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