Special Issue "Algorithms for Managing, Querying and Processing Big Data in Cloud Environments"

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

Deadline for manuscript submissions: closed (30 April 2015)

Special Issue Editor

Guest Editor
Dr. Alfredo Cuzzocrea

ICAR-CNR & University of Calabria, via P.Bucci 41C 87036 Rende (CS), Italy
Website | E-Mail
Phone: +39 0984 831730
Fax: +39 0984 839054

Special Issue Information

Dear Colleagues,

Big Data has become one of the most challenging research topics in current years. Big data are everywhere, from social networks to Web advertisement, from sensor and stream systems to bio-informatics, from graph management tools to smart cities, and so forth. Cloud computing environments represent the “natural” context for such data, as embedding several emerging trends, both at the research level and the technological level, which comprise high-performance, high reliability, high availability, transparence, abstraction, virtualization, and so forth.

At the convergence of these emerging trends, managing, querying and processing big data in Cloud environments plays a leading role, and algorithmic approaches to these challenges are very promising at now. These approaches come from a rich variety of multi-disciplinary areas, ranging from mathematical models to approximation models, from resource-constrained paradigms to memory-bounded methods, and so forth. On another side, algorithms for managing big data according to a “systematic” view of the problem are gaining momentum. For instance, algorithms for efficiently managing MapReduce tasks over Clouds are a clear instance of the latter scientific area.

Inspired by these exciting research challenges, the Special Issue “Algorithms for Managing, Querying and Processing Big Data in Cloud Environments” will explore a wide range of topics related to theory and practice of algorithms for managing big data in Cloud environments, design and analysis of algorithms for managing big data in Cloud environments, tuning and experimental evaluation of algorithms for managing big data in Cloud environments, and so forth.

More Specifically, the special issue will cover a wide collection of research topics of algorithms for managing big data in Cloud environments, including (but not limited to): •Theory of Algorithms for Managing, Querying and Processing Big Data in Cloud Environments;
•Design of Algorithms for Managing, Querying and Processing Big Data in Cloud Environments;
•Analysis of Algorithms for Managing, Querying and Processing Big Data in Cloud Environments;
•Tuning of Algorithms for Managing, Querying and Processing Big Data in Cloud Environments
•Experimental Evaluation and Analysis of Algorithms for Managing, Querying and Processing Big Data in Cloud Environments;
•Complexity Issues of Algorithms for Managing, Querying and Processing Big Data in Cloud Environments;
•Case Studies of Algorithms for Managing, Querying and Processing Big Data in Cloud Environments;
•Approximation Algorithms for Managing, Querying and Processing Big Data in Cloud Environments;
•(Data) Compression Algorithms for Managing, Querying and Processing Big Data in Cloud Environments;
•Security Aspects of Algorithms for Managing, Querying and Processing Big Data in Cloud Environments;
•Privacy-Preserving Aspects of Algorithms for Managing, Querying and Processing Big Data in Cloud Environments;
•Resource-Constrained Algorithms for Managing, Querying and Processing Big Data in Cloud Environments

Dr. Alfredo Cuzzocrea
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 850 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 (5 papers)

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Editorial

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Open AccessEditorial Algorithms for Managing, Querying and Processing Big Data in Cloud Environments
Algorithms 2016, 9(1), 13; https://doi.org/10.3390/a9010013
Received: 12 January 2016 / Revised: 26 January 2016 / Accepted: 27 January 2016 / Published: 1 February 2016
PDF Full-text (151 KB) | HTML Full-text | XML Full-text
Abstract
Big data (e.g., [1–3]) has become one of the most challenging research topics in current years. Big data is everywhere, from social networks to web advertisements, from sensor and stream systems to bio-informatics, from graph management tools to smart cities, and so forth.
[...] Read more.
Big data (e.g., [1–3]) has become one of the most challenging research topics in current years. Big data is everywhere, from social networks to web advertisements, from sensor and stream systems to bio-informatics, from graph management tools to smart cities, and so forth. [...] Full article

Research

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Open AccessArticle An Effective and Efficient MapReduce Algorithm for Computing BFS-Based Traversals of Large-Scale RDF Graphs
Algorithms 2016, 9(1), 7; https://doi.org/10.3390/a9010007
Received: 2 September 2015 / Revised: 30 December 2015 / Accepted: 4 January 2016 / Published: 11 January 2016
Cited by 2 | PDF Full-text (2170 KB) | HTML Full-text | XML Full-text
Abstract
Nowadays, a leading instance of big data is represented by Web data that lead to the definition of so-called big Web data. Indeed, extending beyond to a large number of critical applications (e.g., Web advertisement), these data expose several characteristics that
[...] Read more.
Nowadays, a leading instance of big data is represented by Web data that lead to the definition of so-called big Web data. Indeed, extending beyond to a large number of critical applications (e.g., Web advertisement), these data expose several characteristics that clearly adhere to the well-known 3V properties (i.e., volume, velocity, variety). Resource Description Framework (RDF) is a significant formalism and language for the so-called Semantic Web, due to the fact that a very wide family of Web entities can be naturally modeled in a graph-shaped manner. In this context, RDF graphs play a first-class role, because they are widely used in the context of modern Web applications and systems, including the emerging context of social networks. When RDF graphs are defined on top of big (Web) data, they lead to the so-called large-scale RDF graphs, which reasonably populate the next-generation Semantic Web. In order to process such kind of big data, MapReduce, an open source computational framework specifically tailored to big data processing, has emerged during the last years as the reference implementation for this critical setting. In line with this trend, in this paper, we present an approach for efficiently implementing traversals of large-scale RDF graphs over MapReduce that is based on the Breadth First Search (BFS) strategy for visiting (RDF) graphs to be decomposed and processed according to the MapReduce framework. We demonstrate how such implementation speeds-up the analysis of RDF graphs with respect to competitor approaches. Experimental results clearly support our contributions. Full article
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Open AccessArticle A Data Analytic Algorithm for Managing, Querying, and Processing Uncertain Big Data in Cloud Environments
Algorithms 2015, 8(4), 1175-1194; https://doi.org/10.3390/a8041175
Received: 26 September 2015 / Revised: 20 November 2015 / Accepted: 3 December 2015 / Published: 11 December 2015
Cited by 7 | PDF Full-text (374 KB) | HTML Full-text | XML Full-text
Abstract
Big data are everywhere as high volumes of varieties of valuable precise and uncertain data can be easily collected or generated at high velocity in various real-life applications. Embedded in these big data are rich sets of useful information and knowledge. To mine
[...] Read more.
Big data are everywhere as high volumes of varieties of valuable precise and uncertain data can be easily collected or generated at high velocity in various real-life applications. Embedded in these big data are rich sets of useful information and knowledge. To mine these big data and to discover useful information and knowledge, we present a data analytic algorithm in this article. Our algorithm manages, queries, and processes uncertain big data in cloud environments. More specifically, it manages transactions of uncertain big data, allows users to query these big data by specifying constraints expressing their interests, and processes the user-specified constraints to discover useful information and knowledge from the uncertain big data. As each item in every transaction in these uncertain big data is associated with an existential probability value expressing the likelihood of that item to be present in a particular transaction, computation could be intensive. Our algorithm uses the MapReduce model on a cloud environment for effective data analytics on these uncertain big data. Experimental results show the effectiveness of our data analytic algorithm for managing, querying, and processing uncertain big data in cloud environments. Full article
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Open AccessArticle Implementation of a Parallel Algorithm Based on a Spark Cloud Computing Platform
Algorithms 2015, 8(3), 407-414; https://doi.org/10.3390/a8030407
Received: 19 March 2015 / Revised: 1 June 2015 / Accepted: 23 June 2015 / Published: 3 July 2015
Cited by 5 | PDF Full-text (741 KB) | HTML Full-text | XML Full-text
Abstract
Parallel algorithms, such as the ant colony algorithm, take a long time when solving large-scale problems. In this paper, the MAX-MIN Ant System algorithm (MMAS) is parallelized to solve Traveling Salesman Problem (TSP) based on a Spark cloud computing platform. We combine MMAS
[...] Read more.
Parallel algorithms, such as the ant colony algorithm, take a long time when solving large-scale problems. In this paper, the MAX-MIN Ant System algorithm (MMAS) is parallelized to solve Traveling Salesman Problem (TSP) based on a Spark cloud computing platform. We combine MMAS with Spark MapReduce to execute the path building and the pheromone operation in a distributed computer cluster. To improve the precision of the solution, local optimization strategy 2-opt is adapted in MMAS. The experimental results show that Spark has a very great accelerating effect on the ant colony algorithm when the city scale of TSP or the number of ants is relatively large. Full article
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Open AccessArticle Multiobjective Cloud Particle Optimization Algorithm Based on Decomposition
Algorithms 2015, 8(2), 157-176; https://doi.org/10.3390/a8020157
Received: 12 January 2015 / Revised: 17 March 2015 / Accepted: 17 April 2015 / Published: 23 April 2015
Cited by 5 | PDF Full-text (1114 KB) | HTML Full-text | XML Full-text
Abstract
The multiobjective evolutionary algorithm based on decomposition (MOEA/D) has received attention from researchers in recent years. This paper presents a new multiobjective algorithm based on decomposition and the cloud model called multiobjective decomposition evolutionary algorithm based on Cloud Particle Differential Evolution (MOEA/D-CPDE). In
[...] Read more.
The multiobjective evolutionary algorithm based on decomposition (MOEA/D) has received attention from researchers in recent years. This paper presents a new multiobjective algorithm based on decomposition and the cloud model called multiobjective decomposition evolutionary algorithm based on Cloud Particle Differential Evolution (MOEA/D-CPDE). In the proposed method, the best solution found so far acts as a seed in each generation and evolves two individuals by cloud generator. A new individual is produced by updating the current individual with the position vector difference of these two individuals. The performance of the proposed algorithm is carried on 16 well-known multi-objective problems. The experimental results indicate that MOEA/D-CPDE is competitive. Full article
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