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
- 2. Raymond Chi-Wing Wong
- 3. Ke Wang
- 4. Ada Wai-Chee Fu
Incoming Citations (Sorted by Pagerank)
Showing 7 of 7 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,925 | Efficient Algorithms for Answering the m-Closest Keywords Query | 2015 | SIGMOD | 5.8226582e-05 |
| 7,277 | Exact Top-k Nearest Keyword Search in Large Networks | 2015 | SIGMOD | 4.7794907e-05 |
| 7,281 | Retrieving Regions of Interest for User Exploration | 2014 | VLDB | 4.7770174e-05 |
| 7,645 | Selectivity Estimation on Streaming Spatio-Textual Data Using Local Correlations | 2015 | VLDB | 4.6896215e-05 |
| 8,822 | Querying Geo-Textual Data: Spatial Keyword Queries and Beyond | 2016 | SIGMOD | 4.4417735e-05 |
| 9,193 | SkyGraph: Retrieving Regions of Interest using Skyline Subgraph Queries | 2017 | VLDB | 4.3764958e-05 |
| 11,172 | Effectiveness Perspectives and a Deep Relevance Model for Spatial Keyword Queries | 2023 | SIGMOD | 4.1945683e-05 |
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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 |
|---|---|---|---|---|
| 648 | Efficient Retrieval of the Top-k Most Relevant Spatial Web Objects | 2009 | VLDB | 0.00018666267 |
| 1,403 | Efficient Processing of Top-k Spatial Preference Queries | 2011 | VLDB | 0.00012176993 |
| 4,525 | Retrieving Top-k Prestige-Based Relevant Spatial Web Objects | 2010 | VLDB | 6.1116751e-05 |
| 4,786 | Collective Spatial Keyword Querying | 2011 | SIGMOD | 5.9235651e-05 |
| 4,960 | Reverse Spatial and Textual k Nearest Neighbor Search | 2011 | SIGMOD | 5.7987607e-05 |
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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,636 | WISK: A Workload-aware Learned Index for Spatial Keyword Queries | 2023 | SIGMOD | 4.4801284e-05 |
| 8,505 | Top-K Nearest Keyword Search on Large Graphs | 2013 | VLDB | 4.4958064e-05 |
| 1,403 | Efficient Processing of Top-k Spatial Preference Queries | 2011 | VLDB | 0.00012176993 |
| 10,009 | The Space-Time Complexity of Sum-Product Queries | 2026 | PODS | 4.1945683e-05 |
| 2,906 | A Scalable Algorithm for Maximizing Range Sum in Spatial Databases | 2012 | VLDB | 7.9350108e-05 |
| 7,462 | Maximizing Bichromatic Reverse Spatial and Textual k Nearest Neighbor Queries | 2016 | VLDB | 4.7233035e-05 |
| 1,073 | Finding and Approximating Top-k Answers in Keyword Proximity Search | 2006 | PODS | 0.00014264992 |
| 7,693 | Processing and Optimizing Main Memory Spatial-Keyword Queries | 2016 | VLDB | 4.6759281e-05 |
| 4,925 | Efficient Algorithms for Answering the m-Closest Keywords Query | 2015 | SIGMOD | 5.8226582e-05 |
| 4,786 | Collective Spatial Keyword Querying | 2011 | SIGMOD | 5.9235651e-05 |