Retrieving Regions of Interest for User Exploration
Summary: Introduces LCMSR, selecting a size-bounded spatial-network region within an ROI that maximizes keyword-relevant PoI weight for user exploration. Establishes NP-hardness and gives a (5+ε)-approximation via integer weight scaling, plus efficient heuristic and greedy methods. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Xin Cao (Nanyang Technological University)
- 2. Gao Cong (Nanyang Technological University)
- 3. Christian S. Jensen (Aalborg University)
- 4. Man Lung Yiu (Hong Kong Polytechnic University)
BibTeX Citation
@article{cao_vldb14,
title = {{Retrieving Regions of Interest for User Exploration}},
author = {Cao, Xin and Cong, Gao and Jensen, Christian S. and Yiu, Man Lung},
journal = {PVLDB},
series = {{VLDB} '14},
volume = {7},
number = {9},
pages = {733--744},
doi = {10.14778/2732939.2732942},
url = {https://doi.org/10.14778/2732939.2732942},
year = {2014}
}Incoming Citations (Sorted by Pagerank)
Showing 6 of 6 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,715 | Efficient Algorithms for Answering the m-Closest Keywords Query | 2015 | SIGMOD | 6.5487034e-05 |
| 7,600 | Towards Best Region Search for Data Exploration | 2016 | SIGMOD | 5.5867987e-05 |
| 8,823 | Querying Geo-Textual Data: Spatial Keyword Queries and Beyond | 2016 | SIGMOD | 5.3624668e-05 |
| 9,358 | SkyGraph: Retrieving Regions of Interest using Skyline Subgraph Queries | 2017 | VLDB | 5.2822032e-05 |
| 10,052 | Top-k Relevant Semantic Place Retrieval on Spatial RDF Data | 2016 | SIGMOD | 5.1685424e-05 |
| 10,173 | A Bouquet of Results on Maximum Range Sum: General Techniques and Hardness Reductions | 2026 | PODS | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 6 of 6 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,819 | Efficient Retrieval of the Top-k Most Relevant Spatial Web Objects | 2009 | VLDB | 9.6810907e-05 |
| 3,024 | A Scalable Algorithm for Maximizing Range Sum in Spatial Databases | 2012 | VLDB | 7.8388143e-05 |
| 3,458 | Collective Spatial Keyword Queries: A Distance Owner-Driven Approach | 2013 | SIGMOD | 7.3961486e-05 |
| 3,700 | Approximate MaxRS in Spatial Databases | 2013 | VLDB | 7.1861452e-05 |
| 4,179 | Retrieving Top-k Prestige-Based Relevant Spatial Web Objects | 2010 | VLDB | 6.8487923e-05 |
| 4,486 | Collective Spatial Keyword Querying | 2011 | SIGMOD | 6.6693992e-05 |
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Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 3,458 | Collective Spatial Keyword Queries: A Distance Owner-Driven Approach | 2013 | SIGMOD |
| 2 | 6,251 | The Flexible Socio Spatial Group Queries | 2019 | VLDB |
| 3 | 9,358 | SkyGraph: Retrieving Regions of Interest using Skyline Subgraph Queries | 2017 | VLDB |
| 4 | 1,819 | Efficient Retrieval of the Top-k Most Relevant Spatial Web Objects | 2009 | VLDB |
| 5 | 1,212 | Efficient Query Processing in Geographic Web Search Engines | 2006 | SIGMOD |
| 6 | 3,024 | A Scalable Algorithm for Maximizing Range Sum in Spatial Databases | 2012 | VLDB |
| 7 | 4,715 | Efficient Algorithms for Answering the m-Closest Keywords Query | 2015 | SIGMOD |
| 8 | 6,179 | Densely Connected User Community and Location Cluster Search in Location-Based Social Networks | 2020 | SIGMOD |
| 9 | 7,600 | Towards Best Region Search for Data Exploration | 2016 | SIGMOD |
| 10 | 4,486 | Collective Spatial Keyword Querying | 2011 | SIGMOD |