Aggregate Skyline: Analysis for Online Users

Antony, Shyam; Wu, Peng; Agrawal, Divyakant; Abbadi, Amr El

doi:10.1109/saint.2009.17

Cited by 8 publications

(3 citation statements)

References 6 publications

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“…The functions map sets to scalar values. The space of sets may consist of all kelement subsets of a given set of tuples [28,35,52], or all the tuple groups in a set of tuples [2]. Now a set T dominates another set S if the profile of T Pareto-dominates the profile of S. This approach to set dominance can be generalized to arbitrary dominance relations [52].…”

Section: Dominance In Other Spacesmentioning

confidence: 99%

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Skyline queries, front and back

2013

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Skyline queries are a popular way to obtain preferred answers from the database by providing only the orderings of attribute values. The result of a skyline query consists of those input tuples for which there is no input tuple having better or equal values in all the attributes and a better value in at least one attribute. In this article, we summarize the basic notions and properties of skyline queries, and discuss their extensions and generalizations. In particular, we consider skyline algorithms and skyline cardinality issues.

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Section: Dominance In Other Spacesmentioning

confidence: 99%

“…It is important to keep in mind the distinctions between (1) Pareto dominance, (2) skyline queries that return all the non-Pareto-dominated objects, (3) skylines: the results of skyline queries, and (4) skyline algorithms that compute skylines.…”

Section: Introductionmentioning

confidence: 99%

Skyline queries, front and back

2013

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“…Skyline queries have been adapted to the OLAP context [29] as the Multi-Objective OLAP (MOOLAP) framework. Like diamonds, the goal is to allow an analyst to focus on interesting data.…”

Section: Skyline Operatormentioning

confidence: 99%

Diamond dicing

Webb

Lemire

Kaser

2013

Data & Knowledge Engineering

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In OLAP, analysts often select an interesting sample of the data. For example, an analyst might focus on products bringing revenues of at least $100 000, or on shops having sales greater than $400 000. However, current systems do not allow the application of both of these thresholds simultaneously, selecting products and shops satisfying both thresholds. For such purposes, we introduce the diamond cube operator, filling a gap among existing data warehouse operations.Because of the interaction between dimensions the computation of diamond cubes is challenging. We compare and test various algorithms on large data sets of more than 100 million facts. We find that while it is possible to implement diamonds in SQL, it is inefficient. Indeed, our custom implementation can be a hundred times faster than popular database engines (including a row-store and a column-store).

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Preference Specification

Chomicki¹

2018

Encyclopedia of Database Systems

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Aggregate Skyline: Analysis for Online Users

Cited by 8 publications

References 6 publications

Skyline queries, front and back

Skyline queries, front and back

Diamond dicing

Preference Specification

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