Extracting valuable information from raw data is especially difficult considering the velocity of growing data from year to year and the fact that 80% of data is unstructured. In addition, data sources are heterogeneous (various sensors, users with different profiles, etc.) and are located in different situations or contexts. This is why the Smart City infrastructure runs reliably and permanently to provide the context as a “public utility” to different services. Context-aware applications exploit the context to adapt accordingly the timing, quality and functionality of their services. The value of these applications and their supporting infrastructure lies in the fact that end-users always operate in a context: their role, intentions, locations and working environment constantly change. Since the Internet introduction, we witness an explosive growth in the volume, velocity, and variety of the data created on a daily basis. This data is originated from numerous sources including mobile devices, sensors, individual archives, Internet of Things, government data holdings, software logs, public profiles on social networks, commercial datasets, etc. The issue so-called the ‘Big Data’ problem requires the continuous increase of the processing speeds of the servers and of the whole network infrastructure. In this context, new models for resource management are required. This poses a critically difficult challenge and striking development opportunities to Data Intensive (DI) and HighPerformance Computing (HPC): how to efficiently turn massively large data into valuable information and meaningful knowledge. Computational-effective DI and HPC are required in a fast-increasing number of data-intensive domains.
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