C
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.
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Journal ArticleDOI
Indeterminacy and Spatiotemporal Data: Basic Definitions and Case Study
TL;DR: This work explores fuzziness and uncertainty, subsumed under the term indeterminacy, in the spatiotemporal context and shows how the fundamental modeling concepts of spatial objects, attributes, and relationships and time points and periods are influenced by ind determinacy and how they can be combined.
Journal ArticleDOI
Authentication of Moving Top-k Spatial Keyword Queries
TL;DR: New authentication data structures, the MIR-tree and MIR*-tree are proposed that enable the authentication of MkSK queries at low computation and communication costs and are capable of outperforming two baseline algorithms by orders of magnitude.
Adding Transaction Time to SQL/Temporal
TL;DR: This change proposal specifies the addition of transaction time, in a fashion consistent with that already proposed for valid time, and constructs to create tables with valid-time and transaction-time support and query such tables with temporal upward compatibility, sequenced semantics, and nonsequenced semantics are defined.
Journal ArticleDOI
On spatio-temporal blockchain query processing
Qiang Qu,Ildar Nurgaliev,Muhammad Muzammal,Muhammad Muzammal,Christian S. Jensen,Jianping Fan +5 more
TL;DR: The evaluation indicates that TGS-BSI is a promising solution for efficient spatio-temporal query processing on blockchains, which is a modification of the Merkle KD-tree.
Proceedings ArticleDOI
Effective and Efficient Reuse of Past Travel Behavior for Route Recommendation
TL;DR: To enable efficient and effective RSL-Psc computation on massive route data, novel search space pruning techniques are developed and the two algorithms are capable of achieving high efficiency and scalability.