Page 24 - ICDL 2019 - Background Paper
P. 24
Application of Big Data and Analytics
Big Data has become ubiquitous in modern society. It challenges state-of-the-art data acquisition, computation, and
analysis methods. A lot of focus has been placed on the application of Big Data methods and less on the theoretical
underpinning of the field.
The recent availability of data administrative records, mobile devices, sensors, and many private sources, as well as
new processing and analytical techniques, can potentially transform the practice of science. In the social-science
context, the new data can potentially offer policymakers information that is much more current, granular, and richer
in environmental information than data produced by statistical agencies from surveys. New research opportunities,
however, are accompanied by challenges associated with using the new data that is no longer generated and
disseminated by statistical agencies, but can be harvested from many individual, public, and some private actions.
Nonetheless, important scholarly work has been done that uses Big Data in a way that is valuable to policymakers—in
areas as varied as finance, labour, education, science, innovation, transportation, and development.
The workshop topics include, but are not limited to, the following:
Big Data comprises bits on one side and processing on the other. The 5 Vs we are confronted with are:
VIRTUALIZATION
This might be happening in the Internet
VARIETY of Things (IoT) where you don’t actually
VOLUME Differences are deeper than the expression of have Volume, nor Velocity, nor Variety,
in principle, since a lot is happening locally
can present architecture scale to independent values based on different metrics. in IoT. However, the challenge for the future
They relate to differences in the capturing of
crunch the volume of data involved those values, in their stability, and in the is to consider all the data spread around
credibility of the sources. and look at it as a ‘virtual Big Data’.
The rapid and asynchronous
change in data over several who is benefiting from the
sources makes consistency a metadata, how are data
major issue and replication sources rewarded, and
basically impossible. who is accountable?
VELOCITY VALUE
Social Media and Analytics
The explosion of social media—in the form of user generated content on blogs, microblogs (Twitter), discussion
forums, product reviews, and multimedia sharing sites—presents many new opportunities and challenges to both
producers and consumers of information. For producers, this user generated content provides a rich source of implicit
consumer feedback. Tracking the pulse of the ever expanding social media outlets enables companies to discern what
consumers are saying about their products, which provides useful insights on how to improve and market products
better. For consumers, the plethora of information and opinions, from diverse sources, helps them tap into the
wisdom of crowds, which aids in making more informed decisions. Though a vast quantity of information is available,
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Big Data has become ubiquitous in modern society. It challenges state-of-the-art data acquisition, computation, and
analysis methods. A lot of focus has been placed on the application of Big Data methods and less on the theoretical
underpinning of the field.
The recent availability of data administrative records, mobile devices, sensors, and many private sources, as well as
new processing and analytical techniques, can potentially transform the practice of science. In the social-science
context, the new data can potentially offer policymakers information that is much more current, granular, and richer
in environmental information than data produced by statistical agencies from surveys. New research opportunities,
however, are accompanied by challenges associated with using the new data that is no longer generated and
disseminated by statistical agencies, but can be harvested from many individual, public, and some private actions.
Nonetheless, important scholarly work has been done that uses Big Data in a way that is valuable to policymakers—in
areas as varied as finance, labour, education, science, innovation, transportation, and development.
The workshop topics include, but are not limited to, the following:
Big Data comprises bits on one side and processing on the other. The 5 Vs we are confronted with are:
VIRTUALIZATION
This might be happening in the Internet
VARIETY of Things (IoT) where you don’t actually
VOLUME Differences are deeper than the expression of have Volume, nor Velocity, nor Variety,
in principle, since a lot is happening locally
can present architecture scale to independent values based on different metrics. in IoT. However, the challenge for the future
They relate to differences in the capturing of
crunch the volume of data involved those values, in their stability, and in the is to consider all the data spread around
credibility of the sources. and look at it as a ‘virtual Big Data’.
The rapid and asynchronous
change in data over several who is benefiting from the
sources makes consistency a metadata, how are data
major issue and replication sources rewarded, and
basically impossible. who is accountable?
VELOCITY VALUE
Social Media and Analytics
The explosion of social media—in the form of user generated content on blogs, microblogs (Twitter), discussion
forums, product reviews, and multimedia sharing sites—presents many new opportunities and challenges to both
producers and consumers of information. For producers, this user generated content provides a rich source of implicit
consumer feedback. Tracking the pulse of the ever expanding social media outlets enables companies to discern what
consumers are saying about their products, which provides useful insights on how to improve and market products
better. For consumers, the plethora of information and opinions, from diverse sources, helps them tap into the
wisdom of crowds, which aids in making more informed decisions. Though a vast quantity of information is available,
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