27,509 research outputs found
Big Data and the Internet of Things
Advances in sensing and computing capabilities are making it possible to
embed increasing computing power in small devices. This has enabled the sensing
devices not just to passively capture data at very high resolution but also to
take sophisticated actions in response. Combined with advances in
communication, this is resulting in an ecosystem of highly interconnected
devices referred to as the Internet of Things - IoT. In conjunction, the
advances in machine learning have allowed building models on this ever
increasing amounts of data. Consequently, devices all the way from heavy assets
such as aircraft engines to wearables such as health monitors can all now not
only generate massive amounts of data but can draw back on aggregate analytics
to "improve" their performance over time. Big data analytics has been
identified as a key enabler for the IoT. In this chapter, we discuss various
avenues of the IoT where big data analytics either is already making a
significant impact or is on the cusp of doing so. We also discuss social
implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski
(eds.) Big Data Analysis: New algorithms for a new society, Springer Series
on Studies in Big Data, to appea
Real-time predictive maintenance for wind turbines using Big Data frameworks
This work presents the evolution of a solution for predictive maintenance to
a Big Data environment. The proposed adaptation aims for predicting failures on
wind turbines using a data-driven solution deployed in the cloud and which is
composed by three main modules. (i) A predictive model generator which
generates predictive models for each monitored wind turbine by means of Random
Forest algorithm. (ii) A monitoring agent that makes predictions every 10
minutes about failures in wind turbines during the next hour. Finally, (iii) a
dashboard where given predictions can be visualized. To implement the solution
Apache Spark, Apache Kafka, Apache Mesos and HDFS have been used. Therefore, we
have improved the previous work in terms of data process speed, scalability and
automation. In addition, we have provided fault-tolerant functionality with a
centralized access point from where the status of all the wind turbines of a
company localized all over the world can be monitored, reducing O&M costs
Research and Education in Computational Science and Engineering
Over the past two decades the field of computational science and engineering
(CSE) has penetrated both basic and applied research in academia, industry, and
laboratories to advance discovery, optimize systems, support decision-makers,
and educate the scientific and engineering workforce. Informed by centuries of
theory and experiment, CSE performs computational experiments to answer
questions that neither theory nor experiment alone is equipped to answer. CSE
provides scientists and engineers of all persuasions with algorithmic
inventions and software systems that transcend disciplines and scales. Carried
on a wave of digital technology, CSE brings the power of parallelism to bear on
troves of data. Mathematics-based advanced computing has become a prevalent
means of discovery and innovation in essentially all areas of science,
engineering, technology, and society; and the CSE community is at the core of
this transformation. However, a combination of disruptive
developments---including the architectural complexity of extreme-scale
computing, the data revolution that engulfs the planet, and the specialization
required to follow the applications to new frontiers---is redefining the scope
and reach of the CSE endeavor. This report describes the rapid expansion of CSE
and the challenges to sustaining its bold advances. The report also presents
strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie
Smart Asset Management for Electric Utilities: Big Data and Future
This paper discusses about future challenges in terms of big data and new
technologies. Utilities have been collecting data in large amounts but they are
hardly utilized because they are huge in amount and also there is uncertainty
associated with it. Condition monitoring of assets collects large amounts of
data during daily operations. The question arises "How to extract information
from large chunk of data?" The concept of "rich data and poor information" is
being challenged by big data analytics with advent of machine learning
techniques. Along with technological advancements like Internet of Things
(IoT), big data analytics will play an important role for electric utilities.
In this paper, challenges are answered by pathways and guidelines to make the
current asset management practices smarter for the future.Comment: 13 pages, 3 figures, Proceedings of 12th World Congress on
Engineering Asset Management (WCEAM) 201
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