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Sam Chamberlain

dblp:31/1436 · DBLP profile ↗
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11ranked-venue papers
0as first author
0since 2021 · last 2004
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 10Computer networks · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
5 papers
Spatial and temporal data management · 77% Data models and query languages · 12% Data mining · 7%

Topics — the 10 heaviest of 12, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Spatial and temporal data management
moving object databases
0.152004
Managing uncertainty in moving objects databases · ACM Trans. Database Syst. 2004
Location Prediction and Queries for Tracking Moving Objects · ICDE 2000
DOMINO: Databases fOr MovINg Objects tracking · SIGMOD Conference 1999
Spatial and temporal data management › trajectory data management
trajectory representation
0.012004
Managing uncertainty in moving objects databases · ACM Trans. Database Syst. 2004
Data models and query languages › uncertain data management
uncertain trajectory
0.012004
Managing uncertainty in moving objects databases · ACM Trans. Database Syst. 2004
Data mining › spatiotemporal data mining › mobility data mining
location prediction
0.012000
Location Prediction and Queries for Tracking Moving Objects · ICDE 2000
Spatial and temporal data management
spatio-temporal query processing
0.012000
Location Prediction and Queries for Tracking Moving Objects · ICDE 2000
Spatial and temporal data management › moving object databases
moving object query
0.011999
DOMINO: Databases fOr MovINg Objects tracking · SIGMOD Conference 1999
Spatial and temporal data management › spatial data model
spatio-temporal data model
0.011997
Modeling and Querying Moving Objects · ICDE 1997
Spatial and temporal data management
spatio-temporal query language
0.011997
Modeling and Querying Moving Objects · ICDE 1997
Data integration and cleaning
heterogeneous data sources
0.012000
Location Prediction and Queries for Tracking Moving Objects · ICDE 2000
Spatial and temporal data management
spatial query processing
0.011999
DOMINO: Databases fOr MovINg Objects tracking · SIGMOD Conference 1999

Methods — techniques the papers use, named apart from their topics

chebyshev's inequality · 0.0simulation · 0.0location tracking · 0.0cost-based analysis · 0.0query processing algorithms · 0.0future temporal logic · 0.0
YearPublicationVenuePosition
2004 Managing uncertainty in moving objects databases
abstract
This article addresses the problem of managing Moving Objects Databases (MODs) which capture the inherent imprecision of the information about the moving object's location at a given time. We deal systematically with the issues of constructing and representing the trajectories of moving objects and querying the MOD. We propose to model an uncertain trajectory as a three-dimensional (3D) cylindrical body and we introduce a set of novel but natural spatio-temporal operators which capture the uncertainty and are used to express spatio-temporal range queries. We devise and analyze algorithms for processing the operators and demonstrate that the model incorporates the uncertainty in a manner which enables efficient querying, thus striking a balance between the modeling power and computational efficiency. We address some implementation aspects which we experienced in our DOMINO project, as a part of which the operators that we introduce have been implemented. We also report on some experimental observations of a practical relevance.
Goce Trajcevski, Ouri Wolfson, Klaus H. Hinrichs, Sam Chamberlain
ACM Trans. Database Syst.4
2002 The Geometry of Uncertainty in Moving Objects Databases
Goce Trajcevski, Ouri Wolfson, Fengli Zhang, Sam Chamberlain
EDBT4
2002 Cost Based Data Dissemination in Satellite Networks
Bo Xu 0001, Ouri Wolfson, Sam Chamberlain, Naphtali Rishe
Mob. Networks Appl.3
2001 Cost Based Data Dissemination in Broadcast Networks with Disconnection
Bo Xu 0001, Ouri Wolfson, Sam Chamberlain
ICDT3
2000 Location Prediction and Queries for Tracking Moving Objects
abstract
Our Mobitrack prototype is intended to serve as a platform, or a toolkit for developing moving-objects-database type of applications. The system is the third in a three-layer architecture. The first layer is an object relational DBMS. The database stores the information about each moving object, including its plan of motion. The second layer is a GIS that adds capabilities and user interface primitives for storing, querying, and manipulating geographic information. The third layer, Mobitrack, adds temporal capabilities, capabilities of managing the uncertainty that is inherent in future motion plans, capabilities for location prediction, and a simulation testbed. Currently, Mobitrack runs on both Unix and MS Windows. On both platforms Mobitrack uses the Arc-View GIS. It uses the Informix DBMS on Unix, and DBAccess on MS Windows.
Ouri Wolfson, Bo Xu 0001, Sam Chamberlain
ICDE3
1999 Databases for Tracking Mobile Units in Real Time
Ouri Wolfson, Liqin Jiang, A. Prasad Sistla, Sam Chamberlain, Naphtali Rishe, Minglin Deng
ICDT4
1999 DOMINO: Databases fOr MovINg Objects tracking
abstract
Consider a database that represents information about moving objects and their location. For example, for a database representing the location of taxi-cabs a typical query may be: retrieve the free cabs that are currently within 1 mile of 33 N. Michigan Ave., Chicago (to pick-up a customer); or for a trucking company database a typical query may be: retrieve the trucks that are currently within 1 mile of truck ABT312 (which needs assistance); or for a database representing the current location of objects in a battlefield a typical query may be: retrieve the friendly helicopters that are in a given region, or, retrieve the friendly helicopters that are expected to enter the region within the next 10 minutes. The queries may originate from the moving objects, or from stationary users. We will refer to applications with the above characteristics as moving-objects-database (MOD) applications, and to queries as the ones mentioned above as MOD queries.
Ouri Wolfson, A. Prasad Sistla, Bo Xu 0001, Jutai Zhou, Sam Chamberlain
SIGMOD Conference5
1999 Updating and Querying Databases that Track Mobile Units
Ouri Wolfson, A. Prasad Sistla, Sam Chamberlain, Yelena Yesha
Distributed Parallel Databases3
1998 Cost and Imprecision in Modeling the Position of Moving Objects
abstract
Consider a database that represents the location of moving objects, such as taxi-cabs (typical query: "retrieve the cabs that are currently within 1 mile of 33 Michigan Ave., Chicago"), or objects in a battle-field. Existing database management systems (DBMSs) are not well equipped to handle continuously changing data, such as the position of moving objects, since data is assumed to be constant unless it is explicitly modified. In this paper, we address position-update policies and imprecision. Assuming that the actual position of a moving object m deviates from the position computed by the DBMS, when should m update its position in the database in order to eliminate the deviation? Furthermore, how can the DBMS provide a bound on the error (i.e. the deviation) when it replies to a query, such as: "what is the current position of m?" We propose a cost-based approach to update policies that answers both questions. We develop several update policies and analyze them theoretically and experimentally.
Ouri Wolfson, Sam Chamberlain, Son Dao, Liqin Jiang, Gisela Mendez
ICDE2
1998 Moving Objects Databases: Issues and Solutions
abstract
Consider a database that represents information about moving objects and their location. For example, for a database representing the location of taxi-cabs a typical query may be: retrieve the free cabs that are currently within 1 mile of 33 N. Michigan Ave., Chicago (to pickup a customer). In the military, moving object database applications arise in the context of the digital battlefield and in the civilian industry they arise in transportation systems. Currently, moving object database applications are being developed in an ad hoc fashion. Database management system (DBMS) technology provides a potential foundation upon which to develop these applications, however DBMSs are currently not used for this purpose. The reason is that there is a critical set of capabilities that are needed by moving object database applications and are lacking in existing DBMSs. The objective of our Databases fOr MovINg Objects (DOMINO) project is to build an envelope containing these capabilities on top of existing DBMSs. We describe the problems and our proposed solutions.
Ouri Wolfson, Bo Xu 0001, Sam Chamberlain, Liqin Jiang
SSDBM3
1997 Modeling and Querying Moving Objects
abstract
We propose a data model for representing moving objects in database systems. It is called the Moving Objects Spatio-Temporal (MOST) data model. We also propose Future Temporal Logic (FTL) as the query language for the MOST model, and devise an algorithm for processing FTL queries in MOST.
A. Prasad Sistla, Ouri Wolfson, Sam Chamberlain, Son Dao
ICDE3