns V shares in Y where V is expressed as a percentage of the total number of shares available for Y. We define rules for two predicates controls and owns such that: ....

S. Ceri, G. Gottlob, and L. Tanca. Logic programming and databases. Springer-Verlag, 1990. Surveys in Computer Science.

....data models. H.2.3[Database Management] Languages query languages. General Terms : Design, Languages Additional Key Words and Phrases : Nested graph, complex object, types, rule based query and update language, object store. 2 1. INTRODUCTION Recent database research has focussed on deductive [7, 14, 26] and object oriented [5, 22] databases. Deductive databases extend the relational data model with rule based computation. Rules enable the derivation of further, intentional, tuples from the stored, extensional, tuples. These derived tuples can be used purely for querying purposes or can be ....

Ceri S., Gottlob G. and Tanca L. Logic Programming and Databases, Surveys in Computer Science, SpringerVerlag (1990).

....remarks and suggestions for further research. We would like to stress the fact that, in the present paper, we address conceptual aspects. Computational and performance aspects of our model are addressed in a forthcoming paper. 2 Basic Definitions and Notations In this section we recall from [Bid91, CGT90, Ull89] some facts concerning Datalog neg databases under well founded semantics and then introduce our basic definitions and notations concerning what we call literal rules. 2.1 Datalog neg Databases A database is seen as a finite function free set of facts and rules over an alphabet A consisting ....

S. Ceri, G. Gottlob, L. Tanca, Logic Programming and Databases, Surveys in Computer Science, Springer-Verlag, 1990.

....is related to work on nonmonotonic reasoning and inheritance networks in artificial intelligence [15] but is more general in that it assumes property (or method) values can be defined by deductive rules. We assume the reader has some knowledge of logic programming [16] deductive databases [8], and object oriented databases [6, 10] For simplicity, we restrict attention to ground answers throughout. For brevity, we omit proofs of our results; these can be found in [11] 2 Syntax In this section we describe our language through an extended example. Our language is based loosely on LIFE ....

S. Ceri, G. Gottlob, and L. Tanca. Logic Programming and Databases. Surveys in Computer Science. Springer-Verlag, 1990.

.... for defining and efficiently evaluating recursive views, thus significantly extending the class of queries that can be expressed, by providing structured data in the form of Prolog terms and sometimes sets, and by using a logic programming language as the uniform query and application language [9, 12, 29, 36, 37]. Their most appealing property is that they preserve the firm logical foundation of relational systems, and much work has been done on their application to problems involving recursive or cyclic data, on the semantics of databases with increasingly complex combinations of recursion, negation and ....

S. Ceri, G. Gottlob, and L. Tanca. Logic Programming and Databases. Surveys in Computer Science. Springer-Verlag, 1990.

....and planned extensions in section VII. In the appendix we describe reducer programs for a simplified company database. II. Preliminaries In the next paragraph we will define some notations used in the rest of this paper. For a more general introduction to recursive logic programs see [21]. Additionally we show how to describe fragmentation in a distributed database using non recursive logic rules. After these preliminaries we will be able to concentrate on the logical optimization and execution strategies assuming fragmentation transparency. A. Notations Definition 1 (Logic ....

S. Ceri, G. Gottlob, and L. Tanca, Logic Programming and Databases. Surveys in Computer Science, Springer-Verlag, 1990.

....in linear time [9, 10] Datalog. In database theory, a database is identified with a finite relational structure. A query associates to each input database over a given schema (vocabulary) a result consisting of one or more output relations. Queries are formulated in query languages. Datalog (cf. [33, 7] is a very powerful, well studied declarative query language based on Horn clause logic. Datalog adapts the paradigm of Logic Programming to the database setting. A Datalog query ( Pi; P ) is a finite set of (recursive or nonrecursive) rules Pi and a distinguished output relation P . Example 1. ....

....databases as well as for databases stored on disk. Several Datalog query processors interacting directly with secondary storage have been developed. Sophisticated query optimization strategies (e.g. the magic set method [4] for minimizing disk access have been developed and implemented, cf. [7]. The relational input structure to a Datalog query is not necessarily a plain relational representation of the relevant application data. It could be instead a succinct representation of the same data, in form of a (relational representation) of e.g. a Boolean 2 circuit or an ordered binary ....

S. Ceri, G. Gottlob, and L. Tanca, Logic Programming and Databases, Surveys in Computer Science, Springer, 1990.

....in the number of the constituent SPJ subqueries, which in turn is exponential in the size of the original fusion query. Examples of systems in our first category are Information Manifold [23] TSIMMIS [29] HERMES [1] and Infomaster [13] Query processing in these systems is based on resolution ([8, 16]) which leads to the distribution of the join over the union. For example, Information Manifold would process our sample fusion query by generating nine datalog ( 37] subqueries, one for each X 1 : sq(c 1 ; R 1 ) X 2 : sq(c 1 ; R 2 ) X 3 : sq(c 1 ; R 3 ) X : X 1 [ X 2 X : X [ X 3 Y 1 ....

S. Ceri, G. Gottlobb, and L. Tanca. Logic Programming and Databases, Surveys in Computer Science. Springer-Verlag, 1990.

....evaluation in the presence of defaults; this can be taken as a basis for different optimizations. Second, in contrast to other deduction algorithms, it is able to handle partially ordered defaults with a skeptical minimal model semantics. 1 Introduction Our goal is to extend deductive databases [14, 10, 20] by overridable rules in order to increase the reusability of such specifications. This closely corresponds to inheritance in object oriented approaches. Viewing the object classes as the modules of the specification, it seems clear that it should be possible to define rules inside the classes ....

S. Ceri, G. Gottlob, L. Tanca: Logic Programming and Databases. Surveys in Computer Science. Springer-Verlag, Berlin, 1990.

....in the number of the constituent SPJ subqueries, which in turn is exponential in the size of the fusion query. Examples of systems in our first category are Information Manifold [12] TSIMMIS [17] HERMES [23] and Infomaster [6] Query processing in these systems is based on resolution ([3]) which leads to the distribution of the join over the union. One obvious way in which systems taking this approach to fusion query processing can incorporate techniques discussed in our paper is to implement a module that checks if a query is a fusion query (by looking for the distinctive ....

S. Ceri, G. Gottlobb, and L. Tanca. Logic Programming and Databases, Surveys in Computer Science. Springer-Verlag, 1990.

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S. Ceri, G. Gotlob, L. Tanca, \Logic Programming and Databases", Surveys in Computer Science, Springer Verlag, 1990

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S. Ceri, G. Gottlob, L. Tanca, Logic Programming and Databases, Surveys in Computer Science, Springer-Verlag, Heidelberg, 1990, iSBN: 0387517286.

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S. Ceri, G. Gottlob, and L. Tanca. Logic Programming and Databases. Surveys in Computer Science, Springer Verlag, Berlin, 1990.

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S. Ceri, G. Gottlob, L. Tanca: Logic Programming and Databases, Surveys in Computer Science, Springer-Verlag, 1990.

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