J.A. Thom, K. Ramamohanarao, and L. Naish. A Superjoin Algorithm for Deductive Databases. In Procs. VLDB, pages 189--196, 1986. 20  Home/Search   Document Not in Database   Summary   Related Articles   Check

....for adding new methods to the existing framework. Another area that deserves further investigation is the processing of joins in the case that more than one relation is maintained by an LSD tree. For other multi dimensional structures methods for executing equi joins have been proposed, e.g. in [OB88, CFMT86, TRN86]# [KHT89]even considers k d trees. For more complex joins, we only know of one approach for grid files [NHS84] which is presented in [BHF93] ....

J.A. Thom, K. Ramamohanarao, and L. Naish. A Superjoin Algorithm for Deductive Databases. In Procs. VLDB, pages 189--196, 1986. 20

.... key access methods map a real file on a cartesian product file, for example, multiattribute hashing [19] 1] or the grid file [16] and its derivatives [12] All these methods are used to answer efficiently partial match or range queries, or to perform fast joins (e.g. the superjoin algorithm [21] for disk resident, deductive databases) In a cartesian product file, let d i be the number of ranges that domain D i is divided into. Thus, a bucket is characterized by a string of k numbers [i 1 ; i 2 ; i k ] called bucket id. Clearly, each i j should belong to the correct range, 0,d ....

J.A. Thom, K. Ramamohanarao, and L. Naish. A superjoin algorithm for deductive databases. In Proc. 12th International Conference on VLDB, pages 189--196, Kyoto, Japan, August 1986.

....need to store many thousands of rectangles [15] representing electronic gates and higher level elements. Rectangles can be divided in pieces; each piece is assigned a z value , according to the Peano curve [13] 3) Computer vision and robotics. 4) Retrieval in large knowledge bases [9] 11] [18]. 5) Clustering of data in data base machines [3] 4] 6) In numerical analysis, large k d arrays that have to be stored on disk [6] 7) In computational geometry. Heuristics in geometric complexity problems use distancepreserving mappings: e.g. to solve the traveling salesman problem, the ....

Thom, J.A., K. Ramamohanarao, and L. Naish, "A Superjoin Algorithm for Deductive Databases," Proc. 12th International Conference on VLDB, pp. 189-196, Kyoto, Japan, Aug. 1986.

....management systems [53, 78, 79] A large amount of research has been conducted to find methods of efficiently implementing the join. Using the clustering provided by a data structure to increase the performance of the join has been considered in the past, by Ozkarahan and Ouksel [63] Thom et al. [77] and Harada et al. 29] However, none of the authors attempted to find the optimal clustering organisation. As the join operation is so expensive, any increase in its cost can result in a significant degradation of the performance of the database management system. 2 An optimal clustering ....

....of the value of the attribute is to define an order for different attribute values which have the same hash value. The relations can be sorted using the hashed sort key instead of the attribute value in the sort merge algorithm. A variation of this idea appears in the superjoin of Thom et al. [77], and all hash joins. We now define a notation to denote sorting on combinations of attributes. Let Pi(A 1 ; A n ) be the result of sorting a relation on A 1 , then A 2 , and so on. Within each distinct value of A i the records will be ordered on increasing (or decreasing) values of A ....

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J. A. Thom, K. Ramamohanarao, and L. Naish. A superjoin algorithm for deductive databases. In Proceedings of the Twelfth International Conference on Very Large Data Bases, pages 189--196, Kyoto, Japan, August 1986.

....operations. 4 Join operations. The join operation is a frequently occurring one in relational database systems. It is an expensive operation. Several join algorithms have been described and analysed in the literature, amongst these are the nested loop, sort merge, hashjoin [4] and superjoin [24]. In [16] Merrett argued that the sort merge algorithm gives the best implementation of the natural join based on the theory of clustering of relations. The conclusion was disputed by Bratbergsengen [4] who showed that the hash join algorithm always performs better than the sort merge when the ....

....of partitioning which needs to be performed by the hash join algorithm. Instead of commencing with one data file which needs to be partitioned, we can start with a number of smaller data files which are distinguished by having different indexes for the appropriate attributes. The superjoin method [24] does this, however Thom, et al. do not attempt to construct an optimal index for the data. We will shortly show how the optimal index may be determined. 8.1 Cost of partitioning in the hash join method. The cost of partitioning the data in the hash join method was given by Bratbergsengen in ....

J. A. Thom, K. Ramamohanarao, and L. Naish. A superjoin algorithm for deductive databases. In Proceedings of the 12th International Conference on Very Large Databases, pages 189--196, Kyoto, Japan, August 1986.

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