%0 Conference Paper %1 iiwas_21_barents %A Stach, Christoph %A Bräcker, Julia %A Eichler, Rebecca %A Giebler, Corinna %A Mitschang, Bernhard %B Proceedings of the 23rd International Conference on Information Integration and Web Intelligence %C Linz %D 2021 %E Indrawan-Santiago, Maria %E Pardede, Eric %E Salvadori, Ivan Luiz %E Steinbauer, Matthias %E Khalil, Ismail %E Kotsis, Gabriele %I ACM %K Data_Lakes data_management data_pre-processing data_transformation food_analysis knowledge_modeling myown ontology zone_model %P 191--202 %R 10.1145/3487664.3487784 %T Demand-Driven Data Provisioning in Data Lakes: BARENTS - A Tailorable Data Preparation Zone %X 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. %@ 978-1-4503-9556-4

@inproceedings{iiwas_21_barents, abstract = {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.}, added-at = {2021-12-01T17:47:29.000+0100}, address = {Linz}, author = {Stach, Christoph and Bräcker, Julia and Eichler, Rebecca and Giebler, Corinna and Mitschang, Bernhard}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/22e90555f70ed2f9def6a047c38dd05dc/christophstach}, booktitle = {Proceedings of the 23\textsuperscript{rd} International Conference on Information Integration and Web Intelligence}, doi = {10.1145/3487664.3487784}, editor = {Indrawan-Santiago, Maria and Pardede, Eric and Salvadori, Ivan Luiz and Steinbauer, Matthias and Khalil, Ismail and Kotsis, Gabriele}, interhash = {6fd3f804e56aa0e4b7d5581948d048b1}, intrahash = {2e90555f70ed2f9def6a047c38dd05dc}, isbn = {978-1-4503-9556-4}, keywords = {Data_Lakes data_management data_pre-processing data_transformation food_analysis knowledge_modeling myown ontology zone_model}, month = nov, note = {iiWAS 2021 Best Paper Award}, pages = {191--202}, publisher = {ACM}, series = {iiWAS '21}, timestamp = {2021-12-01T16:47:29.000+0100}, title = {Demand-Driven Data Provisioning in Data Lakes: BARENTS - A Tailorable Data Preparation Zone}, year = 2021 }