Efficient Geometric Pruning Strategies for Continuous Skyline Queries Article Swipe
Jiping Zheng
,
Jialiang Chen
,
Haixiang Wang
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.3390/ijgi6030091
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.3390/ijgi6030091
The skyline query processing problem has been well studied for many years. The literature on skyline algorithms so far mainly considers static query points on static attributes. With the popular usage of mobile devices along with the increasing number of mobile applications and users, continuous skyline query processing on both static and dynamic attributes has become more pressing. Existing efforts on supporting moving query points assume that the query point moves with only one direction and constant speed. In this paper, we propose continuous skyline computation over an incremental motion model. The query point moves incrementally in discrete time steps with no restrictions and predictability. Geometric properties over incremental motion denoted by a kinetic data structure are utilized to prune the portion of data points not included in final skyline query results. Various geometric strategies are asymptotically proposed to prune the querying dataset, and event-driven mechanisms are adopted to process continuous skyline queries. Extensive experiments under different data sets and parameters demonstrate that the proposed method is robust and more efficient than multiple snapshots of I/O optimal branch-and-bound skyline (BBS) skyline queries.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/ijgi6030091
- https://www.mdpi.com/2220-9964/6/3/91/pdf?version=1490177661
- OA Status
- gold
- Cited By
- 4
- References
- 38
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2604100891
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Raw OpenAlex JSON
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https://openalex.org/W2604100891Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/ijgi6030091Digital Object Identifier
- Title
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Efficient Geometric Pruning Strategies for Continuous Skyline QueriesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2017Year of publication
- Publication date
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2017-03-22Full publication date if available
- Authors
-
Jiping Zheng, Jialiang Chen, Haixiang WangList of authors in order
- Landing page
-
https://doi.org/10.3390/ijgi6030091Publisher landing page
- PDF URL
-
https://www.mdpi.com/2220-9964/6/3/91/pdf?version=1490177661Direct link to full text PDF
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2220-9964/6/3/91/pdf?version=1490177661Direct OA link when available
- Concepts
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Skyline, Computer science, Pruning, Point (geometry), Process (computing), Data mining, Theoretical computer science, Mathematics, Geometry, Operating system, Biology, AgronomyTop concepts (fields/topics) attached by OpenAlex
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4Total citation count in OpenAlex
- Citations by year (recent)
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2021: 2, 2018: 1, 2017: 1Per-year citation counts (last 5 years)
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38Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.I/O | 175 |
| abstract_inverted_index.The | 0, 12, 91 |
| abstract_inverted_index.and | 42, 51, 75, 103, 143, 159, 168 |
| abstract_inverted_index.are | 116, 135, 146 |
| abstract_inverted_index.far | 18 |
| abstract_inverted_index.for | 9 |
| abstract_inverted_index.has | 5, 54 |
| abstract_inverted_index.not | 125 |
| abstract_inverted_index.one | 73 |
| abstract_inverted_index.the | 28, 36, 67, 120, 140, 163 |
| abstract_inverted_index.With | 27 |
| abstract_inverted_index.been | 6 |
| abstract_inverted_index.both | 49 |
| abstract_inverted_index.data | 114, 123, 157 |
| abstract_inverted_index.many | 10 |
| abstract_inverted_index.more | 56, 169 |
| abstract_inverted_index.only | 72 |
| abstract_inverted_index.over | 86, 107 |
| abstract_inverted_index.sets | 158 |
| abstract_inverted_index.than | 171 |
| abstract_inverted_index.that | 66, 162 |
| abstract_inverted_index.this | 79 |
| abstract_inverted_index.time | 98 |
| abstract_inverted_index.well | 7 |
| abstract_inverted_index.with | 35, 71, 100 |
| abstract_inverted_index.(BBS) | 179 |
| abstract_inverted_index.along | 34 |
| abstract_inverted_index.final | 128 |
| abstract_inverted_index.moves | 70, 94 |
| abstract_inverted_index.point | 69, 93 |
| abstract_inverted_index.prune | 119, 139 |
| abstract_inverted_index.query | 2, 22, 46, 63, 68, 92, 130 |
| abstract_inverted_index.steps | 99 |
| abstract_inverted_index.under | 155 |
| abstract_inverted_index.usage | 30 |
| abstract_inverted_index.assume | 65 |
| abstract_inverted_index.become | 55 |
| abstract_inverted_index.mainly | 19 |
| abstract_inverted_index.method | 165 |
| abstract_inverted_index.mobile | 32, 40 |
| abstract_inverted_index.model. | 90 |
| abstract_inverted_index.motion | 89, 109 |
| abstract_inverted_index.moving | 62 |
| abstract_inverted_index.number | 38 |
| abstract_inverted_index.paper, | 80 |
| abstract_inverted_index.points | 23, 64, 124 |
| abstract_inverted_index.robust | 167 |
| abstract_inverted_index.speed. | 77 |
| abstract_inverted_index.static | 21, 25, 50 |
| abstract_inverted_index.users, | 43 |
| abstract_inverted_index.years. | 11 |
| abstract_inverted_index.Various | 132 |
| abstract_inverted_index.adopted | 147 |
| abstract_inverted_index.denoted | 110 |
| abstract_inverted_index.devices | 33 |
| abstract_inverted_index.dynamic | 52 |
| abstract_inverted_index.efforts | 59 |
| abstract_inverted_index.kinetic | 113 |
| abstract_inverted_index.optimal | 176 |
| abstract_inverted_index.popular | 29 |
| abstract_inverted_index.portion | 121 |
| abstract_inverted_index.problem | 4 |
| abstract_inverted_index.process | 149 |
| abstract_inverted_index.propose | 82 |
| abstract_inverted_index.skyline | 1, 15, 45, 84, 129, 151, 178, 180 |
| abstract_inverted_index.studied | 8 |
| abstract_inverted_index.Existing | 58 |
| abstract_inverted_index.constant | 76 |
| abstract_inverted_index.dataset, | 142 |
| abstract_inverted_index.discrete | 97 |
| abstract_inverted_index.included | 126 |
| abstract_inverted_index.multiple | 172 |
| abstract_inverted_index.proposed | 137, 164 |
| abstract_inverted_index.queries. | 152, 181 |
| abstract_inverted_index.querying | 141 |
| abstract_inverted_index.results. | 131 |
| abstract_inverted_index.utilized | 117 |
| abstract_inverted_index.Extensive | 153 |
| abstract_inverted_index.Geometric | 105 |
| abstract_inverted_index.considers | 20 |
| abstract_inverted_index.different | 156 |
| abstract_inverted_index.direction | 74 |
| abstract_inverted_index.efficient | 170 |
| abstract_inverted_index.geometric | 133 |
| abstract_inverted_index.pressing. | 57 |
| abstract_inverted_index.snapshots | 173 |
| abstract_inverted_index.structure | 115 |
| abstract_inverted_index.algorithms | 16 |
| abstract_inverted_index.attributes | 53 |
| abstract_inverted_index.continuous | 44, 83, 150 |
| abstract_inverted_index.increasing | 37 |
| abstract_inverted_index.literature | 13 |
| abstract_inverted_index.mechanisms | 145 |
| abstract_inverted_index.parameters | 160 |
| abstract_inverted_index.processing | 3, 47 |
| abstract_inverted_index.properties | 106 |
| abstract_inverted_index.strategies | 134 |
| abstract_inverted_index.supporting | 61 |
| abstract_inverted_index.attributes. | 26 |
| abstract_inverted_index.computation | 85 |
| abstract_inverted_index.demonstrate | 161 |
| abstract_inverted_index.experiments | 154 |
| abstract_inverted_index.incremental | 88, 108 |
| abstract_inverted_index.applications | 41 |
| abstract_inverted_index.event-driven | 144 |
| abstract_inverted_index.restrictions | 102 |
| abstract_inverted_index.incrementally | 95 |
| abstract_inverted_index.asymptotically | 136 |
| abstract_inverted_index.predictability. | 104 |
| abstract_inverted_index.branch-and-bound | 177 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5046551456 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I31746571, https://openalex.org/I9842412 |
| citation_normalized_percentile.value | 0.54890246 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |