TY  - CONF
AU  - Stach, Christoph
AU  - Bräcker, Julia
AU  - Eichler, Rebecca
AU  - Giebler, Corinna
AU  - Mitschang, Bernhard
A2  - Indrawan-Santiago, Maria
A2  - Pardede, Eric
A2  - Salvadori, Ivan Luiz
A2  - Steinbauer, Matthias
A2  - Khalil, Ismail
A2  - Kotsis, Gabriele
T1  - Demand-Driven Data Provisioning in Data Lakes: BARENTS\,—\,A Tailorable Data Preparation Zone
T2  - Proceedings of the 23rd International Conference on Information Integration and Web Intelligence
PB  - ACM
AD  - Linz
Y1  - 2021/november
SP  - 187
EP  - 198
M3  - https://doi.org/10.1145/3487664.3487784
KW  - data pre-processing; data transformation; knowledge modeling; ontology; data management; Data Lakes; zone model; food analysis
N1  - iiWAS 2021 Best Paper Award
N2  - Data has never been as significant as it is today. It can be acquired virtually at will on any subject. Yet, this poses new challenges towards data management, especially in terms of storage (data is not consumed during processing, i.\,e., the data volume keeps growing), flexibility (new applications emerge), and operability (analysts are no IT experts). The goal has to be a demand-driven data provisioning, i.\,e., the right data must be available in the right form at the right time. Therefore, we introduce a tailorable data preparation zone for Data Lakes called BARENTS\@. It enables users to model in an ontology how to derive information from data and assign the information to use cases. The data is automatically processed based on this model and the refined data is made available to the appropriate use cases. Here, we focus on a resource-efficient data management strategy. BARENTS can be embedded seamlessly into established Big Data infrastructures, e.\,g., Data Lakes.
ER  -