Special Issue "Machine Learning Algorithms for Big Data"
A special issue of Algorithms (ISSN 1999-4893).
Deadline for manuscript submissions: closed (30 June 2015).
Interests: signal processing; machine learning; image processing
Interests: data processing; machine learning; image processing
Interests: pattern recognition; machine learning; image processing
Algorithms will publish a Special Issue on Machine Learning for Big Data in 2015. Big Data refers to, not only the ever-increasing data size, as its name suggests, but also to those attributes associated with efficient and flexible data processing for dealing with various data formats to meet the real-time requirements, constituting its three defining properties, namely, Volume, Velocity, and Variety, which greatly challenge traditional data processing techniques from collecting, storing, transferring, mining, processing and visualizing massive data. Applications of Big Data in various aspects, be it politics, economy and culture, or our daily life, are creating increasingly stronger demands for efficient and accurate data processing techniques to acquire valuable information from pre-existing or dynamic Big Data. Machine Learning provides intelligent and comprehensive analysis to automatically detect properties and patterns buried in the Big Data, which allows the processing systems of Big Data attain better performance. The main focus of this Special Issue is on the recent advancement of machine learning, including the challenges and solutions in designing, developing, and deploying modern machine learning algorithms and systems for the various applications of Big Data. We welcome authors to submit their original research articles, as well as comprehensive reviews. This Special Issue is expected to be an effective platform for researchers to present their state-of-the-art work on machine learning for Big Data, and to present new ideas and directions for future development.
- Big Data management and analysis
- Cloud computing and cloud data mining
- Efficient learning algorithms for scalable social media analysis
- Big Data analysis and social media applications
- Large-scale social multimedia content analysis and retrieval
- Social Big Data transport and sharing
- Text information and knowledge mining on large scale social media
- Algorithms and systems for Big Data search
- Link and graph mining for Big Data
- BigData pre-processing
- Multimedia and multi-structured data mining
- Streaming data processing
- Visualization for Big Data
- Advanced machine learning methods for Big Data
Jeng-Shyang Pan, Shen Wang and Jun-Bao Li
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. Algorithms 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 1400 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
- Machine Learning
- Data Processing
- Cloud Computing