Topologically Sorted Skylines for Partially Ordered Domains

Sacharidis, Dimitris; Papadopoulos, Stavros; Papadias, Dimitris

doi:10.1109/icde.2009.129

Cited by 41 publications

(35 citation statements)

References 17 publications

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“…In the dynamic, or contextual skyline query (CSQ) [2], preferences among attribute values can vary and are specified at query time. Two recent works [30], [7] discuss CSQs in the presence of attributes with partially ordered domains. In the simplest CSQ form, an exemplar record q is provided so that all dominance relationships are defined relative to q, rather than the axes origin.…”

Section: B Skyline Query Processingmentioning

confidence: 99%

“…The majority of skyline algorithms visit tuples in a monotonic order from more to less preferred attribute values, either explicitly, e.g., by pre-sorting [21], [22], [23], [11], [24], or implicitly, e.g., using a heap [27], [2], [12], [7]. Note that even when tuples are uncertain, such an order exists if one considers the minimum bounding box of each record [11], [12].…”

Section: A Basic Iterative Algorithmmentioning

confidence: 99%

“…A skyline query returns those hotels for which there is no cheaper and, at the same time, closer to the beach alternative. In many cases, it is meaningful to specify preferences with respect to the context, i.e., the current query situation, and pose dynamic skyline queries [2], [3], [4], [5], [6], [7]. To emphasize that preferences and dominance relationships are defined relative to a context, we adopt the term contextual skyline queries (CSQ).…”

Section: Introductionmentioning

confidence: 99%

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Probabilistic contextual skylines

Sacharidis¹,

Arvanitis²,

Sellis³

2010

2010 IEEE 26th International Conference on Data Engineering (ICDE 2010)

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Abstract-The skyline query returns the most interesting tuples according to a set of explicitly defined preferences among attribute values. This work relaxes this requirement, and allows users to pose meaningful skyline queries without stating their choices. To compensate for missing knowledge, we first determine a set of uncertain preferences based on user profiles, i.e., information collected for previous contexts. Then, we define a probabilistic contextual skyline query (p-CSQ) that returns the tuples which are interesting with high probability. We emphasize that, unlike past work, uncertainty lies within the query and not the data, i.e., it is in the relationships among tuples rather than in their attribute values. Furthermore, due to the nature of this uncertainty, popular skyline methods, which rely on a particular tuple visit order, do not apply for p-CSQs. Therefore, we present novel non-indexed and index-based algorithms for answering p-CSQs. Our experimental evaluation concludes that the proposed techniques are significantly more efficient compared to a standard block nested loops approach.

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Section: B Skyline Query Processingmentioning

confidence: 99%

Section: A Basic Iterative Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Probabilistic contextual skylines

Sacharidis¹,

Arvanitis²,

Sellis³

2010

2010 IEEE 26th International Conference on Data Engineering (ICDE 2010)

Self Cite

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“…Several recent work on skyline queries have broadened the scope to include categorical attributes with partially-ordered domains in both offline [2,9] as well as online [10] environment. Categorical attributes are more general than numerical attributes as the dominance relationships among the domain values for a categorical attribute are based on a partial ordering instead of a total ordering.…”

Section: Introductionmentioning

confidence: 99%

Efficient Skyline Maintenance for Streaming Data with Partially-Ordered Domains

Chan

2010

Database Systems for Advanced Applications

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Abstract. We address the problem of skyline query processing for a count-based window of continuous streaming data that involves both totally-and partially-ordered attribute domains. In this problem, a fixedsize buffer of the N most recent tuples is dynamically maintained and the key challenge is how to efficiently maintain the skyline of the sliding window of N tuples as new tuples arrive and old tuples expire. We identify the limitations of the state-of-the-art approach STARS, and propose two new approaches, STARS + and SkyGrid, to address its drawbacks. STARS+ is an enhancement of STARS with three new optimization techniques, while SkyGrid is a simplification STARS that eliminates a key data structure used in STARS. While both new approaches outperform STARS significantly, the surprising result is that the best approach turns out to be the simplest approach, SkyGrid.

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“…Previous work [6,24,25,31] relies on partial-to-total order mapping schemes. A single value in the POD is mapped to a set of * Work supported by grant HKU 715509E from Hong Kong RGC.…”

Section: Introductionmentioning

confidence: 99%

Efficient skyline evaluation over partially ordered domains

et al. 2010

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Although there has been a considerable body of work on skyline evaluation in multidimensional data with totally ordered attribute domains, there are only a few methods that consider attributes with partially ordered domains. Existing work maps each partially ordered domain to a total order and then adapts algorithms for totallyordered domains to solve the problem. Nevertheless these methods either use stronger notions of dominance, which generate false positives, or require expensive dominance checks. In this paper, we propose two new methods, which do not have these drawbacks. The first method uses an appropriate mapping of a partial order to a total order, inspired by the lattice theorem and an off-the-shelf skyline algorithm. The second technique uses an appropriate storage and indexing approach, inspired by column stores, which enables efficient verification of whether a pair of objects are incompatible. We demonstrate that both our methods are up to an order of magnitude more efficient than previous work and scale well with different problem parameters, such as complexity of partial orders.

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Topologically Sorted Skylines for Partially Ordered Domains

Cited by 41 publications

References 17 publications

Probabilistic contextual skylines

Probabilistic contextual skylines

Efficient Skyline Maintenance for Streaming Data with Partially-Ordered Domains

Efficient skyline evaluation over partially ordered domains

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