Research Overview Kyriakos Mouratidis Assistant Professor School of

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” - K nearest neighbors (if K=2) 2

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” 3

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 4

Spatial Optimization Queries • E. g. : At which 10 positions in S’pore should Mc. Donalds 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. 5

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 Qo. S) drops with distance • How can we assign laptops to routers so that we 1. Serve the maximum possible number of users, AND 2. Minimize the average distance between laptops-routers? • Assignments by “local” criteria (eg, NN below) would fail! 3 -Nearest Neighbor Queries

Spatial Optimization Example • Optimal Assignment: • Aim: quickly compute the optimal assignment over large datasets [SIGMOD’ 08, TODS’ 10]

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

Building block: Hardware-aided PIR • Practical PIR = hardware-aided PIR [Williams & Sion: Usable PIR. NDSS’ 08] Fetching a disk page: amortized comp. cost O(log 2 N) i. e. , approx. 1 sec for a Gigabyte database 9

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]… 10

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

Thank you! 12