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Research Overview

Research Overview. Kyriakos Mouratidis Assistant Professor School of Information Systems Singapore Management University http://www.mysmu.edu/faculty/kyriakos/. Spatial Queries. - Indexing spatial data and query processing E.g., “find the 10 closest restaurants to my location”

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Research Overview

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  1. Research Overview Kyriakos Mouratidis Assistant Professor School of Information Systems Singapore Management University http://www.mysmu.edu/faculty/kyriakos/

  2. Spatial Queries - Indexing spatial data and query processing E.g., “find the 10 closest restaurants to my location” - K nearest neighbors (if K=2)

  3. Continuous Queries Continuous re-evaluation as data change. Eg: • “monitor who are the 10 SMU students that are closest to my location as I walk around”

  4. Continuous Queries • Cont. NN in Euclidean space: SIGMOD’05 • Cont. NN in road networks: VLDB’06 • Cont. Top-k monitoring: SIGMOD’06 • Eg: "continuously report the 5 most interesting stocks according to my investment criteria” • Cont. Text queries on document streams: TKDE’11

  5. Spatial Optimization Queries • E.g.: At which 10 positions in S’pore should McDonalds open branches so that the average distance between clients and their closest branch is minimized? • E.g.: Given a coverage radius and a maximum capacity of a Mobile Service Provider’s base stations, find a (dynamic) assignment of mobile phone users to a base station so that the average distance between them is minimal.

  6. Spatial Optimization Example • A set of wireless routers serve a set of laptops • each router can serve at most 3 laptops concurrently • the signal strength (ie, the QoS) drops with distance • How can we assign laptops to routers so that we • Serve the maximum possible number of users, AND • Minimize the average distance between laptops-routers? • Assignments by “local” criteria (eg, NN below) would fail! 3-Nearest Neighbor Queries

  7. Spatial Optimization Example • Optimal Assignment: • Aim: quicklycompute the optimalassignment over large datasets [SIGMOD’08, TODS’10]

  8. Location Privacy • How could an untrusted server answer your spatial queries without learning your location? • Example: shortest path query [VLDB’12]

  9. Building block: Hardware-aided PIR • Practical PIR = hardware-aided PIR[Williams & Sion: Usable PIR. NDSS’08] Fetching a disk page: amortized comp. cost O(log2N) i.e., approx. 1 sec for a Gigabyte database

  10. Verification in Outsourced Databases: • Model: Database as a Service • Data Owner uploads DB to untrusted server • Server hosts the DB and answers queries from users • How can users verify that the results to their queries are authentic and complete? • Examples: text queries [VLDB’08], relational/spatial queries [VLDBJ’09], shortest path queries [ICDE’10]…

  11. Other cool stuff • TripAdvisor has hotel information, such as: price, value, location, cleanliness, user rating • Imagine this interface to select top-10 options: Immutable Regions [VLDB’13]

  12. Thank you!

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