# Location-aware Query Processing and Optimization: A

```Location-aware Query
Processing and
Optimization: A Tutorial
Mohamed F. Mokbel
Walid G. Aref
Department of Computer Science and Engineering, University of Minnesota
Minneapolis, MN, USA
mokbel@cs.umn.edu
Department of Computer Science, Purdue University
West Lafayette, Indiana, USA.
aref@cs.purdue.edu
Motivation
May 2007
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Applications
Traffic Monitoring
 How many cars are in the downtown area?
 Send an alert if a non-friendly vehicle enters a
restricted region
 Report any congestion in the road network
 Once an accident is discovered, immediately send
alarm to the nearest police and ambulance cars
 Make sure that there are no two aircrafts with nearby
paths
May 2007
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Applications (Cont.)
 Where is my nearest Gas station?
 What are the fast food restaurants within 3 miles from
my location?
 Let me know if I am near to a restaurant while any of
my friends are there
 Send E-coupons to all customers within 3 miles of my
stores
 Get me the list of all customers that I am considered
their nearest restaurant
May 2007
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Location-based Database Servers
Built-in Approach
Layered Approach
GIS Interface
Spatio-temporal
GIS
DBMS
DBMS
ST Query
Processing
ST-Index
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Variety of Location-aware Queries
Continuously report the number of cars in the freeway
 Type: Range query
 Query: Stationary
 Time: Present
 Object: Moving
 Duration: Continuous
What are my nearest McDonalds for the next hour?
 Type: Nearest-Neighbor query
 Query: Moving
 Time: Future
 Object: Stationary
 Duration: Continuous
Send E-coupons to all cars that I am their nearest gas station
 Type: Reverse NN query
 Query: Stationary
 Time: Present
 Object: Moving
 Duration: Snapshot
What was the closest dist. between Taxi A &amp; me yesterday?
 Type: Closest-point query
 Query: Moving
 Time: Past
 Object: Moving
 Duration: Snapshot
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Tutorial Outline
 Location-aware Environments
 Location-aware Snapshot Query Processing
 Snapshot Past Queries
 Snapshot Present Queries
 Snapshot Future Queries
 Spatio-temporal Access Methods
 Location-aware Continuous Query Processing
 Scalable Execution of Continuous Queries
 Location-aware Query Optimization
 Uncertainty in Location-aware Query Processing
 Case Study
 Open Research Issues
May 2007
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Location-aware Snapshot Query Processing
Querying the Past
 Examples:
 Querying Along the Temporal Dimension:
What was the location of a certain object
from 7:00 AM to 10:00 AM yesterday?
 Querying Along the Spatial Dimension:
Find all objects that were in a certain area
at 7:00 AM yesterday
 Querying Along the Spatio-temporal
Dimension: Find all objects that were
close to each other from 7:00 AM to 8:00
AM yesterday
 Features:
 Large number of historical trajectories
 The ability to query the spatial and/or
temporal dimensions
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Location-aware Snapshot Query Processing
Indexing the Time Dimension
 Historical trajectories are represented by their three-dimensional
Minimum Bounding Rectangle (MBR)
Time
 3D-R-tree is used to index the
MBRs
 Technique simple and easy to
implement
 Does not scale well
 Does not provide efficient
query support
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Location-aware Snapshot Query Processing
Multi-version Index Structures
 Maintain an R-tree for each time instance
 R-tree nodes that are not changed across consecutive time
Timestamp 1
3D-R-tree
Timestamp 0
 A multi-version R-tree can be combined with a 3D-R-tree to
support interval queries
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Location-aware Snapshot Query Processing
Querying the Present
 Time is always NOW
 Example Queries:
 Find the number of objects in a certain area
 What is the current location of a certain
object?
 Features:
 Continuously changing data
 Real-time query support is required
 Index structures should be update-tolerant
 Present data is always accessed through
continuous queries
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Location-aware Snapshot Query Processing
Updating Index Structures
top-down
and insert transactions
 Updates can be managed in
space without the need for
deletion or insertions
 Bottom-up approaches
through auxiliary index
structures to locate the object
identifier
May 2007
MDM Tutorial
Hash based on OID
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Location-aware Snapshot Query Processing
Update Memos
 Keep a memo with the R-tree
Spatio-temporal Queries
 The memo contains the
R-tree
from the R-tree should be
passed through the memo
Update
Memo
 The update memo is reflected
to the R-tree once the
relevant disk page is retrieved
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Location-aware Snapshot Query Processing
Querying the Future
 Examples:
 What will my nearest restaurant be
after 30 minutes?
 Does my path conflict with any other
cars for the next hour?
 Features:
 Predict the movement through a
velocity vector
 Prediction could be valid for only a
limited time horizon in the future
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Location-aware Snapshot Query Processing
Duality Transformation
 A line (trajectory) in the two-dimensional space can be
transformed into a point in another dual two-dimensional
space
 Trajectory: x(t) = vt + a  Point: (v,a)
 All queries will need to be transformed into the dual space
 Rectangular queries will be represented as polygons
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Location-aware Snapshot Query Processing
Time-Parameterized Data Structures
 The Time-parameterized R-tree (TPR-tree) consists of:
 Minimum bounding rectangles (MBR)
 Velocity bounding rectangles (VBR)
 A bounding rectangle with MBR
&amp; VBR is guaranteed to contain
all its moving objects as long as
they maintain their velocity
vector
 High degree of overlap when
the velocity vector is not
updated
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Location-aware Snapshot Query Processing
Indexing Past, Present, and Future
 A unified index structure for both past, present, and future data
 Makes use of the partial-persistent R-tree for past data and the
TPR-tree for current and future data
 Double Time-Parameterized Bounding rectangles are used to
bound moving objects. Double TPBR has two components:
 Tail MBR that starts at the time of the last update and
extends to infinity. The tail is a regular TPBR of the TPR-tree
 Head MBR to bound the finite historical trajectories. The
 Querying is similar to regular PPR-tree search with the exception
of redefining the intersection function to accommodate for the
double TPBR
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Spatio-temporal Access Methods
RPPF-tree
Red: Future
Blue: Past
Green: Present
Brown: All
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Tutorial Outline
 Location-aware Environments
 Location-aware Snapshot Query Processing
 Location-aware Continuous Query Processing


Continuous Queries Vs. Snapshot Queries
Approaches for Continuous Query Evaluation
 Scalable Execution of Continuous Queries
 Location-aware Query Optimizer
 Uncertainty in Location-aware Query Processing
 Case Study
 Open Research Issues
May 2007
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Snapshot vs. Continuous Query Processing
Data
Query
 Continuous Queries
Query
Data
Query
Data
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Location-aware Continuous Query Processing
Approaches
 Straightforward Approach

Abstract the continuous queries to a series of
snapshot queries evaluated periodically
 Result Validation
 Result Caching
 Result Prediction
 Incremental Evaluation
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Location-aware Continuous Query Processing
Result Validation
 Associate a validation condition with each query answer
 Valid time (t):
The query answer is valid for the
next t time units
 Valid region (R)
 The query answer is valid as long
as you are within a region R

 It is challenging to maintain the computation of valid time/region
for querying moving objects
 Once the associated validation condition expires, the query will
be reevaluated
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Location-aware Continuous Query Processing
Caching the Result
 Observation: Consecutive evaluations of a
continuous query yield very similar results
 Idea: Upon evaluation of a continuous query,
retrieve more data that can be used later
 K-NN query
 Initially, retrieve more than k
 Range query
 Evaluate the query with a larger range
 How much we need to pre-compute?
 How do we do re-caching?
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Location-aware Continuous Query Processing
Predicting the Result
 Given a future trajectory movement, the query answer
 The trajectory movement is
divided into N intervals,
each with its own query
Nearest-Neighbor Query
 The query is evaluated once (as a snapshot query). Yet,
the answer is valid for longer time periods
 Once the trajectory changes, the query will be reevaluated
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Location-aware Continuous Query Processing
Incremental Evaluation
 The query is evaluated only once.
Then, only the updates of the query
 There are two types of updates.
Query Result
 Only the objects that cross the query
boundary are taken into account
 Need to continuously listen for
query boundary
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+
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Tutorial Outline
 Location-aware Environments
 Location-aware Snapshot Query Processing
 Location-aware Continuous Query Processing
 Scalable Execution of Continuous Queries



Location-aware Centralized Database Systems
Location-aware Distributed Database Systems
Location-aware Data Stream Management Systems
 Location-aware Query Optimizer
 Uncertainty in Location-aware Query Processing
 Case Study
 Open Research Issues
May 2007
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Scalability of Location-aware Continuous Queries
Motivation
Continuous
K-NN Query
Keep me updated by
nearest 3 hospitals
Make sure that the
nearest 3 airplanes are
FRIENDLY
Continuous
K-NN Query
May 2007
Location-aware
Database Server
Continuous
Range Query
How many cars in
the highlighted
area?
Alert me if there are less
than 3 police cars within
5 miles
Continuous
Range Query
MDM Tutorial
Monitor the traffic
in the red areas
Continuous
Range Query
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Scalability of Location-aware Continuous Queries
Main Concepts
 Continuous queries last for long times at the server side
 While a query is active in the server, other queries will be submitted
 Shared execution among multiple queries
 Should we index data OR queries?
 Data and queries may be stationary or moving
 Data and queries are of large size
 Data and queries arrive to the system with very high rates
 Treat data and queries similarly
 Queries are coming to data OR data are coming to queries?
 Both data and queries are subjected to each other
 Join data with queries
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Scalability of Location-aware Continuous Queries
Main Concepts (Cont.)
One thread for all continuous queries
Q1
...
...
...
Each query is a single thread
Q2
...
QN
Split
. . . ST Query N
ST Query 1
ST Query 2
DIndex
DIndex
DIndex
DIndex
QIndex
Data
Objects
Data
Objects
Data
Objects
Data
Objects
ST
Queries
Shared ST Join
 Evaluating a large number of concurrent continuous spatio-
temporal queries is abstracted as a spatio-temporal join between
moving objects and moving queries
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Scalability of Location-aware Continuous Queries
Location-aware Centralized Database Systems
 Centralized index structures
 Index the queries instead of data
Moving Objects
(Stationary) ST Queries in an R-tree index structure
 Valid only for stationary queries
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Scalability of Location-aware Continuous Queries
Location-aware Centralized Database Systems (Cont.)
 To accommodate for the continuous
movement of both data and queries:

Concurrent continuous queries share
a grid structure

Moving objects are hashed to the
same grid structure as queries
 The spatio-temporal join is done by
overlaying the two grid structures
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Scalability of Location-aware Continuous Queries
Location-aware Distributed Database Systems
 Motivation: Centralized location-aware servers will have a
bottleneck at the server side
 Assumption: Moving objects have devices with the capability of
doing some computations
 Idea:


Server will ship some of its processing to the moving
objects
Server will act as a mediator among moving objects
 Implementation: Moving objects should welcome cooperation in
such environments
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Scalability of Location-aware Continuous Queries
Location-aware Distributed Database Systems (Cont.)
 Each moving object O maintains a list of the queries that O
may be part of their answer
 It is the responsibility of the moving object O to report that O
becomes part of the answer of a certain query
 Once a query updates its location, it sends the new location
to the server, which will propagate the new location to the
interested users
 The server is responsible in determining which objects will be
interested in which queries
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Scalability of Location-aware Continuous Queries
Location-aware Data Stream Management Systems
 Motivation: Very high arrival rates that are beyond the system
capability to store
 Idea: Only store those objects that are likely to produce query
results, i.e., only significant objects are stored, all other data are
simply dropped
 Significant objects: A moving object O is significant if there is at
least one query that is interested in O’s location
 Challenge: Discovering that an object becomes insignificant
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Scalability of Location-aware Continuous Queries
Location-aware Data Stream Management Systems (Cont.)
 Only significant objects are
Cache
Area
stored in-memory
 An object is considered
significant if it is either in the
query area or the cache area
 Due to the query and object movements, a stored object may
become insignificant at any time
 Larger cache area indicates more storage overhead and
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Scalability of Location-aware Continuous Queries
Location-aware Data Stream Management Systems (Cont.)
 The first k objects are considered
 K-NN query is reduced to a circular
range query
However, the query area may
shrink or grow
May 2007
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K=3
36
Scalability of Location-aware Continuous Queries
Location-aware Data Stream Management Systems (Cont.)
+/-
Stationary
Range
Moving
kNN
Q1
...
QN
+/-
+/-
+/-
+/Moving
Range
Shared
Operator
Shared Memory
Buffer among
all C. Queries
Stream of
Moving Objects
May 2007
Q2
..
...
+/-
QN
..
..
Q2
One thread for all continuous queries
...
Q1
...
Each query is a single thread
Split
Shared Spatiotemporal Join
Stream of
Moving Objects
MDM Tutorial
Stream of
Moving Queries
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Scalability of Location-aware Continuous Queries
Location-aware Data Stream Management Systems (Cont.)




Reduce the cache area
Possibly reduce the query area
Immediately drop insignificant tuples
Intuitive and simple to implement




2
Objects that satisfy less than k
queries are insignificant
Lazily drop insignificant tuples
Challenge I: How to choose k?
Challenge II: How to provide a
lower bound for the query
accuracy?
1
6
5
3
4
7
K=2
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Tutorial Outline
 Location-aware Environments
 Location-aware Snapshot Query Processing
 Location-aware Continuous Query Processing
 Scalable Execution of Continuous Queries
 Location-aware Query Optimization
 Uncertainty in Location-aware Query Processing
 Case Study
 Open Research Issues
May 2007
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Location-aware Query Optimization
 Spatio-temporal pipelinable query operators


Range queries
Nearest-neighbor queries
 Selectivity estimation for spatio-temporal
queries/operators
 Spatio-temporal histograms
 Sampling
 Adaptive query optimization for continuous queries
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Spatio-temporal Query Operators
 Existing Approaches are Built on Top of DBMS (at the
Application Level)
Continuously report the
trucks in this area
Scalar functions
(Stored procedure)
Only produce objects in
the areas of interest
The performance
of scalar functions
is limited
Database
Engine
May 2007
SELECT O. ID
FROM Objects O
WHERE O.type = truck
INSIDE Area A
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Database
Engine
Spatio-temporal
Operators
41
Spatio-temporal Query Operators
 “Continuously report the Avis cars in a certain area”
Scalar Function
Spatio-temporal
Operators
2500
+/-
+/INSIDE
3000
Tuples in the Pipeline
SELECT M.ObjectID
FROM MovingObjects M, AvisCars A
WHERE M.ID = A.ID
INSIDE RegionR
JOIN
2000
JOIN
AvisCars Moving Objects
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Scalar
Function
Table Function
1500
1000
500
0
+/AvisCars
INSIDE Operator
INSIDE
2
4
8
16
32
Query Size
Moving Objects
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Spatio-temporal Selectivity Estimation
 Estimating the selectivity of spatio-temporal operators is crucial
in determining the best plan for spatio-temporal queries
SELECT ObjectID
FROM MovingObjects M
WHERE Type = Truck
INSIDE Region R
INSIDE
SELECT
SELECT
INSIDE
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Spatio-temporal Histograms
 Moving objects in D-dimensional space are mapped to 2D-
dimensional histogram buckets
x
x
t
t
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Spatio-temporal Histograms with Query
Feedback
 Estimating the selectivity of spatio-temporal operators is crucial
in determining the best plan for spatio-temporal queries
10%
6.25%
6.98%
6.98% 6.25%
6.01%
Q1 6.25%
6.25%
6.01%
6.25%
6.98% 6.25%
6.98% 6.25%
6.01%
6.25%
6.01%
6.25%
6.01% 6.25%
6.01% 6.25%
6.01%
6.25%
6.01%
6.25%
6.01% 6.25%
6.01% 6.25%
6.01%
6.25%
6.01%
Query
Query Optimizer
Spatio-temporal
Histogram
Query plan
Query Executer
May 2007
Feedback
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 Continuous queries last for
SELECT ObjectID
FROM MovingObjects M
WHERE Type = Truck
INSIDE Region R
long time (hours, days, weeks)


Environment variables are likely
to change
The initial decision for building a
query plan may not be valid after
a while
SELECT
INSIDE
INSIDE
SELECT
Moving Objects
Moving Objects
 Need continuous optimization
and ability to change the query
plan:


Training period: Spatio-temporal
histogram, periodicity mining
Online detection of changes
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Tutorial Outline
 Location-aware Environments
 Location-aware Snapshot Query Processing
 Location-aware Continuous Query Processing
 Scalable Execution of Continuous Queries
 Location-aware Query Optimizer
 Uncertainty in Location-aware Query Processing
 Case Study
 Open Research Issues
May 2007
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Uncertainty in Moving Objects
 Location information from moving objects is inherently
inaccurate
 Sources of uncertainty:



Sampling. A moving object sends its location information
once every t time units. Within any two consecutive
locations, we have no clue about the object’s exact
location
Reading accuracy. Location-aware devices do not
provide the exact location
Object movement and network delay. By the time that a
object has already changed its location
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Uncertainty in Moving Objects
 Historical data (Trajectories)
 Current data
T01+Є210
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Uncertainty in Moving Objects
 Range Queries
 Nearest Neighbor Queries
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Representing Uncertain Data using
Ellipses
 Given :
Start point
 End point
 Maximum possible speed  Maximum traveling
distance S
 If S is greater than the distance between the two end
points, then the moving object may have deviated from the
given route

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Representing Uncertain Data using
Cylinders
 Given:
 Start and end points
 Constraint:
 An object would report its location only if it is deviated by
a certain distance r from the predicted trajectory
r
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Networks
 Given:
 Start and end points
 Constraints :
 Deviation threshold r
 Speed threshold v
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Querying Uncertain Data
Uncertain Keywords
 KEYWORDS:



Probability: possibly, definitely
Temporal: sometimes, always
Spatial: somewhere, everywhere
 Examples:




What are the objects that are possibly sometimes within area
R at time interval T?
What are the objects that definitely passed through a certain
region?
Retrieve all the objects that are always inside a certain region
Retrieve all the objects that are sometimes definitely inside
region R
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Querying Uncertain Data
Uncertain Keywords (Cont.)
Q4
Q2
O
Q3
Q1
 Object O is definitely always in Q1
 Object O is possibly always in Q2
 Object O is definitely sometimes in Q3
 Object O is possibly sometimes in Q4
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Querying Uncertain Data
Probabilistic Queries
 With each query answer, associate a probability that
 The answer set of a query Q is represented as a set of
tuples &lt;ID, p&gt; where ID is the tuple identifier and p is
the probability that the object ID belongs to the answer
set of Q
 Assumptions:

Objects can lie anywhere uniformly within their
uncertainty region
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Querying Uncertain Data
Probabilistic Range Queries
E
A
C
D
F
B
 (B, 50%)
 (C, 90%)
 D
 E
 (F, 30%)
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Querying Uncertain Data
Probabilistic Nearest-Neighbor Queries
E
A
C
D
F
B
 (C, p1)
 (D, p2)
 (E, p3)
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Tutorial Outline
 Location-aware Environments
 Location-aware Snapshot Query Processing
 Location-aware Continuous Query Processing
 Scalable Execution of Continuous Queries
 Location-aware Query Optimizer
 Uncertainty in Location-aware Query Processing
 Case Studies



DOMINO
SECONDO
PLACE
 Open Research Issues
May 2007
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Case Study I
DOMINO
 DOMINO: Databases fOr MovINg Objects tracking
 Built on top of database management systems using a three-
layers approach; the DBMS layer, the GIS layer, and the DOMINO
layer
 Utilize dynamic attributes for future predicted locations
 Manage uncertainty that is inherent in future motion plans
 Support various location models:




Exact point location
An area in which the object is located in
An approximate motion plan
A complete motion plan
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DOMINO Architecture
DOMINO
Arc-View GIS
Object-Relational
DBMS
Informix/Oracle
May 2007
Provide temporal capabilities, uncertainty
management, and location prediction
Provide capabilities and user interface
primitives for storing, querying, and
manipulating geographic information
moving object, including each object’s
plan of motion
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Uncertainty Management in DOMINO
 Uncertainty operators are implemented as user-
defined functions (UDFs) in Oracle
 Uncertainty operators:

E.g., Always_Definitely_Inside, Sometime_Definitely_Inside,
Possibly_Always_Inside, Possibly_Sometime_Inside
 Example:
SELECT oid
FROM
MovingObjects
WHERE
Possibly_Always_Inside (trajectory,
region,
time interval)
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Case Study II
SECONDO
 SECONDO: An Extensible DBMS Architecture and Prototype
 A generic database system frame that can be filled with
implementation of various data models (relational, objectoriented, or XML) and data types (spatial data, moving objects)
 A database is a set of SECONDO objects of the form (name, type,
value), where type is one of the implemented algebras
 About 20 implemented algebras, e.g., standard algebra, relational
algebra, R-Tree algebra, and spatial algebra
 Query optimizer includes optimization of conjunctive queries,
selectivity estimation, and implementation of an SQL-like query
language
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SECONDO Architecture
Generic GUI independent of data models. The interface includes
command prompt and is extensible by a set of different viewers
GUI
Java
The core functionality is the optimization
of conjunctive queries, i.e., producing an
efficient query plan
Optimizer
PROLOG
SECONDO
Kernel
Berkeley DB (C++)
On top of the query optimizer, there is a
SQL-like language in a notation adopted
to PROLOG
Built on top of Berkeley DB. Includes specific data models, algebra
modules, and query processors over the implemented algebra.
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Case Study III
The PLACE Server
 PLACE: Pervasive Location-Aware Computing Environments
 Scalable execution of continuous queries over spatio-temporal
data streams
 Shared execution among concurrent continuous queries
 Built inside a database engine
 Incremental evaluation of
continuous queries
 Spatio-temporal query operators
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PLACE Architecture
DBMS
PLACE
Query
Parser
INSIDE, kNN
Negative
Query
Processor
Continuous /
Moving Queries
Scalable shared
operators
Relational
Operators
INSIDE, KNN,
operators
Storage
Engine
Stream of Moving
Objects/Queries
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PLACE Architecture
PLACE
A Query Processor for Real-time Spatio-temporal Data Streams
NILE
Continuous
A Query Processing Engine for Data Streams
Predicate-based
Window Queries
Continuous time-based
Sliding Window Queries
Moving Queries
PREDATOR
SQL Language
Query processor
Storage engine
Abstract data types
May 2007
WINDOW window_clause INSIDE inside_clause
kNN knn_clause
W-Expire Operator
INSIDE Operator
Negative Tuples
kNN Operator
Stream_Scan Operator
Stream of Moving
Stream data types
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Objects/Queries
67
Extended SQL Syntax
 inside_clause:


Stationary query: (x1,y1,x2,y2)
Moving query: (‘M’,OID, width, length)
 knn_clause:


Stationary query: (k,x,y)
Moving query: (‘M’, OID, k)
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Tutorial Outline
 Location-aware Environments
 Location-aware Snapshot Query Processing
 Location-aware Continuous Query Processing
 Scalable Execution of Continuous Queries
 Location-aware Query Optimizer
 Uncertainty in Location-aware Query Processing
 Case Study
 Open Research Issues
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Open Research Issues
Location Privacy
YOU ARE
TRACKED…
!!!!
“New technologies can pinpoint your location at any time and place.
They promise safety and convenience but threaten privacy and security”
Cover story, IEEE Spectrum, July 2003
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Open Research Issues
Spatio-temporal Data Mining
 Mining the history 
Predicting the future
 Online outlier detection for
moving objects
 Suspicious movement in video
surveillance
 Analysis of tsunami,
hurricanes, or earthquakes
 Phenomena detection and
tracking
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Open Research Issues
Reducing the Gap between ST Databases and DBMSs/DSMSs
 What do Spatio-temporal researchers offer?


50+ spatial index structure, 30+ spatio-temporal indexing structure
Wide variety of spatio-temporal query processing techniques
 What do DBMS designers want?


Little disturbance to their code
Large number of customers
 The result is:


DB2 and SQLServer do not support the R-tree (and may not be willing
to)
Oracle supports only R-tree and Quadtree
 Can we reduce this gap?




YES. Think in the minimal additions to the engine
Example I: B-tree with SFC
Example II: GiST and SP-GiST
May 2007
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References
Overview Papers:

1.
2.
3.
4.
Ouri Wolfson, Bo Xu, Sam Chamberlain, and Liqin Jiang. Moving Objects Databases: Issues
and Solutions. In Proceeding of the International Conference on Scientific and Statistical
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Mohamed F. Mokbel, Walid G. Aref, Susanne E. Hambrusch, and Sunil Prabhakar. Towards
Scalable Location-aware Services: Requirements and Research Issues. In Proceeding of the
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New Orleans, LA, November 2003.
Christian S. Jensen. Database Aspects of Location-based Services. In Location-based
Services, pages 115-148. Morgan Kaufmann, 2004.
Dik Lun Lee, Manli Zhu, and Haibo Hu. When Location-based Services Meet Databases.
Mobile Information Systems, 1(2):81-90, 2005.
Spatio-temporal Access Methods:

5.
6.
7.
8.
9.
Mohamed F. Mokbel, Thanaa M. Ghanem, and Walid G. Aref. Spatio-temporal Access Methods.
IEEE Data Engineering Bulletin, 26(2):40-49, June 2003.
X. Xu, Jiawei Han, and W. Lu. RT-Tree: An Improved R-Tree Indexing Structure for Temporal
Spatial Databases. In Proceeding of the International Symposium on Spatial Data Handling,
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Yannis Theodoridis, Michael Vazirgiannis, and Timos Sellis. Spatio-temporal Indexing for Large
Multimedia Applications. In Proceeding of the IEEE Conference on Multimedia Computing and
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Mario A. Nascimento and Jeerson R. O. Silva. Towards Historical R-Trees. In Proceeding of the
ACM Sympo-sium on Applied Computing, SAC, pages 235-240, Atlanta, GA, February 1998.
Jamel Tayeb, Ozgur Ulusoy, and Ouri Wolfson. A Quadtree-Based Dynamic Attribute Indexing
Method. The Computer Journal, 41(3):185-200, 1998.
May 2007
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73
References
 Spatio-temporal Access Methods (Cont.):
10. Dieter
Pfoser, Christian S. Jensen, and Yannis Theodoridis. Novel Approaches in Query
Processing for Moving Object Trajectories. In Proceeding of the International Conference on Very
Large Data Bases, VLDB, pages 395-406, Cairo, Egypt, September 2000.
11. Yufei Tao and Dimitris Papadias. MV3R-Tree: A Spatio-temporal Access Method for Timestamp
and Interval Queries. In Proceeding of the International Conference on Very Large Data Bases,
VLDB, pages 431-440, Roma, Italy, September 2001.
12. Yufei Tao and Dimitris Papadias. Efficient Historical R-Trees. In Proceeding of the International
Conference on Scientific and Statistical Database Management, SSDBM, pages 223-232,
Fairfax, VA, July 2001.
13. George Kollios, Vassilis J. Tsotras, Dimitrios Gunopulos, Alex Delis, and Marios Hadjieleftheriou.
Indexing Animated Objects Using Spatiotemporal Access Methods. IEEE Transactions on
Knowledge and Data Engineering, TKDE, 13(5):758-777, 2001.
14. Marios Hadjieleftheriou, George Kollios, Vassilis J. Tsotras, and Dimitrios Gunopulos. Efficient
Indexing of Spatiotemporal Objects. In Proceeding of the International Conference on Extending
Database Technology, EDBT, pages 251-268, Prague, Czech Republic, March 2002.
15. Zhexuan Song and Nick Roussopoulos. SEB-Tree: An Approach to Index Continuously Moving
Objects. In Proceeding of the International Conference on Mobile Data Management, MDM,
pages 340-344, Melbourne, Australia, January 2003.
16. Elias Frentzos. Indexing Objects Moving on Fixed Networks. In Proceeding of the International
Symposium on Advances in Spatial and Temporal Databases, SSTD, pages 289-305, Santorini
Island, Greece, July 2003.
17. V. Prasad Chakka, Adam Everspaugh, and Jignesh M. Patel. Indexing Large Trajectory Data
Sets with SETI. In Proceeding of the International Conference on Innovative Data Systems
Research, CIDR, Asilomar, CA, January 2003.
18. Yuhan Cai and Raymond T. Ng. Indexing Spatio-temporal Trajectories with Chebyshev
Polynomials. In Proceeding of the ACM International Conference on Management of Data,
SIGMOD, pages 599-610, Paris, France, June 2004.
May 2007
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74
References
 Spatio-temporal Access Methods (Cont.):
19. Dieter
Pfoser and Christian S. Jensen. Trajectory Indexing Using Movement Constraints.
GeoInformatica, 9(2):93-115, June 2005.
20. Jinfeng Ni and Chinya V. Ravishankar. PA-Tree: A Parametric Indexing Scheme for Spatio-temporal
Trajectories. In Proceeding of the International Symposium on Advances in Spatial and Temporal
Databases, SSTD, pages 254-272, Angra dos Reis, Brazil, August 2005.
21. Mario A. Nascimento, Jeerson R. O. Silva, and Yannis Theodoridis. Evaluation of Access Structures for
Discretely Moving Points. In Proceeding of the International Workshop on Spatio-temporal Database
Management, STDBM, pages 171-188, Edinburgh, UK, September 1999.
22. Zhexuan Song and Nick Roussopoulos. Hashing Moving Objects. In Proceeding of the International
Conference on Mobile Data Management, MDM, pages 161-172, Hong Kong, January 2001.
23. Dongseop Kwon, Sangjun Lee, and Sukho Lee. Indexing the Current Positions of Moving Objects
Using the Lazy Update R-Tree. In Proceeding of the International Conference on Mobile Data
Management, MDM, pages 113-120, Singapore, January 2002.
24. Mahdi Abdelguer, Julie Givaudan, Kevin Shaw, and Roy Ladner. The 2-3 TR-Tree, A Trajectory-Oriented
Index Structure for Fully Evolving Valid-time Spatio-temporal Datasets. In Proceeding of the ACM
Symposium on Advances in Geographic Information Systems, ACM GIS, pages 29-34, McLean, VA,
November 2002.
25. Mong-Li Lee, Wynne Hsu, Christian S. Jensen, Bin Cui, and Keng Lik Teo. Supporting Frequent
Updates in R-Trees: A Bottom-Up Approach. In Proceeding of the International Conference on Very
Large Data Bases, VLDB, pages 608-619, Berlin, Germany, September 2003.
26. Yuni Xia and Sunil Prabhakar. Q+R-Tree: Efficient Indexing for Moving Object Database. In Proceeding
of the International Conference on Database Systems for Advanced Applications, DASFAA, pages 175182, Kyoto, Japan, March 2003.
27. Christian S. Jensen, Dan Lin, and Beng Chin Ooi. Query and Update Efficient B+-Tree Based Indexing
of Moving Objects. In Proceeding of the International Conference on Very Large Data Bases, VLDB,
pages 768-779, Toronto, Canada, August 2004.
May 2007
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75
References

28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
Spatio-temporal Access Methods (Cont.):
Reynold Cheng, Yuni Xia, Sunil Prabhakar, and Rahul Shah. Change Tolerant Indexing for Constantly
Evolving Data. In Proceeding of the International Conference on Data Engineering, ICDE, pages 391402, Tokyo, Japan, April 2005.
Bin Lin and Jianwen Su. Handling Frequent Updates of Moving Objects. In Proceeding of the
International Conference on Information and Knowledge Management, CIKM, pages 493-500, Bremen,
Germany, October 2005.
Xiaopeng Xiong, Mohamed F. Mokbel, and Walid G. Aref. LUGrid: Update-tolerant Grid-based Indexing
for Moving Objects. In Proceeding of the International Conference on Mobile Data Management, MDM,
Nara, Japan, May 2006.
Xiaopeng Xiong and Walid G. Aref. R-Trees with Update Memos. In Proceeding of the International
Conference on Data Engineering, ICDE, Atlanta, GA, April 2006.
George Kollios, Dimitrios Gunopulos, and Vassilis J. Tsotras. On Indexing Mobile Objects. In
Proceeding of the ACM Symposium on Principles of Database Systems, PODS, pages 261-272,
Simonas Saltenis, Christian S. Jensen, Scott T. Leutenegger, and Mario A. Lopez. Indexing the
Positions of Continuously Moving Objects. In Proceeding of the ACM International Conference on
Management of Data, SIGMOD, pages 331-342, Dallas, TX, May 2000.
Pankaj K. Agarwal, Lars Arge, and Je Erickson. Indexing Moving Points. In Proceeding of the ACM
Symposium on Principles of Database Systems, PODS, pages 175-186, Dallas, TX, May 2000.
Mengchu Cai and Peter Revesz. Parametric R-Tree: An Index Structure for Moving Objects. In
International Conference on Management of Data, COMAD, 57-64, Pune, India, December 2000.
Hae Don Chon, Divyakant Agrawal, and Amr El Abbadi. Storage and Retrieval of Moving Objects. In
International Conference on Mobile Data Management, MDM, 173-184, Hong Kong, January 2001.
Kriengkrai Porkaew, Iosif Lazaridis, and Sharad Mehrotra. Querying Mobile Objects in Spatio-temporal
Databases. In Proceeding of the International Symposium on Advances in Spatial and Temporal
Databases, SSTD, pages 59-78, Redondo Beach, CA, July 2001.
May 2007
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76
References

38.
39.
40.
41.
42.
43.
44.
45.
46.
Spatio-temporal Access Methods (Cont.):
Cecilia Magdalena Procopiuc, Pankaj K. Agarwal, and Sariel Har-Peled. STAR-Tree: An Efficient
Self-Adjusting Index for Moving Objects. In Proceeding of the International Workshop on Algorithm
Engineering and Experimentation, ALENEX, pages 178-193, San Francisco, CA, January 2002.
Simonas Saltenis and Christian S. Jensen. Indexing of Moving Objects for Location-based
Services. In Proceeding of the International Conference on Data Engineering, ICDE, pages 463472, San Jose, CA, February 2002.
Khaled M. Elbassioni and Ibrahim Kamel Amr Elmasry. An Efficient Indexing Scheme for Multidimensional Moving Objects. In Proceeding of the International Conference on Database Theory,
ICDT, pages 425-439, Siena, Italy, January 2003.
Yufei Tao, Dimitris Papadias, and Jimeng Sun. The TPR*-Tree: An Optimized Spatio-temporal
Access Method for Predictive Queries. In Proceeding of the International Conference on Very Large
Data Bases, VLDB, pages 129-140, Berlin, Germany, September 2003.
Jignesh M. Patel, Yun Chen, and V. Prasad Chakka. STRIPES: An Efficient Index for Predicted
Trajectories. In Proceeding of the ACM International Conference on Management of Data,
SIGMOD, pages 637-646, Paris, France, June 2004.
George Kollios, Dimitris Papadopoulos, Dimitrios Gunopulos, and Vassilis J. Tsotras. Indexing
Mobile Objects Using Dual Transformations. VLDB Journal, 14(2):238-256, April 2005.
Dan Lin, Christian S. Jensen, Beng Chin Ooi, and Simonas Saltenis. Efficient Indexing of the
Historical, Present, and Future Positions of Moving Objects. In Proceeding of the International
Conference on Mobile Data Management, MDM, pages 59-66, Ayia Napa, Cyprus, May 2005.
Zhao-Hong Liu, Xiao-Li Liu, Jun-Wei Ge, and Hae-Young Bae. Indexing Large Moving Objects from
Past to Future with PCFI+-Index. In Proceeding of the International Conference on Management of
Data, COMAD, pages 131-137, January 2005.
Mindaugas Pelanis, Simonas Saltenis, and Christian Jensen. Indexing the Past, Present, and
Anticipated Future Positions of Moving Objects. ACM Transactions of Database Systems, TODS,
31(1), 255-298, March 2006.
May 2007
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
47.
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49.
50.
51.
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Ouri Wolfson, Bo Xu, and Sam Chamberlain. Location Prediction and Queries for Tracking Moving
Objects. In Proceeding of the International Conference on Data Engineering, ICDE, pages 687-688,
San Diego, CA, February 2000.
Rimantas Benetis, Christian S. Jensen, Gytis Karciauskas, and Simonas Saltenis. Nearest Neighbor
and Reverse Nearest Neighbor Queries for Moving Objects. In Proceeding of the International
Database Engineering and Applications Symposium, IDEAS, pages 44-53, Alberta, Canada, July
2002.
Yufei Tao and Dimitris Papadias. Time Parameterized Queries in Spatio-temporal Databases. In
Proceeding of the ACM International Conference on Management of Data, SIGMOD, pages 334-345,
Yufei Tao and Dimitris Papadias. Spatial Queries in Dynamic Environments. ACM Transactions on
Database Systems, TODS, 28(2):101-139, June 2003.
Yufei Tao, Jimeng Sun, and Dimitris Papadias. Analysis of Predictive Spatio-temporal Queries. ACM
Transactions on Database Systems, TODS, 28(4):295-336, December 2003.
Dimitris Papadias, Qiongmao Shen, Yufei Tao, and Kyriakos Mouratidis. Group Nearest Neighbor
Queries. In Proceeding of the International Conference on Data Engineering, ICDE, pages 301{312,
Boston, MA, March 2004.
Jimeng Sun, Dimitris Papadias, Yufei Tao, and Bin Liu. Querying about the Past, the Present and the
Future in Spatio-temporal Databases. In Proceeding of the International Conference on Data
Engineering, ICDE, pages 202-213, Boston, MA, March 2004.
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
54.
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56.
57.
58.
59.
60.
61.
Bin Lin and Jianwen Su. Shapes Based Trajectory Queries for Moving Objects. In Proceeding of the
ACM Symposium on Advances in Geographic Information Systems, ACM GIS, pages 21-30,
Bremen, Germany, November 2005.
Panfeng Zhou, Donghui Zhang, Betty Salzberg, Gene Cooperman, and George Kollios. Close Pair
Queries in Moving Object Databases. In Proceeding of the ACM Symposium on Advances in
Geographic Information Systems, ACM GIS, pages 2-11, Bremen, Germany, November 2005.
Yufei Tao and Dimitris Papadias. Historical Spatio-temporal Aggregation. ACM Transactions on
Information Systems, TOIS, 23(1):61-102, January 2005.
Man Lung Yiu, Nikos Mamoulis, and Dimitris Papadias. Aggregate Nearest Neighbor Queries in Road
Networks. IEEE Transactions on Knowledge and Data Engineering, TKDE, 17(6):820-833, June
2005.
Elias Frentzos, Kostas Gratsias, Nikos Pelekis, and Yannis Theodoridis. Nearest Neighbor Search on
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Hyung-Ju Cho and Chin-Wan Chung. An Efficient and Scalable Approach to CNN Queries in a Road
Network. In Proceeding of the International Conference on Very Large Data Bases, VLDB, pages
865-876, Trondheim, Norway, August 2005.
Marios Hadjieleftheriou, George Kollios, Petko Bakalov, and Vassilis J. Tsotras. Complex Spatiotemporal Pattern Queries. In Proceeding of the International Conference on Very Large Data Bases,
VLDB, pages 877-888, Trondheim, Norway, August 2005.
Lei Chen, M. Tamer Ozsu, and Vincent Oria. Robust and Fast Similarity Search for Moving Object
Trajectories. In Proceeding of the ACM International Conference on Management of Data, SIGMOD,
pages 491-502, Baltimore, MD, June 2005.
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
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65.
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Redondo Beach, CA, July 2001.
Zhexuan Song and Nick Roussopoulos. K-Nearest Neighbor Search for Moving Query Point. In Proceeding
of the International Symposium on Advances in Spatial and Temporal Databases, SSTD, pages 79-96,
Redondo Beach, CA, July 2001.
Iosif Lazaridis, Kriengkrai Porkaew, and Sharad Mehrotra. Dynamic Queries over Mobile Objects. In
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Prague, Czech Republic, March 2002.
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the International Conference on Very Large Data Bases, VLDB, pages 287-298, Hong Kong, August 2002.
Marios Hadjieleftheriou, George Kollios, Dimitrios Gunopulos, and Vassilis J. Tsotras. On-Line Discovery of
Dense Areas in Spatio-temporal Databases. In Proceeding of the International Symposium on Advances in
Spatial and Temporal Databases, SSTD, pages 306-324, Santorini Island, Greece, July 2003.
Glenn S. Iwerks, Hanan Samet, and Ken Smith. Continuous K-Nearest Neighbor Queries for Continuously
Moving Points with Updates. In Proceeding of the International Conference on Very Large Data Bases,
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
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Mohamed F. Mokbel, Xiaopeng Xiong, and Walid G. Aref. SINA: Scalable Incremental Processing of
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Database Technology, EDBT, Crete, Greece, March 2004.
Xiaopeng Xiong, Mohamed F. Mokbel, Walid G. Aref, Susanne Hambrusch, and Sunil Prabhakar.
Scalable Spatio-temporal Continuous Query Processing for Location-aware Services. In Proceeding
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Continuous K-Nearest Neighbor Queries in Spatio-temporal Databases. In Proceeding of the
International Conference on Data Engineering, ICDE, pages 643-654, Tokyo, Japan, April 2005.
Donghui Zhang, Dimitrios Gunopulos, Vassilis J. Tsotras, and Bernhard Seeger. Temporal and
Spatio-temporal Aggregations over Data Streams Using Multiple Time Granularities. Journal of
Information Systems, 28(1-2):61-84, March 2003.
Xuegang Huang and Christian S. Jensen. Towards A Streams-Based Framework for Defining
Location-based Queries. In Proceedings of the International Workshop on Spatio-temporal Database
Management, STDBM, pages 73-80, Toronto, Canada, August 2004.
Yufei Tao, George Kollios, Jerey Considine, Feifei Li, and Dimitris Papadias. Spatio-temporal
Aggregation Using Sketches. In Proceeding of the International Conference on Data Engineering,
ICDE, pages 214-226, Boston, MA, March 2004.
Mohamed F. Mokbel and Walid G. Aref. SOLE: Scalable Online Execution of Continuous Queries on
Spatiotemporal Data Streams. Technical Report TR CSD-05-016, submitted for a journal publication,
Purdue University Department of Computer Science, July 2005.
Mohamed F. Mokbel and Walid G. Aref. GPAC: Generic and Progressive Processing of Mobile
Queries over Mobile Data. In Proceeding of the International Conference on Mobile Data
Management, MDM, pages 155-163, Ayia Napa, Cyprus, May 2005.
Rimma Nehme and Elke Rundensteiner. SCUBA: Scalable Cluster-Based Algorithm for Evaluating
Continuous Spatio-Temporal Queries on Moving Objects. In Proceeding of the International
Conference on Extending Database Technology, EDBT, Munich, Germany, March 2006.
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Yong-Jin Choi and Chin-Wan Chung. Selectivity Estimation for Spatio-temporal Queries to Moving
Objects. In Proceeding of the ACM International Conference on Management of Data, SIGMOD,
pages 440-451, Madison, WI, June 2002.
Yufei Tao, Jimeng Sun, and Dimitris Papadias. Selectivity Estimation for Predictive Spatio-temporal
Queries. In Proceeding of the International Conference on Data Engineering, ICDE, pages 417-428,
Bangalore, India, March 2003.
Marios Hadjieleftheriou, George Kollios, and Vassilis J. Tsotras. Performance Evaluation of Spatiotemporal Selectivity Estimation Techniques. In Proceeding of the International Conference on
Scientific and Statistical Database Management, SSDBM, pages 202-211, Cambridge, MA, July
2003.
Qing Zhang and Xuemin Lin. Clustering Moving Objects for Spatio-temporal Selectivity Estimation. In
Proceedings of the Australasian Database Conference, pages 123-130, Dunedin, New Zealand,
January 2004.
Yufei Tao, Dimitris Papadias, Jian Zhai, and Qing Li. Venn Sampling: A Novel Prediction Technique
for Moving Objects. In Proceeding of the International Conference on Data Engineering, ICDE, pages
680-691, Tokyo, Japan, April 2005.
Hicham G. Elmongui, Mohamed F. Mokbel, and Walid G. Aref. Spatio-temporal Histograms. In
Proceeding of the International Symposium on Advances in Spatial and Temporal Databases, SSTD,
pages 19-36, Angra dos Reis, Brazil, August 2005.
Slobodan Rasetic, J•org Sander, James Elding, and Mario A. Nascimento. A Trajectory Splitting
Model for Efficient Spatio-temporal Indexing. In Proceeding of the International Conference on Very
Large Data Bases, VLDB, pages 934-945, Trondheim, Norway, August 2005.
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Uncertainty and Probabilistic Queries:
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96.
97.
98.
99.
100.
101.
Dieter Pfoser and Christian S. Jensen. Capturing the Uncertainty of Moving-Object Representations. In
Proceeding of the International Symposium on Advances in Spatial Databases, SSD, pages 111{132, Hong
Kong, July 1999.
Reynold Cheng, Dmitri V. Kalashnikov, and Sunil Prabhakar. Evaluating Probabilistic Queries over Imprecise
Data. In Proceeding of the ACM International Conference on Management of Data, SIGMOD, pages
551{562, San Diego, CA, June 2003.
Reynold Cheng, Dmitri V. Kalashnikov, and Sunil Prabhakar. Querying Imprecise Data in Moving Object
Environments. IEEE Transactions on Knowledge and Data Engineering, TKDE, 16(9):1112{1127, September
2004.
Jinfeng Ni, Chinya V. Ravishankar, and Bir Bhanu. Probabilistic Spatial Database Operations. In Proceeding
of the International Symposium on Advances in Spatial and Temporal Databases, SSTD, pages 140{158,
Santorini Island, Greece, July 2003.
Goce Trajcevski, Ouri Wolfson, Fengli Zhang, and Sam Chamberlain. The Geometry of Uncertainty in
Moving Objects Databases. In Proceeding of the International Conference on Extending Database
Technology, EDBT, pages 233{250, Prague, Czech Republic, March 2002.
Ouri Wolfson and Huabei Yin. Accuracy and Resource Concumption in Tracking and Location Prediction. In
Proceeding of the International Symposium on Advances in Spatial and Temporal Databases, SSTD, pages
325{343, Santorini Island, Greece, July 2003.
Goce Trajcevski, OuriWolfson, Klaus Hinrichs, and Sam Chamberlain. Managing Uncertainty in Moving
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