Enormous amounts of data are already present and still rapidly growing due to data sources such as sensors and social networks. There has been an increasing interest in incorporating these huge amounts of external and unstructured data, normally referred to as â€śBig Dataâ€ť, into traditional applications. This necessity has made that traditional database systems and processing need to evolve and accommodate them. We view that several key themes with the Big Data trends include (i) managing Big Data projects to discover business values; (ii) developing an architecture for a Big Data environment to conceptualize goals, tasks, and problem-solving methods to apply to domains; (iii) exploring problem-solving methods for Big Data; (iv) using a cloud for managing large-scale external and internal data; (v) providing an easy-to-use but powerful services to access/manage/analyze the Big Data in the cloud; and (vi) exploring and improving the security and privacy of these repositories.
Therefore, this new era of Big Data and cloud environment requires conceptualization and methods to effectively manage Big Data and accomplish intended business goals. Thus, the objective of MoBiDâ€™ 17 is to be an international forum for exchanging ideas on the latest and best proposals for modeling and managing Big Data in this new data-driven paradigm. Papers focusing on novel applications and using conceptual modeling approaches for any aspects of Big Data such as Hadoop and its ecosystems, Big Data Analytics, social networking, security/cyber resilience/privacy, hybrid cloud, Big Data warehousing, data science topics, and industry-specific challenges that arise in Big Data scenarios (e.g. in Customer Relationship Management), and how to approach them from a modelling as well as from an implementation perspective are highly encouraged. The workshop will be a forum for researchers and practitioners who are interested in the different facets related to the use of the conceptual modeling approaches for the development of next generation applications based on Big Data.