618,218 research outputs found
Intelligent Management and Efficient Operation of Big Data
This chapter details how Big Data can be used and implemented in networking
and computing infrastructures. Specifically, it addresses three main aspects:
the timely extraction of relevant knowledge from heterogeneous, and very often
unstructured large data sources, the enhancement on the performance of
processing and networking (cloud) infrastructures that are the most important
foundational pillars of Big Data applications or services, and novel ways to
efficiently manage network infrastructures with high-level composed policies
for supporting the transmission of large amounts of data with distinct
requisites (video vs. non-video). A case study involving an intelligent
management solution to route data traffic with diverse requirements in a wide
area Internet Exchange Point is presented, discussed in the context of Big
Data, and evaluated.Comment: In book Handbook of Research on Trends and Future Directions in Big
Data and Web Intelligence, IGI Global, 201
Security and Privacy Issues of Big Data
This chapter revises the most important aspects in how computing
infrastructures should be configured and intelligently managed to fulfill the
most notably security aspects required by Big Data applications. One of them is
privacy. It is a pertinent aspect to be addressed because users share more and
more personal data and content through their devices and computers to social
networks and public clouds. So, a secure framework to social networks is a very
hot topic research. This last topic is addressed in one of the two sections of
the current chapter with case studies. In addition, the traditional mechanisms
to support security such as firewalls and demilitarized zones are not suitable
to be applied in computing systems to support Big Data. SDN is an emergent
management solution that could become a convenient mechanism to implement
security in Big Data systems, as we show through a second case study at the end
of the chapter. This also discusses current relevant work and identifies open
issues.Comment: In book Handbook of Research on Trends and Future Directions in Big
Data and Web Intelligence, IGI Global, 201
Recommended from our members
Handbook of Big Data
Engineering and Physical Sciences Research Council, Leverhulme TrustThis is the author accepted manuscript. The final version is available from Wiley via https://doi.org/10.1002/sim.707
Big-Data-Driven Materials Science and its FAIR Data Infrastructure
This chapter addresses the forth paradigm of materials research -- big-data
driven materials science. Its concepts and state-of-the-art are described, and
its challenges and chances are discussed. For furthering the field, Open Data
and an all-embracing sharing, an efficient data infrastructure, and the rich
ecosystem of computer codes used in the community are of critical importance.
For shaping this forth paradigm and contributing to the development or
discovery of improved and novel materials, data must be what is now called FAIR
-- Findable, Accessible, Interoperable and Re-purposable/Re-usable. This sets
the stage for advances of methods from artificial intelligence that operate on
large data sets to find trends and patterns that cannot be obtained from
individual calculations and not even directly from high-throughput studies.
Recent progress is reviewed and demonstrated, and the chapter is concluded by a
forward-looking perspective, addressing important not yet solved challenges.Comment: submitted to the Handbook of Materials Modeling (eds. S. Yip and W.
Andreoni), Springer 2018/201
Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications
In the era when the market segment of Internet of Things (IoT) tops the chart
in various business reports, it is apparently envisioned that the field of
medicine expects to gain a large benefit from the explosion of wearables and
internet-connected sensors that surround us to acquire and communicate
unprecedented data on symptoms, medication, food intake, and daily-life
activities impacting one's health and wellness. However, IoT-driven healthcare
would have to overcome many barriers, such as: 1) There is an increasing demand
for data storage on cloud servers where the analysis of the medical big data
becomes increasingly complex, 2) The data, when communicated, are vulnerable to
security and privacy issues, 3) The communication of the continuously collected
data is not only costly but also energy hungry, 4) Operating and maintaining
the sensors directly from the cloud servers are non-trial tasks. This book
chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog
Computing is a service-oriented intermediate layer in IoT, providing the
interfaces between the sensors and cloud servers for facilitating connectivity,
data transfer, and queryable local database. The centerpiece of Fog computing
is a low-power, intelligent, wireless, embedded computing node that carries out
signal conditioning and data analytics on raw data collected from wearables or
other medical sensors and offers efficient means to serve telehealth
interventions. We implemented and tested an fog computing system using the
Intel Edison and Raspberry Pi that allows acquisition, computing, storage and
communication of the various medical data such as pathological speech data of
individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate
estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area
Network, Body Sensor Network, Edge Computing, Fog Computing, Medical
Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment,
Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in
Smart Healthcare (2017), Springe
Chapter 19 Unsupervised Methods
The Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches. The Handbook is divided into two volumes written by outstanding, internationally renowned scholars in the field. This second volume focuses on foundations and advances in data science, statistical modeling, and machine learning. It covers a range of key issues, including the management of big data in terms of record linkage, streaming, and missing data. Machine learning, agent-based and statistical modeling, as well as data quality in relation to digital trace and textual data, as well as probability, non-probability, and crowdsourced samples represent further foci. The volume not only makes major contributions to the consolidation of this growing research field, but also encourages growth into new directions. With its broad coverage of perspectives (theoretical, methodological, computational), international scope, and interdisciplinary approach, this important resource is integral reading for advanced undergraduates, postgraduates, and researchers engaging with computational methods across the social sciences, as well as those within the scientific and engineering sectors
Digital Twins: Potentials, Ethical Issues, and Limitations
After Big Data and Artificial Intelligence (AI), the subject of Digital Twins
has emerged as another promising technology, advocated, built, and sold by
various IT companies. The approach aims to produce highly realistic models of
real systems. In the case of dynamically changing systems, such digital twins
would have a life, i.e. they would change their behaviour over time and, in
perspective, take decisions like their real counterparts \textemdash so the
vision. In contrast to animated avatars, however, which only imitate the
behaviour of real systems, like deep fakes, digital twins aim to be accurate
"digital copies", i.e. "duplicates" of reality, which may interact with reality
and with their physical counterparts. This chapter explores, what are possible
applications and implications, limitations, and threats.Comment: 22 pages, in Andrej Zwitter and Oskar Gstrein, Handbook on the
Politics and Governance of Big Data and Artificial Intelligence, Edward Elgar
[forthcoming] (Handbooks in Political Science series
Scientific Realism and Primordial Cosmology
We discuss scientific realism from the perspective of modern cosmology,
especially primordial cosmology: i.e. the cosmological investigation of the
very early universe.
We first (Section 2) state our allegiance to scientific realism, and discuss
what insights about it cosmology might yield, as against "just" supplying
scientific claims that philosophers can then evaluate. In particular, we
discuss: the idea of laws of cosmology, and limitations on ascertaining the
global structure of spacetime. Then we review some of what is now known about
the early universe (Section 3): meaning, roughly, from a thousandth of a second
after the Big Bang onwards(!).
The rest of the paper takes up two issues about primordial cosmology, i.e.
the very early universe, where "very early" means, roughly, much earlier
(logarithmically) than one second after the Big Bang: say, less than
seconds. Both issues illustrate that familiar philosophical threat to
scientific realism, the under-determination of theory by data---on a cosmic
scale.
The first issue (Section 4) concerns the difficulty of observationally
probing the very early universe. More specifically, the difficulty is to
ascertain details of the putative inflationary epoch. The second issue (Section
5) concerns difficulties about confirming a cosmological theory that postulates
a multiverse, i.e. a set of domains (universes) each of whose inhabitants (if
any) cannot directly observe, or otherwise causally interact with, other
domains. This again concerns inflation, since many inflationary models
postulate a multiverse.
For all these issues, it will be clear that much remains unsettled, as
regards both physics and philosophy. But we will maintain that these remaining
controversies do not threaten scientific realism.Comment: 52 pages. An abridged version will appear in "The Routledge Handbook
of Scientific Realism", ed. Juha Saats
Interoperability in IoT
Interoperability refers to the ability of IoT systems and components to communicate and share information among them. This crucial feature is key to unlock all of the IoT paradigm´s potential, including immense technological, economic, and social benefits. Interoperability is currently a major challenge in IoT, mainly due to the lack of a reference standard and the vast heterogeneity of IoT systems. IoT interoperability has also a significant importance in big data analytics because it substantively eases data processing. This chapter analyzes the critical importance of IoT interoperability, its different types, challenges to face, diverse use cases, and prospective interoperability solutions. Given that it is a complex concept that involves multiple aspects and elements of IoT, for a deeper insight, interoperability is studied across different levels of IoT systems. Furthermore, interoperability is also re-examined from a global approach among platforms and systems.González-Usach, R.; Yacchirema-Vargas, DC.; Julián-Seguí, M.; Palau Salvador, CE. (2019). Interoperability in IoT. Handbook of Research on Big Data and the IoT. 149-173. http://hdl.handle.net/10251/150250S14917
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