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Glenn Simmons Iwerks

dblp:61/3506 · DBLP profile ↗
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4ranked-venue papers
4as first author
0since 2021 · last 2006
—ORCID · none

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

Databases, data management, data science and information retrieval · 4 · 4 first-authorArtificial intelligence and machine learning · 1 · 1 first-authorApplied, interdisciplinary, general and emerging computing · 1 · 1 first-author

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
3 papers
Spatial and temporal data management · 100%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Spatial and temporal data management › moving object databases
moving object query
0.122006
Maintenance of K-nn and spatial join queries on continuously moving points · ACM Trans. Database Syst. 2006
Continuous K-Nearest Neighbor Queries for Continuously Moving Points with Updates · VLDB 2003
Spatial and temporal data management › spatial query processing
spatial join
0.112006
Maintenance of K-nn and spatial join queries on continuously moving points · ACM Trans. Database Syst. 2006
Spatial and temporal data management › spatial query processing › continuous spatial queries
continuous k-nearest neighbor queries
0.012003
Continuous K-Nearest Neighbor Queries for Continuously Moving Points with Updates · VLDB 2003
Spatial and temporal data management
spatial indexing
0.012006
Maintenance of K-nn and spatial join queries on continuously moving points · ACM Trans. Database Syst. 2006

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

kinematic representation · 0.1
YearPublicationVenuePosition
2006 Maintenance of K-nn and spatial join queries on continuously moving points
abstract
Cars, aircraft, mobile cell phones, ships, tanks, and mobile robots all have the common property that they are moving objects. A kinematic representation can be used to describe the location of these objects as a function of time. For example, a moving point can be represented by the function p ( t ) = x → 0 + ( t - t 0 ) v → , where x → 0 is the start location, t 0 is the start time, and v → is its velocity vector. Instead of storing the location of the object at a given time in a database, the coefficients of the function are stored. When an object's behavior changes enough so that the function describing its location is no longer accurate, the function coefficients for the object are updated. Because the location of each object is represented as a function of time, spatial query results can change even when no transactions update the database. We present efficient algorithms to maintain k -nearest neighbor, and spatial join queries in this domain as time advances and updates occur. We assume no previous knowledge of what the updates will be before they occur. We experimentally compare these new algorithms with more straight forward adaptations of previous work to support updates. Experiments are conducted using synthetic uniformly distributed data, and real aircraft flight data. The primary metric of comparison is the number of I/O disk accesses needed to maintain the query results and the supporting data structures.
Glenn Simmons Iwerks, Hanan Samet, Kenneth P. Smith
ACM Trans. Database Syst.1
2004 Maintenance of Spatial Semijoin Queries on Moving Points
Glenn Simmons Iwerks, Hanan Samet, Kenneth P. Smith
VLDB1
2003 The internet spatial spreadsheet: enabling remote visualization of dynamic spatial data and ongoing query results over a network
abstract
Moving object databases store and process data for objects that change location frequently. Materialized views maintained over time must be updated to reflect changes due to the motion of objects in their environment. To visualize view query results, displays must be updated to reflect the change. In this paper we present the Internet Spatial Spreadsheet (ISS) as a means to organize, query, and visualize changing spatial data in a network environment such as the Internet.The goal of the ISS is to keep client visualizations of query results up to date with the server state. This is accomplished by pushing the minimal set of spatial data needed for rendering query results on the client. Incremental changes to query results are subsequently transmitted to the client as the database is updated to keep the visualization current. Additional constraints in the network environment such as firewall limitations are also considered.
Glenn Simmons Iwerks, Hanan Samet
GIS1
2003 Continuous K-Nearest Neighbor Queries for Continuously Moving Points with Updates
Glenn Simmons Iwerks, Hanan Samet, Kenneth P. Smith
VLDB1