A. Gupta, V. Harinarayan, and D. Quass. Aggregate-Query Processing in Data Warehousing Environments. In Proc. Int. Conf. on Very Large Data Bases (VLDB), pages 358-369, 1995. Home/Search Document Details and Download
Summary Related Articles Check |
This paper is cited in the following contexts:
First 50 documents Next 50
On Relational Support for XML Publishing: Beyond.. - Chaudhuri, Kaushik.. (2003) (8 citations) (Correct)
....with this, if the condition is selective, then the latter plan is likely to be cost e#ective. This rule can be formulated in a manner analogous to the previous rule. 4. 3 Pushing GApply below joins Finally, we consider a generalization of the rules for optimizing groupby in relational systems [7, 14, 19, 20]. The idea there briefly is to think of groupby as an operator and push it below joins, the motivation being that groupby reduces the number of output tuples considerably since it outputs one tuple per group. Rules to pull groupby above joins are also discussed in this body of work. The same ....
....describes when the optimizer considers the use of GApply, given an operator tree without GApply. We study the impact of GApply on query optimization in detail to complete the picture. Some of our transformation rules are a generalization of the rules involving groupby in the traditional setting [7, 14, 19, 20]. Thus, our rules to push GApply below joins generalize similar operations on groupby, addressed in [7, 19] while the rules to pull GApply above joins generalize the lazy aggregation technique proposed in [20] 7. CONCLUSIONS In this paper, we asked what impact the requirement for e#cient XML ....
A. Gupta, V. Harinarayan, and D. Quass. Aggregate query processing in data warehousing environments. In VLDB, 1995.
Rewriting Aggregate Queries using Description Logic - DeHaan, Toman, Weddell (Correct)
....an aggregate function. Aggregate queries are a relatively unexplored area of relational query optimization. However, aggregate functions may occur frequently in certain application domains; for example, query workloads in OLAP and data warehousing environments make substantial use of aggregation [1, 6]. The most commonly discussed aggregate functions are likely those that are de ned by SQL Sum, Average, Maximum, Minimum, and Count. However, many other aggregates are possible: Median, Standard Deviation, Parity, Product, just to name a few with mathematical signi cance. Description Logics can ....
....the constraints in T and the rest of the query. This condition has been used within the context of a system for answering queries using aggregate views [5] but it no longer remains necessary when the aggregation block is not at the root of the query tree. Similar rewrites have been considered in [2, 6]. 4 DL Reasoning for Rewriting Aggregate Queries In this section we show how a reasoner for DLFDE terminologies can be applied to rewriting aggregate queries expressed in QLA. The rewrite rule examined by Yan and Larson [10] and described in the previous section can be generalized in several ....
A. Gupta, V. Harinarayan, and D. Quass. Aggregate-query processing in data warehousing environments. In Proc. 21th International Conference on Very Large Data Bases (VLDB'95), pages 358-369. Morgan Kaufmann, 1995.
Efficient Maintenance of Materialized Top-k Views - Yi, Yu, Yang, Xia, Chen (2003) (Correct)
....complete self maintenance; none has considered using auxiliary data to increase the probability of runtime self maintenance, which is the one of the key observations in this paper. Until recently, most papers that deal with SQL MIN and MAX views (which are special cases of top k views) e.g. [11, 21, 1, 17, 24], cannot efficiently handle deletions or updates to the base table. Recent work by Palpanas et al. 18] proposes using work areas to maintain MIN and MAX views. Their approach has the same underlying idea as our algorithm in Section 3, which we have developed independently. Besides this basic ....
A. Gupta, V. Harinarayan, and D. Quass. Aggregate-query processing in data warehousing environments. In Proc. of the 1995.
A Powerful and SQL-Compatible Data Model and Query.. - Pedersen, Riis, Pedersen (2001) (Correct)
.... of advantages over traditional types of DBMSs, including automatic application of the pre specified aggregation functions (automatic aggregation) Rafanelli et al. 1990, Thomsen, 1997] visual querying [Thomsen, 1997, Thomsen, 1999] and good query performance due to the use of preaggregation [Gupta et al. 1995, Pedersen et al. 1999b] Additionally, the dimensional approach is most often a natural fit for data analysis problems. To be able to capture the complex data found in many real world applications, the data model for the OLAP system must be able to handle irregular dimension hierarchies ....
A. Gupta, V. Harinarayan, and D. Quass. Aggregate-Query Processing in Data Warehousing Environments. In Proceedings of 21th International Conference on Very Large Data Bases, pp. 358-- 369, 1995.
Finding an Efficient Rewriting of OLAP Queries Using.. - Park, Kim, Lee (Correct)
....process efficiently, a query rewriting technique that can make effective use of the materialized views is required. Several rewriting strategies for conjunctive queries and aggregation queries have been proposed in the literature of relational databases, data integration, and data warehouses [3,5,11,16,22,23,26]. Few of them, however, exploited the characteristics of DWs and OLAP queries effectively and utilized existing MVs sufficiently. In this paper, we propose a new algorithm for rewriting OLAP queries which improves the usability of MVs significantly in contrast to the previous studies. We consider ....
....We also observed that the greedy algorithm scales up to large number of candidate MVs well in contrast to the A algorithm whose execution time increases exponentially. 7. Related Work Several work on answering conjunctive queries using materialized views has been proposed in the literature [3,5,6,11,16,22,23,26]. 16] formalized the problem of finding rewritings of a conjunctive query under set semantics in terms of containment mappings from views to the query. 5] addressed the problem of optimizing queries using MVs. They proposed a rewriting method for conjunctive SPJ queries and integrated it into a ....
[Article contains additional citation context not shown here]
A. Gupta, V. Harinarayan, and D. Quass, Aggregate-Query Processing in Data Warehousing Environments, Proceedings of the 21st Int'l Conf. on Very Large Data Bases, 1995, pp. 358-369.
Online Generation of Association Rules - Aggarwal, Yu (1998) (25 citations) (Correct)
....transaction data each time a user makes a query. By preprocessing the data set just once, a user may be able to query the system efficiently multiple times at the cost of a single phase of preprocessing. Considerable work has been done in online analytical processing, as applied to the data cube [5, 7, 8, 10, 20]. This paper also discusses an approach for online mining by using one phase of preprocessing. 1.1 Contributions of this paper In this paper, we present an intuitive framework for performing online mining of association rules. Past work has concentrated on a two phase approach: 1) Large ....
Gupta A., Harinarayan V., and Quass D. Aggregatequery processing in data warehousing environments. Proceedings of the 21st Conference on Very Large Databases, Zurich, Switzerland, September 1995.
CUBIST: A New Approach to Speeding Up OLAP Queries in Data Cubes - Fu, Hammer (2001) (Correct)
....is very expensive. 6 Other related research in this area has focused on indexing pre computed aggregates [31] and incrementally maintaining them [22] Also relevant is the work on maintenance of materialized views (see [21] for a summary of excellent papers) and processing of aggregation queries [13, 32]. However, in order to be able to support true ad hoc OLAP queries, indexing and pre computation of results alone will not produce good results. For example, building an index for each attribute of the warehouse or pre computing every sub cube requires too much space and results in unacceptable ....
A. Gupta, V. Harinarayan, and D. Quass, "Aggregate-query Processing in Data Warehousing Environments," in Proceedings of the Eighth International Conference on Very Large Databases, Zurich, Switzerland, pp. 358-369, 1995.
Algebraic XML Construction and its Optimization in Natix - Fiebig, Moerkotte (2002) (2 citations) (Correct)
No context found.
A. Gupta, V. Harinarayan, and D. Quass. Aggregate-Query Processing in Data Warehousing Environments. In Proc. Int. Conf. on Very Large Data Bases (VLDB), pages 358-369, 1995.
Variations in Grouping and Aggregation - Westmann, Moerkotte (1999) (Correct)
No context found.
Ashish Gupta, Venky Harinarayan, and Dallan Quass. Aggregate-query processing in data warehousing environments. In VLDB '95 [VLD95], pages 358-369.
Supporting Ad-hoc Ranking Aggregates - Chengkai Li Kevin (2006) (Correct)
No context found.
A. Gupta, V. Harinarayan, and D. Quass. Aggregate query processing in data warehousing environments. In VLDB, pages 358--369, 1995.
A Relational Operator Approach to Data Fusion - Jens Bleiholder.. (Correct)
No context found.
A. Gupta, V. Harinarayan, and D. Quass. Aggregatequery processing in data warehousing environments. In Proc. of VLDB, pages 358--369, 1995.
Answering Queries with Aggregation Using Views - Divesh Srivastava Divesh (1996) (61 citations) (Correct)
No context found.
A. Gupta, V. Harinarayan, and D. Quass. Aggregatequery processing in data warehousing environments. In Proc. VLDB, 1995.
Efficient Processing of Ad-Hoc Top-k Aggregate Queries in OLAP - Li, Chang, Ilyas (2005) (Correct)
No context found.
A. Gupta, V. Harinarayan, and D. Quass. Aggregate query processing in data warehousing environments. In VLDB, pages 358--369, 1995.
Lifespan of Data in a Warehouse - Makkonen (1999) (Correct)
No context found.
Gupta, A., Harinarayan, V., Quass, D., Aggregate-Query Processing in Data Warehousing Environments. Proc. 21st Conference on Very Large Data Bases (VLDB), Zurich 1995, 358-369.
Extending Practical Pre-Aggregation - In On-Line Analytical (1999) (Correct)
No context found.
A. Gupta et al. Aggregate Query Processing in Data WarehousingEnvironments. In Proc. of VLDB, pp. 358--369, 1995.
Hierarchical Compact Cube for Range-Max Queries - Sin Yeung Lee (2000) (3 citations) (Correct)
No context found.
Ashish Gupta, Venky Harinarayan, Dallan Quass, "Aggregate-query processing in data warehousing environments". In Proceedings of the 18 Inernational Conference on Very Large Databases, pages 358-369, Zurich, Switzerland, September 1995.
Extending Practical Pre-Aggregation - In On-Line Analytical (1999) (Correct)
No context found.
A. Gupta et al. Aggregate Query Processing in Data Warehousing Environments. In Proc. of VLDB, pp. 358--369, 1995.
Extending Practical Pre-Aggregation - In On-Line Analytical (Correct)
No context found.
A. Gupta, V. Harinarayan, and D. Quass. Aggregate Query Processing in Data Warehousing Environments. In Proceedings of the Twenty-First International Conference on Very Large Data Bases, pp. 358--369, 1995.
Network Sensitivity to Hot-Potato Disruptions - Teixeira, Shaikh, Griffin.. (2004) (6 citations) (Correct)
No context found.
A. Gupta, V. Harinarayan, and D. Quass, "Aggregate query processing in data warehousing environments," in VLDB, pp. 358--369, 1995.
MiniCon: A Scalable Algorithm for Answering Queries Using Views - Pottinger, Halevy (2001) (1 citation) (Correct)
No context found.
Ashish Gupta, Venky Harinarayan, and Dallan Quass. Aggregate-query processing in data warehousing environments. In VLDB, 1995.
Rewriting Aggregate Queries using Description Logic - David Dehaan David (Correct)
No context found.
A. Gupta, V. Harinarayan, and D. Quass. Aggregate-query processing in data warehousing environments. In Proc. 21th Int'l Conference on Very Large Data Bases (VLDB'95), pages 358--369, 1995.
Unknown - Programma Di Ricerca (Correct)
No context found.
A. Gupta, V. Harinarayan, and D. Quass, Aggregate query processing in data warehousing environments. In Proc 21 Very Large Database Conf. (VLDB95), Zurich, Switzerland, 1995.
Unknown - Integrazione Warehousing Mining (Correct)
No context found.
A. Gupta, V. Harinarayan, and D. Quass. Aggregate-Query Processing in Data Warehousing Environments. In Proc. 21st VLDB, Zurich, Swizerland, 1995.
Answering Queries Using Views: A Survey - Halevy (2000) (103 citations) (Correct)
No context found.
Ashish Gupta, Venky Harinarayan, and Dallan Quass. Aggregate-query processing in data warehousing environments. In Proc. of VLDB, pages 358-369, 1995.
Dynamic Caching of Query Results for Decision Support.. - Shim, Scheuermann.. (1999) (4 citations) (Correct)
No context found.
A. Gupta, V. Harinarayan, and D. Quass. Aggregate-query processing in data warehousing environments. In Proceedings of the International Conference on Very Large Databases, 1995.
First 50 documents Next 50
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