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Range Monitoring Queries in Locationbased Services

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Title: Range Monitoring Queries in Locationbased Services


1
Range Monitoring Queries in Location-based
Services
Kien A. Hua School of EECS University of Central
Florida
2
Location-Based Services
Integrate a mobile devices position with other
information so as to provide added value to the
users.
  • 6 a month
  • The phone uploads its GPS coordinates to the
    Mologogo server every few minutes.
  • You can view up to 100 of the last reported spots
    the person has been on a google map.

3
Other Location-Based Services
  • Emergency services (E911 in US and 112 in Europe)
  • Traveler information systems in transportation
  • Traffic and incident Management
  • Other industries
  • Location-aware gaming
  • Advertising services
  • Environmental Monitoring
  • We focus our discussion on Location-based Queries
    that are important to Location-based Services.

4
Location-Based Queries
  • Two kinds of location-based queries
  • Snapshot queries Tell me 3 nearest cars around
    me now
  • Continuous queries Monitor 3 nearest
    restaurants around me for the next 10 minutes
  • We focus on one continuous query type called
    Range Monitoring Query (RMQ).

5
Range-Monitoring Query
  • What is range-monitoring query ?
  • Retrieve mobile objects in a spatial region, and
  • Continuously monitor the population in the area

6
Range Monitoring Queries
Q2
a
d
e
Q1
c
b
f
7
Range Monitoring Queries
Q2
a
d
Q1
e
b
c
f
8
Research Issues
  • How to minimize location updates ?
  • Each update involves mobile communication costs
    and server processing costs
  • How to minimize query processing cost ?
  • Query results keep changing
  • Traditional and spatial databases are not
    suitable for these tasks

9
Safe Region
Rectangular Safe Region
Q1
Q2
Q5
a
Q3
Q4
Circular Safe Region
10
Problems with Safe Regions
  • Computing a safe region takes from O(n) to O(n
    log3n)
  • Adding a new query requires recomputation of
    safe regions for all objects
  • A solution - Monitoring Query Management (MQM)

11
MQM - Resident Domain
Q9
Q1
  • A mobile object A contacts the server when A
  • exits the current resident domain, or
  • enters or exits a query in the resident domain

Q6
Q3
Resident Domain
Q2
A
Q5
Q7
Q4
N 3
Q8
12
Safe Region vs. Resident Domain
Q9
Safe Region incurs substantially more
communication messages
Q1
Q6
Q3
Safe Region
Q2
A
Resident Domain
Q5
Q7
Q4
Q8
13
Determine the Resident Domain
Q2 and Q3 are relevant to monitoring region R22
Space is dynamically partitioned into disjoint
subdomains
Q2
Q3
R1
R21
R22
Q1
R31
Q4
R42
R41
A monitoring region
Query Q2 overlaps query Q3
14
Determine the Resident Domain
Q2
Q3
R1
R21
R22
Q1
R31
a
Q4
R42
R41
Too small
15
Determine the Resident Domain
Q2
Q3
R1
R21
R22
Q1
R31
a
Q4
R42
R41
Resident domain for a
16
Domain Decomposition
  • Suddomains and monitoring regions are maintained
    using BP-tree (Binary Partitioning Tree)
  • For each new query,
  • Search BP-tree to find the overlapping
    subdomains, each corresponding to a monitoring
    region.
  • Insert the monitoring regions into their
    subdomain
  • Split a subdomain if its number of monitoring
    regions exceeds the threshold

Overlapping subdomains
Query
R12
R11
Two monitoring regions
17
BP-tree Example
D
domain node
D
data node
Q1
Q1
18
BP-tree Example
D
d1
d2
d1
d2
Q1
Q7
R71
R72
19
BP-tree Example
D
d21
d1
d2
d1
Q9
d21
d22
R91
d22
R911
R912
20
Advantages over Safe Regions
  • Resident domains can be determined efficiently
  • A new query generally affects only a small number
    of existing resident domains
  • Resident domain are generally much larger
    resulting in less location updates
  • Offloads query processing tasks to mobile units
  • Distributed processing
  • Trading computation for communications to
    conserve energy

21
Mobile Communication Cost
30
25
20
Safe Region
15
Number of messages sent by mobile objects
(millions)
MQM
10
5
0
10
20
30
40
50
60
70
80
90
100
Number of monitoring queries (thousands)
22
Server Processing Cost
1000
100
Safe Region
10
MQM
Number of index nodes accessed (millions)
1
0.1
10
20
30
40
50
60
70
80
90
100
Number of monitoring queries (thousands)
23
MQM - Summary
  • MQM is highly scalable in terms of
  • Mobile communication costs, and
  • Server processing costs
  • for real-time range monitoring queries

24
Dynamic Range Query in Spatial Network
Environments
25
Moving Range Query
  • Defined by a range (e.g., within 5 miles)
  • Moves in accordance with a specific moving object
    (e.g., car)
  • Results include objects (e.g., gas stations,
    other cars) currently inside the specified
    range.

26
Example - Moving Range Query
UCF
Show me Italian restaurants within 5 miles
Airport
27
Query Properties
  • Query Mobility moving vs. stationary
  • Query Shape static vs. dynamic
  • Objects moving vs. stationary
  • Environment open space vs. network
  • Open space dealing with Euclidean distance
  • Network dealing with network distance

28
Dynamic Range Query (DRQ)
Shape of query footprint changes dynamically
29
Network Distance
Included in the query result
d
Moving Range Query
Not included in the query result
d
Dynamic Range Query
30
Example Dynamic Range Query
  • Give me all the AAA vehicles on service within
    five miles from me, while I am driving from
    Orlando to Miami.
  • How to answer such queries efficiently ?

31
DRQ - Dynamic Footprint
Query Object
32
DRQ - Dynamic Footprint
33
DRQ - Dynamic Footprint
34
DRQ - Dynamic Footprint
35
DRQ - Dynamic Footprint
36
Challenges
  • Server workload
  • Communication bandwidth
  • Limited battery power on client side
  • Dynamic query footprints

37
System Assumptions
  • Every moving object is equipped with a
    positioning device.
  • Every moving object has some computing capability.

38
Modeling Graph
  • Network
  • Undirected graph G (N, E)
  • N a set of nodes
  • E a set of edges
  • Edge
  • e ltni, njgt
  • ni start node
  • nj end node
  • i lt j

n4 - end node
n3 - start node
39
Edge Distance
  • Network Distance between two edges
  • Four types of edge distance between two distinct
    edges SS, SE, ES, EE
  • If the two edges are the same, we have SM type
  • Edge distance is the shortest netwrok distance
    between two edges
  • d(ei , ej) min( (dSS(ei , ej), dSE(ei , ej),
    dES(ei , ej), dEE(ei , ej) )

40
Moving Objects
  • Two types of moving objects for a given query
  • Query object the moving object defined as the
    spatial center of the dynamic range query
  • Data object other objects
  • A moving object is a moving point in the road
    network
  • lt o, pos, direction, speed, reportTime, IsQuery gt
  • pos relative position from the S-node
  • direction 1 if moving from S-node to E-node
    -1, otherwise.
  • Speed object speed. Query objects must report
    new speed
  • IsQuery 1 if the object is a query object

Compute New position of a moving object newPos
(currentTime reportTime) ? speed ? direction
pos
41
Object Distance
  • Four possible network distances between two
    objects

The object distance is the minimum of the four
42
Dynamic Range Query (DRQ)
Query object Q, query range 5
  • Query has two parameters
  • q ltOq , lengthgt
  • Oq query object
  • length query range
  • The network space within the length distance from
    Oq makes up the query footprint
  • Query result includes all moving objects within
    the query footprint (e.g., Od )

Query Footprint
Od
Oq
Query result oi oi ? O, d(oi , oq )
length
43
Monitoring Region
  • Position of query object o determines the set of
    edges overlapping with the current query
    footprint
  • As o moves over an edge e, the distinct
    footprints define a set of edges, referred to as
    the monitoring region of the DRQ when o moves on
    e.

MonitoringRegion ei ei ? E, d(ei , e) ?
length
44
Monitoring Region Example
  • For a query object Q moving on edge n1 n6 with a
    query range as 5, the monitoring region is as
    follows

ltn1n6, SM, 0gt
n10
ltn1n2, SS, 0gt, ltn1n8, SS, 0gt, ltn1n9, SS, 0gt
4
ltn2n3, SS, 3gt, ltn2n10, SS, 3gt
n3
n4
5
n2
2
ltn3n6, EE, 0gt, ltn5n6, EE, 0gt, ltn6n7, SE, 0gt
2
3
n5
6
n6
ltn2n3, EE, 2gt, ltn3n4, SE, 2gt
n1
Q
4
ltn1n2, EE, 4gt, ltn2n10, SE, 4gt
6
7
6
n9
n7
The SE-distance from n1n6 is 4
n8
The server computes and multicasts this list to
objects in the monitoring region
45
Some Storage Techniques for Networks
  • J. Zhao and A. Zaki, Spatial Data Traversal in
    Road Map Databases A Graph Indexing Approach,
    CIKM 94
  • D. Papadias, J. Zhang, N. Mamoulis and Y. Tao,
    Query Processing in Spatial Network Databases,
    VLDB 03
  • S. Shekhar and D. Liu, CCAM A Connectivity
    Clustered Access Method for Networks and Network
    Computations, IEEE Trans. on Knowledge and Data
    Engineering, 9(1), 1997

46
Processing on Mobile Host (1)
  • Query object Q /w range 5, at location 3.6 on
    edge n1n6.
  • Data object D at location 0.5 on edge n2n10.

Multicast Message
n10
4
ltn1n6, SM, 0gt
n3
n4
5
2
D
n2
ltn1n2, SS, 0gt, ltn1n8, SS, 0gt, ltn1n9, SS, 0gt
2
4
6
n5
3
ltn2n3, SS, 3gt, ltn2n10, SS, 3gt
n6
Q
n1
ltn3n6, EE, 0gt, ltn5n6, EE, 0gt, ltn6n7, SE, 0gt
6
7
ltn2n3, EE, 2gt, ltn3n4, SE, 2gt
6
n9
n7
ltn1n2, EE, 4gt, ltn2n10, SE, 4gt
n8
Object D picks up only ltn2n10, SS, 3gt ,
ltn2n10, SE, 4gt , object Qs information
Edge distance from n1n6
47
Processing on Mobile Host (2)
  • Query object Q /w Range 5, at location 3.6 on
    edge n1n6
  • Data object D at location 0.5 on edge n2n10.

Object D uses the multicast information to
compute its distance to Q
n10
E
4
n3
n4
5
2
D
S
n2
2
ltn2n10, SS, 3gt ltn2n10, SE, 4gt
4
6
n5
3
E
n6
Q
S
n1
6
7
0.5 4 (4 3.6) 4.9 lt 5
0.5 3 3.6 7.1 gt 5
6
n9
n7
n8
  • Object D should be included in the querys
    result.
  • Object D continues to monitor its distance from Q
    and updates the query accordingly

48
Summary
  • The server
  • computes the monitoring region for each DRQ, and
  • multicasts the information to moving objects
    inside the monitoring region.
  • Moving object
  • uses the information received from the server to
    monitor if it is inside a querys range.

49
Simulation Setup
  • Area of interest
  • a square shaped region of 10,000 square miles
  • 2000 nodes
  • 4000 edges
  • 100, 000 moving objects
  • Speeds vary between 0.5 and 1 mile per time unit
  • Initial speeds follow a Zipf distribution with
    deviation of 0.7
  • Every time step, 10 of the objects change their
    speed at a small increment
  • 10 to 1,000 queries

50
Performance Comparisons
  • Communication cost
  • Compared to Query-Blind Optimal (QBO) technique
  • moving objects send messages to server whenever
    they change speed or move to a new road segment.
  • Query processing is done on server - very
    expensive. QBO is used just as a reference to
    study communication costs
  • Server computation cost
  • Compared to a centralized scheme, which we
    adapted from the Query Indexing technique
    Prabhakar, 2002 for spatial network
    environments.

51
Server Communication Cost
  • Effect of of queries on server communication
    cost

Naïve Every object repeatedly reports its new
location
52
Object Communication Cost
  • Effect of of queries on object-side
    communication cost

Query-blind Optimal - Server Computation Cost
is very high
53
Server Computation Cost(segments loaded per
time unit)
  • Effect of of queries on server workload

54
Remarks
  • Use road segment as the unit for monitoring
    regions
  • Moving objects utilize their own computing power
    to help reduce server load and save wireless
    bandwidth
  • Distributed servers can be used for a very-large
    deployment, in which case the proposed technique
    keeps the number of servers low
  • A limitation - query result is an approximation
    due to location estimation
  • A solution Query objects must report their new
    speed

55
A P2P Approach
  • Query Processing
  • Tracking the moving objects and the query regions
  • Update query results when objects move in or out
    of the query regions
  • In the UCF techniques,
  • every moving object participates in query
    processing as a peer
  • server only provides the database service
  • It is a P2P computing technique

56
Four categories ofmoving object databases
  • Centralized Server
  • Distributed Servers
  • P2P
  • Hybrid (Distributed Servers P2P)

57
Four Categories ofMoving Object Databases
58
Hybrid System
Communication Network
Location-based Services
Queries
Network
Distributed Servers
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