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Heidi Gregersen

dblp:76/6932 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 1999
—ORCID · none

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

Databases, data management, data science and information retrieval · 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
1 paper
Data models and query languages · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Data models and query languages
conceptual modeling
0.011999
Temporal Entity-Relationship Models - A Survey · IEEE Trans. Knowl. Data Eng. 1999
Data models and query languages
entity-relationship model
0.011999
Temporal Entity-Relationship Models - A Survey · IEEE Trans. Knowl. Data Eng. 1999
Data models and query languages › temporal data model
temporal ER model
0.011999
Temporal Entity-Relationship Models - A Survey · IEEE Trans. Knowl. Data Eng. 1999

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

survey · 0.0
YearPublicationVenuePosition
1999 Temporal Entity-Relationship Models - A Survey
abstract
The entity-relationship (ER) model, using varying notations and with some semantic variations, is enjoying a remarkable and increasing popularity in both the research community-the computer science curriculum-and in industry. In step with the increasing diffusion of relational platforms, ER modeling is growing in popularity. It has been widely recognized that temporal aspects of database schemas are prevalent and difficult to model using the ER model. As a result, how to enable the ER model to properly capture time-varying information has, for a decade and a half, been an active area in the database research community. This has led to the proposal of close to a dozen temporally enhanced ER models. This paper surveys all temporally enhanced ER models known to the authors. It provides a comprehensive overview of temporal ER modeling and it thus meets a need for consolidating and providing easy access to the research in temporal ER modeling. In the presentation of each model, the paper examines how the time-varying information is captured in the model and presents the new concepts and modeling constructs of the model. A total of 19 different design properties for temporally enhanced ER models are defined, and each model is characterized according to these properties.
Heidi Gregersen, Christian S. Jensen
IEEE Trans. Knowl. Data Eng.1