![](bg8.png)
In the IoT domain, privacy is a critical issue con-
cerning the processing of data. Future work has to
consider, how to realize privacy protection in BRAID
without compromising analytic quality. A possible
approach towards this goal is PATRON (Stach et al.,
2017; Stach et al., 2018).
ACKNOWLEDGEMENTS
This paper is part of the PATRON research project
which is commissioned by the Baden-Wrttemberg Stif-
tung gGmbH. The authors would like to thank the
BW-Stiftung for financing this research.
REFERENCES
Abadi, D. J., Carney, D.,
C¸
etintemel, U., Cherniack, M.,
Convey, C., Lee, S., Stonebraker, M., Tatbul, N., and
Zdonik, S. (2003). Aurora: A New Model and Ar-
chitecture for Data Stream Management. The VLDB
Journal — The International Journal on Very Large
Data Bases, 12(2):120–139.
Azzedin, F. (2013). Towards a Scalable HDFS Architecture.
In Proceedings of the 2013 International Conference
on Collaboration Technologies and Systems, CTS ’13,
pages 155–161.
Barbierato, E., Gribaudo, M., and Iacono, M. (2013). Mo-
deling Apache Hive Based Applications in Big Data
Architectures. In Proceedings of the 7
th
International
Conference on Performance Evaluation Methodologies
and Tools, ValueTools ’13, pages 30–38.
Beheshti, A., Benatallah, B., Nouri, R., Chhieng, V. M., Xi-
ong, H., and Zhao, X. (2017). CoreDB: A Data Lake
Service. In Proceedings of the 2017 ACM on Con-
ference on Information and Knowledge Management,
CIKM ’17, pages 2451–2454.
Casado, R. and Younas, M. (2015). Emerging Trends and
Technologies in Big Data Processing. Concurrency and
Computation: Practice and Experience, 27(8):2078–
2091.
Chang, F., Dean, J., Ghemawat, S., Hsieh, W. C., Wallach,
D. A., Burrows, M., Chandra, T., Fikes, A., and Gruber,
R. E. (2008). Bigtable: A Distributed Storage System
for Structured Data. ACM Transactions on Computer
Systems, 26(2):4:1–4:26.
Columbus, L. (2016). Industry 4.0 Is Enabling A New Era
Of Manufacturing Intelligence And Analytics. Forbes.
https://www.forbes.com/sites/louiscolumbus/2016/08/
07/industry-4-0-is-enabling-a-new-era-of-
manufacturing-intelligence-and-analytics.
Dean, J. and Ghemawat, S. (2004). MapReduce: Simplified
Data Processing on Large Clusters. In Proceedings
of the 6
th
Conference on Symposium on Opearting
Systems Design & Implementation – Volume 6, OSDI
’04, pages 137–149.
Domingos, P. and Hulten, G. (2000). Mining High-Speed
Data Streams. In Proceedings of the Sixth ACM
SIGKDD International Conference on Knowledge Dis-
covery and Data Mining, KDD ’00, pages 71–80.
Geissbauer, R., Schrauf, S., Koch, V., and Kuge, S. (2014).
Industry 4.0 – Opportunities and Challenges of the
Industrial Internet. PricewaterhouseCoopers.
Gr
¨
oger, C. (2018). Building an Industry4.0 Analytics Plat-
form. Datenbank-Spektrum, 18(1):5–14.
Gr
¨
oger, C., Schwarz, H., and Mitschang, B. (2014). Prescrip-
tive Analytics for Recommendation-Based Business
Process Optimization. In Proceedings of the 17
th
Inter-
national Conference on Business Information Systems,
BIS ’14, pages 25–37.
Kreps, J. (2014). Questioning the Lambda Architec-
ture. OReilly Media. https://www.oreilly.com/ideas/
questioning-the-lambda-architecture.
Kreps, J., Narkhede, N., and Rao, J. (2011). Kafka: A Dis-
tributed Messaging System for Log Processing. In
Proceedings of the 6
th
International Workshop on Net-
working Meets Databases, NetDB ’11, pages 1–7.
Marz, N. (2011). How to beat the CAP theo-
rem. http://nathanmarz.com/blog/how-to-beat-the-cap-
theorem.html.
Marz, N. and Warren, J. (2015). Big Data - Principles
and best practices of scalable real-time data systems.
Manning Publications Co.
Middleton, P., Kjeldsen, P., and Tully, J. (2013). Fore-
cast: The Internet of Things, Worldwide. Gartner,
Inc. http://www.gartner.com/document/2625419.
Quix, C., Hai, R., and Vatov, I. (2016). Metadata Ex-
traction and Management in Data Lakes With GEMMS.
Complex Systems Informatics and Modeling Quarterly,
9(9):67–83.
Shvachko, K., Kuang, H., Radia, S., and Chansler, R. (2010).
The Hadoop Distributed File System. In Proceedings
of the 2010 IEEE 26
th
Symposium on Mass Storage
Systems and Technologies (MSST), MSST ’10, pages
1–10.
Stach, C., D
¨
urr, F., Mindermann, K., Palanisamy, S. M., Ta-
riq, M. A., Mitschang, B., and Wagner, S. (2017). PA-
TRON – Datenschutz in Datenstromverarbeitungssys-
temen. In Tagungsband der 47. Jahrestagung der Ge-
sellschaft f
¨
ur Informatik e.V., pages 1085–1096. (in
German).
Stach, C., D
¨
urr, F., Mindermann, K., Palanisamy, S. M.,
and Wagner, S. (2018). How a Pattern-based Privacy
System Contributes to Improve Context Recognition.
In Proceedings of the 2018 IEEE International Confe-
rence on Pervasive Computing and Communications
Workshops, CoMoRea ’18, pages 238–243.
Vavilapalli, V. K., Murthy, A. C., Douglas, C., Agarwal, S.,
Konar, M., Evans, R., Graves, T., Lowe, J., Shah, H.,
Seth, S., Saha, B., Curino, C., O’Malley, O., Radia,
S., Reed, B., and Baldeschwieler, E. (2013). Apache
Hadoop YARN: Yet Another Resource Negotiator. In
Proceedings of the 4
th
Annual Symposium on Cloud
Computing, SOCC ’13, pages 5:1–5:16.
Zaharia, M., Chowdhury, M., Franklin, M. J., Shenker, S.,
and Stoica, I. (2010). Spark: Cluster Computing with
Working Sets. In Proceedings of the 2
nd
USENIX Con-
ference on Hot Topics in Cloud Computing, HotCloud
’10, pages 1–7.
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