V. Harinarayan, A. Rajaraman, and J.D. Ullman. Implementing data cubes e#ciently. In Proceedings of ACM SIGMOD Conference on Management of Data (SIGMOD'96), pages 205 -- 216, 1996.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....makes use of the support threshold in pruning away the unnecessary cubes. We should also mention approach suggested in the literature for avoiding to generate all possible group bys. The idea is to materialize a selective few and then evaluate the user queries using the materialized views [HRU96, BPT97, SDJ98] The challenge is that it requires additional work for query optimizations on top of the views materialized and view maintenance for cases where the detailed data gets updated. Just like in databases and association rules, query languages have been an important part of OLAP also. ....

V. Harinarayan, A. Rajaraman, and J.D. Ullman. Implementing data cubes e#ciently. In Proceedings of ACM SIGMOD Conference on Management of Data (SIGMOD'96), pages 205 -- 216, 1996.

....answer a query quickly based on the results of prior queries that have been stored by the system. Since the space available for storing these results is limited, various algorithms that make use of a cost benefit function to maximize the utilization of available cache space have been proposed [3][4]. Data mining algorithms are actually even more computationally expensive than OLAP algorithms and so, until recently, data mining could only be done in batch mode. In our previous work[7] we proposed a caching solution for association rule mining queries that can dramatically reduce query ....

V. Harinarayan, A. Rajaraman, J. Ullmann "Implementing Data Cubes E#ciently". SIGMOD Conf. 1996.

....queries that have been stored by the system. The problem with this is that the space required to store the results of all possible queries would be huge. Hence several algorithms that make use of a cost benefit function to maximize the utilization of available cache space have been proposed [3][5]. Data mining algorithms are actually even more computationally expensive than OLAP algorithms and so, until recently, data mining could only be done in batch mode. In our previous work [8] we proposed a caching solution for association rule mining queries that can dramatically reduce query ....

V. Harinarayan, A. Rajaraman, J. Ullmann "Implementing Data Cubes E#ciently". SIGMOD Conf. 1996.

....is superior to both MOLAP and ROLAP in handling sparse but clustered multidimensional data. Moreover, our EDEM algorithm is e#cient and e#ective in identifying dense regions. 1 Introduction On Line Analytical Processing (OLAP) has emerged recently as an important decision support technology. [4, 8, 10, 12] It supports queries and data analysis on aggregated databases built from data warehouses. Recently, Jim Gray et al. has introduced the data cube model for OLAP systems, and the Data Cube operator to support multiple aggregates. 6] The Cube operator is an n dimensional generalization of the ....

V. Harinarayan, A. Rajaraman, and J. D. Ullman. Implementing data cubes e#- ciently. In Proceedings of the ACM SIGMOD Conference on Management of Data, pages 205-216, Montreal, Quebec, June 1996.

....how to schedule the web crawler to improve the freshness. The model used for web pages is similar to ours; however, the model for the crawler and freshness is very di#erent. In data warehousing context, a lot of work has been done to e#ciently maintain the local copy, or the materialized view [6, 7, 12]. However, most of the work focused on di#erent issues, such as minimizing the size of the view while reducing the query response time [7] 8 Conclusion In this paper we studied how to synchronize a local database to improve its freshness and age. We presented a formal framework, which provides a ....

....and freshness is very di#erent. In data warehousing context, a lot of work has been done to e#ciently maintain the local copy, or the materialized view [6, 7, 12] However, most of the work focused on di#erent issues, such as minimizing the size of the view while reducing the query response time [7]. 8 Conclusion In this paper we studied how to synchronize a local database to improve its freshness and age. We presented a formal framework, which provides a theoretical foundation for this problem, and 6 Many popular search engines report numbers similar to these. 18 we studied the ....

V. Harinarayan, A. Rajaraman, and J. D. Ullman. Implementing data cubes e#ciently. In ACM SIGMOD Conference, 1996.

....work can be saved. 1.1 Notation and Terminology The computation of the various cuboids are not independent of each other, but are closely related in that some of them can be computed using others. The relationship between cuboids can be captured in terms of the search lattice of the data cube [6]. Each granularity # B i # B 1 , B k is a node in the search lattice, and there is an edge from node # B i to # B j if # B j is a subset of and has one fewer element than # B i ; # B i is said to be a parent of # B j in the search lattice. If there is a path from # B i to # ....

V. Harinarayan, A. Rajaraman, and J. Ullman. Implementing data cubes e#ciently. In Proceedings of the 1996 ACM SIGMOD Conference on Management of Data, Montreal,Canada, 1996. Association for Computing Machinery.

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V. Harinarayan, A. Rajaraman, and J. Ullman. Implementing data cubes e#- ciently.InACM SIGMOD International Conf. on Management of Data, pages 205#216, 1996.

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