Big Data and Cloud Surya Nepal1 and Athman Bouguettaya2 1
CSIRO ICT Centre Marsfield, NSW, Australia
[email protected] 2 RMIT University Melbourne, NSW, Australia
[email protected]
The rise of multimedia, social media, sensor networks and the Internet of Things will fan the flames of data explosion in many application domains. These emerging Big Data applications present enterprises with very high data-to-compute ratio. For example, in Square Kilometre Array (SKA), a telescope generates more data than the whole Internet with a single file often reaching multiple terabytes in size. As data volumes continue to increase exponentially, the challenge is not only how to store and manage data but also to effectively analyse the data to gain insight knowledge to make smarter decisions. One of the key challenges is transforming the raw data available to your business into business value and strategic advantage. The better management and analysis of Big Data will become the next frontier of innovation, competition and productivity. For example, according to a McKinsey Global Institute study, a retailer exploiting the full potential of big data could increase its operating margin by more than 60 percent; efficient and effective use of big data could save more than $300 billion dollars for US government in healthcare alone. Therefore, there is a need of effective and efficient management and analysis of big data. Recently, Cloud computing has emerged as a promising technology towards handling Big Data. The driving forces behind cloud computing are: (a) significantly reduced Total Cost of Ownership (TCO) of the required IT infrastructure and software including (but not limited to) purchasing, operating, maintaining and updating costs; (b) high Quality of Service (QoS) provided by cloud service providers such as availability, reliability and Pay-As-You-Go (PAYG) based low prices; and (c) easy access to organizational information and services anytime anywhere. In a nutshell, cloud computing provides a new paradigm for delivering computing resources (e.g., infrastructure, platform, software, etc.) to customers such as utilities (e.g., water, electricity, gas, etc.) on demand. Despite its own shortcomings, cloud becomes an attractive technology platform for developing and deploying Big Data analytics. The international workshop on Big Data and Cloud (BDC 2012) held in conjunction with WISE 2012 provided the scientific community a dedicated forum for discussing state-of-the-art research, development, and deployment efforts of Big Data in Cloud. We have selected two papers to be presented at the workshop. The first paper “A Service Oriented Framework for Animating Big Spatiotemporal Datasets” proposes a service oriented distributed system framework for annimating big spatiotemporal vector datasets. The second paper “Searching frequent itemsets by clustering data: towards a parallel approach using MapReduce” proposes a new algorithm for searching frequent itemsets in large data bases. A. Haller et al. (Eds.): WISE 2011 and 2012 Combined Workshops, LNCS 7652, p. 237, 2013. © Springer-Verlag Berlin Heidelberg 2013
