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Christian S. Jensen
Researcher at Aalborg University
Publications - 541
Citations - 26166
Christian S. Jensen is an academic researcher from Aalborg University. The author has contributed to research in topics: Temporal database & Query language. The author has an hindex of 80, co-authored 507 publications receiving 24234 citations. Previous affiliations of Christian S. Jensen include University of Maryland, College Park & Zhejiang University.
Papers
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Journal ArticleDOI
EcoMark 2.0: empowering eco-routing with vehicular environmental models and actual vehicle fuel consumption data
TL;DR: EcoMark 2.0 proposes a general framework for eco-weight assignment and indicates that the instantaneous model EMIT and the aggregated model SIDRA-Running are suitable for assigning eco-weights under varying circumstances.
Proceedings ArticleDOI
Supporting imprecision in multidimensional databases using granularities
TL;DR: Techniques for handling imprecision are developed that aim to maximally reuse existing OLAP modeling constructs such as dimension hierarchies and granularities, yielding an effective approach with low computational overhead and that may be implemented using current technology.
Book ChapterDOI
Location Privacy Techniques in Client-Server Architectures
TL;DR: This chapter considers location privacy techniques that work in traditional client-server architectures without any trusted components other than the client's mobile device, and characterizes the privacy models assumed by existing techniques and categorizes these according to their approach.
Journal ArticleDOI
Design and analysis of a ranking approach to private location-based services
TL;DR: The article's proposal, SpaceTwist, aims to offer location privacy for k nearest neighbor (kNN) queries at low communication cost without requiring a trusted anonymizer and is believed to be the first solution that expresses the server-side functionality in a single SQL statement.
Journal ArticleDOI
Risk-aware path selection with time-varying, uncertain travel costs: a time series approach
TL;DR: This work addresses the problem of choosing the best paths among a set of candidate paths between the same origin–destination pair by developing techniques that, for each time interval, are able to find paths with non-dominated lowest costs while taking the users’ risk preferences into account.