Projects per year
Database and web technologies is one of the three research groups at the Department of Computer Science. The group covers two broad areas:
- Data-intensive systems
- Web science and knowledge engineering
In the area of data-intensive systems, the research concerns data management and analytics. Within this broad area, substantial research concerns temporal, spatial, and spatio-temporal data; multidimensional data; time-series data; and metric data. Prominent more specific areas include business intelligence, data warehousing, OLAP, and data integration. In the context of analytics, the research covers query processing, data mining, and machine learning, while in the context of data management, the research covers data modeling and database design, data models, query languages, and indexing.
In the area of web science and knowledge engineering, the research concerns web personalization, web data management and querying, recommender systems, semantic web and (linked) open data, information retrieval, web and social media mining, web engineering, and knowledge mining and integration.
The research approach is primarily constructive in nature: theoretically well-founded, purposeful artefacts such as frameworks, data structures, indexes, algorithms, languages, tools, and systems are prototyped and subjected to empirical study. Further, the research is mostly driven by novel and challenging real-world applications, with primary application areas being intelligent transport systems, energy grids, web querying, and healthcare. The research has impact on (at least) SDGs 3, 4, 6, 7, 11, 12, and 13.
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Forresi, C., Gallinucci, E., Golfarelli, M. & Ben Hamadou, H., 2021, In: VLDB Journal.
Research output: Contribution to journal › Journal article › peer-reviewOpen Access
Iqbal, M., Lissandrini, M. & Pedersen, T. B., 2021, In: CEUR Workshop Proceedings. 2840, p. 11-20 10 p.
Research output: Contribution to journal › Conference article in Journal › peer-review
AMIC: An Adaptive Information Theoretic Method to Identify Multi-Scale Temporal Correlations in Big Time Series DataHo, N. T. T., Vo, H., Vu, M. & Pedersen, T. B., 2021, In: IEEE Transactions on Big Data. 7, 1, p. 128 - 146 19 p., 8676277.
Research output: Contribution to journal › Journal article › peer-reviewOpen AccessFile
Christian S. Jensen (Chairman)2020 → …
Activity: Memberships › Board duties in companies, associations, or public organisations
Press / Media
1 item of Media coverage
Press/Media: Press / Media
Danske forskere vil i samarbejde med Rambøll og MIR bygge et datavarehus, der skal øge mulighederne med positionsdata
30/08/2021 → 05/09/2021
6 items of Media coverage
Press/Media: Press / Media