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Information Technology (Some) Research Trends in Location-based Services Muhammad Aamir Cheema Faculty of Information Technology Monash University, Australia.

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Presentation on theme: "Information Technology (Some) Research Trends in Location-based Services Muhammad Aamir Cheema Faculty of Information Technology Monash University, Australia."— Presentation transcript:

1 Information Technology (Some) Research Trends in Location-based Services Muhammad Aamir Cheema Faculty of Information Technology Monash University, Australia aamir.cheema@monash.edu www.aamircheema.com

2 Faculty of Information Technology Outline  Introduction  Preliminary Research  Advanced Research  Our Contributions

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

4 Faculty of Information Technology Examples  Navigation and travel  Geo-social networking  Gaming  Retail  Advertisement and many many more…

5 Faculty of Information Technology Significance  Location-based services have become ubiquitous Smart Phones > old fashioned phones Number of mobiles > World’s population 60% 40% LBS are a bonanza for start-ups (est. market $13B in 2014) $21B in 2015

6 Faculty of Information Technology Preliminary research  Shortest Path Query  Range Query  Nearest Neighbors Query  Reverse Nearest Neighbors Queries  K-closest Pairs Queries and other similar queries…

7 Faculty of Information Technology Preliminary Research  Shortest Path Query: What is the shortest path from here to airport

8 Faculty of Information Technology Preliminary research  Range Query: Return the coffee shops within 300 meters.

9 Faculty of Information Technology Preliminary research  Nearest Neighbor Query: Return the nearest fuel station.

10 Faculty of Information Technology Preliminary research  Reverse Nearest Neighbor Query: Return every object for which the query object is the closest object.

11 Faculty of Information Technology Preliminary research  K-Closest Pairs Query: Return k-closest pairs of objects.

12 Faculty of Information Technology Preliminary 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 Faculty of Information Technology Our research  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)"Multi-Guarded Safe Zone: An Effective Technique to Monitor Moving Circular Range Queries" 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. "Continuous Monitoring of Distance Based Range Queries"

14 Faculty of Information Technology Our research  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"Quantile-Based KNN Over Multi-Valued Objects" M. Hasan, M. A. Cheema, X. Lin, Y. Zhang. "Efficient Construction of Safe Regions for moving kNN Queries over Dynamic Datasets", SSTD 2009."Efficient Construction of Safe Regions for moving kNN Queries over Dynamic Datasets" M. Hasan, M. A. Cheema, W. Qu, X. Lin "Efficient Algorithms to Monitor Continuous Constrained k Nearest Neighbor Queries", DASFAA 2010."Efficient Algorithms to Monitor Continuous Constrained k Nearest Neighbor Queries" M. Hasan, M. A. Cheema, X. Lin, W. Zhang. "A Unified Algorithm for Continuous Monitoring of Spatial Queries, DASFAA 2011."A Unified Algorithm for Continuous Monitoring of Spatial Queries

15 Faculty of Information Technology Our research  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)"Lazy Updates: An Efficient Technique to Continuously Monitoring Reverse kNN“ 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."Continuous Reverse k Nearest Neighbors Queries in Euclidean Space and in Spatial Networks" 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)"Influence Zone: Efficiently Processing Reverse k Nearest Neighbors Queries" M. A. Cheema, W. Zhang, X. Lin, Y. Zhang. "Efficiently Processing Snapshot and Continuous Reverse k Nearest Neighbors Queries", VLDB Journal 2012."Efficiently Processing Snapshot and Continuous Reverse k Nearest Neighbors Queries" S. Yang, M. A. Cheema, Xuemin Lin, Ying Zhang. “Reviving Regions-based Pruning for Reverse k Nearest Neigbhors Queries", ICDE 2014“Reviving Regions-based Pruning for Reverse k Nearest Neigbhors Queries" S. Yang, M. A. Cheema, Xuemin Lin, Wei Wang. “Reverse k Nearest Neighbors Query Processing: Experiments and Analysis", PVLDB 2015“Reverse k Nearest Neighbors Query Processing: Experiments and Analysis"

16 Faculty of Information Technology Our research  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."A Unified Approach for Computing Top-k Pairs in Multidimensional Space" M. A. Cheema, X. Lin, H. Wang, J. Wang, W. Zhang "A Unified Framework for Answering k Closest Pairs Queries and Variants", IEEE TKDE 2014"A Unified Framework for Answering k Closest Pairs Queries and Variants" 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)"Efficiently Monitoring Top-k Pairs over Sliding Windows" 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 TKDE 2013."A Generic Framework for Top-k Pairs and Top-k Objects Queries over Sliding Windows"

17 Faculty of Information Technology Advanced Research  Personalized and context-aware results The query results should be based on location as well as the user profile (e.g., age, gender, interests, friends etc.) context (e.g., time, weather etc.)

18 Faculty of Information Technology Advanced Research  Handling Inaccuracy in data

19 Faculty of Information Technology Advanced Research  Handling Inaccuracy in data Apple Maps directs drivers through Alaska airport runway

20 Faculty of Information Technology Advanced Research  Handling Inaccuracy and uncertainty Inaccuracy of GPS devices User created data Automatically annotated data Entity resolution etc …

21 Faculty of Information Technology Advanced Research  Privacy and security

22 Faculty of Information Technology Advanced Research  Privacy and security

23 Faculty of Information Technology Advanced Research  Privacy and security

24 Faculty of Information Technology Advanced Research  Privacy and security User awareness pleaserobme.com robmenow.com

25 Faculty of Information Technology Advanced Research  Privacy and security User awareness Privacy preserving techniques (e.g., spatial cloaking, k-anonymity)

26 Faculty of Information Technology Advanced Research  Indoor location data management  We spend 85% time indoor – 30% outside of home  800 Million mobiles using indoor location technology by 2018  More than 200,000 indoor maps in USA by 2016  Apple allowed indoor maps for businesses - service crashed Indoor LBS is the next frontier for LBS – Forbes Indoor LBS is expected to have bigger impact than outdoor LBS – Sillicon Valley

27 Faculty of Information Technology Advanced Research  Indoor location data management  Fundamental queries (shortest path, kNN etc.)  Spatial keyword queries  Route planning  Handling uncertainty  Data analytics  …

28 Faculty of Information Technology Our Research  On-going Projects 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.  Upcoming/New Projects Efficient Query Processing Techniques for Indoor Location based Services – with Hua Lu (Aalborg University, Denmark) Query Processing in Location-Based Social Networks – with Wei Wang (UNSW Australia) and Mohamed Mokbel (University of Minnesota)

29 Faculty of Information Technology Our Research  Representative Published Research Results W. Zhang, X. Lin, Y. Zhang, M. A. Cheema, Qing Zhang. ”Stochastic Skylines”, ACM TODS, 2012. X. Wang, Y. Zhang, W. Zhang, X. Lin, M. A. Cheema. "Optimal Spatial Dominance: An effective search of Nearest Neighbor Candidates”, SIGMOD 2015 M. A. Cheema, X. Lin, W. Wang, W. Zhang, J. Pei. "Probabilistic Reverse Nearest Neighbor Queries on Uncertain Data", IEEE TKDE 2010" X. Lin, Y. Zhang, W. Zhang, M. A. Cheema. "Stochastic Skyline Operator", ICDE 2011" W. Zhang, A. Li, M. A. Cheema, Y. Zhang, L. Chang. "Probabilistic n-of-N Skyline Computation over Uncertain Data Streams”, WISE 2013. (Best Paper Award)" C. Zhang, Y. Zhang, W. Zhang, X. Lin, "Inverted Linear Quadtree: Efficient Top K Spatial Keyword Search", ICDE 2013. C. Zhang, Y. Zhang, W. Zhang, X. Lin, M. A. Cheema, X. Wang "Diversified Spatial Keyword Search On Road Networks”, EDBT 2014"

30 Faculty of Information Technology Acknowledgments

31 Faculty of Information Technology Thanks

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