Yu C. and Sun W., `Automatic knowledge acquisition and maintenance for semantic query optimisation', IEEE Transactions on Knowledge and Data Engineering, Vol. 1, No. 3, 362-375, 1989.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....interesting knowledge discovery techniques and systems prototypes, such as INLEN [18] KDW [24] Quest [1] IMACS [3] Datalogic R [31] 49er [32] etc. which demonstrates the promising future of knowledge dis covery. Different from most of the previous studies on cooper ative query answering [26, 25, 15, 7, 6, 30] and querying database knowledge [20] which emphasize on the ap plication or inquiry of deduction rules and integrity constraints in relational or deductive databases, this study extends the domain of study from a deductive database to a knowledge rich database assisted with generalized knowledge ....

C. T. Yu and W. Sun. Automatic knowledge acquisition and maintenance for semantic query optimization. IEEE Trans. Knowledge and Data Engineering, 1:362 375, 1989.

....using check constraints. An SQO prototype has been implemented in DB2. 5 Summary and Related Work Extracting semantic information from database schemas and contents, often called rule discovery, has been studied over the last several years. Rules can be inferred from integrity constraints [3, 2, 24] or can be discovered from database content using machine learning or data mining approaches [5, 7, 10, 21, 22, 24] It has also been suggested that such rules be used for query optimization [11, 21, 22, 24] in a similar way that traditional integrity constraints are used in semantic query ....

....Extracting semantic information from database schemas and contents, often called rule discovery, has been studied over the last several years. Rules can be inferred from integrity constraints [3, 2, 24] or can be discovered from database content using machine learning or data mining approaches [5, 7, 10, 21, 22, 24]. It has also been suggested that such rules be used for query optimization [11, 21, 22, 24] in a similar way that traditional integrity constraints are used in semantic query optimization [4, 15, 6] Many algorithms for mining functional dependencies, which can be considered a special type of ....

[Article contains additional citation context not shown here]

C. T. Yu and W. Sun. Automatic knowledge acquisition and maintenance for semantic query optimization. IEEE Transactions on Knowledge and Data Engineering, 1(3):362--375, Sept. 1989.

....table. In experiments, good optimization has been demonstrated through range restriction using the holes. Extracting semantic information from database schemas and contents, often called rule discovery, has been studied over the last several years. Rules can be inferred from integrity constraints [2, 3, 30] or can be discovered from database content using machine learning or data mining approaches [5, 7, 12, 27, 28, 30] It has also been suggested that such rules be used for query optimization [13, 27, 28, 30] in a similar way that traditional integrity constraints are used in semantic query ....

....Extracting semantic information from database schemas and contents, often called rule discovery, has been studied over the last several years. Rules can be inferred from integrity constraints [2, 3, 30] or can be discovered from database content using machine learning or data mining approaches [5, 7, 12, 27, 28, 30]. It has also been suggested that such rules be used for query optimization [13, 27, 28, 30] in a similar way that traditional integrity constraints are used in semantic query optimization [4, 6, 17] A lot of work has been devoted to the problem of estimating the size of the result of a query ....

[Article contains additional citation context not shown here]

C. T. Yu and W. Sun. Automatic knowledge acquisition and maintenance for semantic query optimization. IEEE Transactions on Knowledge and Data Engineering, 1(3):362--375, Sept. 1989.

....many applications it is very difficult to identify all of the relevant query transformation rules. Further, the set of query transformation rules can be 2 expanded significantly by incrementally adding additional discovered query transformation rules based on the current state of the database[7, 10, 11]. In this paper we propose a data driven discovery approach to learning query transformation rules. A data distribution based approach is useful for two reasons. First, discovery of the specific patterns in the data distribution can identify useful query transformation rules. Second, it can ....

....contexts, namely Databases and AI. We summarize the alternative approaches and bring out our main contributions in this section. 2.1. Learning in Databases for Semantic Query Optimization The learning of query transformation rules can be query driven or data driven. In querydriven frameworks [7, 10 12], the search for new query transformation rules is guided by the set of queries which arrive at the database using query comparisons [7] and hypothesis generation and testing[10 12] In query comparison, the set of queries arriving after the last update are analyzed by comparing the set of tuples ....

[Article contains additional citation context not shown here]

C. T. Yu and W. Sun, Automatic Knowledge Acquisition and Maintenance for Semantic Query Optimization, IEEE Transactions on Knowledge and Data Engineering, pp. 362-375 (1989).

....equivalent queries, which produce the same result for all database instances that satisfy the integrity constraints and functional dependencies. The objective of a semantic query optimizer at this point is to find a semantically equivalent query which yields a more efficient execution plan[1 7]. Semantic query optimization also provides the flexibility to add new information and optimization methods to an existing optimizer. A modular arrangement of optimization methods makes it possible to add, delete and modify individual methods, without affecting other methods. Since this system is ....

....in many applications it is very difficult to identify all of the relevant query transformation rules. Further, the set of query transformation rules can be expanded significantly by incrementally adding additional discovered query transformation rules based on the current state of the database[7]. We regard the unearthing of query transformation rules for semantic query optimization as a discovery task [7, 10] rather than as a concept learning task. While there may exist some general heuristics that facilitate the search for regularities, the direction of the search for such ....

[Article contains additional citation context not shown here]

C. T. Yu and W. Sun, Automatic Knowledge Acquisition and Maintenance for Semantic Query Optimization, IEEE Transactions on Knowledge and Data Engineering, pp. 362-375 (1989).

....R1:A1 R2:A1 R3:A2 R4:A2 R3:A3 R5:A3 m m m m m R5 R2 R3 R1 R4 A3 A1 A1 A2 a. Join Graph b. Query Graph Fig. 1. Sample Query and Join Graph In general, the join graph of a query consists of a set of connected components, each of which forms an equivalent class of the join predicates [22]. For example, Figure 1 gives the query graph and the join graph for the following query: Q = fR 1 :A 2 ; R 2 :A 4 j R 1 :A 1 = R 2 :A 1 R 2 :A 1 = R 3 :A 1 R 3 :A 2 = R 4 :A 2 R 3 :A 3 = R 5 :A 3 g A query Q is called acyclic either if its query graph is acyclic or it is equivalent to a ....

Yu, C. and Sun, W.: Automatic knowledge acquisition and maintenance for semantic query optimization. IEEE TKDE, Vol. 1, No. 3, pp. 362-375, Sept. 1989.

....scenarios while the algorithm that is based on a partitioning scheme produces optimal plans in more scenarios. The problem remains NP hard. 4. 5 Semantic Query Optimization An alternative approach to optimize query plans generated by information mediators is to use semantic query optimization [24,44, 49, 8, 50]. Semantic Query Optimization as described in [7] is the process of optimizing (increasing the potential for an efficientevaluation) of database queries using the semantic information contained in the integrity constraints. The essential idea is to use semantic knowledge (suchasknown integrity ....

W. Sun and C.T. Yu. Automatic knowledge acquisition and maintenance for semantic query optimization. IEEE Trans. on Data and Knowledge Engineering, 1(3):362--375, 1989.

....into other queries that are semantically equivalent i.e. that produce the same answer as the original queries but have a lower execution cost. The knowledge on which the transformations are based are the semantic integrity constraints of the database. Apart from integrity constraints, Yu et. al [YU89] suggested the use of Dynamic Constraints. These are rules that are frequently satisfied by the database but need not always be true. These constraints would need to be updated when updates are made to the database as an update may affect the truth value of the constraint. These Semantic Query ....

Yu, C., Sun, W. Automatic Knowledge Acquisition and Maintenance of Semantic Query Optimization IEEE Trans. Know. Data Eng. 1,3 (Sept. 1989) Pg. 362 - 375.

....partitioning constraint sets may not be significant for small and static constraint sets. However, as database applications involve large numbers of constraints (Kung 1985) the partitioning approach increases the performance of the maintenance algorithms drastically. Also, availability of methods (Yu and Sun 1989, Siegel 1988) that automatically generate constraints to be used in semantic query optimization from query answers is an indication for the existence of large and volatile constraint sets. At the end of Sect. 6, we elaborate on this issue, and give an example of the performance improvement ....

....variables, but the variables in the constraints are universally quantified implicitly. Thus, checking for unsatisfiability is faster in our context. The class of constraints we consider here or its special cases have also been utilized in semantic query optimization (Shenoy and Ozsoyoglu 1987, Yu and Sun 1989). A method that detects queries with empty set answers by utilizing semantic integrity constraints is presented by Illarramendi et al. 1994) If one thinks of a query as a constraint, this method can also be considered as a method for detecting the inconsistency of a set of constraints. ....

[Article contains additional citation context not shown here]

Yu, T. C., Sun, W., Automatic Knowledge Acquisition and Maintenance for Semantic Query Optimization, IEEE Transactions on Knowledge and Data Engineering, September 1989, pp. 362-375.

....unfolds the query, removes that unfolding, and refolds back the query to its compact form. 9 Besides the set of integrity constraints given by the database administrator one can also use for optimization the so called propagated integrity constraints [10] as well as dynamic integrity constraints [18]. 10 Informally, N 1 is before N 2 in a sequence of ordered unfoldings if all atoms of N 1 are below atoms of N 2 in the query tree. A formal definition of the ordering and a discussion of a possibility of such ordering is beyond the scope of this paper. We assume that all nice unfoldings ....

Clement T. Yu and Wei Su. Automatic knowledge acquisition and maintenance for semantic query optimization. IEEE Transactions on Knowledge and Data Engineering, 1(3):362--375, September 1989.

....interesting knowledge discovery techniques and systems prototypes, such as INLEN [18] KDW [24] Quest [1] IMACS [3] Datalogic R [31] 49er [32] etc. which demonstrates the promising future of knowledge discovery. Different from most of the previous studies on cooperative query answering [26, 25, 15, 7, 6, 30] and querying database knowledge [20] which emphasize on the application or inquiry of deduction rules and integrity constraints in relational or deductive databases, this study extends the domain of study from a deductive database to a knowledge rich database assisted with generalized knowledge ....

C. T. Yu and W. Sun. Automatic knowledge acquisition and maintenance for semantic query optimization. IEEE Trans. Knowledge and Data Engineering, 1:362--375, 1989.

....Pos From Employees Where Salary 90,000 Although dynamic rules are more useful for semantic query optimization than schema based constraints, dynamic rules must be maintained when changes are made to the database. Although several researchers have acknowledged the need to maintain dynamic rules [YS89, SSS92], no one has formally addressed the problem. This paper presents a set of strategies, methods, and algorithms for maintaining dynamic semantic rules in the Metadata View Graph Framework. The rest of the paper is organized as follows. Section 2 describes the Metadata View Graph, Section 3 ....

C. Yu and W. Sun. Automatic knowledge acquisition and maintenance for semantic query optimization. IEEE Transactions on Knowledge and Data Engineering, 1(3):362--375, September 1989.

....sources. However, it is difficult for conventional query optimization techniques to solve all the problems for the next generation information systems. Semantic query optimization (SQO) Hammer and Zdonik, 1980, King, 1981, Siegel, 1988, Shekhar et al. 1988, Shenoy and Ozsoyoglu, 1989, Yu and Sun, 1989, Chakravarthy et al. 1990, Sun and Yu, 1994] is a promising query optimization technique that can complement conventional techniques to overcome the heterogeneity and considerably reduce query execution cost. The essential idea of semantic query optimization is to use semantic rules about data, ....

....all redundant literals that can be deleted from an input query and all newly inserted literals. An example of such a closure is indicated by the literals underlined in Q1.3. Researchers have developed several polynomial algorithms to generate closures of implications [Shenoy and Ozsoyoglu, 1989, Yu and Sun, 1989, Hsu and Knoblock, 1993b] With the closures, the optimizer can search for the least expensive equivalent query by identifying an optimal combination of insertions and deletions from the closure. In our example, since the equivalent query Q1.2 does not involve access to the large ship relation ....

[Article contains additional citation context not shown here]

Clement T. Yu and Wei Sun. Automatic knowledge acquisition and maintenance for semantic query optimization. IEEE Trans. Knowledge and Data Engineering, I(3):362--375, 1989.

....to the database since changes cannot violate the integrity constraints. Unfortunately, scheme based constraints are typically so general that they are of little use. For example, an integrity constraint may require an employee s salary to be greater than zero. Recently, a number of researchers [YS89, SSS92, SHKC93, HK94] have proposed methods for discovering instance based constraints (also referred to as dynamic constraints in [YS89] and derived constraints in [SSS92] which are only valid for particular instances of the database. Instance based constraints contain more information than scheme based constraints ....

....that they are of little use. For example, an integrity constraint may require an employee s salary to be greater than zero. Recently, a number of researchers [YS89, SSS92, SHKC93, HK94] have proposed methods for discovering instance based constraints (also referred to as dynamic constraints in [YS89] and derived constraints in [SSS92] which are only valid for particular instances of the database. Instance based constraints contain more information than scheme based constraints because they represent the actual contents of the database. For example, an instance based constraint may assert ....

[Article contains additional citation context not shown here]

C. Yu and W. Sun. Automatic knowledge acquisition and maintenance for semantic query optimization. IEEE Transactions on Knowledge and Data Engineering, 1(3):362--375, September 1989.

.... this issue, researchers have proposed interleaving query planning and execution so that the query processor can use intermediate data to refine the part of the query plan that has not been completely executed [9, 12, 13] A relatively unexplored area is the use of semantic query optimization (SQO) [14, 15, 16, 17, 18, 19, 20, 21] for multi source query plan optimization. The advantage of SQO is that the optimizer can infer the information about intermediate data from semantic knowledge prepared prior to query execution time. Another reason is that SQO supports the extensibility of multidatabase systems because it ....

....of multidatabase systems because it minimizes the dependency on how individual sources execute a query. When a new information source is integrated into the system, the optimizer can still be used with minimal modification. Many algorithms are available for learning useful semantic knowledge [16, 22, 19, 23, 24, 25]. 1.1 Query Plans A query plan is a directed acyclic graph with its nodes as plan steps and its edges as the ordering constraints that specify data flow direction as well as the order in which the plan steps should be executed. Query plans generated by existing multidatabase query processing ....

[Article contains additional citation context not shown here]

C. T. Yu and W. Sun, "Automatic knowledge acquisition and maintenance for semantic query optimization, " IEEE Trans. Knowledge and Data Engineering, vol. I, no. 3, pp. 362--375, 1989.

....much query execution cost. 2 A set of high utility semantic rules is crucial to the performance of a semantic query optimizer. Since it is difficult to encode sufficient semantic rules, researchers have proposed several approaches to rule induction for semantic query optimization (Siegel 1988; Yu Sun 1989; Shekhar et al. 1993; Hsu Knoblock 1994) A rule maintenance approach is also necessary because the learned rules may become inconsistent with data after updates to the database, and the number of rules may grow so large that they may slow down the optimization and reduce the savings. ....

Yu, C. T., and Sun, W. 1989. Automatic knowledge acquisition and maintenance for semantic query optimization.

No context found.

Yu C. and Sun W., `Automatic knowledge acquisition and maintenance for semantic query optimisation', IEEE Transactions on Knowledge and Data Engineering, Vol. 1, No. 3, 362-375, 1989.

No context found.

Yu C. and Sun W., Automatic knowledge acquisition and maintenance for semantic query optimisation, IEEE Transactions on Knowledge and Data Engineering, Vol. 1, No. 3, 362-375, 1989.

No context found.

C. T. Yu and W. Sun. Automatic knowledge acquisition and maintenance for semantic query optimization. IEEE Trans. Knowledge Data Engrg., 1:362-- 375, 1989.

No context found.

C. Yu and W. Sun. Automatic knowledge acquisition and maintenance for semantic query optimisation. IEEE Trans. Knowl. Data Eng., 1, 3, 362-375, Sept. 1989.

No context found.

Yu, T. C., and W. Sun, (1989), Automatic Knowledge Acquisition and Maintenance for Semantic Query Optimization, IEEE Transactions on Knowledge and Data Engineering, pp. 362-375.

Online articles have much greater impact   More about CiteSeer.IST   Add search form to your site   Submit documents   Feedback  

CiteSeer.IST - Copyright Penn State and NEC