Scalable Programming Models and Algorithms for Big Data
A special issue of Algorithms (ISSN 1999-4893).
Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 2796
Special Issue Editors
Interests: big data analysis; social media analysis; cloud computing; data mining; machine learning; Internet of Things
Special Issues, Collections and Topics in MDPI journals
Interests: cloud computing; social media and Big Data analysis; distributed knowledge discovery; data mining
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
It is our pleasure to announce the opening of a new Special Issue of Algorithms entitled “Scalable Programming Models and Algorithms for Big Data”.
Through the pervasive use of computers, smartphones, and other digital objects, huge amounts of digital data are generated and collected. These data, commonly referred as big data, represent a challenge for the current storage, process, and analysis capabilities.
To extract value from such data, novel architectures, programming modelss and frameworks have been developed in recent years for capturing and analyzing complex and/or high velocity data. For example, in the scientific and business fields, researchers and data scientists are analyzing big data to extract information and knowledge useful for making new discoveries and supporting decision-making processes. Moreover, high-performance computers, such as clouds and clusters, paired with scalable algorithms are commonly being used by data analysts to solve big data problems and obtain valuable information and knowledge within a reasonable time.
From this perspective, this Special Issue aims to contribute to the field, presenting the most relevant advances in this research area.
The following are some examples of the topics proposed for this Special Issue, though submissions are not restricted to this list:
- Programming models, algorithms, and applications for big data;
- Systems for data processing on cloud platforms;
- Data analysis workflows for distributed environments;
- Scalable data mining/machine learning algorithms;
- Big data analytics and applications;
- Applications of machine learning in big data;
- Cloud-based data mining applications;
- Libraries, algorithms, and applications for big social data analysis.
We hope you will contribute your high-quality research, and we look forward to reading about your results.
Dr. Fabrizio Marozzo
Dr. Loris Belcastro
Guest Editors
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. 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 1600 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.
Keywords
- big data
- big data analysis
- cloud computing
- scalable data mining
- data analysis workflows
- social media analysis
- parallel and distributed algorithms
- high-performance computing
- machine learning applications
