Chang, C.L., DEDUCE 2: Further Investigations of Deduction in Relational Data Bases, In Logic and Data Bases, pages 201-236, Plenum Publishing Corporation, 227 W. 17th St. New York, N.Y. 10011, 1978.  Home/Search   Document Not in Database   Summary   Related Articles   Check

.... (GEN) These descriptions are refined using a model of parallel execution (TASK) into a natural language database (NLP RBD) The ATN is based on the work of Woods [Woods70] and Burton [Burton76] The relational database is based on the work of Codd [Codd70] and uses the DEDUCE systems as a model [Chang76, Chang78]. The eight domains used in the refinement of this larger example are organized as shown in figure 31. Figure 31. NLP RDB Domain Organization A Simple Example In this section we will be discussing the refinement of the SIMAL program given in figure 32. The program represents a simple program for ....

Chang, C.L., DEDUCE 2: Further Investigations of Deduction in Relational Data Bases, In Logic and Data Bases, pages 201-236, Plenum Publishing Corporation, 227 W. 17th St. New York, N.Y. 10011, 1978.

.... Datadriven approaches can be based on the learning algorithms developed in Artificial Intelligence (AI) Many of these learning algorithms discover rules represented in languages similar to First Order Predicate Logic (FOPL) and these rules can be used to represent general integrity constraints [13, 14] and query transformation rules. For example, the representation languages used in AQ15[15] and in the conceptual clustering algorithm Cluster 2[16] are fairly close to FOPL. 2.2. Learning and Discovery techniques in AI The AI learning algorithms are based on supervised concept learning and ....

....be learned for semantic query optimization. We then show the correspondence between basic transformation rules and patterns in the data distribution, which forms the basis of the datadriven rule discovery algorithm. 3.1. Representation Language We follow a logic based representation proposed in [1, 13, 14] for queries, integrity constraints and query transformation rules. For relations P, the atomic formula will be written as P(a 1 op t 1 , a n op t n ) where a 1 , a n are some attributes of P. The operation op is a comparison operator which will include = and . Well formed ....

C. L. Chang, DEDUCE 2: Further Investigations of Deduction in Relational Data bases, pp. 201-236 in Logic and Data Bases, ed. J. Minker, Plenum Press, New York (1978).

....before. Datadriven approaches can be based on the learning algorithms developed in Artificial Intelligence (AI) Many of these learning algorithms discover rules represented in languages similar to first order predicate logic (FOPL) which can be used to represent general integrity constraints [14, 15] and query transformation rules. For example, the representation languages used in AQ15[16] and in the conceptual clustering algorithm Cluster 2[17] are fairly close to FOPL. The AI learning algorithms can be classified into two categories, supervised concept learning and unsupervised discovery. ....

....be learned for semantic query optimization. We then show the correspondence between basic transformation rules and patterns in the data distribution, which forms the basis of the datadriven rule discovery algorithm. 3.1. Representation Language We follow a logic based representation proposed in [1, 14, 15] for queries, integrity constraints and query transformation rules in this paper. For relations P, the atomic formula will be written as P(a 1 op t 1 , a n op t n ) where a 1 , a n are some attributes of P. The operation op is a comparison operator which will include = and . ....

C. L. Chang, DEDUCE 2: Further Investigations of Deduction in Relational Data bases, pp. 201-236 in Logic and Data Bases, ed. J. Minker, Plenum Press, New York (1978).

.... to model theory [Nicolas and Gallaire 1978] Reiter introduced the Closed World Assumption [Reiter 1978] Clark presented some new results on negation [Clark 1978] Kowalski discussed using logic to describe data [Kowalski 1978] and there were reports on several implemented systems [Minker 1978, Chang 1978, Kellogg et al. 1978] To say that this book has been the basis of most of the research into deductive databases in the last decade would not be an understatement. It focussed attention on the use of logic for deductive databases, unlike anything that had gone before. However, it cannot take all ....

C.L. Chang. DEDUCE 2: Further investigations of deduction in relational databases. In H. Gallaire and J. Minker, editors, Logic and Data Bases, pages 291--236, Plenum, New York, 1978.

....is a lecturer constrain some s in staff so that position(s) lecturer ; not exists: denoted by no, e.g. no postgraduate student is younger than 20 constrain no p in postgrad to have age(p) 20; CHAPTER 3. INTEGRITY CONSTRAINTS IN P FDM 67 and three numerical (or cardinality) quantifiers [22] exists at least: denoted by at least n , e.g. there are at least 2 secretaries on the staff constrain at least 2 s in staff so that position(s) secretary ; exists at most: denoted by at most n , e.g. the Computing Science Department can support at most 100 undergraduate students ....

C.L. Chang. DEDUCE 2: Further Investigations of Deduction in Relational Data Bases. In H. Gallaire and J. Minker, editors, Logic and Databases, pages 201--236. Plenum Press, 1978.

....and semantic query optimization. One of the first papers on processing recursive queries was [MN82] it contained the first description of bounded recursive queries, which are recursive queries that can be replaced by nonrecursive equivalents. DEDUCE was implemented at IBM in the mid 1970 s [Cha78] and supported left linear recursive Horn clause rules using a compiled approach. DADM [KT81] emphasized the distinction between EDB and IDB and studied the representation of the IDB in the form of connection graphs closely related to Sickel s interconnectivity graphs [Sic76] to aid in ....

C.L. Chang. Deduce 2: Further investigations of deduction in relational databases. In H. Gallaire and J. Minker, editors, Logic and Databases. Plenum Press, 1978.

.... denoted by some and any, e.g. some staff member is a lecturer constrain some s in staff to have position(s) lecturer ; not exists: denoted by no, e.g. no postgraduate student is younger than 20 constrain no p in postgrad to have age(p) 20; and three numerical (or cardinality) quantifiers [5] exists at least: denoted by at least n , e.g. there are at least 2 secretaries on the staff constrain at least 2 s in staff to have position(s) secretary ; exists at most: denoted by at most n , e.g. the Computing Science Department can support at most 100 undergraduate students ....

C.L. Chang. DEDUCE 2: Further Investigations of Deduction in Relational Data Bases. In H. Gallaire and J. Minker, editors, Logic and Databases, pages 201--236. Plenum Press, 1978.

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