Transcript Slide 1

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)
p4
p3
p2
p1
p5
q
p6
p7
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”
p4
p3
p2
p5
q
p6
p1
p7
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
Sliding Window
... d
3
d
-k 1
p
o
T
d2
Incoming Document Stream
d1
o cs
User 1
Top-k2 docs
Monitoring Server
User 2
4
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.
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 QoS) 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]
Destination
Source
8
Building block: Hardware-aided PIR
• Practical PIR = hardware-aided PIR
[Williams & Sion: Usable PIR. NDSS’08]
LBS
Page requests
Data pages
Client
SSL connection
SCP
Database
Fetching a disk page: amortized comp. cost O(log2N)
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
• TripAdvisor 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