S.Naqvi,S. Tsur. A Logical Language for Data andKnowledgeBases. Computer SciencePress1989.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....based that support nondeterministic tuple at a time updates and fail to provide a natural account for setoriented (or bulk) updates in deductive databases syntactically and semantically. Some database oriented approaches either lack intuitive semantics, such as DatalogA [25] and LDL [26] or expressive power, such as U Datalog [23] Surprisingly, most of the proposals, logic programming based or database oriented, cannot even support the following simple bulk updates that are expressible in SQL: give every employee a 10 salary increase if some employee s salary is zero. The ....

....Deductive rules are given the standard static semantics while update rules are not interpreted at all. Update rules are simply treated as named transaction de nitions with parameters and are given semantics only when they are invoked by the user, which is similar to DatalogA [25] and LDL [26], but di erent from U Datalog [23] in which xpoint and operational semantics are given to update rules, which have to account for all possible situations that may never occur. Example 4 Consider the following transactions: raise(tom; shoe; 0:1) raise(E; shoe; 0:1) raise(E; D; 0:1) ....

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S. Naqvi and S. Tsur. A Logical Language for Data and Knowledge Bases. Computer Science Press, 1989.

....introduce a complex (in nite) equational theory capable of specifying the uni ability of set terms with di erent main functors, like in fg 3 (X; Y; Z) fg 2 (a; b) Representation (2) has been frequently used in the context of logic languages embedding sets. It is used for instance in [31] in [44], where with is called scons, in the language flogg [16] and also in the G odel language [25] 26] uses the [ operator but its behavior is that of the with operator of approach (2) Representation (2) is also adopted, for instance, in [46] Representation (1) on the contrary, has been often ....

....proposed by other authors [45, 28] 7.4 Simpli ed (Ab) C ) Uni cation Various authors have considered simpli ed versions of the (Ab) C ) problem obtained by imposing restrictions on the form of the set terms. Most notable is the use of sets in the context of relational and deductive databases [37, 1, 44, 34]. Typical restrictions which have been considered are: requiring sets to be at, i.e. for every (sub)term of the form fs j tg, s does not contain any further occurrence of the functor f j g; 22 requiring sets to be completely speci ed (also known as Bound condition [4] i.e. for every ....

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Naqvi, S. and Tsur, S. A Logical Language for Data and Knowledge Bases. Computer Science Press, 1989.

....rules. In this paper, we restrict ourselves to the common denominator: negation in the conclusion literals L c is disallowed and negated literals in the rule body must obey the stratification condition. The latter restriction outcasts rules of the form A:B ) B. Few deductive databases like LDL [34] allow complex terms as arguments. Here, we assume that all arguments are either quantified variables or constants. This is also known under the term Datalog in the database community. With these assumptions the preferred interpretation is a certain minimal Herbrand model, the so called ....

S. Naqvi and T. Tsur, A logical language for data and knowledge bases, Computer Science Press, 1989.

.... [19] was later revised by Sacca and Zaniolo [21] and refined in Giannotti, Pedreschi, Sacca and Zaniolo [14] While the declarative semantics of choice models is based on and stable models semantics, it leads to e#cient implementations, and it is actually supported in logic database language [20, 8]. The objective of this paper is to provide a characterization of the expressive power of various forms of non deterministic constructs. Thus, in addition to FO W(itness) we study the following three languages: Datalog with static choice, i.e. the choice construct in [19] Datalog with ....

....A query is in NDB PTIME i# it is expressed in FO IFP W. # 4 The family of choice operators The choice construct captures non determinism while preserving the model theoretic semantics of Datalog. Chocie is amenable to e#cient implementation, has been adopted in the logic database language [20, 8] and it has been used widely in applications developed in these languages. The construct was introduced by Krishnamurthy and Naqvi [19] and later refined by Sacca and Zaniolo [21] and Giannotti, Pedreschi, Sacca and Zaniolo [14] these improvements were motivated by the realization that di#erent ....

S. Naqvi, S. Tsur. A Logical Language for Data and Knowledge Bases. Computer Science Press, New York (1989).

....tuple at a time semantics often leading to non determinism. However, relational databases support a set at a time update semantics, where all the applicable updates are fired at once, in parallel. A setat a time semantics based on dynamic logic was adopted and e#ciently implemented in [25, 6]. While the design has progressed further than other systems toward a complete integration of database oriented updates into a logic based language, several problems remain, such as multiple update rules sharing the same heads, and failing goals after update goals [16] Furthermore, since ....

.... goals after update goals [16] Furthermore, since dynamic logic is quite di#erent from standard logic, the two do not mix well, and, as a result, updates are not allowed in recursive rules; a special construct, called forever had to be introduced to express do while iterations over updates [25]. A further illustration of the di#culties encountered by declarative logic based languages in dealing with updates is provided by the design of the Glue Nail system [22] In this second generation deductive database system, updates were banned from the core declarative language and relegated to ....

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S. A. Naqvi, S. Tsur "A Logical Language for Data and Knowledge Bases", W. H. Freeman, 1989.

....is, however, one aspect in which SDML modellers do have control over ordering when firing rules. Antecedents are evaluated in the order in which they appear in a rule, since the most efficient ordering would be hard to determine automatically (although some work has been done in this area, see [11]) Due to the declarative semantics of SDML, changing the order of antecedents cannot yield different results. Some primitives cannot be evaluated when certain arguments are uninstantiated because they have an infinite number of solutions (e.g. sum a b 4) Such antecedents have to be delayed ....

....7.3 Other declarative rule based systems Most mainly declarative rule based systems also provide some imperative facilities. In Prolog, rules are fired in the order they are stored, and the cut facility can be used to prevent later rules or instances of the same rule from being fired. LDL [11] overcomes these imperative features of Prolog by automatically ordering rules, and using a declarative form of cut called choice which is similar to SDML s arbitraryChoice primitive. However, even LDL resorts to imperative techniques for asserting information to and retracting it from ....

S. Naqvi and S. Tsur. A Logical Language for Data and Knowledge Bases. Computer Science Press, 1989.

....of the refutation process. Much work has been done on the topic of updating derived (intensional) predicates. These approaches typically rely on SLD , SLDNF Resolution or Abduction (e.g. AT, Dec90, KM90, Tom88] Examples for approaches considering updates of base predicates are Prolog, LDL [NT89] and DLP [MW88] DLP manages updates of derived predicates, too. Bottomup approaches for updates also have been proposed. In [AV91] various extensions of Datalog including deletions are investigated, and the language RDL1 [dMS88] provides a seperate component for explicit control of the bottom up ....

....leads to an increase of computational power. In rulebased update languages based on top down reasoning, different control mechanisms are encountered : Tom88, Dec90, KM90, MW88] use the implicit control strategies offered by different variants of resolution. The update language proposed by [NT89] provides in addition explicit control by allowing sequential , conditional and iterative operators in rule bodies. A comprehensive study of various extensions of Datalog with fixpoint semantics can be found in [AV91] deterministic and nondeterministic extensions are studied w.r.t. their ....

S. Naqvi and S. Tsur. A Logical Language for Data and Knowledge Bases. Computer Science Press, 1989.

....is available as Technical Report TR 94 030, Department of Computer Science, SUNYBuffalo, August 1994. 1 Introduction The use of sets has been advocated by several authors in the literature on logic programming ( DOPR91] Jay92] JP89] Kup90] and deductive databases ( AG91] BNST91] [NT89]) In studying the inclusion of sets in logic programs, it is natural to study finite sets at first. A representation often chosen for finite sets is that of scons, parallel to the list constructor cons. The use of this constructor for declarative programming can be traced back to [MW85] and has ....

....different ground terms to be related for example, f1=f2= gg = f2=f1= gg. By virtue of EqAx, is an equivalence relation. Let [t] denote the equivalence class containing ground term t. A somewhat simpler characterization of the = relation is usually stated in the literature ([JP89, NT89]) viz. using just CoIdAx and EqAx to define the relation. Since set appears in CoIdAx we also need some axioms from FinSetAx. Let SetCoIdAx be FS1 [ FS2 [ CoIdAx [ EqAx. The next proposition justifies this more intuitive characterisation of = Proposition 11 Let s, t be ground terms. Then ....

Naqvi, S. and Tsur, S.: A Logical Language for Data and Knowledge Bases, Computer Science Press, New York, 1989.

....that can com municate with multiple back ends for the actual evaluation of the queries. The front end, written in Smalltalk, includes the user interface of the system. We have experimented with several different back end query processors: Prolog [8] the LDL deductive database system from MCC [13], and CORAL [14] the deduc tive database system from Wisconsin. The current implementation uses CORAL. The architecture of the Hy system is shown in Figure 2. An examination of the survey [12] reveals that most distributed program analysis systems do not provide all the facilities provided by ....

Shamim Naqvi and Tsur Shalom. A logical language for data and knowledge bases. Computer Science Press, New York, 1989.

.... at least two di#erent topics: An update and a transaction language should be provided and typical atomicity, consistency, isolation, and durability (ACID) properties have to be guaranteed [24] This requirement has led to the definition of several languages integrating logic and updates [1,2,9,10,15,16,31,32,35]. In general, all those proposals are based on including special atoms denoting updates in (typically Datalog) rules. The proposed approaches di#er for several aspects, such as the semantics assigned to the resulting language (declarative vs. operational) the evaluation techniques, the update ....

S. Naqvi and S. Tsur. A Logical Language for Data and Knowledge Bases.Com- puter Science Press, 1989.

....variables, then h X; Y i is a set term which allows X and Y to range over the elements of the set which h X; Y i denotes. Similarly, X; Y ] is a tuple term which allows X and Y to range over the first and second elements of the tuple which [ X; Y ] denotes respectively. Languages such as LDL [27] and COL [4] do not support such kind of set terms so that it is cumbersome to access deeply nested data [23] Relationlog is a deductive database language that supports rules. Rules are used to define intensional relations from the extensional relations stored explicitly in the database. Figure ....

S. Naqvi and S. Tsur. A Logical Language for Data and Knowledge Bases. Computer Science Press, 1989.

.... database application, therefore they guarantee the correct and efficient semantic analysis of the input sentence, e.g. for our example: update, raw material, St 50 H , quantity, 25] 5 Implementation As implementation platform we used the deductive database language LDL (Logic Data Language) [22]. LDL was implemented at MCC as an efficient and portable prototype system for UNIX, called SALAD. An important facility represents the possibility of defining external predicates and functions in the procedural language C. SALAD consists of four main components which are strictly separated into ....

S. Naqvi and S. Tsur. A Logical Language for Data and Knowledge Bases. Computer Science Press, Rockville, 1989.

....atom, also denoted as p( t) 2 Deduction atoms are partitioned in extensional deduction atoms, those built on predicates in Pi e , and intensional deduction atoms, those built on predicates in Pi i . Update operations are expressed in our language (as in U Datalog [MBM97] and in LDL [NT89] by action atoms in rule bodies. Definition 2. Action Atom) An action atom is an extensional deduction atom prefixed by (denoting insertion) or Gamma (denoting deletion) that is, if p(t 1 ; t n ) is an extensional deduction atom, then p(t 1 ; t n ) and Gammap(t 1 ; ....

S. Naqvi and S. Tsur. A Logical Language for Data and Knowledge Bases, volume 2. Computer Science Press, 1989.

.... and implementation of a German natural language interface to a production planning and control system (PPC) Due to the high complexity of the application domain exceeding the capabilities of traditional relational DBMS, the PPC was modelled by use of the deductive database LDL implemented at MCC [2, 3]. The dictionary used in the natural language interface is built out of canonical forms as hierarchical deductive database in LDL. This allows to assign all morphological, syntactic, and semantic features to the appropriate level of abstraction (e.g. special derivations, word stems, endings, word ....

S. Naqvi & S. Tsur. A Logical Language for Data and Knowledge Bases, Rockville: Computer Science Press, 1989.

....include the definition of built in predicates like = or for arithmetic operations ( Gamma; the use of functors for dealing with complex objects, or the the use of sets as arguments. Such extensions can be studied in LDL, a deductive database systems implementing extended Datalog [NT89]. 4 Conclusions Logic turned out to provide a solid and fruitful basis for the integration of database technology with knowledge processing capabilities. Research in logic and databases brought theoretically sound foundations for the building of deductive database systems. Several prototype ....

....databases will have a major impact on future knowledge based systems. We will conclude with some references for further reading. The topic of logic and databases is subject of a specialized textbook [CGT90] and is also extensively covered in [Ull89] CGT89] provides an introduction to Datalog, [NT89] presents the logic database language LDL, an already implemented extension of Datalog. An Overview of logic and databases can be found in [GM92] UZ90] discuss achievements and furture directions of research in the field. ....

S. Naqvi and S. Tsur. A Logical Language for Data and Knowledge Bases. Computer Science Press, New York, 1989.

.... can thus be viewed as an extension of relational database query languages, such as SQL [22] In particular, Datalog is a language of logic programming, based on Horn clauses without function symbols [22] Recently, Datalog has been extended to include negation (Datalog ) 2, 10] sets and lists [16] and functions [1] 3 In this paper we present a formalism for distributed knowledge bases based on modal logic [9] which has also been the basis for modal logics of knowledge and belief [7, 14] In modal logics of knowledge and belief a single agent (or more generally, multiple agents) is ....

....the reader the KL PL checks if the data represented by L is fragmented or partially replicated across the network. LP1) Find the transitive closure of the flights: tc flights(Y, Z) Pflights(X, Y, Z) tc flights(X, Z) tc flights(X, Y) tc flights(Y, Z) 12 (LP2) Find the same generation [16] relation of clients who made bookings: same gen(X, X) Pbookings(X, Y, W, Z) same gen(Y, Y) Pbookings(X, Y, W, Z) same gen(Y1, Y2) bookings(X1, Y1, W1, Z1) bookings(X2, Y2, W2, Z2) same gen(X1, X2) LP3) Replicate the facts on flights from larnaca: Kflights(X, larnaca, Y) flights(X, ....

S. Naqvi and S. Tsur, A Logical Language for Data and Knowledge Bases, Computer Science Press, New York, 1989.

.... defined functions in functional databases) one can distinguish two fundamentally different approaches to bulk type support within a language framework: Built In Bulk Types are provided as first class parameterized type constructors in several programming languages [Sch77, LRV88, OBBT89, NT89] their syntax, type rules, semantics and implementation being hard wired into the language processor and the run time system support. Add On Bulk Types are defined and implemented utilizing standard built in language mechanisms (typing, naming, binding, scoping or recursion) of a sufficiently ....

....adequately exploit the key uniqueness constraint and one has to resort to standard bulk iteration constructs: for each p in persons: p.name = Peter do p.age: p. age 1 end Associative set and element selection is also a basis for logic and constraint based programming languages like LDL [NT89] or Life [AKN89] person(Name, Age) Age 60. person( Peter , PetersAge) It should be emphasized that in the above discussion on mutability, copy vs. reference semantics, the handling of constraint violations or basic iteration facilities, we do not argue in favour of a specific solution. ....

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S. Naqvi and S. Tsur. A Logical Language for Data and Knowledge Bases. Computer Science Press, 1989.

....becomes an issue. WF97] present an update language based on deferred updates which solves this problem. Other well known approaches in the deductive database community subsumable under the updates in the body paradigm are the early works on DLP [MW88] based on dynamic logic) and LDL updates [NT89] In contrast, frameworks with semantics similar to production rules typically express updates in the head of rules (cf. Sections 2.2 and 4) A main difference between update languages and active rules is that in the former, updates are initiated explicitly by the user, whereas the latter specify ....

S. Naqvi and S. Tsur. A Logical Language for Data and Knowledge Bases. Computer Science Press, New York, 1989.

....system, which was completed at UCLA in the summer of 2000, concludes a research project that was started at MCC in 1989 in response of the lessons learned from of its predecessor, the LDL system. The LDL system, which was completed 1988, featured many technical advances in language design [28], and implementation techniques [9] However, its deployment in actual applications [49, 50] revealed many problems and needed improvements, which motivated the design of the new LDL system. Many of these problems were addressed in the early versions of the LDL prototype that were built at ....

....data required, by o# loading much of the search work to the underlying database. Special constructs and operators were also added to express cyclone queries [27] Data Mining and Decision Support. The potential of the LDL technology in this important application area was clear from the start [28], and e#orts concentrated, on providing the analyst with powerful tools for the verification and refinement of scientific hypotheses [49] In our early experiments, the expert would write complex verification rules that were then applied to the data. The LDL proved well suited for the rapid ....

S. A. Naqvi, S. Tsur "A Logical Language for Data and Knowledge Bases", W. H. Freeman, 1989.

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S.Naqvi,S. Tsur. A Logical Language for Data andKnowledgeBases. Computer SciencePress1989.

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S. Naqvi and S. Tsur, "A Logical Language for Data and Knowledge Bases," Computer Science Press, 1989.

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S. Naqvi and S. Tsur. A Logical Language for Data and Knowledge Bases. Computer Science Press, 1989.

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S. Naqvi and S. Tsur. A Logical Language for Data and Knowledge Bases. Computer Science Press, Potomac, Maryland, 1989.

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Naqvi, S., and Tsur, S. A Logical Language for Data and Knowledge Bases. Computer Science Press, 1989.

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S. Naqvi and S. Tsur. A Logical Language for Data and Knowledge Bases. Computer Science Press, 1989.

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