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Title: Locationaware Query Processing: A Tutorial


1
Location-aware Query Processing A Tutorial
  • Mohamed F. Mokbel Walid G. Aref
  • Department of Computer Science and Engineering,
    University of Minnesota
  • mokbel_at_cs.umn.edu
  • Department of Computer Science, Purdue University
  • West Lafayette, Indiana, U.S.A.
  • aref_at_cs.purdue.edu

2
Motivation
3
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

4
Applications (Cont.) Location-based Store Finder
/ Advertisement
  • 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

5
Location-based Database Servers
Layered Approach
6
Variety of Location-aware Queries
  • Query Stationary
  • Object Moving

7
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

8
Location-aware Snapshot Query Processing
Querying the Past
  • Examples
  • Querying Along the Temporal Dimension What was
    the location of a certain object at 700 AM
    yesterday?
  • Querying Along the Spatial Dimension Find all
    objects that were in a certain area at 700 AM
    yesterday
  • Querying Along the Spatio-temporal Dimension
    Find all objects that were close to each other
    from 700 AM to 800 AM yesterday
  • Features
  • Large number of historical trajectories
  • Persistent read-only data
  • The ability to query the spatial and/or temporal
    dimensions

9
Location-aware Snapshot Query ProcessingIndexing
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

10
Location-aware Snapshot Query ProcessingMulti-ver
sion Index Structures
  • Maintain an R-tree for each time instance
  • R-tree nodes that are not changed across
    consecutive time instances are linked together

Timestamp 1
  • A multi-version R-tree can be combined with a
    3D-R-tree to support interval queries

11
Location-aware Snapshot Query ProcessingQuerying
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

12
Location-aware Snapshot Query ProcessingUpdating
Index Structures
  • Traditional R-tree updates are top-down
  • Updates translated to delete and insert
    transactions
  • To support frequent updates
  • 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

Hash based on OID
13
Location-aware Snapshot Query ProcessingUpdate
Memos
  • Keep a memo with the R-tree
  • The memo contains the recent updates to the
    existing R-tree
  • The query answer returned from the R-tree should
    be passed through the memo
  • The update memo is reflected to the R-tree once
    the relevant disk page is retrieved

Spatio-temporal Queries
Raw answer set
Final answer set
14
Location-aware Snapshot Query ProcessingQuerying
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

15
Location-aware Snapshot Query ProcessingDuality
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

16
Location-aware Snapshot Query ProcessingTime-Para
meterized Data Structures
  • The Time-parameterized R-tree (TPR-tree) consists
    of
  • Minimum bounding rectangles (MBR)
  • Velocity bounding rectangles (VBR)
  • A bounding rectangle with MBR 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

17
Spatio-temporal Access Methods
Red Future Blue Past Green Present Brown All
18
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

19
Snapshot vs. Continuous Query Processing
  • Traditional (Snapshot) Queries

Data
20
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

21
Location-aware Continuous Query ProcessingResult
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

22
Location-aware Continuous Query
ProcessingCaching 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?

23
Location-aware Continuous Query
ProcessingPredicting the Result
  • Given a future trajectory movement, the query
    answer can be pre-computed in advance
  • The trajectory movement is divided into N
    intervals, each with its own query answers Ai

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

24
Location-aware Continuous Query
ProcessingIncremental Evaluation
  • The query is evaluated only once. Then, only the
    updates of the query answer are evaluated
  • There are two types of updates. Positive and
    Negative updates

Query Result
  • Only the objects that cross the query boundary
    are taken into account
  • Need to continuously listen for notifications
    that someone cross the query boundary

25
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

26
Scalability of Location-aware Continuous Queries
Motivation
27
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

28
Scalability of Location-aware Continuous Queries
Main Concepts (Cont.)
  • Evaluating a large number of concurrent
    continuous spatio-temporal queries is abstracted
    as a spatio-temporal join between moving objects
    and moving queries

29
Scalability of Location-aware Continuous Queries
Location-aware Centralized Database Systems
  • Centralized index structures
  • Index the queries instead of data
  • Valid only for stationary queries

30
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

31
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

32
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

33
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 Os location
  • Challenge Discovering that an object becomes
    insignificant

34
Scalability of Location-aware Continuous Queries
Location-aware Data Stream Management Systems
(Cont.)
  • Only significant objects are 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 more accurate answer

35
Scalability of Location-aware Continuous Queries
Location-aware Data Stream Management Systems
(Cont.)
  • The first k objects are considered an initial
    answer
  • K-NN query is reduced to a circular range query

However, the query area may shrink or grow
K 3
36
Scalability of Location-aware Continuous Queries
Location-aware Data Stream Management Systems
(Cont.)
Each query is a single thread
One thread for all continuous queries
37
Scalability of Location-aware Continuous Queries
Location-aware Data Stream Management Systems
(Cont.)
  • Query Load Shedding
  • Reduce the cache area
  • Possibly reduce the query area
  • Immediately drop insignificant tuples
  • Intuitive and simple to implement
  • Object Load Shedding
  • 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?

38
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

39
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

40
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)
SELECT O. ID FROM Objects O WHERE O.type
truck INSIDE Area A
41
Spatio-temporal Query Operators
  • Continuously report the Avis cars in a certain
    area

SELECT M.ObjectID FROM MovingObjects M,
AvisCars A WHERE M.ID A.ID INSIDE RegionR
Spatio-temporal Operators
Scalar Function
/-
/-
INSIDE
/-
AvisCars
Moving Objects
AvisCars
Moving Objects
42
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
43
Spatio-temporal Histograms
  • Moving objects in D-dimensional space are mapped
    to 2D-dimensional histogram buckets

x
t
44
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
Q1
Query
Query Optimizer
Query plan
Feedback
45
Adaptive Query Optimization
  • Continuous queries last for 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
  • Need continuous optimization and ability to
    change the query plan
  • Training period Spatio-temporal histogram,
    periodicity mining
  • Online detection of changes

46
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

47
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 objects exact location
  • Reading accuracy. Location-aware devices do not
    provide the exact location
  • Object movement and network delay. By the time
    that a certain reading is received by the server,
    the moving object has already changed its location

48
Uncertainty in Moving Objects
  • Historical data (Trajectories)
  • Current data

T0?0
T0?1
T0?2
T0
T1
49
Uncertainty in Moving ObjectsError in Query
Answer
  • Range Queries
  • Nearest Neighbor Queries

50
Representing Uncertain Data usingEllipses
  • 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

51
Representing Uncertain Data usingCylinders
  • 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
52
Representing Uncertain Data in Road Networks
  • Given
  • Start and end points
  • Constraints
  • Deviation threshold r
  • Speed threshold v

53
Querying Uncertain DataUncertain 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

54
Querying Uncertain DataUncertain Keywords (Cont.)
O
  • 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

55
Querying Uncertain DataProbabilistic Queries
  • With each query answer, associate a probability
    that this answer is true
  • The answer set of a query Q is represented as a
    set of tuples ltID, pgt 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

56
Querying Uncertain DataProbabilistic Range
Queries
E
A
C
D
F
B
  • Query Answer
  • (B, 50)
  • (C, 90)
  • D
  • E
  • (F, 30)

57
Querying Uncertain DataProbabilistic
Nearest-Neighbor Queries
E
A
C
D
F
B
  • Query Answer (k1)
  • (C, p1)
  • (D, p2)
  • (E, p3)

58
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

59
Case Study IDOMINO
  • 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

60
DOMINO Architecture
61
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)

62
Case Study IISECONDO
  • SECONDO An Extensible DBMS Architecture and
    Prototype
  • A generic database system frame that can be
    filled with implementation of various data models
    (relational, object-oriented, 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

63
SECONDO Architecture
Generic GUI independent of data models. The
interface includes command prompt and is
extensible by a set of different viewers
The core functionality is the optimization of
conjunctive queries, i.e., producing an efficient
query plan
On top of the query optimizer, there is a
SQL-like language in a notation adopted to PROLOG
SECONDO Kernel
Berkeley DB (C)
Built on top of Berkeley DB. Includes specific
data models, algebra modules, and query
processors over the implemented algebra.
64
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

65
PLACE Architecture
DBMS
Query Parser
Query Processor
Relational Operators
Storage Engine
66
PLACE Architecture
PLACE
A Query Processor for Real-time Spatio-temporal
Data Streams
NILE
  • Continuous Predicate-based Window Queries
  • Moving Queries

A Query Processing Engine for Data Streams
Continuous time-based Sliding Window Queries
PREDATOR
INSIDE inside_clause
WINDOW window_clause
SQL Language
kNN knn_clause
W-Expire Operator
INSIDE Operator
Query processor
Negative Tuples
kNN Operator
Storage engine
Stream_Scan Operator
Stream of Moving Objects/Queries
Stream data types
Abstract data types
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)

68
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

69
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
70
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

71
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
  • Example III Add-in query operators

72
References
  • Overview Papers
  • 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 Database
    Management, SSDBM, pages 111-122, Capri, Italy,
    July 1998.
  • 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 ACM
    Symposium on Advances in Geographic Information
    Systems, ACM GIS, pages 110-117, 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
  • 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,
    SSDH, pages 1040-1049, Zurich, Switzerland, July
    1990.
  • Yannis Theodoridis, Michael Vazirgiannis, and
    Timos Sellis. Spatio-temporal Indexing for Large
    Multimedia Applications. In Proceeding of the
    IEEE Conference on Multimedia Computing and
    Systems, ICMCS, pages 441-448, Hiroshima, Japan,
    June 1996.
  • 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.

73
References
  • Spatio-temporal Access Methods (Cont.)
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.

74
References
  • Spatio-temporal Access Methods (Cont.)
  • Dieter Pfoser and Christian S. Jensen. Trajectory
    Indexing Using Movement Constraints.
    GeoInformatica, 9(2)93-115, June 2005.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Yuni Xia and Sunil Prabhakar. QR-Tree Efficient
    Indexing for Moving Object Database. In
    Proceeding of the International Conference on
    Database Systems for Advanced Applications,
    DASFAA, pages 175-182, Kyoto, Japan, March 2003.
  • 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.

75
References
  • 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 391-402, 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,
    Philadelphia. PA, May 1999.
  • 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
    Proceeding of the International Conference on
    Management of Data, COMAD, pages 57-64, Pune,
    India, December 2000.
  • Hae Don Chon, Divyakant Agrawal, and Amr El
    Abbadi. Storage and Retrieval of Moving Objects.
    In Proceeding of the International Conference on
    Mobile Data Management, MDM, pages 173-184, Hong
    Kong, January 2001.

76
References
  • Spatio-temporal Access Methods (Cont.)
  • 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.
  • 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
    463-472, San Jose, CA, February 2002.
  • Khaled M. Elbassioni and Ibrahim Kamel Amr
    Elmasry. An Efficient Indexing Scheme for
    Multi-dimensional 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.

77
References
  • Location-aware Snapshot Query Processing
  • 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, Madison, WI, June 2002.
  • 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
    301312, 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.

78
References
  • Location-aware Snapshot Query Processing
    (Cont.)
  • 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 Moving Object Trajectories. In Proceeding of
    the International Symposium on Advances in
    Spatial and Temporal Databases, SSTD, pages
    328-345, Angra dos Reis, Brazil, August 2005.
  • 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
    Spatio-temporal 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.

79
References
  • Location-aware Continuous Query Processing
  • Baihua Zheng and Dik Lun Lee. Semantic Caching in
    Location-Dependent Query Processing. In
    Proceeding of the International Symposium on
    Advances in Spatial and Temporal Databases, SSTD,
    pages 97-116, 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
    Proceeding of the International Conference on
    Extending Database Technology, EDBT, pages
    269-286, Prague, Czech Republic, March 2002.
  • Sunil Prabhakar, Yuni Xia, Dmitri V. Kalashnikov,
    Walid G. Aref, and Susanne E. Hambrusch. Query
    Indexing and Velocity Constrained Indexing
    Scalable Techniques for Continuous Queries on
    Moving Objects. IEEE Transactions on Computers,
    51(10)1124-1140, October 2002.
  • Yufei Tao, Dimitris Papadias, and Qiongmao Shen.
    Continuous Nearest Neighbor Search. In Proceeding
    of 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, VLDB, pages 512-523,
    Berlin, Germany, September 2003.

80
References
  • Location-aware Continuous Query Processing
    (Cont.)
  • Jun Zhang, Manli Zhu, Dimitris Papadias, Yufei
    Tao, and Dik Lun Lee. Location-based Spatial
    Queries. In Proceeding of the ACM International
    Conference on Management of Data, SIGMOD, pages
    443-454, San Diego, CA, June 2003.
  • Bugra Gedik, Kun-Lung Wu, Philip S. Yu, and Ling
    Liu. Motion Adaptive Indexing for Moving
    Continual Queries over Moving Objects. In
    Proceeding of the International Conference on
    Information and Knowledge Management, CIKM, pages
    427-436, Washington, DC, November 2004.
  • Mohamed F. Mokbel, Xiaopeng Xiong, and Walid G.
    Aref. SINA Scalable Incremental Processing of
    Continuous Queries in Spatio-temporal Databases.
    In Proceeding of the ACM International Conference
    on Management of Data, SIGMOD, pages 623-634,
    Paris, France, June 2004.
  • Ying Cai, Kien A. Hua, and Guohong Cao.
    Processing Range-Monitoring Queries on
    Heterogeneous Mobile Objects. In Proceeding of
    the International Conference on Mobile Data
    Management, MDM, page January, Berkeley, CA,
    2004.
  • Bugra Gedik and Ling Liu. MobiEyes Distributed
    Processing of Continuously Moving Queries on
    Moving Objects in a Mobile System. In Proceeding
    of the International Conference on Extending
    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 of the
    International Conference on Scientific and
    Statistical Database Management, SSDBM, pages
    317-328, Santorini Island, Greece, June 2004.
  • Haibo Hu, Jianliang Xu, and Dik Lun Lee. A
    Generic Framework for Monitoring Continuous
    Spatial Queries over Moving Objects. In
    Proceeding of the ACM International Conference on
    Management of Data, SIGMOD, pages 479-490,
    Baltimore, MD, June 2005.
  • Kyriakos Mouratidis, Dimitris Papadias, and
    Marios Hadjieleftheriou. Conceptual Partitioning
    An Efficient Method for Continuous Nearest
    Neighbor Monitoring. In Proceeding of the ACM
    International Conference on Management of Data,
    SIGMOD, pages 634-645, Baltimore, MD, June 2005.

81
References
  • Location-aware Continuous Query Processing
    (cont.)
  • Mohammad R. Kolahdouzan and Cyrus Shahabi.
    Alternative Solutions for Continuous K Nearest
    Neighbor Queries in Spatial Network Databases.
    GeoInformatica, 9(4)321-341, December 2005.
  • Xiaopeng Xiong, Mohamed F. Mokbel, and Walid G.
    Aref. SEA-CNN Scalable Processing of 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.

82
References
  • Location-aware Query Optimization
  • 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
    Spatio-temporal 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, Jorg 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.

83
References
  • Uncertainty and Probabilistic Queries
  • 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
    111132, 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 551562, 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)11121127, 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 140158, 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 233250, 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 325343, Santorini Island,
    Greece, July 2003.
  • Goce Trajcevski, OuriWolfson, Klaus Hinrichs, and
    Sam Chamberlain. Managing Uncertainty in Moving
    Objects Databases. ACM Transactions on Database
    Systems, TODS, 29(3)463507, September 2004.
  • Victor Teixeira de Almeida and Ralf Hartmut
    Guting. Supporting Uncertainty in Moving Objects
    in Network Databases. In Proceeding of the ACM
    Symposium on Advances in Geographic Information
    Systems, ACM GIS, pages 3140, Bremen, Germany,
    November 2005.
  • Dieter Pfoser, Nectaria Tryfona, and Christian S.
    Jensen. Indeterminacy and Spatiotemporal Data
    Basic Denitions and Case Study. GeoInformatica,
    9(3)211236, September 2005.
  • Xiangyuan Dai, Man Lung Yiu, Nikos Mamoulis,
    Yufei Tao, and Michail Vaitis. Probabilistic
    Spatial Queries on Existentially Uncertain Data.
    In Proceeding of the International Symposium on
    Advances in Spatial and Temporal Databases, SSTD,
    pages 400417, Angra dos Reis, Brazil, August
    2005.

84
References
  • Case Studies
  • Mohamed F. Mokbel, Xiaopeng Xiong, Walid G. Aref,
    Susanne Hambrusch, Sunil Prabhakar, and Moustafa
    Hammad. PLACE A Query Processor for Handling
    Real-time Spatio-temporal Data Streams (Demo). In
    Proceeding of the International Conference on
    Very Large Data Bases, VLDB, pages 13771380,
    Toronto, Canada, August 2004.
  • Mohamed F. Mokbel, Xiaopeng Xiong, Moustafa A.
    Hammad, and Walid G. Aref. Continuous Query
    Processing of Spatio-temporal Data Streams in
    PLACE. In Proceedings of the International
    Workshop on Spatio-temporal Database Management,
    STDBM, pages 5764, Toronto, Canada, August 2004.
  • Mohamed F. Mokbel and Walid G. Aref. PLACE A
    Scalable Location-aware Database Server for
    Spatiotemporal Data Streams. IEEE Data
    Engineering Bulletin, 28(3)310, September 2005.
  • Mohamed F. Mokbel, Xiaopeng Xiong, Moustafa A.
    Hammad, and Walid G. Aref. Continuous Query
    Processing of Spatio-temporal Data Streams in
    PLACE. GeoInformatica, 9(4)343365, December
    2005.
  • Stefan Dieker and Ralf Hartmut Guting. Plug and
    Play with Query Algebras SECONDO-A Generic DBMS
    Development Environment. In Proceeding of the
    International Database Engineering and
    Applications Symposium, IDEAS, pages 380392,
    Yokohoma, Japan, September 2000.
  • Ralf Hartmut Guting, Thomas Behr, Victor Teixeira
    de Almeida, Zhiming Ding, Frank Homann, and
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