Track 2: Call for Paper

Big Data computing has emerged as a new paradigm for understanding the market and detecting business opportunities to reap the benefits the market has to offer. The Internet of Things (IoT), which enables ¡®things¡¯ to interact with other ¡®things¡¯ connected through the Internet, allows for collecting new types of data such as from sensors and social networks. IoT thus complements the vision of big data computing by generating massive amounts of data related to a particular domain of interests.

In order to realize these benefits many challenges still need to be addressed. For example, generating a holistic view over data from different sources requires a better understanding of integration problems along different dimensions. Firstly, the data collection and management has to be achieved. This requirement demands new ways of collecting, storing and managing data, to achieve the desired data or information at the desired time. Secondly, the data consolidation has to be achieved. This requirement for data consolidation calls for a common formalism to which different data formats can be mapped resolving apparent data heterogeneity. Thirdly, both the data requirements and the data sources will change over time. This evolution will impact existing solutions, and thus require the adaptation, management and maintenance of data models, data life-cycle management and governance. Furthermore, the data collected from different sources contain not only public data but also the private and sensitive data. This implies that data ownership, security and privacy issues need to be addressed accordingly.

This track aims to focus on fundamental and practical challenges related to big data computing in practical settings where an overwhelming amount of data is generated by internet of things and continuously exploited to improve processes and services.

Topics of interest for the track include, but are not limited to:
- Scalable solution for data collection and storage
- Combining big data with traditional data warehouses
- Internet of business related things
- Big data analytics
- Big data and smart applications
- Data relationships mining
- Data ownership, security and privacy
- Efficient processing of massively large datasets
- Just-in-time data availability
- Evolving Data Model
- Semantic big data computing