Report on the first international workshop on cloud intelligence (Cloud-I 2012)

Abstract

Business intelligence (BI) is a broad field related to integrating, storing and analyzing data to help decisionmakers in many domains (from “real” business to administration, health, and environment) make better decisions. Front-end analytics methods include reporting, on-line analytical processing (OLAP), and data mining. With the increasing success of cloud computing, cloud BI “as a service” offerings have started appearing, both from cloud start-ups and major BI industry vendors. Beyond porting BI features into the cloud, which already implies numerous issues (e.g., BigData/NoSQL database modeling and storage, data localization, security and privacy, performance, cost and usage models...), this trend also poses new, broader challenges for making data analytics available to small and middle-size enterprises (SMEs), non-governmental organizations, Web communities (e.g., supported by social networks), and even the average citizen; this vision presumably requiring a mixture of both private and open data. Thus, Cloud Intelligence is not only a current technological and research challenge, but also an important economic and societal stake, since people increasingly demand open data, which must be easily accessible on the Web, possibly mixed with private data, and analyzed with intelligible on-line tools with advanced collaborative features enabling users to share and reuse BI concepts and analyses in large scale fashion. Analysis results can then be be shared world-wide. The Second International Workshop on Cloud Intelligence (Cloud-I 2013) [3] was held in conjunction with VLDB 2013 in Riva del Garda, Italy on August 26, 2013. In continuation of the first edition, it brought together researchers and engineers from academia and industry to discuss and exchange ideas related to Cloud Intelligence. The workshop featured a joint keynote with the BIRTE workshop and two research sessions, the latter including a panel discussion. The topics of this year’s accepted papers mainly focused on MapReduce-based computations and indexing.