Special Issue "Big Data Processing and Analytics in the Era of Extreme Connectivity and Automation"
Deadline for manuscript submissions: 30 May 2019
Prof. Simon James Fong
Department of Computer and Information Science, Data Analytics and Collaborative Computing Laboratory, University of Macau, Taipa, Macau SAR
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Interests: data stream mining; big data; advanced analytics; bio-inspired optimization algorithms and applications; business intelligence; e-commerce; biomedical applications; wireless sensor networks
Big data is a term that has risen to prominence describing data that exceeds the processing capacity of conventional database systems. McKinsey and company announced the big data revolution in 2011 and suggested that the age of relational database management systems and SQL-based data manipulation and access methods was drawing to a close because those technologies could not keep pace with what McKinsey projected was a coming deluge of new and complex data sets, for most organizations in most industries. This projection is getting more challenging with the lack of mechanisms for managing all of the metadata associated with the big data “data pool” — where data sets reside, how they entered the pool, what data engineering flows they are implicated in, what kinds of algorithms and decision-making processes the data sets are suitable (and unsuitable) for, what kinds of governance, regulatory and compliance restrictions are associated with the data sets, and so forth. This technology is tagged today as big data connectivity, which is a growing area of importance as the world is entering the new Industry 4.0 with the increasing reliance on extreme connectivity and automation. The heterogeneity of the big data landscape is characterized not only by the distributed nature and connectivity of its most prominent technologies, but also by their underlying architectures. There is great need for mechanisms for expressing, managing, versioning, executing and monitoring data engineering work streams and models and algorithms consuming data from the data pool and from data warehouses as well as mechanisms for orchestrating data flows that cross the logical boundary between the data pool. This Special Issue is an attempt to study big data and analytics in the era of extreme connectivity and automation that relies on technologies like IoT, big data connectivity, blockchain, named data networking and artificial intelligence.Prof. Simon James Fong
Prof. Sabah Mohammed
Prof. Luiz Moutinho
Prof. Jinan Fiaidhi
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 850 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- Big Data Automation
- Extreme Automation and Connectivity
- Internet of Things
- Connected Data Analytics
- Named Data Networking