1 research outputs found
Industrial Big Data Analytics: Challenges, Methodologies, and Applications
While manufacturers have been generating highly distributed data from various
systems, devices and applications, a number of challenges in both data
management and data analysis require new approaches to support the big data
era. These challenges for industrial big data analytics is real-time analysis
and decision-making from massive heterogeneous data sources in manufacturing
space. This survey presents new concepts, methodologies, and applications
scenarios of industrial big data analytics, which can provide dramatic
improvements in velocity and veracity problem solving. We focus on five
important methodologies of industrial big data analytics: 1) Highly distributed
industrial data ingestion: access and integrate to highly distributed data
sources from various systems, devices and applications; 2) Industrial big data
repository: cope with sampling biases and heterogeneity, and store different
data formats and structures; 3) Large-scale industrial data management:
organizes massive heterogeneous data and share large-scale data; 4) Industrial
data analytics: track data provenance, from data generation through data
preparation; 5) Industrial data governance: ensures data trust, integrity and
security. For each phase, we introduce to current research in industries and
academia, and discusses challenges and potential solutions. We also examine the
typical applications of industrial big data, including smart factory
visibility, machine fleet, energy management, proactive maintenance, and just
in time supply chain. These discussions aim to understand the value of
industrial big data. Lastly, this survey is concluded with a discussion of open
problems and future directions