In the era of big data, the complexity of data analysis and information extraction has grown dramatically. There is an increasing demand for scalable solutions that can handle the storage and acquisition bandwidth of current sensing and data analysis systems. Every discussion on big data usually includes a passing reference to the ubiquity of sensors: as we collect more data through sensors, we'll need tools to take advantage of data. The focus has been primarily on the tools that help you store and manage, visualise and summarise, and finally, analyse and make sense of big data collections. Since the leading big data innovators have tended to be web companies, data acquisition has often involved data exhaust or crawled data. The mechanics and details of collecting data from the physical world have garnered less attention.
Rather than store and discard many signals/measurements, it would be more efficient if one can acquire only the necessary signals to begin with. Nowadays, to keep abreast with the development in computational technologies, the big data processing system and the information management system have undergone a great change with the new computation method is getting more popular and the internet technology has also evolved swiftly, the new method has impact on the big data processing and information management system changes.
Suitable topics include, but are not limited to, the following:
- Big data analysis algorithms
- Sparse representation for pattern recognition and classification
- Dictionary learning for sparse representation and modelling
- Sparsity-based event detection and classification in sensor networks
- Algorithms for big data mining
- Compressive sensing methods in big data storage
- Measurement/sampling procedures
- Mathematical theory of compressive sensing
- Compressive sensing for multiple signals or with additional information
- Hardware implementation of compressive sensing systems
- Applications of compressive sensing
- Big data systems and engineering method in big data era
- Big data novel theory, algorithm and applications
- Big data Infrastructure, MapReduce and cloud computing
- Big data visualisation
- Big data semantics, scientific discovery and intelligence
- Big data performance analysis and large-scale deployment
- Big data placement, scheduling, and optimisation
- Storage and computation management of big data
- Large-scale big data workflow management
- Mobility and big data
- Sensor network, social network and big data
- Big data applications in social network/web service/health/and so on
Important Dates
Submission of manuscripts: 15 April, 2017
Notification to authors: 15 June, 2017
Final versions due: 15 August, 2017
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