Dept. of Electrical Engineering and Computer Science, Northwestern University Context-Aware Optimization of Continuous Query Maintenance for Trajectories

Dept. of Electrical Engineering and Computer Science,Northwestern University

Context-Aware Optimization of Continuous Query Maintenance

for Trajectories

Context-Aware Optimization of Continuous Query Maintenance

for Trajectories

For the Degree Master of Science

Hui DingDecember, 2005

Dept. of Electrical Engineering and Computer Science,

Northwestern University

OutlineOutline

1. Motivation1. Motivation

2. System Architecture2. System Architecture

3. Context-Aware Optimization3. Context-Aware Optimization

4. Experimental Evaluation4. Experimental Evaluation

3. Conclusion3. Conclusion



MotivationMotivation• Location-Based Services has become an enabling techn

ology for many novel classes of applications– Transportation planning– Context-Aware Tourist Information Provider– Digital Battlefield

• The key problem is the efficient management of:– Transient (location, time)

information of the moving objects– Various types of

spatio-temporal queries

pertaining to the objectsLocationupdate

Database Server

Moving Objects

Spatial Queries

Business Rules

Locationupdate



Motivation: Continuous Query Reevaluation

Motivation: Continuous Query Reevaluation

• Moving Object Database (MOD) differs from traditional database in that the data stored changes over time, due to the dynamic nature of moving objects

• Accordingly, the queries on such database may be instantaneous as well as continuous

• Consider the following query:

A

B

I-90

Andi: Give me the 4-star hotels within 5 miles on my way I-90 north



Motivation: Modeling Moving Objects in the Database

Motivation: Modeling Moving Objects in the Database

Moving objects can be modeled as follows:sequence of (location,time)updates (e.g., GPS-based)

Electronic maps + traffic distributionElectronic maps + traffic distributionpatterns + set of (to be visited) pointspatterns + set of (to be visited) points

=> => full trajectory

(location,time, Velocity Vector )updates

now ->

A trajectory is a piece-wise lA trajectory is a piece-wise linear function f: T -> (x, y) rinear function f: T -> (x, y) represented as a sequence epresented as a sequence of points (x1, y1, t1), (x2, yof points (x1, y1, t1), (x2, y2, t2), …, (xn, yn, tn)2, t2), …, (xn, yn, tn)

Realistic description:Realistic description:transportation vehicles, patrtransportation vehicles, patrol cars, delivery trucks, indiol cars, delivery trucks, individuals travel between homviduals travel between home and work…e and work…

Continuous Queries are Maintained on the trajectories!



Motivation: Reevaluating Continuous queries on trajectories

Motivation: Reevaluating Continuous queries on trajectories

• Traffic disturbances (accident, road work, fire, storm, etc.) , insertion/deletion of certain trajectory, modification to the query pattern

Q: It’s 12:30pm now, retrieve the public transportation buses within half a mile around NU campus between 2:00pm and 2:30pm, and send me the answer at 1:15pm.

=> answer set of query invalid, => re-evaluation necessary.

For example, a bus is delayed by an accident at 1:40pm and cannot be on campus between 2:00pm and 2:30pm



Motivation: ScenarioMotivation: Scenario

• Disturbance zone affects some moving objects’ trajectories in the future

• Queries need re-evaluation

• Our goal: optimize the response time it takes for re-evaluation



Motivation: Problem Statement -- Optimize the Reactive

Maintenance of Continuous Queries

Motivation: Problem Statement -- Optimize the Reactive

Maintenance of Continuous Queries

• Q3 need not be re-evaluated since its answer not affected

• Tr5 and Tr6 need not be considered when reevaluating Q1 and Q2 (not affected by disturbance!)

• Tr4 need not be considered since it is not relevant to pending queries



System ArchitectureSystem Architecture

Stored Procedures

Main Memory

CacheTable

Moving Object Table

Answers andAnswer Updates

TrafficAbnormality

PostQuery

DatabaseDist.Zone

ZoneID

...Time

Duration

Traffic Abnormality Table

DatabaseQueryRegion

QueryID

...CurrentAnswer

Query Table

Traj.Shape ...

PendingQuery

... ......

TrajID

DatabaseQuery Optimizer

SDO_GEOMETRY type

Linear ReferencingSystem

Index EngineQuery Operators

GeometryEngine

Triggers

Context Parser andExtraction

User Interface

...

Traj.Shape ...

PendingQuery

TrajID

Traj.Shape ...

PendingQuery

TrajID



System Architecture :Main Components

System Architecture :Main Components

Database Tables

Database Triggers Query Trigger TR_Q:

ON UPDATE TO MOT

IF A_Q Affected

Update A_Q

Queries Table (QT)

Traj.Shape

. . . PendingQuery

. . . . . . . . . . . .

TrajID

Moving Object TableDatabaseQuery

RegionQuery

ID...

CurrentAnswer

Query TableDatabaseDist.

ZoneZone

ID...

TimeDuration

Traffic Abnormality Table

Moving Objects table (MOT)Traffic Abnormalities Table (TAT)

ON INSERT/UPDATE TO TAT

IF trajectories in MOT affected

UPDATE MOT.traj_shape

Traffic monitoring Trigger TR_TAT:



System Architecture: Queries Considered in Our System

System Architecture: Queries Considered in Our System

• Range Query– Retrieve the moving objects within a given region

during a time interval

• Within Distance Query– Retrieve the moving objects within a given distance

to a querying object

• KNN Query– Retrieve the K nearest neighbors to the querying

object



Context-Aware Optimization:Guideline

Context-Aware Optimization:Guideline

• Carefully avoid context-switching cost– System level– Intra-query level

• Intelligently reduce disk access and computation volume– Utilizing spatio-temporal context information e

mbedded in the queries/moving objects



Context-Aware Optimization: System-level Context-awarenessContext-Aware Optimization: System-level Context-awareness

• minimize the context-switching among OS processes (semantics of individual trigger execution)

Set-level vs. Instance-level query re-evaluation

Traji d1Traji d2Traji d3Traji d4Traji d5

fl ags

fl ags

fl ags

fl ags

fl ags

………

………

Queryi d1

Queryi d2

Queryi d3

……

……

……

……

Moving Object TableQuery Table

Re-eval uateaf ter al l

are updated


fl ags

fl ags

fl ags

fl ags

fl ags

………

………

Queryi d1

Queryi d2

Queryi d3

……

……

……

……

Moving Object Table Query TableRe-evaluateafter Update

Re-evaluate afterUpdate






Context-Aware Optimization: System-level Context-awarenessContext-Aware Optimization: System-level Context-awareness“Before” vs. “After” trigger for query re-evaluation


fl ags

fl ags

fl ags

fl ags

fl ags

………

………

Movi ng Obj ects Tabl e

Traji d6Traji d7 fl ags

fl ags

Database

Check foraffected

trajectories

LoadTrajectories for

Updates

Update Queryanswer

?Before After



Context-Aware Optimization:Intra-query level Context-Awareness

Context-Aware Optimization:Intra-query level Context-Awareness

• Chances of performance tuning: the three steps of reevaluation1. Between affected moving objects and unaffected queries

2. Between affected queries and unaffected moving objects

3. Between affected moving objects and affected queries

Report TrafficAbnormality

Moving Object Trajectories

Pending Queries

UpdatedTrajectories

Affected?

UpdatedQueries

Affected?

3

2 1



Context-Aware Optimization: Query Indexing

Context-Aware Optimization: Query Indexing

• When reevaluating unaffected queries against affected trajectories, index on the pending queries is used to limit search space

• Construct Minimum Bounding Box on the fly and use it to retrieve queries that need to be reevaluated

• For Within Distance query and KNN query, index is maintained on the segments of the queries instead of the trajectories to improve selectivity

R1

WD1

WD2

K NN1

K NN2

R2



Context-Aware Optimization: Query Ordering

Context-Aware Optimization: Query Ordering

• When reevaluating affected queries against unaffected trajectories, we use query ordering to take advantage of the operating system cache

• The space ordering is imposed by using space-filling curve

R3

R1

R2

R4



Context-Aware Optimization: Join++ -- Spatio-Temporal PeculiaritiesContext-Aware Optimization: Join++ -- Spatio-Temporal Peculiarities

• When reevaluating affected queries against affected trajectories, we proposed a spatio-temporal join algorithm to reduce the amount of computation required

Tra

ffic

A

bno

rmal

ity

Pen

ding

Q

uerie

sM

ovin

g O

bjec

t T

raje

ctor

ies

Updat

e Q

uery

Updat

e T

raje

ctor

y

Spl

itT

raje

ctor

y

Update GridLists forQueries

Sort UpdatedTrajectorySegments

Spatio-Temporal Join



Context-Aware Optimization:Spatio-temporal Join ExampleContext-Aware Optimization:

Spatio-temporal Join Example

Q1

Q2

Q3

Tr1

Tr2

Tr3 Tr4

Tr5Tr6

(0, 10)

05

10

10

16 20

25

40

0

3

17

2

11

15

20

25

0 4

8 12

0

6

12

1824

0

2

6

10



Experimental EvaluationExperimental Evaluation

• System-level Context-Awareness

• Intra-Query level Context-Awareness– Query Indexing– Query Ordering– Spatio-temporal Join

• Overall performance study



Experimental Evaluation: system-level context-awareness


• “Before “ trigger reduce response time significantly due to the savings in disk access

• Set-level trigger out performs tuple-level trigger, but this is somewhat diminished by the dominant disk access and computation time





Range Query





Within Distance Query





K-Nearest Neighbor Query



Experimental Evaluation: Query IndexingExperimental Evaluation: Query Indexing

Range Query – 100 Affected Trajectories




Range Query – 200 Affected Trajectories




Within Distance Query – 100 Affected Trajectories




KNN Query – 100 Affected Trajectories



Experimental Evaluation: Query OrderingExperimental Evaluation: Query Ordering

Ordering of A Group of Pending Range Queries



Experimental Evaluation: Query orderingExperimental Evaluation: Query ordering

Ordering of A Group of Within Distance Queries



Experimental Evaluation:Spatio-temporal Join

Experimental Evaluation:Spatio-temporal Join



Experimental evaluation:overall performance

Experimental evaluation:overall performance

• Context-aware approach can make query reevaluation 3 times faster than the naive approach



ConclusionConclusion

• Investigated the impact of various context-dimension on the reevaluation of pending queries

• Reduced response time to traffic abnormality by utilizing the spatio-temporal correlation between the queries and the (updated) trajectories

• Implemented a system that maintains correct answers to three types of major continuous queries



Ongoing work: OMCAT DemoOngoing work: OMCAT Demo

Dept. of Electrical Engineering and Computer Science,Northwestern University

THANK YOU!THANK YOU!

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Dept. of Electrical Engineering and Computer Science, Northwestern University Context-Aware Optimization of Continuous Query Maintenance for Trajectories