Efficient skyline querying with variable user preferences on nominal attributes

Wong, Raymond Chi-Wing; Fu, Ada Wai-Chee; Pei, Jian; Ho, Yip Sing; Wong, Tai; Liu, Yubao

doi:10.14778/1453856.1453967

Cited by 53 publications

(29 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Since it is not possible to list a complete survey of all papers, we mention a few here. Algorithms using the bitmap method [25] and for partially-ordered attributes to capture dynamic user preferences [6,5,32,22] , computing cardinality or exploiting low cardinality domains [7,17], sliding window or time-series skyline queries [15,27,12,35], distributed and super-peer architectures [2,31,36,19], representative skylines [26], probabilistic skylines on uncertain data [20] have been studied. We mention the streaming and distributed related works again in Sections 3 and 5.1 respectively.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Randomized multi-pass streaming skyline algorithms

et al. 2009

View full text Add to dashboard Cite

We consider external algorithms for skyline computation without pre-processing. Our goal is to develop an algorithm with a good worst case guarantee while performing well on average. Due to the nature of disks, it is desirable that such algorithms access the input as a stream (even if in multiple passes). Using the tools of randomness, proved to be useful in many applications, we present an efficient multi-pass streaming algorithm, RAND, for skyline computation. As far as we are aware, RAND is the first randomized skyline algorithm in the literature.RAND is near-optimal for the streaming model, which we prove via a simple lower bound. Additionally, our algorithm is distributable and can handle partially ordered domains on each attribute. Finally, we demonstrate the robustness of RAND via extensive experiments on both real and synthetic datasets. RAND is comparable to the existing algorithms in average case and additionally tolerant to simple modifications of the data, while other algorithms degrade considerably with such variation.

show abstract

Section: Related Workmentioning

confidence: 99%

“…This version of our algorithm on posets can be adapted to compute skylines with partially-ordered domains (see, e.g., [6,32,33,22] and references therein).…”

Section: Posetsmentioning

confidence: 99%

Randomized multi-pass streaming skyline algorithms

et al. 2009

View full text Add to dashboard Cite

show abstract

“…A recent work by Wong et al [24] studies the efficient computation of skylines on datasets with dynamic preferences on nominal attributes. Nominal attributes do not have a pre-defined (i.e., objective) order, but a custom-based preference for their values can be set.…”

Section: Skyline Queriesmentioning

confidence: 99%

“…The skyline for such data can be computed by an indexindependent method (e.g., [3]), but index-based methods cannot be applied because it is infeasible to maintain an index for each of the (exponentially many) possible orderings. In view of this, [24] proposes a technique, which precomputes and materializes the skylines for a subset of the possible orderings of the nominal attributes. Given a skyline query with arbitrary user preferences on the nominal attributes, the technique computes the result efficiently from the materialized skylines.…”

Section: Skyline Queriesmentioning

confidence: 99%

A fair assignment algorithm for multiple preference queries

Mamoulis

Mouratidis

2009

Proc. VLDB Endow.

View full text Add to dashboard Cite

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.

show abstract

“…The skyline query, as an efficient tool for preference-based data analysis [27,13], has attracted a lot of attention in the database community. It has a wide range of real applications [8,26] and it can easily be incorporated into commercial database systems, as SQL is being extended with clauses for the support of preference queries [3,10,19,8].…”

Section: Introductionmentioning

confidence: 99%

Scalable skyline computation using object-based space partitioning

Zhang

Mamoulis

Cheung

2009

Proceedings of the 2009 ACM SIGMOD International Conference on Management of Data

114

View full text Add to dashboard Cite

The skyline operator returns from a set of multi-dimensional objects a subset of superior objects that are not dominated by others. This operation is considered very important in multi-objective analysis of large datasets. Although a large number of skyline methods have been proposed, the majority of them focuses on minimizing the I/O cost. However, in high dimensional spaces, the problem can easily become CPU-bound due to the large number of computations required for comparing objects with current skyline points while scanning the database. Based on this observation, we propose a dynamic indexing technique for skyline points that can be integrated into state-of-the-art sort-based skyline algorithms to boost their computational performance. The new indexing and dominance checking approach is supported by a theoretical analysis, while our experiments show that it scales well with the input size and dimensionality not only because unnecessary dominance checks are avoided but also because it allows efficient dominance checking with the help of bitwise operations.

show abstract

Efficient skyline querying with variable user preferences on nominal attributes

Cited by 53 publications

References 22 publications

Randomized multi-pass streaming skyline algorithms

Randomized multi-pass streaming skyline algorithms

A fair assignment algorithm for multiple preference queries

Scalable skyline computation using object-based space partitioning

Contact Info

Product

Resources

About