M. Kantola, H. Mannila, K.-J. Raiha & H. Siirtola, `Discovering Functional and Inclusion Dependencies in Relational Databases', Int. Journal of Intelligent Systems 7, 1992, pp. 591-607.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....all, of the data and not that any probabilistic techniques are used. that FDs represent interesting patterns existent in the data. In this setting, FDs are not regarded as declared constraints. Researchers have investigated the problem of efficiently discovering FDs that hold in a given instance [11, 13, 16, 18, 20, 21, 23, 38]. Researchers have also considered the concept of an FD approximately holding in an instance and have developed measures to characterize the degree of approximation . Piatetsky Shapiro [26] describe a measure derived from probabilistic considerations (this measure corresponds to the measure of ....

Kantola M., Mannila H., R aih a K., and Siirtola H. Discovering functional and inclusion dependencies in relational databases. International Journal of Intelligent Systems 7 (1992), 591--607.

....measures from the 2D cuboid need not be changed) this may result in significant savings of time. ut As an aside, we point out the following application of the dependency discovery problem in database design. Mannila et al. developed a database design and analysis tool called Design By Example [KMR 92] which makes use of discovered FDs. Their tool, among other things, allows a database designer to see all of the FDs which hold on an example table of a database. The designer can then modify the database schema if necessary, for example to normalize with respect to some of these FDs. The schema ....

Kantola, Martti; Mannila, Heikki; Raiha, Kari-Jouko and Siirtola, Harri. "Discovering Functional and Inclusion Dependencies in Relational Databases." Journal of Intelligent Systems, vol. 7, pg. 591-607, 1992.

....(Y ) g (3) For the relation: R A B 0 0 0 1 1 2 the result is: X Y fBg fAg fBg fA; Bg . The two lines of the result encode the functional dependencies fBg fAg and fBg fA; Bg respectively. The discovery of functional dependencies has been studied for many years now (see for example [5, 8]) 4.2 The use of binary schema variables: inclusion dependencies In all examples introduced so far, all schema variables were unary, i.e. had arity = 1. We now illustrate the need for binary schema variables. In the example, sets of attribute pairs are used to encode inclusion dependencies ....

M. Kantola, H. Mannila, K.-J. Raiha, and H. Siirtola. Discovering functional and inclusion dependencies in relational databases. Internat. Journal of Intelligent Systems, 7:591--607, 1992.

....1 2 1 4 is 4 7 . The con dence is 4 5 . 2 There are multiple similarities between association rules and binary association rules. Both rules give frequent dependencies that hold within the tuples themselves. Unlike for example roll up dependencies [16] or approximate dependencies [9][10], that describe relations between di erent tuples, association rules and binary association rules relate properties of attributes. In association rule mining, frequent itemsets can be considered as a conjunction of unary predicates. In this setting, binary association rules are a straightforward ....

M. Kantola, H. Mannila, K.-J. Raiha, and H. Siirtola. Discovering functional and inclusion dependencies in relational databases. International Journal of Intelligent Systems, 7, 1992.

.... of the degree to which an FD is approximate 1 Introduction Over approximately the last ten years, a new research direction has emerged involving functional dependencies (FDs) Researchers have been addressing the problem of finding all of the FDs which hold in a given relation instance ( 4] [5], 6] 7] 9] 10] 12] 15] We call this FD discovery research. The primary motivation for FD discovery research is different than that for the original FD research in the 70s. The research in the 70s was primarily motivated by database design (e.g. schema normal forms) The primary ....

Kantola M., Mannila H., Raiha K., and Siirtola H. Discovering functional and inclusion dependencies in relational databases. International Journal of Intelligent Systems, 7:591--607, 1992.

....is also possible to discover formats (e.g. mmddyy or dd mm yyyy for a date) Dependency Analysis. Finding functional, multivalued and inclusion dependencies in large databases is an expensive operation. But at least for functional dependencies it is feasible for narrow data samples [9] 30] [31]. The results gained from data samples will probably not be exact, but can nevertheless be helpful as candidates which can then be confirmed or rejected by the user. The general problem of finding inclusion dependencies is NP complete [31] It can be reduced to O(n 2 p log p) with p = number of ....

....it is feasible for narrow data samples [9] 30] 31] The results gained from data samples will probably not be exact, but can nevertheless be helpful as candidates which can then be confirmed or rejected by the user. The general problem of finding inclusion dependencies is NP complete [31]. It can be reduced to O(n 2 p log p) with p = number of rows and n = number of attributes) by considering only unary inclusion dependencies. The factor n 2 can be lowered further because there is no need to investigate attributes with different domains. Until now we do not consider ....

Mannila, H., Raiha, K.J. et al.: Discovering Functional and Inclusion Dependencies in Relational Databases. International Journal of Intelligent Systems, Vol 7, 1992.

....in the number of attributes. We give an algorithm for this problem. Experimental results show that the algorithm uses linear time in the number of tuples of the input database. 1 Introduction The problem of discovering functional dependencies (FDs) from relational databases has been studied [KMRS92] Although FDs are the most important dependencies in database design, FDs are not prevalent in data mining. This may be attributed to the fact that FDs other than those known at design time, are fairly rare. We propose Roll Up Dependencies (RUDs) with clear and interesting applications in data ....

....50 , while the RUD fg (Fname : EXTENSION) had a confidence of less than 2 . 6 Related Work RUDs generalize FDs for relational databases that support roll up drill down. The discovery of FDs from relational databases has already been studied before the explosive growth of data mining research [KMRS92] Roll up plays an important role in data mining and related areas. A lattice framework for OLAP has been 8 provided by Harinarayan et al. HRU96] As we pointed out in Section 3, our notion of roll up lattice is more general than what has so far been proposed in the literature. We believe that ....

M. Kantola, H. Mannila, K.-J. Raiha, and H. Siirtola. Discovering functional and inclusion dependencies in relational databases. Internat. Journal of Intelligent Systems, 7:591--607, 1992.

....implication is provided. The problem of mining RUDs is at the center of this paper. The problem is shown NP hard, and an outline of an algorithm is provided. 1 Introduction The problem of discovering functional dependencies (FDs) from relational databases has been studied several years ago [8], before the explosive growth of data mining. Although FDs are the single most important dependencies in database design, FDs are not prevalent in the data mining literature. This may be attributed to the fact that FDs other than those known at design time, are fairly rare. In this paper we ....

....not grouped together. Second, if a RUD Psi Upsilon fails the threshold support, we can prune away all RUDs Omega Upsilon with Omega Theta Psi. 7 Related Work The discovery of FDs from relational databases was already studied before the explosive growth of research in data mining. See [8], which also contains a discussion of earlier work. Kantola et al. 8] apply FD discovery in a database design tool called Design By Example. The tool mines FDs from an example database and reports them to the designer, who can then decide which of them should hold in general. FD mining has not ....

[Article contains additional citation context not shown here]

M. Kantola, H. Mannila, K.-J. Raiha, and H. Siirtola. Discovering functional and inclusion dependencies in relational databases. Internat. Journal of Intelligent Systems, 7:591--607, 1992.

....only to the efficiency of the algorithm, but also to the quality of the output. AprioriRel is a generalization of Apriori [Agrawal et al. 1996] and is otherwise related to a family of algorithms that mine association rules [Agrawal et al. 1993; Houtsma and Swami, 1993] functional dependencies [Kantola et al. 1992], clauses [Shen et al. 1996; De Raedt and Dehaspe, 1997] determinations [Schlimmer, 1991] and general rulepatterns [Kl osgen, 1996] in large databases. Numerous opportunities remain for further integration of association rule research and techniques from inductive logic programming. From the ....

M. Kantola, H. Mannila, K.J. Raiha, and H. Siirtola. Discovering functional and inclusion dependencies in relational databases. International Journal of Intelligent Systems, 7(7):561--607, 1992.

....this paper we address this problem of dependency discovery, understood as characterising the set of dependencies that are satisfied by a given collection of data. 1 Some previous work has been done on algorithms for dependency discovery, mostly restricted to discovery of functional dependencies [18, 14, 20, 13]. In this paper we propose some new algorithms for discovery of functional dependencies, and we study the new problem of discovery of multivalued dependencies. The major contribution, however, is the elaboration of the connection between the problem of dependency discovery and the problem of ....

....the prototype can serve as a basis for the development of a system which can induce multivalued dependencies from larger relations. 6. Related work Most of the published work on dependency discovery has been done by Mannila and co workers, who concentrated on inferring functional dependencies [18, 14, 20]. There are a number of resemblances between the algorithms of Mannila et al. and ours; however, their algorithms make more use of the particulars of functional dependencies, and are thus less easily generalised to other kinds of database constraints. We will follow the survey in [20] Algorithm 1 ....

M. Kantola, H. Mannila, K.-J. Raiha & H. Siirtola, `Discovering Functional and Inclusion Dependencies in Relational Databases', Int. Journal of Intelligent Systems 7, 1992, pp. 591-607.

....Here, we present both the basic primitives for describing the conceptual structure of object types (classes) and a method for modeling the structure on the basis of primitives, a method we call the design by example method. Similar approaches have been used for example in database design [e.g. Kantola et al. 1992] and in formulation of complicated 2 queries 1 and in designing of frameworks [Koskimies and M ssenb ck, 1995] To begin with, we need two kinds of relations among the object types for describing the modality of containment in the application domain at hand. One to describe whether a complex ....

....when connections between individuals are ensued from this [Artale et al. 1996] We can make a hierarchy for attributes, but this approach is more like an ad hoc solution. In this paper, we adapt the term design by examples (DBE) from relational database design [Mannila and R ih , 1986; Kantola et al. 1992] In that appraoch the extensional level is called database instances whereas the term database schema can be interpreted to correspond with the conceptual level. The database design is typically begun by using an example ER model. If the modeler does not know what kind of demands the database ....

Martti Kantola, Heikki Mannila, Kari-Jouko Räihä and Harri Siirtola, Discovering functional and inclusion dependencies in relational databases, International journal of intelligent systems. Vol. 7, 1992. 591-607

....can simplify the database design process and can be of a great help when relationships among the attributes of the relation are not obvious, due to the complex structure of the University of Discourse. Much work on the discovery of functional dependencies has been done by Mannila and Raiha; see [5] for an overview. Discovery of database dependencies can also be viewed as an induction process, where the tuples in a relation represent instances of that relation, and dependencies represent hypotheses about the relation. In [3] it was shown how inductive learning techniques can be applied to ....

M.Kantola, H.Mannila, K.Raiha, H.Siirtola, Discovering Functional and Inclusion Dependencies in Relational Databases, Int. Journal of Intelligent Systems, Vol.7, 591607, 1992

....Asymptotically, both this algorithm and the one above are exponential in the number of attributes; however, the algorithm presented in this paper can easily be bounded by limiting the maximum domain size. This algorithm also has a time complexity that is only linear in the number of data. Kantola, Mannila, R ih , and Siirtola (1992) identify a revision of their algorithm that makes use of an intermediate sort, lowering the time complexity to O( They also point out specific ways dependencies can be used during the design of a database and demonstrate an implemented system. Shen (1992) also explores learning regularities, ....

Kantola, M., Mannila, H., Räihä, K, & Siirtola, H. (1992). Discovering functional and inclusion dependencies in relational databases. International Journal of Intelligent Systems, 7, 591--607.

....files, databases, or spread sheets. Studies have shown that data is an invaluable resource for extracting business knowledge [23] as well as systems design information [26] Research in constraints discovery have also confirmed that data is a reliable source for recovering database constraints [3, 5, 9, 12, 13, 20, 21]. There might be situation where data is the only resource available for design recovery. However, there is no research dedicated to that scenario. Our focus in this part of the information systems reverse engineering research is on extraction of design constraints from data for design recovery ....

....is on extraction of design constraints from data for design recovery purpose. The discovery of functional dependencies (FDs) is a fundamental activity in the database design recovery process. Many techniques and algorithms for discovering functional dependencies from the data have been proposed [3, 5, 9, 12, 13, 20, 21]. These techniques and algorithms share a common goal: generate minimum number of functional dependency (FD) hypotheses and verify them against the database. These algorithms suffer from a common braw back: the performance of the algorithm deteriorated when the number of attributes or and the size ....

[Article contains additional citation context not shown here]

Martti Kantola, Heikki Mannila, Kari-Jouko Raiha, Harri Siirtola, Discovering Functional and Inclusion Dependencies in Relational Databases, International Journal of Intelligent Systems, Vol. 7, 591-607 (1992).

....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) ....

M. Kantola, H. Mannila, K.J. Raiha, and H. Siirtola. Discovering functional and inclusion dependencies in relational databases. International Journal of Intelligent Systems, 7(7), 1992.

....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 that because of the ....

M. Kantola, H. Mannila, K.J. Raiha, and H. Siirtola. Discovering functional and inclusion dependencies in relational databases. International Journal of Intelligent Systems, 7(7):561--607, 1992.

....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. Kantola, H. Mannila, K.-J. Raiha, and H. Siirtola. Discovering functional and inclusion dependencies in relational databases. International Journal of Intelligent Systems, 7(7):591 -- 607, Sept. 1992.

....algorithm suffices. 6 The verification problem Consider the following idealized statement about the guess and correct method. Assume somebody gives us L, r, q, and a set S L, and claims that S = Th(L; r; q) How many evaluations of q do we have to do to check this claim Theorem 12 Given L, r, q, and a set S L, determining whether S = Th(L; r; q) requires in the worst case at least jBd(S)j evaluations of the predicate q, and it can be solved using exactly this number of evaluations of q. 2 Example 13 Given a relation r over fA; B; C; Dg, suppose a sample or some person ....

....number of evaluations of q. 2 Example 13 Given a relation r over fA; B; C; Dg, suppose a sample or some person tells us that fA; Bg and fA; Cg and their supersets are the only keys of r. Recall that for this case X Y if and only if Y ae X. To verify this, we have to check according to Theorem 12 the set Bd(S) for S = fX fA; B; C; Dg j fA; Bg X fA; Cg Xg. The positive border of S is ffA; Bg; fA; Cgg, and Bd Gamma (S) ffB; C; Dg; fA; Dgg, and we have to inspect the sets fA; Bg; fA; Cg; fB; C; Dg; fA; Dg to determine whether fA; Bg and fA; Cg and their supersets really are the ....

M. Kantola, H. Mannila, K.-J. Raiha, and H. Siirtola. Discovering functional and inclusion dependencies in relational databases. International Journal of Intelligent Systems, 7(7):591 -- 607, Sept. 1992.

....can be found using the above algorithm. Several choices of the specialization relation are possible, and the number of iterations in the outermost loop of the algorithm depends on that choice. Example 7 Consider the discovery of all inclusion dependencies that hold in a given database instance [17, 21, 24]. Given a database schema R, an inclusion dependency over R is an expression R[X] S[Y ] where R and S are relation schemas of R, and X and Y are equal length sequences of attributes of R and S, respectively, that do not contain duplicates. Suppose r is a database over R, and let r and s be ....

M. Kantola, H. Mannila, K.-J. R#ih#, and H. Siirtola. Discovering functional and inclusion dependencies in relational databases. International Journal of Intelligent Systems, 7(7):591 607, Sept. 1992.

No context found.

M. Kantola, H. Mannila, K.-J. Raiha & H. Siirtola, `Discovering Functional and Inclusion Dependencies in Relational Databases', Int. Journal of Intelligent Systems 7, 1992, pp. 591-607.

No context found.

M. Kantola, H. Mannila, K.-J. Rih, and H. Siirtola. Discovering Functional and Inclusion Dependencies in Relational Databases. International Journal of Intelligent Systems, 7(7):591--607, September 1992.

No context found.

M. Kantola, H. Mannila, K. J. Raiha, and H. Siirtola. Discovering functional and inclusion dependencies in relational databases. International J. of Intelligent Systems, 7:591--607, 1992.

No context found.

Mannila, H. et al: Discovering Functional and Inclusion Dependencies in Relational Databases. International Journal of Intelligent Systems. Vol 7. 1992

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