Special Issue "Algorithms for Large Scale Data Analysis"

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: 15 February 2020.

Special Issue Editor

Guest Editor
Dr. Luca Becchetti Website E-Mail
Associate Professor, Department of Computer, Control and Management Engineering Antonio Ruberti, Sapienza University of Rome, via Ariosto 25, 00185 Rome, Italy
Interests: design and analysis of randomized algorithms and probabilistic analysis; spectral graph theory and graph clustering; algorithms for large scale data analysis; algorithmic modelling of complex systems

Special Issue Information

Dear Colleagues,

We invite submission of papers describing original and solid research on algorithmic aspects of information retrieval and data mining over large, or very large, datasets. Topics can range from theoretical foundations to novel algorithmic approaches to tackle data mining problems arising in science, business, medicine, and engineering, when data size is an important issue in practice. In particular, we welcome contributions that are methodologically solid, supporting proposed approaches through a sound theoretical and/or experimental analysis, on scenarios of practical relevance. We also welcome application-oriented papers that make innovative technical contributions to research. Authors are explicitly discouraged from submitting incremental results that do not provide any significant advances over existing approaches.

The aim of this Special Issue is to present recent contributions of practical relevance from these areas, as well as contributions investigating the more theoretical aspects of large-scale data analysis.

Topics include but are not limited to the following areas:

  • Large-scale information retrieval systems;
  • Algorithmic and statistical techniques for big data analysis;
  • Large-scale collaborative filtering;
  • Algorithms for large-scale graph analysis;
  • Large-scale machine learning and optimization;
  • Algorithms and tools for distributed data mining (e.g., map reduction);
  • Streaming algorithms;
  • Applications of large-scale data analysis.
Dr. Luca Becchetti
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 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 1000 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.

Published Papers (2 papers)

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Research

Open AccessArticle
Parallelism Strategies for Big Data Delayed Transfer Entropy Evaluation
Algorithms 2019, 12(9), 190; https://doi.org/10.3390/a12090190 - 09 Sep 2019
Abstract
Generated and collected data have been rising with the popularization of technologies such as Internet of Things, social media, and smartphone, leading big data term creation. One class of big data hidden information is causality. Among the tools to infer causal relationships, there [...] Read more.
Generated and collected data have been rising with the popularization of technologies such as Internet of Things, social media, and smartphone, leading big data term creation. One class of big data hidden information is causality. Among the tools to infer causal relationships, there is Delay Transfer Entropy (DTE); however, it has a high demanding processing power. Many approaches were proposed to overcome DTE performance issues such as GPU and FPGA implementations. Our study compared different parallel strategies to calculate DTE from big data series using a heterogeneous Beowulf cluster. Task Parallelism was significantly faster in comparison to Data Parallelism. With big data trend in sight, these results may enable bigger datasets analysis or better statistical evidence. Full article
(This article belongs to the Special Issue Algorithms for Large Scale Data Analysis)
Open AccessArticle
Pruning Optimization over Threshold-Based Historical Continuous Query
Algorithms 2019, 12(5), 107; https://doi.org/10.3390/a12050107 - 19 May 2019
Abstract
With the increase in mobile location service applications, spatiotemporal queries over the trajectory data of moving objects have become a research hotspot, and continuous query is one of the key types of various spatiotemporal queries. In this paper, we study the sub-domain of [...] Read more.
With the increase in mobile location service applications, spatiotemporal queries over the trajectory data of moving objects have become a research hotspot, and continuous query is one of the key types of various spatiotemporal queries. In this paper, we study the sub-domain of the continuous query of moving objects, namely the pruning optimization over historical continuous query based on threshold. Firstly, for the problem that the processing cost of the Mindist-based pruning strategy is too large, a pruning strategy based on extended Minimum Bounding Rectangle overlap is proposed to optimize the processing overhead. Secondly, a best-first traversal algorithm based on E3DR-tree is proposed to ensure that an accurate pruning candidate set can be obtained with accessing as few index nodes as possible. Finally, experiments on real data sets prove that our method significantly outperforms other similar methods. Full article
(This article belongs to the Special Issue Algorithms for Large Scale Data Analysis)
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