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Online Aggregation

Online Aggregation

Online Aggregation. Joseph M. Hellerstein Peter J. Haas Helen J. Wang. Motivation for Online Aggregation. Traditional aggregation takes a long time to return a very small final result from a large amount of data The result does not have to be very precise!

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685 views • 51 slides
User Interaction

User Interaction

User Interaction. CS511 Presentation: Dec-02-2005 Jesus Alvarez Jianwen Lai James Lin Long Vu. Road Map. Introduction & Motivation Application scenarios for Online Query Processing CONTROL online query processing Implementation Study of CONTROL Summary Future work. Road Map.

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592 views • 49 slides
Supporting Top- k join Queries in Relational Databases

Supporting Top- k join Queries in Relational Databases

Supporting Top- k join Queries in Relational Databases. Ihab F. Ilyas , Walid G. Aref , Ahmed K. Elmagarmid. Presented by: Richa Varshney. Introduction. O rdered set of join results according to some provided function. Often searches are done on multiple features.

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487 views • 35 slides
Supporting Top- k join Queries in Relational Databases

Supporting Top- k join Queries in Relational Databases

Supporting Top- k join Queries in Relational Databases. By:Ihab F. Ilyas, Walid G. Aref, Ahmed K. Elmagarmid Presented by: Calvin R Noronha (1000578539) Deepak Anand (1000603813).

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398 views • 29 slides
Ripple Joins for Online Aggregation

Ripple Joins for Online Aggregation

Ripple Joins for Online Aggregation. by Peter J. Haas and Joseph M. Hellerstein published in June 1999 presented by Nag Prajval B.C. In Simple words.

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423 views • 24 slides
Supporting Top- k join Queries in Relational Databases

Supporting Top- k join Queries in Relational Databases

Supporting Top- k join Queries in Relational Databases. Ihab F. Ilyas, Walid G. Aref, Ahmed K. Elmagarmid. Presented by: Z. Joseph, CSE-UT Arlington. Introduction. Often searches are done on multiple features Each feature produces a different ranking for the query

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229 views • 13 slides

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