I. Savnik & P.A. Flach. Bottom-up induction of functional dependencies from relations. Proc. AAAI '93 Workshop Knowledge Discovery in Databases, G. PiatetskyShapiro (editor), pp.174--185, 1993.  Home/Search   Document Details and Download   Summary   Related Articles   Check

.... 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 been devoted to the problem of estimating the size of the result of a query expression. Approaches based on sampling were explored in [9, 16] and histograms [13, 19] 18] provides a survey of several techniques and [14] provides an analysis of error propagation in size ....

I. Savnik and P. Flach. Bottom-up induction of functional dependencies from relations. In G. Piatetsky-Shapiro, editor, Knowledge Discovery in Databases, pages 284--290. Morgan Kaufman Pub., 1993.

....be quite useful in further ways for an optimizer. If so, FD information (beyond keys) could be explicitly represented when known. The opportunity to discover FDs in databases should also generally be good. There has been a fair amount of work in recent years that has improved data mining for FDs [1, 14, 19, 20, 22, 26]. With a good FD mining tool, FD information could be made available as SCs. 3. SOFT CONSTRAINTS 3.1 Application In DB2, there are three ways in which soft constraints could be used in query optimization and execution. ffl In rewrite. Currently, certain types of integrity constraints are used ....

I. Savnik and P. Flach. Bottom-up induction of functional dependencies from relations. In G. Piatetsky-Shapiro, editor, Knowledge Discovery in Databases, pages 284--290. Morgan Kaufman Pub., 1993.

.... Shekhar et al. 1993] However, having a large rules set remains a problem in all the existing systems since rules are produced automatically regardless of how effective they might be in the query transformation process [Chan and Wong, 1991; Han et al. 1993; Piatetsky Shapiro and Matheus, 1993; Savnik and Flach, 1993; Ziarko, 1991] This is known as the utility problem. For this reason we are suggesting the use of the chi square test in statistical methods to measure the relationship degree of a Candidate Rule (CR) with a given confidence level and degree of freedom [Chan and Wong, 1991] If the relationship ....

I. Savnik and P. A. Flach. Bottom-up induction of functional dependencies from relations. Knowledge Discovery in Databases Workshop , 174-185, 1993.

....Functional dependencies are relationships between attributes of a relation: a functional dependency states that the value of an attribute is uniquely determined by the values of some other attributes. The discovery of functional dependencies from relations has received considerable interest (e.g. [3, 13, 21, 23, 14, 2, 8, 4]) Automated database analysis is, of course, interesting for knowledge discovery and data mining (KDD) purposes, and functional dependencies have applications in the areas of database management, reverse engineering [18, 24] and query optimization [25] Formally, a functional dependency over a ....

....with even hundreds of thousands of rows. Dependency discovery tasks that have been reported to take minutes or even hours are solved with the new algorithm in seconds or fractions of a second on a PC. Related work Several algorithms for the discovery of functional dependencies have been presented [10, 3, 12, 22, 21, 14, 2]. We review these algorithms and compare them with our method in Section 6. The complexity of discovering functional dependencies has been studied in [11, 13, 12] Approximate functional dependencies have been considered in [7, 19, 8, 4] Kivinen and Mannila [7] define several measures for the ....

[Article contains additional citation context not shown here]

I. Savnik and P. Flach. Bottom-up induction of functional dependencies from relations. In G. Piatetsky-Shapiro, editor, Knowledge Discovery in Databases, Papers from the 1993 AAAI Workshop (KDD'93), pages 174--185. AAAI, 1993.

....Functional dependencies are relationships between attributes of a relation: a functional dependency states that the value of an attribute is uniquely determined by the values of some other attributes. The discovery of functional dependencies from relations has received considerable interest (e.g. [2, 10, 17, 19, 11, 1, 6, 3]) Automated database analysis is, of course, interesting for knowledge discovery and data mining (KDD) purposes, and functional dependencies have applications in the areas of database management, reverse engineering [14, 20] and query optimization [21] Formally, a functional dependency over a ....

....with even hundreds of thousands of rows. Dependency discovery tasks that have been reported to take minutes or even hours are solved with the new algorithm in seconds or fractions of a second on a PC. Related work Several algorithms for the discovery of functional dependencies have been presented [7, 2, 9, 18, 17, 11, 1]. We review these algorithms and compare them with our method in Section 6. The complexity of discovering functional dependencies has been studied in [8, 10, 9] Approximate functional dependencies have been considered in [5, 15, 6, 3] Kivinen and Mannila [5] define several measures for the error ....

[Article contains additional citation context not shown here]

I. Savnik and P. Flach. Bottom-up induction of functional dependencies from relations. In G. PiatetskyShapiro, editor, Knowledge Discovery in Databases, Papers from the 1993 AAAI Workshop (KDD'93), pages 174--185. AAAI, 1993.

....Axioms (Ullman 1988) and without loss of generality we only regard FDs with one attribute on the right hand side. The discovery of FDs may be visualized as a search in semi lattices. The nodes are labeled with data dependencies and the edges correspond to the more general than relationship as in (Savnik Flach 1993), which implies the partial ordering. Definition 1 (More general FD) Let X and Y be sets of attributes such that X Y , then the FD X A is more general than the dependency Y A, or Y A is more specific than X A. In contrast to the notion of a minimal cover in database theory, the ....

Savnik, I., and Flach, P. A. 1993. Bottom-up induction of functional dependencies from relations. In Piatetsky-Shapiro, G., ed., Proceedings of the AAAI93 Workshop on Knowledge Discovery in Databases, 174--185. Menlo Park, California: The American Association for Artificial Intelligence.

....Axioms (Ullman, 1988) and without loss of generality we only regard FDs with one attribute on the right hand side. The discovery of FDs may be visualized as a search in semilattices. The nodes are labeled with data dependencies and the edges correspond to the more general than relationship as in (Savnik Flach, 1993), which implies the partial ordering. Definition. More general FD) Let X and Y be sets of attributes such that X Y , then the FD X A is more general than the dependency Y A, and Y A is more specific than X A. In contrast to the notion of a minimal cover in database theory, the ....

Savnik, I., & Flach, P. A. (1993). Bottom-up induction of functional dependencies from relations.

....the cost of testing not only unary INDs as in A.1, but also binary and ternary ones. Since the number of binary and ternary INDs is usually low, we can conclude that in general alternative A.1 has a lower total cost. 4. Related and future work Very few work on inferring FDs has been done [7] [8]. The main effort is the one reported in [7] A number of similarities as well as big differences wrt [7] exist. He focuses on the number of operations, while we consider disk accesses as the primary factor of the cost. His algorithm for FDs extraction orders candidates by rhs, while our approach ....

I.Savnik, P.Flach: "Bottom-up Induction of Functional Dependencies from Relations". Proc. of AAAE-93 Workshop on "Knowledge Discovery in Databases".

....and third one would define the view predicate human. 3. 2 Functional dependencies and determinations One of the important topics in knowledge discovery in databases addresses how to efficiently discover specific types of regularities, such as functional and multivalued dependencies (see e.g. [13, 14, 26]) and determinations (see [27, 29] We ran claudien on the following data from Flach (the term train(From,Hour,Min,To) denotes that there is a train from F rom to To at time Hour; Min) train(utrecht,8,8,den bosch) train(tilburg,8,10,tilburg) train(maastricht,8,10,weert) ....

I. Savnik and P.A. Flach. Bottom-up induction of functional dependencies from relations. In Proceedings of the AAAI'93 Workshop on Knowledge Discovery in Databases, pages 174--185. AAAI Press, 1993. Washington DC.

....of the popular subjects in the field of knowledge discovery in databases is to induce large sets of rules of a particular type or syntax, cf. Mannila s definition of data mining in Section 3.2.3. The types of rules considered include: functional and multivalued dependencies (see e.g. Flach, 1993; Savnik and Flach, 1993; Kantola et al. 1992] determinations (see e.g. Schlimmer, 1991; Shen, 1992] association rules (cf. Agrawal et al. 1993] and strong rules (cf. Piatetsky Shapiro, 1991] Various special purpose algorithms have been developed to handle the different types of rules. However, it turns out ....

I. Savnik and P.A. Flach. Bottom-up induction of functional dependencies from relations. In Proceedings of the AAAI'93 Workshop on Knowledge Discovery in Databases, pages 174--185. AAAI Press, 1993. Washington DC.

No context found.

I. Savnik & P.A. Flach. Bottom-up induction of functional dependencies from relations. Proc. AAAI '93 Workshop Knowledge Discovery in Databases, G. PiatetskyShapiro (editor), pp.174--185, 1993.

No context found.

I. Savnik & P.A. Flach, `Bottom-up induction of functional dependencies from relations', Proc. AAAI '93 Workshop on Knowledge Discovery in Databases, G. Piatetsky-Shapiro (ed.), 1993, pp. 174-185.

....used for the representation of the sets of dependencies should be memory efficient. A data structure called MVD tree is defined for representing sets of sentences as defined in Section 2. MVD tree is based on the data structure for the representation of the sets of functional dependencies [13]. It meets all of the above stated requirements: it provides an efficient implementation of the above mentioned operations, and it takes advantage of the repeating patterns which appear in sentences to reduce the space needed to store a set of sentences. Let us first present an antecedent tree ....

....in an efficient procedural programming language could significantly improve the performance of algorithms. 5 Related work The work on the discovery of multivalued dependencies from relations is closely related to the work on the discovery of functional dependencies from relations presented in [3, 13] and the discovery of database dependencies presented in [5] The basic skeletons of the algorithms for the discovery of multivalued dependencies presented in Section 3 are those of algorithms for the discovery of database dependencies presented in [5] With regards to the previous work, this ....

I. Savnik, P.A. Flach, Bottom-up induction of functional dependencies from relations, in Proc. AAAI '93 Workshop on Knowledge Discovery in Databases, (G. Piatetsky-Shapiro, ed.), 1993, pp. 174-185.

....of the kind of dependency that is being induced to specific sub procedures. As a consequence, the algorithms we develop can be adapted to discover other kinds of dependencies than the functional and multivalued dependencies considered in this paper. This paper summarises and extends results from [6, 28, 29, 7, 8, 30]. 1.1. Overview of the paper In Section 2 we review the main concepts and notation from the relational database model. Some additional insights are gained through a reformulation in terms of clausal logic. Section 3 gives a brief introduction to the field of computational induction, and ....

I. Savnik & P.A. Flach, `Bottom-up induction of functional dependencies from relations', Proc. AAAI '93 Workshop on Knowledge Discovery in Databases, G. Piatetsky-Shapiro (ed.), 1993, pp. 174-185.

....one starts with the set of most general dependencies, specialising them until they are no longer violated by the data. In this way one obtains a cover for the set of satisfied dependencies (although the cover may contain some redundant elements) An alternative bottom up approach runs as follows [38]. From the data one first constructs a negative cover, which is the set of least general violated dependencies (this can be done in O(n 2 ) steps in the case of functional dependencies, and O(n 3 ) steps in the case of multivalued dependencies) The positive cover can then be constructed from ....

I. Savnik & P.A. Flach. Bottom-up induction of functional dependencies from relations. Proc. AAAI '93 Workshop Knowledge Discovery in Databases, G. Piatetsky-Shapiro (editor) , pp.174--185, 1993.

No context found.

I. Savnik and P.A. Flach, `Bottom-up induction of functional dependencies from relations', Proc. Knowledge Discovery in Databases Workshop, 174-185, 1993.

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