DeltaSky: Optimal Maintenance of Skyline Deletions without Exclusive Dominance Region Generation

Wu, Peng; Agrawal, Divyakant; Eğecioǧlu, Ömer; Abbadi, Amr El

doi:10.1109/icde.2007.367894

Cited by 44 publications

(38 citation statements)

References 14 publications

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“…BBS requires computing the EDR in response to skyline removals. [26] shows that if we use a systematic way to decompose a Ddimensional EDR into a set of hyper-rectangles, then the number of rectangles is |O sky | D , where D is the dimensionality. The worst case of each intersection check becomes O(|O sky | D ) (if the MBR entry only overlaps with the last rectangle).…”

Section: Skyline Queriesmentioning

confidence: 99%

“…The worst case of each intersection check becomes O(|O sky | D ) (if the MBR entry only overlaps with the last rectangle). In view of this potentially huge number of computations, an algorithm called DeltaSky [26] is proposed, which determines the intersection between an MBR and the EDR without explicitly calculating the EDR itself. The worst case complexity of the intersection check is reduced to O(|O sky | · D).…”

Section: Skyline Queriesmentioning

confidence: 99%

“…We compare our fully optimized algorithm (SB) against SB-DeltaSky and SB-UpdateSkyline. SB-DeltaSky is Algorithm 1 using DeltaSky [26] for skyline maintenance. SB-UpdateSkyline is Algorithm 1 using our UpdateSkyline technique described in Section 5.…”

Section: Effectiveness Of Optimizationsmentioning

confidence: 99%

“…O sky :=O sky ∪ E Compared to DeltaSky [26] (i.e., the state-of-the-art skyline maintenance algorithm), UpdateSkyline performs fewer node accesses. DeltaSky accesses at most | O sky | · h nodes, where O sky is the set of new skyline objects after a removal and h is the height of the R-tree [17].…”

mentioning

confidence: 99%

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A fair assignment algorithm for multiple preference queries

Mamoulis

Mouratidis

2009

Proc. VLDB Endow.

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Consider an internship assignment system, where at the end of each academic year, interested university students search and apply for available positions, based on their preferences (e.g., nature of the job, salary, office location, etc). In a variety of facility, task or position assignment contexts, users have personal preferences expressed by different weights on the attributes of the searched objects. Although individual preference queries can be evaluated by selecting the object in the database with the highest aggregate score, in the case of multiple simultaneous requests, a single object cannot be assigned to more than one users. The challenge is to compute a fair 1-1 matching between the queries and the objects. We model this as a stable-marriage problem and propose an efficient method for its processing. Our algorithm iteratively finds stable query-object pairs and removes them from the problem. At its core lies a novel skyline maintenance technique, which we prove to be I/O optimal. We conduct an extensive experimental evaluation using real and synthetic data, which demonstrates that our approach outperforms adaptations of previous methods by several orders of magnitude.

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Section: Skyline Queriesmentioning

confidence: 99%

Section: Skyline Queriesmentioning

confidence: 99%

Section: Effectiveness Of Optimizationsmentioning

confidence: 99%

mentioning

confidence: 99%

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A fair assignment algorithm for multiple preference queries

Mamoulis

Mouratidis

2009

Proc. VLDB Endow.

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“…A traditional skyline query [1,4,22,13,19,10,23,14] yields deterministic results. It also conforms to all the four properties described in the previous subsection.…”

Section: Related Workmentioning

confidence: 99%

Understanding the meaning of a shifted sky: a general framework on extending skyline query

Zhang

Ooi

et al. 2009

The VLDB Journal

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Skyline queries are often used on data sets in multi-dimensional space for many decision-making applications. Traditionally, an object p is said to dominate another object q if, for all dimension, it is no worse than q and is better on at least one dimension. Therefore, the skyline of a data set consists of all objects not dominated by any other object.To better cater to application requirements such as controlling the size of the skyline or handling data sets that are not well-structured, various works have been proposed to extend the definition of skyline based on variants of the dominance relationship. In view of the proliferation of variants, in this paper, a generalized framework is proposed to guide the extension of skyline query from conventional definition to different variants. Our framework explicitly and carefully examines the various properties that should be preserved in a vari- ant of the dominance relationship so that: (1) maintaining original advantages, while extending adaptivity to application semantics, and (2) keeping computational complexity almost unaffected. We prove that traditional dominance is the only relationship satisfying all desirable properties, and present some new dominance relationships by relaxing some of the properties. These relationships are general enough for us to design new top-k skyline queries that return robust results of a controllable size. We analyze the existing skyline algorithms based on their minimum requirements on dominance properties. We also extend our analysis to data sets with missing values, and present extensive experimental results on the combinations of new dominance relationships and skyline algorithms.

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