TITLE:
Data Modeling and Data Analytics: A Survey from a Big Data Perspective
AUTHORS:
André Ribeiro, Afonso Silva, Alberto Rodrigues da Silva
KEYWORDS:
Data Modeling, Data Analytics, Modeling Language, Big Data
JOURNAL NAME:
Journal of Software Engineering and Applications,
Vol.8 No.12,
December
30,
2015
ABSTRACT: These last years we have been witnessing a
tremendous growth in the volume and availability of data. This fact results
primarily from the emergence of a multitude of sources (e.g. computers, mobile
devices, sensors or social networks) that are continuously producing either
structured, semi-structured or unstructured data. Database Management Systems
and Data Warehouses are no longer the only technologies used to store and
analyze datasets, namely due to the volume and complex structure of nowadays
data that degrade their performance and scalability. Big Data is one of the
recent challenges, since it implies new requirements in terms of data storage,
processing and visualization. Despite that, analyzing properly Big Data can
constitute great advantages because it allows discovering patterns and
correlations in datasets. Users can use this processed information to gain
deeper insights and to get business advantages. Thus, data modeling and data
analytics are evolved in a way that we are able to process huge amounts of data
without compromising performance and availability, but instead by “relaxing”
the usual ACID properties. This paper provides a broad view and discussion of
the current state of this subject with a particular focus on data modeling and
data analytics, describing and clarifying the main differences between the
three main approaches in what concerns these aspects, namely: operational
databases, decision support databases and Big Data technologies.