LocationBased Services

1
Location-Based Services Continuous kNN Query
Processing

• Tai Do
• Data Systems Group, UCF
• Fall 2005

2
Outline

• Introduction to Data Management in Mobile
  Computing.
• Discussion on Location-Based Services and its
  enabling technologies.
• In-depth discussion on Continuous kNN queries (2
  recent papers MHP05, and XMA05)

3
Data Management in Mobile Computing

• Our interest application-driven research that
  involves data management in mobile computing.
• Services/applications that inspire data
  management research
• Location-based services, Transactional services,
  Data mining applications.
• Research problems to support these novel services
  efficiently
• Spatiotemporal Query Processing.
• Data dissemination over limited bandwidth
  channels.
• Data consistency guarantees.
• Advanced interfaces for mobile computers.

4
Location-Based Services (LBS)

• Location-Based Services
• can be defined as services that integrate a
  mobile devices location or position with other
  information so as to provide added value to a
  user.
• Examples
• Military and Government industries
• Emergency services (E911 in US and 112 in Europe)
• Commercial Sector Advanced Traveler Information
  Systems (DoT), location-aware games, Advertising
  services
• Commercial potentials of LBS(S03)
• Optimistic prediction 4B by 2002, 81.9B by
  2005 (Europe only)
• Pessimistic prediction 11M by 2002, 167M by
  2005 (USA only).
• Enabling Technologies
• Mobile Positioning Methods
• Location Update Techniques
• Location-based Query Processing

5
Mobile Positioning

• GPS Global Positioning System. Accuracy up to 3
  meters or more.
• Cell-ID (Europe) Accuracy 100m-3km

Overview of LBS app. And level of accuracy
required (SV04)
6
Location Update Techniques

• Dead-Reckoning Location Update Policies (GS05)
• Periodic Updates

7
Concept of Uncertainty

• Uncertainty is an inherent feature in databases
  storing location information.
• Sources of uncertainty
• Mobile Positioning Methods
• Location Update Techniques
• Capturing uncertainty in the model and query
  language is an ongoing research.

8
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 in the next 10 minutes
• We focus on continuous kNN (CkNN) query
  processing.
• Main memory solution Conceptual Partitioning
  Model CPM MHP05
• Disk-based solution Shared Execution Algorithm
  SEA-CNN XMA05

9
Common Assumptions
10
SEA-CNN Over view

• Overview
• Objects are stored in disk, everything else is in
  memory.
• Centralized processing.
• Support all kinds of mutability between objects
  and queries.
• No movement pattern, in open space.
• Goal
• Minimize I/O cost, and CPU time.
• Two important features
• Incremental evaluation of queries
• Shared execution

11
SEA-CNN Data Structures
12
SEA-CNN Incremental Search

• Key points
• For each query q, define a search region based on
  past answer and recent movements of q and
  objects.
• Only objects inside search region are checked
  against q.
• Given q.ARt0 as the answer radius of q at time t0
  (q.AR distance from q to kth-NN object)
• At time t1, the search radius of query q
  (q.SRt1) is computed
• as follows
• Step 1 check if any object moves in q.ARt0
  during t0, t1. If yes, q.SRt1 q. ARt0. If no,
  q.SRt1 0.
• Step 2 check if any object that was in q.ARt0
  but moves out of q.ARt0 during t0, t1. If yes,
  q.SRt1 equals the distance from q to the furthest
  object.
• Step 3 check if q moves during t0, t1. If yes
• If q. SRt1 0 then q.SRt1 q.ARt0 q.Loct1-
  q.Loct0
• If q. SRt1 !0 then q.SRt1 q. SRt1 q.Loct1-
  q.Loct0

13
SEA-CNN Incremental Search(An Example)
Q1 O5 and Q1 move during T0, T1. So Q1.SRT1
Q1.ART0 Q1.LocT1-Q1.LocT0
Q2 O8 moves out of Q2.ART0 during T0, T1. So
Q2.SRT1 Q2.LocT1-O8.LocT0
14
SEA-CNN Shared Execution

• Key points
• Utilize shared execution to reduce repeated I/O
  operations.
• Group similar queries together. Evaluating this
  set of queries is reduced to a spatial join
  between the objects and the queries.

15
SEA-CNN Algorithm
16
CPM Overview

• Overview
• Objects and queries are stored in memory.
• Centralized processing.
• Support all kinds of mutability between objects
  and queries.
• No movement pattern, in open space.
• Goal
• Minimize CPU time.
• Important features
• Conceptual Partitioning
• Simulate traditional kNN search (using
  branch-and-bound search with breadth-first (or
  best-first) traversal)
• Roadmap
• Initial NN Computation (conceptual partitioning
  branch and bound search breadth-first
  traversal)
• Handling Updates

17
CPM Data Structures
18
CPM NN Computation(Conceptual Partitioning)

• Conceptual Partitioning
• What is CP? Partitioning of cells into
  rectangles based on proximity to the query cell.
  Each rectangle has direction and level.
• Why CP? A natural processing order of the cells.
  Facilitate NN search (search minimal set of
  cells).

19
CPM NN Computation(Algorithm by Example)

• Search heap content (always sorted)
• H ltc4,4,0gt, ltU0,0.1gt, ltL0,0.2gt, ltR0,0.8gt,
  ltD0,0.9gt
• Deheap c4 do nothing.
• Deheap U0
• insert cells of U0
• Insert U1
• Continue until deheap ltc3,3, 1gt and find 1st
  candidate p1
• best_dist dist(p1, q) 1.7
• Continue until deheap c2,4 and find p2
• best_dist dist(p2, q) 1.3
• Terminate because the next entry in the heap has
  min_dist gt best_dist

20
CPM Handling Updates

• Key Points
• Focus on moving objects, static queries. Moving
  queries are treated as new queries.
• Reexamine only queries whose influence regions
  overlap with updated cells.
• Re-compute affected queries incrementally based
  on book keeping information to save computation
  time.

21
CPM Handling Updates(Algorithm by Example)
NN Re-computation Algorithm Input grid G,
affected query q Output new NN for q / Similar
to NN Computation. Utilize the book keeping
information in visit_list and search heap /

• p2 moves from c2,4 to c0,6
• c2,4 has q in the influence list and dist(q,
  p2) gt best_NN dist(q, p2) ? mark q as affected
  query.
• c0,6 has an empty influence list ? ignore
• Re-compute NN for q in the NN Re-computation
  algorithm

22
SEA-CNN CPM A Comparison

• Common features between the two
• Performance metrics
• Use query processing time (or CPU time) at the
  centralized server as the primary metric.
• Ignore communication cost.
• Employ Grid-based Indexing (simple, fast
  maintenance).
• Keep a search region for each query to handle
  updates.
• Are the differences significant?
• CPM saves some computations over SEA-CNN (as
  shown in the CPM paper) because CPM uses an
  optimal search algorithm.
• However, is saving in CPU time still very
  important?

23
Summary

• Monitoring queries to support LBS is an intensive
  research area in the past few years
• Short-term research trend seems to be proposals
  of new, more advance query types (our next
  presentation will discuss Reverse NN, and Group
  NN).
• Long-term research could be a Moving Object
  Databases. Recommend Moving Objects Databases
  textbook to gain perspective
• Location-management perspective vs.
  spatio-temporal data perspective.
• Many LBS-based commercial products Verilocation,
  uLocate, meetro, EarthComber, CellSpotting.
• Standards and Development Software Natural Area
  Coding System, Mobile Location Services Reference
  Architecture by Sun.
• For LBS updated info try LBSZone.

24
References

• B99 D. Barbara. "Mobile Computing and
  Databases- A Survey. In \em IEEE Transactions
  of Knowledge and Data Engineering, 11(1),
  108-117, 1999.
• S03 http//www.wirelessdevnet.com/features/nacja
  n03/
• GS05 R. H. Guting, M. Schneider. Moving Object
  Databases. Book.
• SV04 J. Schiller, A. Voisard. Location-based
  Services. Book.
• MHP05 Kyriakos Mouratidis, Marios
  Hadjieleftheriou, Dimitris Papadias. Conceptual
  Partitioning An Efficient Method for Continuous
  Nearest Neighbor Monitoring Nearest Neighbor
  Monitoring. SIGMOD 2005.   
• YPK05 Yu, X., Pu, K., Koudas, N. Monitoring
  K-Nearest Neighbor Queries Over Moving Objects.
  ICDE, 2005.
• XMA05 Xiong, X., Mokbel, M., Aref, W. SEA-CNN
  Scalable Processing of Continuous K-Nearest
  Neighbor Queries in Spatio-temporal Databases.
  ICDE, 2005.
• CDT00 Jianjun Chen, David J. DeWitt, Feng
  Tian, and Yuan Wang. NiagaraCQ A Scalable
  Continuous Query System for Internet Databases.
  In SIGMOD, 2000.
• CF02 Sirish Chandrasekaran and Michael J.
  Franklin. Streaming Queries over Streaming Data.
  In VLDB, 2002. (Psoup system).

25
Note

• Due date of your presentation slides is November
  14 2005.

26
Aggregate NN Queries in Spatial Databases and
Location-based Services

• Tai Do
• Data Systems Group, UCF
• November 11, 2005

27
Outline

• Aggregate Nearest Neighbor (ANN) queries
• Introduction to ANN.
• Solutions for Group Nearest Neighbor (GNN)
  Queries, a specific type of ANN.
• Solutions for Continuous Group Nearest Neighbor
  Queries (CGNN).

28
Aggregate NNExamples and Applications

• Applications
• Business decision making (construction of new
  facilities)
• Military Rescue (earliest pick-up time)
• Severe weather monitoring (most dangerous area)

29
Aggregate NN Definition

• What is ANN?
• A generalized form of NN search (multiple query
  points vs. single query point)
• Formally
• Given P p1, , pN (set of data points),
  Qq1,qn (set of query points)
• Aggregate distance function adist(p, Q)
  f(pq1, , pqn)
• An ANN query returns the data point p with the
  minimum aggregate distance
• Note AkNN is similar (find k gt1 data points),
  we only focus on ANN.
• When f sum, the ANN is called Group Nearest
  Neighbor Queries.

30
Group NN Queries

• Assumptions
• Queries are in memory.
• Data points are in disk and indexed by R-tree.
• Goal
• Minimize the extent and cost of the search (I/O
  and CPU time)
• Roadmap 3 solutions
• Multiple query method
• Single point method
• Minimum bound method

31
Multiple Query Method (MQM)

• Apply multiple conventional NN queries, then
  combine the results.
• MQM is a straightforward application of the
  threshold algorithm (FLN03)
• Each query point visits incrementally its NN data
  points (1st NN, then 2nd NN, )
• Compute the aggregate distance of the current NN
  data point
• Do the two above steps until we have seen the
  best data point.
• Main idea
• Question how do we know that the aggregate
  distance of the seen data point is smaller than
  the aggregate distance of unseen data points?
• Answer Predict minimum aggregate distance of
  unseen data points (or in other words, use a
  threshold)

32
MQM An Example (1)

• Q q1, q2
• P p1,, p12

33
MQM An Example (2)
q2
q1
t1 0
t2 0
T 0, best_dist ?, best_NN null
34
MQM An Example (3)

• Step 1
• Find the next (1st ) NN of q1

• Update t1 and T

q1
q2
(p10, 2)
t1 2
T t1 t2 2 0 2
35
MQM An Example (4)

• Step 2
• if the current aggregate distance lt best_dist ?
  Update best_dist and best_NN

• If current best aggregate distance lt T ? Stop
• Else go to the next NN of the next query point
  and repeat step 1

q1
q2
(p10, 2)
t1 2
p10
2
5
7
T 2
best_dist ?
best_dist 7
best_NN p10
36
MQM An Example (5)

• Step 1
• Find the next (1st ) NN of q2

• Update t2 and T

q1
q2
(p10, 2)
(p11, 3)
t1 2
7
t2 3
2
5
p10
T t1 t2 2 3 5
best_dist 7
best_NN p10
37
MQM An Example (6)

• Step 2
• if the current aggregate distance lt best_dist ?
  Update best_dist and best_NN

• If current best aggregate distance lt T ? Stop
• Else go to the next NN of the next query point
  and repeat step 1

q1
q2
(p10, 2)
(p11, 3)
t1 2
t2 3
p10
2
5
7
best_dist 7
p11
3
3
6
T 5
best_dist 6
best_NN p11
38
MQM An Example (7)

• Step 1
• Find the next (2nd ) NN of q1

• Update t1 and T

q1
q2
(p10, 2)
(p11, 3)
7
t2 3
(p11, 3)
2
5
p10
t1 3
p11
3
3
6
T t1 t2 3 3 6
best_dist 6
best_NN p11
39
MQM An Example (6)

• Step 2
• if the current aggregate distance lt best_dist ?
  Update best_dist and best_NN

• If current best aggregate distance lt T ? Stop
• Else go to the next NN of the next query point
  and repeat step 1

q1
q2
(p10, 2)
(p11, 3)
t1 3
(p11, 3)
p10
2
5
7
best_dist 6
t1 3
p11
3
3
6
T 6
No Update
p11
3
3
6
STOP
best_dist 6
best_NN p11
40
Single Point Method (SPM)

• Problem with MQM
• Multiple accesses to the same node and retrieve
  the same data point (e.g p11) through different
  queries.
• SPM processes queries by a single traversal.
• Strategy
• Compute the centroid q of Q, which is a point
  with small adist(q, Q)
• The GNN is a point of P near q.
• Challenges
• The computation of q.
• The range around q, in which we should look for
  points of P, before we conclude that no better
  GNN can be found.

41
SPM Illustration
42
SPM The Computation of q
43
SPM Finding the range

• To define the range around q find heuristics
  that can safely prune nodes in R-tree
• Lemma 1
• For each query point qi we have pqi qiqgt
  pq
• Summing up the n inequalities
• ?pqi ?qiq gt npq ? adist (p, Q) gt npq
  - adist (q, Q) (1)
• Lemma 1 can be used for pruning intermediate
  nodes
• Node N can be pruned if
• mindist(N, q) gt (1/n) best_dist
  adist(q,Q) (2)
• Because when we transform this pruning rule we
  have
• n mindist(N, q) adist(q,Q) gt best_dist (3)
• For any p in node N dist(p,q) gt mindist(N,q),
  so
• n dist(p,q) adist(q, Q) gt best_dist (4)
• Using Lemma 1 we have adist(p, Q) gt best_dist,
  hence node N can be safely pruned.

44
SPM Pruning Illustration

• Both N1 and N2 can be pruned
• best_dist adist(best_NN, Q) 9
• adist(q, Q) 3
• (1/n)(best_dist adist(q,Q)) ½ (9 3) 6
• mindist(N1,q) 10 and mindist(N2,q) 6

45
Minimum Bound Method (MBM)

• Like SPM, MBM performs a single query, but uses
  the minimum bounding rectangle M of Q (instead of
  a centroid q) to prune the search space.
• Is MBM obviously better than SPM? No clear
  reason. Must evaluate through experiments.
• Strategy
• Use good heuristics to identify the qualifying
  nodes

46
Minimum Bound Method Heuristics

• Heuristic 1 A node N cant contain qualifying
  points if
• mindist (N, M) gt (1/n)best_dist, because for
  any data point p in N
• adist(p, Q) gt n mindist(N, M) gt
  best_dist
• Heuristic 1 prunes N1 but not N2.

• Heuristic 2 A node N can be safely pruned if
• ?(mindist(N, qi)) gt best_dist
• Heuristic 2 prunes both N1 and N2

47
Performance Study
48
Continuous Group NN

• Assumptions
• Both query points and data points are in memory.
• Method
• Use a grid index.
• Utilize conceptual partitioning of the space
  around query Q.
• Apply Minimum Bound Method.

49
Continuous GNNDetails

• amindist (c, Q) ?(qi in Q) (mindist(c, qi)).
• amindist(c,Q) is the lower bound of mindist(p, Q)
  for any data point p in cell c.
• The GNN computation is similar to the NN
  computation presented in previous class.

50
Summary

• Threshold Algorithm
• Simple, useful, and reusable.
• Aggregate Nearest Neighbor Queries in Spatial
  Database
• Practical applications.
• Good heuristics are important.
• Optimal ANN search remains unsolved???

51
References

• GS05 R. H. Guting, M. Schneider. Moving Object
  Databases. Book.
• PTM05 D. Papadia, Y. Tao, K. Mouratidis, and
  C. K. Hui. Aggregate Nearest Neighbor Queries in
  Spatial Databases. ACM Trans. On Database
  Systems, Vol. 30, No. 2, June 2005, Pages
  529-576.
• MHP05 Kyriakos Mouratidis, Marios
  Hadjieleftheriou, Dimitris Papadias. Conceptual
  Partitioning An Efficient Method for Continuous
  Nearest Neighbor Monitoring Nearest Neighbor
  Monitoring. SIGMOD 2005.
• PST04 Dimitris Papadias, Qiongmao Shen, Yufei
  Tao, Kyriakos Mouratidis.Group Nearest Neighbor
  Queries. ICDE 2004.   
• XZ06 Tian Xia, Donghui Zhang. Continuous
  Reverse Nearest Neighbor Monitoring. ICDE 2006.
• FLN03 Ronald Fagin, Amnon Lotem, and Moni
  Naorc. Optimal aggregation algorithms for
  middleware. Journal of Computer and System
  Sciences 66 (2003) 614656.
• www.cs.fiu.edu/vagelis/ classes/COP6727/slides/fa
  gin.ppt. The animation for the MQM comes from
  this.