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 2915

Special Issue Editors


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Guest Editor
Department of Electronics, Computer Science and System Sciences (DIMES), University of Calabria Via Pietro Bucci – Cubo 41C (5th floor), 87036 Rende (CS), Italy
Interests: cloud computing; social media and Big Data analysis; distributed knowledge discovery; data mining
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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

Published Papers (1 paper)

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Research

15 pages, 355 KiB  
Article
Efficiency of Algorithms for Computing Influence and Information Spreading on Social Networks
by Vesa Kuikka, Henrik Aalto, Matias Ijäs and Kimmo K. Kaski
Algorithms 2022, 15(8), 262; https://doi.org/10.3390/a15080262 - 28 Jul 2022
Cited by 5 | Viewed by 2314
Abstract
Modelling interactions on complex networks needs efficient algorithms for describing processes on a detailed level in the network structure. This kind of modelling enables more realistic applications of spreading processes, network metrics, and analyses of communities. However, different real-world processes may impose requirements [...] Read more.
Modelling interactions on complex networks needs efficient algorithms for describing processes on a detailed level in the network structure. This kind of modelling enables more realistic applications of spreading processes, network metrics, and analyses of communities. However, different real-world processes may impose requirements for implementations and their efficiency. We discuss different transmission and spreading processes and their interrelations. Two pseudo-algorithms are presented, one for the complex contagion spreading mechanism using non-self-avoiding paths in the modelling, and one for simple contagion processes using self-avoiding paths in the modelling. The first algorithm is an efficient implementation that can be used for describing social interaction in a social network structure. The second algorithm is a less efficient implementation for describing specific forms of information transmission and epidemic spreading. Full article
(This article belongs to the Special Issue Scalable Programming Models and Algorithms for Big Data)
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