En la Conferencia
3rd International Symposium on Interval Data Modelling: Theory and Applications (SIDM2017) from 28-29 June 2017, he presentado la ponencia invitada
Big data analytics through
interval data methods
Carlos Maté
Institute for Research in Technology
Universidad Pontificia Comillas – Madrid (Spain)
ABSTRACT
Currently, the big data paradigm faces two colossal issues. The
first one is to design hardware architectures and programming
languages that allow efficient, reliable and relatively fast computation.
Cloud computing, web services, distributed computation, Hadoop and
Spark are some of the solutions offered until now. The second issue
is to discover and develop methods of analysis for the huge and
sometimes unstructured vast amount of information available in big
data contexts, deciding which ones to use for real-time analytics.
Machine learning and statistical analysis methods are two of the key
pillars to be revisited in order to provide efficient solutions for the big
data analytics (BDA) world.
On an apparently different path, interval analysis (IA) has
become an active research area since 1960, and an impressive
development in the field of methods and applications of interval data
has followed. However, the analysis, classification and forecasting of
interval-valued data from the symbolic data analysis (SDA) approach
is a very young research area, dating back less than 20 years, and
still presents a wide array of open issues.
This talk reviews methods of interval-valued data analysis in
comparison with the corresponding crisp or single data methods from
the BDA approach. In addition, it suggests some BDA contexts such
as healthcare management, energy consumption or inflation rates;
where the above approach can provide an efficient alternative way to
process massive data and to get over some problems in classic BDA.
Discussion of some open research questions is considered.
Keywords: analytics, big data, forecasting, interval time series,
interval-valued data, random interval, real-time analytics, symbolic
data analysis.
