Collective Spatial Keyword Queries: A Distance Owner-Driven Approach
Summary: Distance-owner driven framework for CoSKQ (MaxSum-CoSKQ, Dia-CoSKQ) reveals cost dominated by at most three distance owners. Provides faster exact algorithms and improved approximations (MaxSum 1.375, Dia-CoSKQ 3-factor), with experiments showing scalable, near-optimal results. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Cheng Long (Hong Kong University of Science and Technology)
- 2. Raymond Chi-Wing Wong (Hong Kong University of Science and Technology)
- 3. Ke Wang (Simon Fraser University)
- 4. Ada Wai-Chee Fu (Chinese University of Hong Kong)
BibTeX Citation
@inproceedings{long_sigmod13,
title = {{Collective Spatial Keyword Queries: A Distance Owner-Driven Approach}},
author = {Long, Cheng and Wong, Raymond Chi-Wing and Wang, Ke and Fu, Ada Wai-Chee},
series = {{SIGMOD} '13},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/2463676.2465275},
url = {https://dl.acm.org/doi/10.1145/2463676.2465275},
year = {2013}
}
Incoming Citations (Sorted by Pagerank)
Showing 7 of 7 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,814 | Efficient Algorithms for Answering the m-Closest Keywords Query | 2015 | SIGMOD | 6.3995011e-05 |
| 7,053 | Exact Top-k Nearest Keyword Search in Large Networks | 2015 | SIGMOD | 5.6103309e-05 |
| 7,333 | Retrieving Regions of Interest for User Exploration | 2014 | VLDB | 5.5476074e-05 |
| 7,567 | Selectivity Estimation on Streaming Spatio-Textual Data Using Local Correlations | 2015 | VLDB | 5.4933119e-05 |
| 9,000 | Querying Geo-Textual Data: Spatial Keyword Queries and Beyond | 2016 | SIGMOD | 5.2396658e-05 |
| 9,542 | SkyGraph: Retrieving Regions of Interest using Skyline Subgraph Queries | 2017 | VLDB | 5.1612403e-05 |
| 11,700 | Effectiveness Perspectives and a Deep Relevance Model for Spatial Keyword Queries | 2023 | SIGMOD | 4.9769913e-05 |
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 5 of 5 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,477 | Efficient Processing of Top-k Spatial Preference Queries | 2011 | VLDB | 0.00010546245 |
| 1,858 | Efficient Retrieval of the Top-k Most Relevant Spatial Web Objects | 2009 | VLDB | 9.4893333e-05 |
| 4,256 | Retrieving Top-k Prestige-Based Relevant Spatial Web Objects | 2010 | VLDB | 6.6979143e-05 |
| 4,576 | Collective Spatial Keyword Querying | 2011 | SIGMOD | 6.5228866e-05 |
| 4,799 | Reverse Spatial and Textual k Nearest Neighbor Search | 2011 | SIGMOD | 6.4084993e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 8,204 | WISK: A Workload-aware Learned Index for Spatial Keyword Queries | 2023 | SIGMOD |
| 2 | 8,668 | Top-K Nearest Keyword Search on Large Graphs | 2013 | VLDB |
| 3 | 1,477 | Efficient Processing of Top-k Spatial Preference Queries | 2011 | VLDB |
| 4 | 10,410 | The Space-Time Complexity of Sum-Product Queries | 2026 | PODS |
| 5 | 3,089 | A Scalable Algorithm for Maximizing Range Sum in Spatial Databases | 2012 | VLDB |
| 6 | 6,834 | Maximizing Bichromatic Reverse Spatial and Textual k Nearest Neighbor Queries | 2016 | VLDB |
| 7 | 1,446 | Finding and Approximating Top-k Answers in Keyword Proximity Search | 2006 | PODS |
| 8 | 8,006 | Processing and Optimizing Main Memory Spatial-Keyword Queries | 2016 | VLDB |
| 9 | 4,814 | Efficient Algorithms for Answering the m-Closest Keywords Query | 2015 | SIGMOD |
| 10 | 4,576 | Collective Spatial Keyword Querying | 2011 | SIGMOD |