P. J. Haas and J. M. Hellerstein. Ripple joins for online aggregation. In ACM SIGMOD, June 1999. Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
First 50 documents Next 50
Query Optimization to Meet Performance Targets for.. - Vladimir Zadorozhny.. (2002) (1 citation) (Correct)
....has been extensive prior research on query evaluation techniques to accommodate transient behavior. Reactive query evaluation techniques at the plan level are described in [1, 4, 18, 25] Alternately, adaptive evaluation techniques at the plan and operator implementation level are described in [3, 11, 12, 14, 24]. Research in [2, 7, 13, 15, 29] have addressed various aspects of the task of modifying an optimizer to handle distributions for various parameters, e.g. available memory, or intermediate join cardinality. The Tukwila project [14] provides an adaptive framework composed of rules and adaptive ....
....operators that are sensitive to transient factors. Its optimizer also has a re optimization capability based on pipelined fragments of query execution. The Telegraph project explores adaptive fine grained data flow based query processing techniques using rivers, eddies, ripple join, XJoin, etc. [3, 11, 24]. They also focus on continuous and high frequency response to changing feedback. Query scrambling [1, 25] is a query optimization and evaluation technique to deal with transient conditions, e.g. unexpected delay. It is based on plan re organization to avoid idle time, and the synthesis of new ....
P. Haas and J. Hellerstein. Ripple joins for online aggregation. SIGMOD Conf., 1999.
Interactivity, Scalability and Resource control for - Efficient Kdd Support (2002) (Correct)
....rection in online query processing is trading interactivity for accuracy. The goal is to provide early results that reflect the trends within the data, and do so with an increasing level of confidence as query processing progresses. The most prominent operator in this context is the ripple join [4], which provides the possibility to adjust the rate at which the inputs are processed to allow the user to favor the most interesting input. The area of adaptive query processing deals with the goal of providing partial results under conditions of incomplete input data at a limited bandwidth. A ....
P.J. Haas and J.M. Hellerstein. Ripple joins for online aggregation. In A. Delis, C. Faloutsos, and S. Ghandeharizadeh, editors, SIGMOD 1999, Proceedings ACM SIGMOD International Conference on Management of Data, June 1-3, 1999, Philadephia, Pennsylvania, USA, pages 287--298. ACM Press, 1999.
Tracking Join and Self-Join Sizes in Limited Storage - Alon, Gibbons, Matias, Szegedy (2002) (27 citations) (Correct)
....97] for a recent survey. GM99] presented a formal framework for evaluating such sublinear space 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, Olk93, BDF 97, HHW97, GM98, AGPR99, HH99, VW99, IP99, AGP00, GLR00, CCMN00, CGRS00, MVW00, CDN01, LM01, Gib01, GKS01] The work in [HHW97, AGPR99, HH99, IP99, CGRS00] looked at the problem of providing approximate answers to queries seeking aggregates (e.g. count, sum, avg) of attribute values for the tuples satisfying a predicate ....
....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, Olk93, BDF 97, HHW97, GM98, AGPR99, HH99, VW99, IP99, AGP00, GLR00, CCMN00, CGRS00, MVW00, CDN01, LM01, Gib01, GKS01] The work in [HHW97, AGPR99, HH99, IP99, CGRS00] looked at the problem of providing approximate answers to queries seeking aggregates (e.g. count, sum, avg) of attribute values for the tuples satisfying a predicate that occur in the join of multiple relations. The count aggregate (over joins but with no other predicates) ....
[Article contains additional citation context not shown here]
P. Haas and J. Hellerstein. Ripple joins for online aggregation. In Proc. ACM SIGMOD International Conf. on Management of Data, pages 287--298, June 1999.
Managing Large Multidimensional Datasets Inside A Database System - Chakrabarti (2001) (Correct)
....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 ....
....the approximate answer can be computed much faster compared to the exact answer. Sample synopsis can be either precomputed (as shown in the example above) and maintain incrementally [1, 51] or can be obtained progressively at run time by accessing the base data using appropriate access methods [61, 64]. Random samples typically provide accurate estimates for aggregate quantities (e.g. count, sum and average) Random samples can provide probabilistic guarantees on the quality of estimated aggregate [60] Sampling techniques have several disadvantages, especially for non aggregate queries and ....
[Article contains additional citation context not shown here]
Peter J. Haas and Joseph M. Hellerstein. "Ripple Joins for Online Aggregation". In Proceedings of the 1999.
A Wakeup Call for Internet Monitoring Systems: - The Case For Self-citation (Hellerstein) (Correct)
No context found.
P. J. Haas and J. M. Hellerstein. Ripple joins for online aggregation. In ACM SIGMOD, June 1999.
Online Dynamic Reordering for Interactive Data Processing - Vijayshankar Raman Bhaskaran (1999) (16 citations) Self-citation (Hellerstein) (Correct)
No context found.
P. Haas and J. M. Hellerstein. Ripple joins for online aggregation. In SIGMOD, 1999.
Data Triage: An Adaptive Architecture for Load Shedding in.. - Reiss, Hellerstein (2004) Self-citation (Hellerstein) (Correct)
No context found.
P. J. Haas and J. M. Hellerstein. Ripple joins for online aggregation. In A. Delis, C. Faloutsos, and S. Ghandeharizadeh, editors, SIGMOD Proceedings, pages 287--298. ACM Press, 1999.
Using State Modules for Adaptive Query Processing - Raman, Deshpande, Hellerstein (2003) (15 citations) Self-citation (Hellerstein) (Correct)
No context found.
P. J. Haas and J. M. Hellerstein. Ripple joins for Online Aggregation. In SIGMOD, 1999.
The Sensor Network as a Database - Ramesh Govindan Icsi (2002) (2 citations) Self-citation (Hellerstein) (Correct)
....tuples from the other input s hash table and outputs any matching results, then inserts itself into its own hash table. It is symmetric because the action for each tuple from either table is the same. A generalization of symmetric hash joins is the family of join methods called ripple joins [17]. These join methods statistically sample the two tables to be joined, in order to produce a stream of joined tuples. The relative rates at which the two tables are sampled adapt to match the variance produced by the data in each. When used together with an aggregation operator, they provide ....
....these techniques, the performance improvements clearly depend on the underlying data distribution. In the database literature, the statistical quality of approximate results can be robustly described via confidence intervals for aggregate estimators run over i.i.d. samples of the database (e.g. [24, 22, 17]) Such a robust statistical characterization of approximate result quality for sensor networks is a much more complex challenge, since it may require modeling network losses in tandem with sensor sampling rates, noise models, and so on. One way to address this issue would use simulation and ....
Peter J. Haas and Joseph M. Hellerstein. Ripple Joins for Online Aggregation. In Proc. ACM-SIGMOD International Conference on Management of Data, pages 287--298, Philadelphia, 1999.
Supporting Ad-hoc Ranking Aggregates - Chengkai Li Kevin (2006) (Correct)
No context found.
P. J. Haas and J. M. Hellerstein. Ripple joins for online aggregation. In SIGMOD, pages 287--298, 1999.
liquid: Context-Aware Distributed Queries - Heer, Newberger, Beckmann, Hong (2003) (3 citations) (Correct)
No context found.
P. J. Haas and J. M. Hellerstein. "Ripple joins for online aggregation." In A. Delis, C. Faloutsos, and S. Ghandeharizadeh, editors, SIGMOD 1999.
Online Estimation For Subset-Based SQL Queries - Christopher Jermaine Alin (2005) (Correct)
No context found.
P. J. Haas and J. M. Hellerstein. Ripple joins for Online Aggregation. In SIGMOD, pages 287 -- 298, 1999.
Fjordingth - Stream An Archw (Correct)
No context found.
P. Hass and J. Hellerstein. Ripple joins for online aggregation. In ACM SIGMOD, pages287--298, Philadelphia, PA, June 1999.
Early Hash Join: A Configurable Algorithm for the Efficient and.. - Lawrence (2005) (Correct)
No context found.
P. J. Haas and J. M. Hellerstein. Ripple joins for online aggregation. In SIGMOD 1999.
Efficient Processing of Ad-Hoc Top-k Aggregate Queries in OLAP - Li, Chang, Ilyas (2005) (Correct)
No context found.
P. J. Haas and J. M. Hellerstein. Ripple joins for online aggregation. In SIGMOD, pages 287--298, 1999.
RankSQL: Query Algebra and Optimization for Relational.. - Li, Chang, Ilyas, Song (2004) (1 citation) (Correct)
No context found.
P. J. Haas and J. M. Hellerstein. Ripple joins for online aggregation. In SIGMOD, pages 287--298, 1999.
RankSQL: Query Algebra and Optimization for Relational.. - Li, Chang, Ilyas, Song (2005) (1 citation) (Correct)
No context found.
P. J. Haas and J. M. Hellerstein. Ripple joins for online aggregation. In SIGMOD, pages 287--298, 1999.
An Adaptable Distributed Query Processing Architecture - Zhou, Ooi, Tan, Tok (Correct)
No context found.
P. J. Haas, J. M. Hellerstein, Ripple joins for online aggregation, in: Proceedings of the 1999.
Adapting to Source Properties in Processing Data Integration .. - Ives, Halevy, Weld (2004) (Correct)
No context found.
P. J. Haas and J. M. Hellerstein. Ripple joins for online aggregation. In SIGMOD '99.
Mobile E-Services and Their Challenges to Data Warehousing - Christian Jensen And (2001) (Correct)
No context found.
P. J. Haas and J. M. Hellerstein. Ripple Joins for Online Aggregation. In Proceedings of the ACM SIGMOD International Conference on the Management of Data, pp. 287--298, 1999.
Supporting Top-k Join Queries in Relational Databases - Ilyas, Aref, Elmagarmid (2003) (Correct)
No context found.
Peter J. Haas and Joseph M. Hellerstein. Ripple joins for online aggregation. In SIGMOD, Philadelphia, Pennsylvania, USA, june 1999.
The History of Histograms (abridged) - Ioannidis (2003) (Correct)
No context found.
Haas P., Hellerstein J.: Ripple Joins for Online Aggregation. SIGMOD Conf. (1999) 287-298
Supporting Top-k Join Queries in Relational Databases - Ilyas, Aref, Elmagarmid (Correct)
No context found.
Peter J. Haas and Joseph M. Hellerstein. Ripple joins for online aggregation. In SIGMOD, Philadelphia, Pennsylvania, USA, june 1999.
Fjording the Stream: An Architecture for Queries over.. - Samuel Madden Michael (2002) (64 citations) (Correct)
No context found.
P. Hass and J. Hellerstein. Ripple joins for online aggregation. In ACM SIGMOD, pages 287--298, Philadelphia, PA, June 1999.
Efficient Query Processing for Data Integration - Ives (2002) (4 citations) (Correct)
No context found.
Peter J. Haas and Joseph M. Hellerstein. Ripple joins for online aggregation. In SIGMOD 1999, Proceedings ACM SIGMOD International Conference on Management of Data, June 1-3, 1999, Philadelphia, Pennsylvania, USA, pages 287--298, 1999. 176
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