Gibbons, P. B., Matias, Y., Poosala, V., AQUA Project White Paper, At http://www.bell-labs.com/user/pbgibbons/papers, 1997.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....concurrency control in multidimensional AMs. 2. 8 Approximate Query Answering Techniques Approximate query processing has recently emerged as a viable, cost effective solution for dealing with the huge data volumes and stringent response time requirements of today s Decision Support Systems (DSS) [1, 51, 53, 61, 64, 70, 115, 144, 145]. The general approach is to first construct compact synopses of the interesting relations in the database (using a data reduction technique) and then answering the user queries Figure 2.10: Data reduction techniques for approximate query answering. by using just the synopsis. Data reduction ....

....digits of precision will suffice (e.g. the leading few digits of a total in the millions or the nearest percentile of a percentage) 1] Prior Work. The strong incentive for approximate answers has spurred a flurry of research activity on approximate query processing techniques in recent years [1, 51, 53, 61, 64, 70, 115, 144, 145]. The majority 112 of the proposed techniques, however, have been somewhat limited in their query processing scope, typically focusing on specific forms of aggregate queries. Besides the type of queries supported, another crucial aspect of an approximate query processing technique is the employed ....

Phillip B. Gibbons, Yossi Matias, and Viswanath Poosala. "Aqua Project White Paper". Unpublished Manuscript (Bell Laboratories), December 1997.

.... proofs of all theoretical results from this paper and refer the reader to a full version of this paper for all the details [AGPR99b] The research in this paper was conducted as part of our efforts to develop an efficient decision support system based on approximate query answering, called Aqua [GMP97a] A brief introduction of Aqua is presented in the next section. 2 The Aqua System The goal of Aqua is to improve response times for queries by avoiding accesses to the original data altogether. Instead, Aqua maintains smaller sized statistical summaries, called synopses, on the warehouse and ....

....answers along with error bounds for a 4 way join query. The good quality of the approximate answers is in part due to the use of join synopses to answer foreign key join queries. The figure also shows the times taken to generate the two answers. Further details on Aqua are available in [GMP97a, AGPR99b, AGPR99a] In the rest of the paper, we motivate the need for join synopses and present optimal allocation schemes and maintenance techniques for them. 3 The Problem with Joins A natural set of synopses for an approximate query engine would include uniform random samples of each base ....

P. B. Gibbons, Y. Matias, and V. Poosala. Aqua project white paper. Technical report, Bell Laboratories, Murray Hill, New Jersey, December 1997.

....the TPC D benchmark [TPC] for examples of such queries) The goal is to provide an estimated response in orders of magnitude less time than the time to compute an exact answer, by avoiding or minimizing the number of accesses to the base data. In the Approximate query answering (Aqua) project [GMP97a, GPA 98] at Bell Labs, we seek to provide fast, approximate answers to queries using synopsis data structures. Unlike the traditional data warehouse set up depicted in Figure 1, in which each query is answered exactly using the data warehouse, Aqua considers the set up depicted in Figure 2. ....

P. B. Gibbons, Y. Matias, and V. Poosala, Aqua project white paper, Tech. report, Bell Laboratories, Murray Hill, New Jersey, December 1997.

....the TPC D benchmark [TPC] for examples of such queries) The goal is to provide an estimated response in orders of magnitude less time than the time to compute an exact answer, by avoiding or minimizing the number of accesses to the base data. In the Approximate query answering (Aqua) project [GMP97a, GPA 98] at Bell Labs, we seek to provide fast, approximate answers to queries using synopsis data structures. Unlike the traditional data warehouse set up depicted in Figure 1, in which each query is answered exactly using the data warehouse, Aqua considers the set up depicted in Figure 2. ....

P. B. Gibbons, Y. Matias, and V. Poosala. Aqua project white paper. Technical report, Bell Laboratories, Murray Hill, New Jersey, December 1997.

.... While the current system focuses on answers to broad classes of queries, special features can be added to Aqua to improve the accuracy of specific classes of queries, such as those reported in [AMS96, GMP97b, BDF 97, GP97, GM98] Further details can be found in the Aqua Project White Paper [GMP97a] Acknowledgements We thank Minos Garofalakis and Hank Korth for discussions related to this work. ....

P. B. Gibbons, Y. Matias, and V. Poosala. Aqua project white paper. Technical report, Bell Laboratories, Murray Hill, New Jersey, December 1997.

....97] presents a survey of data reduction techniques for massive data sets. GM98b] presents a formal framework for evaluating synopsis data structures and a survey of some of the results in this area. There has been a flurry of recent work in approximate query answering (e.g. VL93, BDF 97, GMP97a, GMP97b, HHW97, GM98a, AGPR99, HH99, AGP99, MS99] The work in [HHW97, AGPR99, HH99] has looked at the problem of providing approximate answers to queries seeking aggregates (e.g. sum, avg) of attribute values for the tuples satisfying a predicate that occur in the join of multiple relations. ....

P. B. Gibbons, Y. Matias, and V. Poosala. Aqua project white paper. Technical report, Bell Laboratories, Murray Hill, New Jersey, December 1997.

....1 Our goal is provide an estimated response in orders of magnitude less time than the time to compute an exact answer, by avoiding or minimizing the number of accesses to the base data. While there has been a flurry of recent work in approximate query answering (e.g. VL93, BDF 97, GMP97a, GMP97b, HHW97, GM98a] only the work by Hellerstein et al. [HHW97] has looked at the problem of approximate join aggregates. We consider this to be an important problem since most non trivial queries, especially on data warehousing schemas, involve joining two or more tables. For example, 13 of ....

....answers to queries of arbitrary low selectivity with a fixed amount of storage. Our goal is to show that for a given amount of space, join synopses work better than schemes that use only samples of base relations. 2 The research in this paper was conducted as part of the Aqua project [GMP97a] at Bell Labs. The goal of the Aqua project is to develop an approximate query answering engine. The engine has been designed to run on top of any commercial DBMS. The engine uses the DBMS to store synopses of the original data and provides approximate answers via query rewriting. This ....

[Article contains additional citation context not shown here]

P. B. Gibbons, Y. Matias, and V. Poosala. Aqua project white paper. Technical report, Bell Laboratories, Murray Hill, New Jersey, December 1997.

.... proofs of all theoretical results from this paper and refer the reader to a full version of this paper for all the details [AGPR99b] The research in this paper was conducted as part of our efforts to develop an efficient decision support system based on approximate query answering, called Aqua [GMP97a] A brief introduction of Aqua is presented in the next section. 2 The Aqua System The goal of Aqua is to improve response times for queries by avoiding accesses to the original data altogether. Instead, Aqua maintains smaller sized statistical summaries, called synopses, on the warehouse and ....

....answers along with error bounds for a 4 way join query. The good quality of the approximate answers is in part due to the use of join synopses to answer foreign key join queries. The figure also shows the times taken to generate the two answers. Further details on Aqua are available in [GMP97a, AGPR99b, AGPR99a] In the rest of the paper, we motivate the need for join synopses and present optimal allocation schemes and maintenance techniques for them. 3 The Problem with Joins A natural set of synopses for an approximate query engine would include uniform random samples of each base ....

P. B. Gibbons, Y. Matias, and V. Poosala. Aqua project white paper. Technical report, Bell Laboratories, Murray Hill, New Jersey, December 1997.

....(often large) advantage over using a traditional random sample. Our algorithms maintain their accuracy in the presence of ongoing insertions to the data warehouse. This work is part of the Approximate QUery Answering (AQUA) project at Bell Labs. Further details on the Aqua project can be found in [GMP97a, GPA 98] Outline. Section 2 discusses previous related work. Concise samples are studied in Section 3, and counting samples are studied in Section 4. Finally, in Section 5, we describe their application to hot list queries. 2 Previous related work Hellerstein, Haas, and Wang [HHW97] ....

P. B. Gibbons, Y. Matias, and V. Poosala. Aqua project white paper. Technical report, Bell Laboratories, Murray Hill, New Jersey, December 1997.

....of the art in data reduction techniques. Our work on synopsis data structures also includes the use of multifractals and wavelets for synopsis data structures [4, 13] and join synopses for queries on the join of multiple sets. This work is part of the Approximate query answering (Aqua) project [9, 11] at Bell Labs; Aqua seeks to provide fast, approximate answers to queries using synopsis data structures. While synopsis data structures have been proposed and studied for a number of query problems (see the full paper for additional examples) many more open questions remain, and we hope that ....

P. B. Gibbons, Y. Matias, and V. Poosala, Aqua project white paper, tech. rep., Bell Laboratories, Murray Hill, New Jersey, Dec. 1997.

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Gibbons, P. B., Matias, Y., Poosala, V., AQUA Project White Paper, At http://www.bell-labs.com/user/pbgibbons/papers, 1997.

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