A Density-Based Approach to the Retrieval of Top-K Spatial Textual Clusters Article Swipe
Dingming Wu
,
Christian S. Jensen
·
YOU?
·
· 2016
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1607.08681
YOU?
·
· 2016
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1607.08681
Keyword-based web queries with local intent retrieve web content that is relevant to supplied keywords and that represent points of interest that are near the query location. Two broad categories of such queries exist. The first encompasses queries that retrieve single spatial web objects that each satisfy the query arguments. Most proposals belong to this category. The second category, to which this paper's proposal belongs, encompasses queries that support exploratory user behavior and retrieve sets of objects that represent regions of space that may be of interest to the user. Specifically, the paper proposes a new type of query, namely the top-k spatial textual clusters (k-STC) query that returns the top-k clusters that (i) are located the closest to a given query location, (ii) contain the most relevant objects with regard to given query keywords, and (iii) have an object density that exceeds a given threshold. To compute this query, we propose a basic algorithm that relies on on-line density-based clustering and exploits an early stop condition. To improve the response time, we design an advanced approach that includes three techniques: (i) an object skipping rule, (ii) spatially gridded posting lists, and (iii) a fast range query algorithm. An empirical study on real data demonstrates that the paper's proposals offer scalability and are capable of excellent performance.
Related Topics
Concepts
Computer science
Web query classification
Information retrieval
Query expansion
Scalability
Range query (database)
Web search query
Spatial query
Query optimization
Object (grammar)
Query language
Sargable
Cluster analysis
Data mining
Search engine
Database
Artificial intelligence
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/1607.08681
- https://arxiv.org/pdf/1607.08681
- OA Status
- green
- References
- 34
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2952501509
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2952501509Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.1607.08681Digital Object Identifier
- Title
-
A Density-Based Approach to the Retrieval of Top-K Spatial Textual ClustersWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2016Year of publication
- Publication date
-
2016-07-29Full publication date if available
- Authors
-
Dingming Wu, Christian S. JensenList of authors in order
- Landing page
-
https://arxiv.org/abs/1607.08681Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/1607.08681Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/1607.08681Direct OA link when available
- Concepts
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Computer science, Web query classification, Information retrieval, Query expansion, Scalability, Range query (database), Web search query, Spatial query, Query optimization, Object (grammar), Query language, Sargable, Cluster analysis, Data mining, Search engine, Database, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
34Number 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.object | 139, 183 |
| abstract_inverted_index.points | 18 |
| abstract_inverted_index.query, | 98, 149 |
| abstract_inverted_index.regard | 130 |
| abstract_inverted_index.relies | 156 |
| abstract_inverted_index.second | 57 |
| abstract_inverted_index.single | 40 |
| abstract_inverted_index.(k-STC) | 105 |
| abstract_inverted_index.capable | 213 |
| abstract_inverted_index.closest | 117 |
| abstract_inverted_index.compute | 147 |
| abstract_inverted_index.contain | 124 |
| abstract_inverted_index.content | 8 |
| abstract_inverted_index.density | 140 |
| abstract_inverted_index.exceeds | 142 |
| abstract_inverted_index.gridded | 188 |
| abstract_inverted_index.improve | 168 |
| abstract_inverted_index.located | 115 |
| abstract_inverted_index.objects | 43, 76, 128 |
| abstract_inverted_index.on-line | 158 |
| abstract_inverted_index.paper's | 62, 207 |
| abstract_inverted_index.posting | 189 |
| abstract_inverted_index.propose | 151 |
| abstract_inverted_index.queries | 2, 32, 37, 66 |
| abstract_inverted_index.regions | 79 |
| abstract_inverted_index.returns | 108 |
| abstract_inverted_index.satisfy | 46 |
| abstract_inverted_index.spatial | 41, 102 |
| abstract_inverted_index.support | 68 |
| abstract_inverted_index.textual | 103 |
| abstract_inverted_index.advanced | 175 |
| abstract_inverted_index.approach | 176 |
| abstract_inverted_index.behavior | 71 |
| abstract_inverted_index.belongs, | 64 |
| abstract_inverted_index.clusters | 104, 111 |
| abstract_inverted_index.exploits | 162 |
| abstract_inverted_index.includes | 178 |
| abstract_inverted_index.interest | 20, 86 |
| abstract_inverted_index.keywords | 14 |
| abstract_inverted_index.proposal | 63 |
| abstract_inverted_index.proposes | 93 |
| abstract_inverted_index.relevant | 11, 127 |
| abstract_inverted_index.response | 170 |
| abstract_inverted_index.retrieve | 6, 39, 73 |
| abstract_inverted_index.skipping | 184 |
| abstract_inverted_index.supplied | 13 |
| abstract_inverted_index.algorithm | 154 |
| abstract_inverted_index.category, | 58 |
| abstract_inverted_index.category. | 55 |
| abstract_inverted_index.empirical | 199 |
| abstract_inverted_index.excellent | 215 |
| abstract_inverted_index.keywords, | 134 |
| abstract_inverted_index.location, | 122 |
| abstract_inverted_index.location. | 26 |
| abstract_inverted_index.proposals | 51, 208 |
| abstract_inverted_index.represent | 17, 78 |
| abstract_inverted_index.spatially | 187 |
| abstract_inverted_index.algorithm. | 197 |
| abstract_inverted_index.arguments. | 49 |
| abstract_inverted_index.categories | 29 |
| abstract_inverted_index.clustering | 160 |
| abstract_inverted_index.condition. | 166 |
| abstract_inverted_index.threshold. | 145 |
| abstract_inverted_index.encompasses | 36, 65 |
| abstract_inverted_index.exploratory | 69 |
| abstract_inverted_index.scalability | 210 |
| abstract_inverted_index.techniques: | 180 |
| abstract_inverted_index.demonstrates | 204 |
| abstract_inverted_index.performance. | 216 |
| abstract_inverted_index.Keyword-based | 0 |
| abstract_inverted_index.Specifically, | 90 |
| abstract_inverted_index.density-based | 159 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 2 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.47999998927116394 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile |