M.D. Siegel, Automatic Rule Derivation for Semantic Query Optimization, proc 2nd Intl Conf on Expert Database Systems, 1988, pp 371-385.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....discovery tom the data itself has been used to provide more query relevant knowledge of the data. Automatic knowledge discovery can lead to large rule sets (much larger than the data sets they describe) whereas a set much smaller than the data is required. Previous workers [e.g. Hs 95, Lo 95, Si 88] have, in effect, used a sample tom the potentially derivable rule set, by generating a few rules in response to each query. But this can provide a random sample with low probability of containing useful rules. The histogram based rule sets described in this paper are small, easily accessed, and ....

....each query. But this can provide a random sample with low probability of containing useful rules. The histogram based rule sets described in this paper are small, easily accessed, and all rules have a good chance of being used. Previous semantic optimisation algorithms [e.g. Hs 95, Ki 81, Sh 88, Si 88] have been iterative, progressively applying new rules as the query is changed by previous rules. Rule application means adding consequent conditions to the query tom rules whose antecedents are implied by query conditions) This is slow. Its sequential character is tindesirable in a process ....

[Article contains additional citation context not shown here]

M.D. Siegel, Automatic Rule Derivation for Semantic Query Optimization, proc 2nd Intl Conf on Expert Database Systems, 1988, pp 371-385.

....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 ....

....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 check constraints, have been developed over the last years [12, 1, 17, 20] A lot of work has ....

M. Siegel. Automatic rule derivation for semantic query optimization. In Proceedings of the 2nd International Conference on Expert Database Systems, pages 371--386, 1988.

....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 ....

....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 expression. Approaches based on sampling were explored in [11, 18] and on histograms in [15, ....

M. Siegel. Automatic rule derivation for semantic query optimization. In Proceedings of the 2nd International Conference on Expert Database Systems, pages 371--386, Vienna, Virginia, 1988.

....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 ....

M. Siegel, Automatic Rule Derivation for Semantic Query Optimization, Proc. of the Second International Conference on Expert Database Systems, pp. 371-385 George Mason Foundation, (1988).

....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]

M. Siegel, Automatic Rule Derivation for Semantic Query Optimization. Ph.D. diss., Boston Univ. (1988).

....Contributions The learning of query transformation rules can be query driven or data driven. In querydriven frameworks [7, 11] the search for new query transformation rules is guided by the set of queries arriving at the database using query comparisons [7] and hypothesis generation and testing [11, 12]. In query comparison, the set of queries arriving after the last update are analyzed by comparing the set of tuples retrieved to answer various queries. If the set of tuples retrieved by two queries are identical then query transformation rules relating the restrictions in the two queries can be ....

....are characterized by universal quantification in the well formed formulas representing those queries. For example, IC 0 is a query transformation rule but IC 1 is not a query transformation rule. Query transformation rules subsume the simple rules learned in rule discovery for query optimization [12], since simple rules are universally quantified. These rules also subsume the rules learned via automatic knowledge acquisition[7] since the latter are based on set comparisons. Query transformation rules represent the useful integrity constraints which can provide savings during semantic ....

M. Siegel, Automatic Rule Derivation for Semantic Query Optimization. Ph.D. diss., Boston Univ. (1988).

....dependencies. The arise of knowledge discovery in databases (KDD) offers a new approach to solve both problems: provides SQO automatically with constraints and extends them to constraints which precisely reflects the present content of the database. Siegel has reported this by the first time (Siegel 1988) and (Siegel, Sciore, Salveter 1991) Such constraints have been termed, for example, Database Abstractions in (Hsu Knoblock 1993) Metadata in (Siegel Madnick 1991) and Meta Knowledge in (Schlimmer 1991) Also, Hsu and Knoblock (Hsu Knoblock 1993) have shown the benefits of optimization ....

Siegel, M. D. 1988. Automatic rule derivation for semantic query optimization. In Second International Conference on Expert Database Systems.

....dependencies. The arise of knowledge discovery in databases (KDD) offers a new approach to solve both problems: provides SQO automatically with constraints and extends them to constraints which precisely reflects the present content of the database. Siegel has reported this by the first time [Siegel, 1988] and [Siegel et al. 1991] Such constraints have been termed, for example, Database Abstractions in [Hsu and Knoblock, 1993] Metadata in [Siegel and Madnick, 1991] and Meta Knowledge in [Schlimmer, 1991] Also, Hsu and Knoblock [Hsu and Knoblock, 1993] have shown the benefits of optimization ....

Siegel, M. D. (1988). Automatic rule derivation for semantic query optimization. In Second International Conference on Expert Database Systems.

....of knowledge discovery in databases offers a new approach to solve both problems: provides SQO automatically with constraints, and extends them to constraints which are not valid in all states of the database, but describe the present state of the database precisely. This has been recognized by Siegel [Siegel, 1988]. Also, Hsu and Knoblock [Hsu and Knoblock, 1993] have shown the benefits of optimization techniques based on automatically discovered constraints. The discovery of functional dependencies and keys has been only regarded under the assumption of integrity constraints. This implies a closed world ....

Siegel, M. D. (1988). Automatic rule derivation for semantic query optimization. In Second International Conference on Expert Database Systems.

....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 gained by ....

.... checking from seconds to milliseconds, or from minutes to seconds if the constraint set is large (e.g. in the order of hundreds) Secondly, however, constraints can be automatically generated from previous query answers, to be used in semantic query optimization (Yu and Sun 1989, Siegel 1988). Naturally, this set of constraints may change frequently. Since their purpose is only to give information about the current instance of the database to the semantic query optimizer, if database updates invalidate any such constraints, the action to be taken should be to delete the invalidated ....

Siegel, M. D, Automatic Rule Derivation for Semantic Query Optimization, Proc. 2nd Int. Conf. Expert Database Systems, L. Kerschberg, Ed. VA: George Mason Foundation, 1988, pp. 371-386.

....of declarative data models and languages [Ullman 88] This is because it is often difficult to efficiently implement declarative queries. The query reformulation approach, also known as semantic query optimization approach in previous work [Chakravarthy et al. 90, Hammer and Zdonik 80, King 81, Siegel 88] addresses the problem differently from the conventional syntactical approaches [Apers et al. 83,Jarke and Koch 84] in that it brings to bear a richer set of knowledge about the contents of databases to optimize queries. The use of semantic knowledge offers more potential for cost reduction than ....

....As indicated in dash lines in Figure 1, we would like a system that can automatically learn the database abstractions. This paper describes an example guided, data driven learning approach to address this problem. The idea to automatically derive rules for reformulation is proposed originally by [Siegel 88] In his approach, although example queries are used, the learning is mainly driven by a fixed set of heuristics, which are designed based on the database structure and implementation. Our approach differs from theirs in that we do not rely on explicit heuristics. Instead, our approach focuses ....

[Article contains additional citation context not shown here]

Michael D. Siegel. Automatic rule derivation for semantic query optimization. In Larry Kerschberg, editor, Proceedings of the Second International Conference on Expert Database Systems, pages 371--385. George Mason Foundation, Fairfax, VA, 1988.

....dependencies The key finding problem is: given a relation r, find all minimal keys of r. It is a special case of the problem of finding the functional dependencies that hold in a given relation. Applications of the key finding problem include database design, semantic query optimization [24, 44, 46]; one can also argue that finding functional dependencies is a necessary step in some types of structure learning. The size of an instance of the key finding problem is given by two parameters: the number of rows, and the number of columns. In the typical database applications the n, the number of ....

M. Siegel. Automatic rule derivation for semantic query optimization. Technical Report BUCS Tech Report # 86-013, Boston University, Computer Science Department, Dec. 1986.

....query optimization. 1. 4 Closely Related Work Previously, three approaches to automating the knowledge acquisition for semantic query optimization were proposed in [Siegel, 1988] Shekhar et al. 1993] and [Yu and Sun, 1989] The first approach is a query driven approach due to [Siegel, 1988, Siegel, 1989] Siegel s system learns simple rules, a limited form of rules that allows exactly one literal on each side of implication. His system uses a set of predefined heuristics combined with example queries to guide the learning for effective rules. The heuristics are identical to those proposed by ....

Michael D. Siegel. Automatic Rule Derivation for Semantic Query Optimization. PhD thesis, Department of Computer Science, Boston University, 1989.

....the increasing complexity of queries and the heterogeneity of information 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 ....

....are expensive in query execution and relational rules allow an SQO optimizer to detect redundant joins, it is important to use relational rules in the optimization. We note that previous work in SQO cannot apply general relational rules in the optimization [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] To detect redundant joins, they use referential integrity constraints [Ullman, 1988] a restrictive form of relational rules that allow only one literal as the antecedent. One of ....

[Article contains additional citation context not shown here]

Michael D. Siegel. Automatic rule derivation for semantic query optimization. In Larry Kerschberg, editor, Proceedings of the Second International Conference on Expert Database Systems, pages 371--385. George Mason Foundation, Fairfax, VA, 1988.

....Pruned rules and their estimated robustness and database applications for information gathering and retrieval from heterogeneous, distributed environment on the Internet. We are currently applying our approach to the problem of learning for semantic query optimization (Hsu Knoblock 1994; 1996b; Siegel 1988; Shekhar et al. 1993) Semantic query optimization (SQO) King 1981; Hsu Knoblock 1993; Sun Yu 1994) optimizes a query by using semantic rules, such as all Maltese seaports have railroad access, to reformulate a query into a less expensive but equivalent query. For example, suppose we have a ....

Siegel, M. D. 1988. Automatic rule derivation for semantic query optimization. In Kerschberg, L., ed., Proceedings of the Second International Conference on Expert Database Systems. Fairfax, VA: George Mason Foundation.

....could then be done in the afsc database as well. 5.2. 2 Knowledge Compiled from Databases Instead of limiting the system to knowledge that must hold for the entire domain, we can use a compilation process that extracts knowledge from the individual databases and stores it in the knowledge base [3, 14, 15]. The compilation of knowledge about a database is driven by the need for particular types of information. Thus, when an expensive query is given and the semantic query reformulator cannot find a reformulation of it, the system makes a note of that along with the aspect of the query that made it ....

....about the ranges could be extracted from the databases and stored in the knowledge base to help with future queries. We explain below how such information may be used. The one existing system that does provide a more general approach to learning for reformulation was developed by Siegel [15]. However, the particular learning mechanisms are quite limited and what the system learns is guided by a set of heuristics instead of being driven by the need to reformulate specific queries. 5.3 Reformulation Processes Using available knowledge sources, reformulation involves modifications to ....

Michael D. Siegel. Automatic rule derivation for semantic query optimization. In Larry Kerschberg, editor, Proceedings of the Second International Conference on Expert Database Systems, pages 371--385. George Mason Foundation, Fairfax, VA, 1988.

....and constraint simplification (section 4) as well as the technique of omitting rules in the query evaluation process (section 5) is not addressed in the literature cited. The latter topics (detecting redundancy in rules and constraints, and omitting rules) are also not addressed in [12] 13] [21] and [20] In none of these works are recursive rules handled. Moreover, 12] and [13] consider no rules of inference, whereas [21] and [20] restrict the constraints to be of the form p( x i ; q( x j ; respectivley to be Horn formulae. In [12] range, functional and referential integrity ....

....is not addressed in the literature cited. The latter topics (detecting redundancy in rules and constraints, and omitting rules) are also not addressed in [12] 13] 21] and [20] In none of these works are recursive rules handled. Moreover, 12] and [13] consider no rules of inference, whereas [21] and [20] restrict the constraints to be of the form p( x i ; q( x j ; respectivley to be Horn formulae. In [12] range, functional and referential integrity constraints are considered to optimize conjunctive, negation free queries. But the constraints are treated separately and may not ....

M.D. Siegel, Automatic Rule Derivation for Semantic Query Optimization, in Proceedings of the Second International Conference on Expert Database Systems, editor L.Kerschberg, 1989. fpp.669-698g

....5.45 8.79 86.78 Time saved 30.99 39.14 1.31 125.46 Gain of total elapsed time 57.1 87.8 12.9 59.1 Average overhead 0.08 0.07 0.07 0.11 Times rule fired 5.00 6.00 4.18 7. 00 6 RELATED WORK Previously, two systems that learn background knowledge for semantic query optimization were proposed by (Siegel 1988) and by (Shekhar et al. 1993) Siegel s system uses predefined heuristics to drive learning by an example query. This approach is limited because the heuristics are unlikely to be comprehensive enough to detect missing rules for various queries and databases. Shekhar s system is a data driven ....

Siegel, M. D. (1988). Automatic rule derivation for semantic query optimization. In Kerschberg, L., ed., Proceedings of the Second International Conference on Expert Database Systems. Fairfax, VA: George Mason Foundation.

.... 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]

M. D. Siegel, "Automatic rule derivation for semantic query optimization," in Proceedings of the Second International Conference on Expert Database Systems (L. Kerschberg, ed.), pp. 371--385, Fairfax, VA: George Mason Foundation, 1988.

....query will save 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 ....

Siegel, M. D. 1988. Automatic rule derivation for semantic query optimization. In Kerschberg, L., ed., Proceedings of the Second International Conference on Expert Database Systems. Fairfax, VA: George Mason Foundation. 371-- 385.

....of semantic query optimization for single database queries in previous work. The goal of query reformulation is to use reformulation to search for the least expensive query from the space of semantically equivalent queries to the original one. Two queries are defined to be semantically equivalent[25] if they return identical answers given the same contents of the database. The alternative definition of semantic equivalence[15] requires that the queries return identical answers given any contents of the database, but this definition would limit us to using only semantic integrity constraints ....

....in a system called QUIST[15] In contrast with syntactic query optimization, which has been widely studied in the database community, QUIST uses the rules of semantic integrity constraint of the database as background knowledge to reformulate the given query. However, QUIST and the following work[25, 7] use heuristics to select the reformulation operators and rules to reformulate the query in a hill climbing manner. Our reformulation algorithm does not require heuristic control and is thus more flexible. Moreover, our algorithm utilizes the database abstractions to the greatest possible extent, ....

[Article contains additional citation context not shown here]

Michael D. Siegel. Automatic rule derivation for semantic query optimization. In Larry Kerschberg, editor, Proceedings of the Second International Conference on Expert Database Systems, pages 371--385. George Mason Foundation, Fairfax, VA, 1988.

....data. The reformulation algorithm presented here is used to reformulate this initial query plan to reduce the cost of retrieval. The query reformulation approach was initially proposed by (King, 1981) and (Hammer and Zdonik, 1980) Our approach differs from theirs and the following related work (Siegel, 1988; Chakravarthy, Grant and Minker, 1990) in that we do not rely on heuristics to guide the search in a hill climbing manner, which often results in local optima. Moreover, we consider queries for data distributed over multiple sources, while they only consider single database queries. The remainder ....

....the entire query plan in the next section. The goal of the query reformulation is to use reformulation to search for the least expensive query from the space of semantically equivalent queries to the original one. Two queries are defined to be semantically equivalent (Chu and Lee, 1990; Siegel, 1988) if they return identical answer given the same contents of the database. The reformulation from one query to another is by logical inference using database abstractions, the abstracted knowledge of the contents of relevant databases. The database abstractions describe the databases in terms of ....

[Article contains additional citation context not shown here]

Siegel, M.D., 1988. Automatic rule derivation for semantic query optimization. In Larry Kerschberg, editor, Proceedings of the Second International Conference on Expert Database Systems, pages 371--385. George Mason Foundation, Fairfax, VA.

....a query that fail while continuing to execute the other subqueries of the overall plan. The semantic approach to query optimization was first developed by King [ King, 1981 ] and has since been extended in a number of systems [ Adam et al. 1993; Shenoy and Ozsoyoglu, 1989; Shekhar et al. 1988; Siegel, 1988 ] Our approach to this problem differs from other related work in that we do not rely on explicit heuristics of the database implementation to guide search for optimized queries in the combinatorially large space of the potential optimizations. Instead, our algorithm considers all possible ....

Michael D. Siegel. Automatic rule derivation for semantic query optimization. In Larry Kerschberg, editor, Proceedings of the Second International Conference on Expert Database Systems, pages 371--385. George Mason Foundation, Fairfax, VA, 1988.

....one of the four codes. seaport( R213, R212, R212 200000 ) member( R213, APFD , ADLS , WMY2 , NPTU ] Table 1: Example rules learned from a database states. Examples include rule discovery for semantic query optimization [ Hsu, 1996, Hsu and Knoblock, 1994, Hsu and Knoblock, 1996b, Siegel, 1988, Siegel et al. 1991, Shekhar et al. 1993 ] learning an integrated ontology of heterogeneous databases [ Dao and Perry, 1995, Ambite and Knoblock, 1995 ] functional dependency discovery [ Bell, 1995, Mannila and Raiha, 1994 ] knowledge discovery for decision support, etc. However, most ....

Michael D. Siegel. Automatic rule derivation for semantic query optimization. In Larry Kerschberg, editor, Proceedings of the Second International Conference on Expert Database Systems, pages 371--385. George Mason Foundation, Fairfax, VA, 1988.

No context found.

Siegel, M. D., (1988), Automatic Rule Derivation for Semantic Query Optimization, Proc. 2nd int. conf. expert database systems, pp.371-386.

First 50 documents

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