TY - JOUR
T1 - Industrial Cyberphysical Systems
T2 - Realizing Cloud-Based Big Data Infrastructures
AU - CHENG, BO
AU - ZHANG, JINGYI
AU - HANCKE, GERHARD P.
AU - KARNOUSKOS, STAMATIS
AU - COLOMBO, ARMANDO WALTER
N1 - Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).
PY - 2018/3
Y1 - 2018/3
N2 - Future industrial systems and applications are expected to be complex constellations of cyberphysical systems (CPSs) where intel l igent networked embedded devices play a pivotal role toward the realization of new sophisticated industrial scenarios. The prevalence of multifaceted devices enables new avenues for monitoring at large scale via Internet of Things (IoT) technologies, and, when coupled with the real-time analysis of massive amounts of data, it results in new insights that can enhance decision-making processes and provide a competitive business advantage. How to collect, process, analyze, and interpret big data is a challenge that affects all industries, and, if effectively addressed, it would offer numerous operational benefits. This article discusses some of the main architectural issues related to collecting and handling big data for analysis linked to IoT and cloud technologies in the industrial context. The aim is to provide a high-level introductory view of this topic, underpinned with examples from popular frameworks, and discuss open research questions and future directions.
AB - Future industrial systems and applications are expected to be complex constellations of cyberphysical systems (CPSs) where intel l igent networked embedded devices play a pivotal role toward the realization of new sophisticated industrial scenarios. The prevalence of multifaceted devices enables new avenues for monitoring at large scale via Internet of Things (IoT) technologies, and, when coupled with the real-time analysis of massive amounts of data, it results in new insights that can enhance decision-making processes and provide a competitive business advantage. How to collect, process, analyze, and interpret big data is a challenge that affects all industries, and, if effectively addressed, it would offer numerous operational benefits. This article discusses some of the main architectural issues related to collecting and handling big data for analysis linked to IoT and cloud technologies in the industrial context. The aim is to provide a high-level introductory view of this topic, underpinned with examples from popular frameworks, and discuss open research questions and future directions.
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U2 - 10.1109/MIE.2017.2788850
DO - 10.1109/MIE.2017.2788850
M3 - RGC 21 - Publication in refereed journal
SN - 1932-4529
VL - 12
SP - 25
EP - 35
JO - IEEE Industrial Electronics Magazine
JF - IEEE Industrial Electronics Magazine
IS - 1
ER -
