Distributed Data Mining Techniques for Big Data Processing

Dear Colleagues,

The last decade has been characterized by an exponential growth of data collection in different fields, including agriculture and environment science, biology, medicine, astronomy, finance, Earth science, etc. Huge volumes of data are constantly produced, ranging from the observation or simulation of complex phenomena to social media platforms, such as Facebook, Twitter, Instagram, and so on. We live in the era of Big Data, and unfortunately, the traditional techniques of data mining and analysis are not capable to process very large datasets efficiently and on demand (or within a reasonable response time) and keep up with high demand for data analytics on real-world applications. Parallel and distributed data mining approaches constitute a promising solution for extracting knowledge with high accuracy and efficiency. The problem is, not only due to the high computational complexity of the existing approaches, but also on the memory capacity required by big data applications. Distributed and parallel solutions can exploit both the processing power, as well as the memory capacity offered by distributed and parallel systems including the cloud computing and data centres. To this end, we can distinguish two categories of solutions; the first category consists of distributing/parallelising existing popular data mining techniques, while the second category consists of developing innovative and breakthrough distributed approaches that can exploit the full potential of these distributed platforms for high-performance computing and high accuracy of the results, while taking into account the big data application characteristics.

This Special Issue seeks submissions from academia, industry and governmental research labs presenting novel research on all theoretical and practical aspects related to the application of Distributed Data Mining techniques for scalable Big Data processing. 

• Topics

This Special Issue calls for original manuscripts, or substantively extended versions of previously-published manuscripts, describing the latest research on (but not limited to) the following topics:

• Distributed Data Mining algorithms for Big data applications
• Programming models and software libraries for big data analysis
• Parallel data mining techniques for very large datasets
• Scalability issues and performance evaluation of distributed/parallel data mining techniques
• High-performance computing for data mining.
• Distributed computing frameworks for distributed data mining
• Distributed data mining benchmarks and performance evaluation
• Big data analytics
• Big data warehouses

Prof. M-Tahar Kechandi
Guest Editor

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 submissions that pass pre-check are 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. Informatics is an international peer-reviewed open access quarterly 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 1800 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.

• Distributed data mining
• Parallel data Mining
• Big Data analytics
• Data Analytics
• Data Mining
• big data warehouses
• Distributed data models