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Trends in Location-based Services

Trends in Location-based Services. Muhammad Aamir Cheema Supported by : Australian Research Council Discovery Early Career Researcher Award. Outline. Introduction Past Research Emerging Trends Concluding Remarks. Definition.

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Trends in Location-based Services

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  1. Trends in Location-based Services Muhammad AamirCheema Supported by: Australian Research Council Discovery Early Career Researcher Award

  2. Outline • Introduction • Past Research • Emerging Trends • Concluding Remarks

  3. Definition Services that integrate a user’s location with other information to provide added value to a user.

  4. Examples • Navigation and travel • Geo-social networking • Gaming • Retail • Advertisement and many many more…

  5. Why LBS? • Location-based services have a bright future Smart Phones > old fashioned phones Number of mobiles > World’s population 24% use LBS and 94% of these find LBS valuable LBS are a bonanza for start-ups (est. market $13B in 2014) $21B in 2015 40% 60%

  6. Past research • Shortest Path Query • Range Query • k-Nearest Neighbors Query • Reverse Nearest Neighbors Query • k-Closest Pairs Query and other similar queries…

  7. Past research • Shortest Path Query: What is the shortest path from here to airport

  8. Past research • Range Query: Return the coffee shops within 300 meters.

  9. Past research • k-Nearest Neighbors Query: Return the closest fuel stations.

  10. Past research • Reverse Nearest Neighbor Query: Return the cars for which my fuel station is the nearest fuel station.

  11. Past research • K-Closest Pairs Query: Return the closest pair of McDonald’s.

  12. Past research • Shortest Path Query • Range Query • k-Nearest Neighbors Query • Reverse Nearest Neighbors Query • k-Closest Pairs Query and other similar queries… Static and continuous queries Euclidean distance and network distance

  13. Contributions by DBG • Range Query: Return the coffee shops within 300 meters. • M. A. Cheema, L. Brankovic, X. Lin, W. Zhang, W. Wang. "Multi-Guarded Safe Zone: An Effective Technique to Monitor Moving Circular Range Queries"ICDE 2010 (One of the best papers) • M. A. Cheema, L. Brankovic, X. Lin, W. Zhang, W. Wang. "Continuous Monitoring of Distance Based Range Queries", IEEE Transactions on Knowledge and Data Engineering (TKDE), 2011.

  14. Contributions by DBG • k-Nearest Neighbors Query: Return k closest fuel stations. • W. Zhang, X. Lin, M. A. Cheema, Y. Zhang, W. Wang. "Quantile-Based KNN Over Multi-Valued Objects", ICDE 2010 • M. Hasan,M. A. Cheema, X. Lin, Y. Zhang. "Efficient Construction of Safe Regions for moving kNN Queries over Dynamic Datasets", SSTD2009. • M. Hasan, M. A. Cheema, W. Qu, X. Lin "Efficient Algorithms to Monitor Continuous Constrained k Nearest Neighbor Queries", DASFAA 2010. • M. Hasan, M. A. Cheema, X. Lin, W. Zhang. "A Unified Algorithm for Continuous Monitoring of Spatial Queries, DASFAA 2011.

  15. Contributions by DBG • Reverse Nearest Neighbor Query: Return the cars for which my fuel station is the nearest fuel station. • M. A. Cheema, X. Lin, Y. Zhang, W. Wang, W. Zhang. "Lazy Updates: An Efficient Technique to Continuously Monitoring Reverse kNN“,PVLDB 2009. (CiSRA Best Research Paper of 2009 Award) • M. A. Cheema, W. Zhang, X. Lin, Y. Zhang, X. Li. "Continuous Reverse k Nearest Neighbors Queries in Euclidean Space and in Spatial Networks", VLDB Journal 2012. • M. A. Cheema, X. Lin, W. Zhang, Y. Zhang. "Influence Zone: Efficiently Processing Reverse k Nearest Neighbors Queries", ICDE 2011. (CiSRA Best Research Paper of 2010 Award) • M. A. Cheema, W. Zhang, X. Lin, Y. Zhang. "Efficiently Processing Snapshot and Continuous Reverse k Nearest Neighbors Queries", VLDB Journal 2012.

  16. Contributions by DBG • K-Closest Pairs Query: Return the closest pair of McDonald’s. • M. A. Cheema, X. Lin, H. Wang, J. Wang, W. Zhang. "A Unified Approach for Computing Top-k Pairs in Multidimensional Space", ICDE 2011. • Z, Shen, M. A. Cheema, X. Lin, W. Zhang, H. Wang. "Efficiently Monitoring Top-k Pairs over Sliding Windows", ICDE 2012. (One of the best papers) • Z. Shen, M. A. Cheema, X. Lin, W. Zhang, H. Wang. "A Generic Framework for Top-k Pairs and Top-k Objects Queries over Sliding Windows", IEEE Transactions on Knowledge and Data Engineering (TKDE), 2013.

  17. Emerging Trends - 1 • Personalized and context-aware results The query results should be based on location as well as • the user profile (e.g., age, gender, choices etc.) • context (e.g., time, weather etc.)

  18. Emerging Trends - 2 • Handling inaccuracy and uncertainty in data • Inaccuracy of GPS devices • User created data • Automatically annotated data • Entity resolution etc …

  19. Emerging Trends - 3 • More travel spaces • In-door spaces • Obstructed spaces • A combination of road network, Euclidean space, in-door and obstructed spaces

  20. Our Progress • Representative Published Research Results • M. A. Cheema, X. Lin, W. Wang, W. Zhang, J. Pei. "Probabilistic Reverse Nearest Neighbor Queries on Uncertain Data", IEEE Transactions on Knowledge and Data Engineering (TKDE) 2010. • W. Zhang, X. Lin, Y. Zhang, M. A. Cheema, Qing Zhang. "Stochastic Skylines", ACM Transactions on Database Systems (TODS), 2012. • M. A. Cheema, X. Lin, W. Zhang, Y. Zhang. "A Safe Zone Based Approach for Monitoring Moving Skyline Queries", EDBT 2013. • C. Zhang, Y. Zhang, W. Zhang, X. Lin, "Inverted Linear Quadtree: Efficient Top K Spatial Keyword Search", ICDE 2013.

  21. Our Progress • On-going Projects DBG@UNSW is generously supported by Australian Research Council (ARC) to conduct research in this area (total worth for on-going projects is more than $2 Million). • M. A. Cheema,"Efficiently Querying Uncertain Spatial Space", ARC Discovery Early Career Researcher Award (2013-2015), $375,000. • W. Wang, M. A. Cheema,"Next-Generation Spatial Keyword Search", ARC Discovery Project, (2013-2015), $360,000. • H. T. Shen, Y. Zhang, “Taming the Uncertainty in Trajectory Data”, ARC Discovery Project (2013-2015), $335,000. • W. Zhang, “Continuously Monitoring Uncertain Objects in Multidimensional Space”, ARC Discovery Early Career Researcher Aware (2012-2014), $375,000. • X. Lin, W. Zhang, “Ranking Complex Objects in a Multidimensional Space”, ARC Discovery Project (2012-2014), $350,000. • Y. Zhang, “Efficient Processing of Distance-based Spatial Queries on Multi-Valued Objects”, ARC Australian Postdoctoral Fellowship (2011-2013), $260,692.

  22. Concluding Remarks • LBS are becoming increasingly popular • Past research mainly focuses only on locations • Emerging trends are to consider user profiles, more travel spaces, data uncertainty etc.

  23. Thanks

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