R
Ramez Elmasri
Researcher at University of Texas at Arlington
Publications - 202
Citations - 10375
Ramez Elmasri is an academic researcher from University of Texas at Arlington. The author has contributed to research in topics: Database design & Temporal database. The author has an hindex of 36, co-authored 201 publications receiving 10157 citations. Previous affiliations of Ramez Elmasri include Honeywell & Stanford University.
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Proceedings ArticleDOI
Temporal database modeling: an object-oriented approach
TL;DR: This work proposes a number of different approaches for incorporating temporal extensions to object-oriented databases by identifying the different techniques for representing temporal databases in an object-riented framework and defines the concepts of object versioning and attribute versioning in temporal objects.
Proceedings ArticleDOI
Web data cleansing and preparation for ontology extraction using WordNet
TL;DR: The hypothesis is that creating ontologies to describe the semantics of Web data is the key to bridging the gap between semi-structured data and structured databases, and hence to facilitating the application of database techniques.
Proceedings ArticleDOI
Quantitative Analysis of Scalable NoSQL Databases
TL;DR: Three of the most commonly used NoSQL databases: MongoDB, Cassandra and HBase are evaluated using the Yahoo Cloud Service Bench-mark, a popular benchmark tool and the horizontal scalability of the three systems under different workload conditions and varying dataset sizes is captured.
Proceedings ArticleDOI
K-DBSCAN: Identifying Spatial Clusters with Differing Density Levels
TL;DR: K-DBSCAN is a novel density based spatial clustering algorithm with the main focus of identifying clusters of points with similar spatial density, which contrasts with many other approaches, whose main focus is spatial contiguity.
Journal ArticleDOI
An integrated temporal data model incorporating time series concept
TL;DR: A generalized temporal database model that integrates the modeling of both version-based and time-series based temporal data into a single conceptual framework is proposed.