%0 Conference Paper %1 Mammen:2014wl %A S. von Mammen, %A Grenz, Carsten %A Hähner, Jörg %A Timpf, Sabine %A Loebenberger, Daniel %A Mandl, Stefan %A Kozachuk, Oleksandr %B Proceedings of INFORMATIK 2014: Big Data - Komplexität meistern %C Stuttgart, Germany %D 2014 %I Springer %K imported myown %P 1241--1252 %T Organic Data: Ein sicheres, dezentralisiertes Big Data Konzept %U https://downloads.hci.informatik.uni-wuerzburg.de/Mammen2014wl.pdf %X Today’s globalized economy forces companies more than ever to constantly adapt their business process executions to present business situations. Companies that are able to analyze the current state of their processes and moreover forecast its most optimal progress as well as proactively control them based on reliable predictions will be a decisive step ahead competitors. The paper at hands examines, based on a case study stemming from the steel manufacturing industry, which production-related data is currently collectable using state of the art sensor technologies forming a potential foundation for a detailed situation awareness and derivation of accurate forecasts. An analysis of this data however shows that its full potential cannot be utilized without dedicated approaches of big data analytics. By proposing an architecture for implementing predictive enterprise systems, the article intends to form a working and discussion basis for further research and implementation efforts in big data analytics.

@inproceedings{Mammen:2014wl, abstract = {Today’s globalized economy forces companies more than ever to constantly adapt their business process executions to present business situations. Companies that are able to analyze the current state of their processes and moreover forecast its most optimal progress as well as proactively control them based on reliable predictions will be a decisive step ahead competitors. The paper at hands examines, based on a case study stemming from the steel manufacturing industry, which production-related data is currently collectable using state of the art sensor technologies forming a potential foundation for a detailed situation awareness and derivation of accurate forecasts. An analysis of this data however shows that its full potential cannot be utilized without dedicated approaches of big data analytics. By proposing an architecture for implementing predictive enterprise systems, the article intends to form a working and discussion basis for further research and implementation efforts in big data analytics.}, added-at = {2022-01-21T13:23:20.000+0100}, address = {Stuttgart, Germany}, author = {\textbf{S. {von Mammen}} and Grenz, Carsten and H\"ahner, J\"org and Timpf, Sabine and Loebenberger, Daniel and Mandl, Stefan and Kozachuk, Oleksandr}, biburl = {https://www.bibsonomy.org/bibtex/2dddccd7edbac560d807d268acd0d69f5/hci-uwb}, booktitle = {Proceedings of INFORMATIK 2014: Big Data - Komplexit{\"a}t meistern}, date-added = {2020-01-28 13:45:16 +0100}, date-modified = {2020-01-28 13:45:16 +0100}, interhash = {c7e9b257407ed560b808798502359c56}, intrahash = {dddccd7edbac560d807d268acd0d69f5}, keywords = {imported myown}, pages = {1241--1252}, publisher = {Springer}, series = {Lecture Notes in Informatics}, timestamp = {2024-05-06T17:22:37.000+0200}, title = {Organic Data: Ein sicheres, dezentralisiertes Big Data Konzept}, url = {https://downloads.hci.informatik.uni-wuerzburg.de/Mammen2014wl.pdf}, year = 2014 }