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

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Databases and Data Structures".

Deadline for manuscript submissions: closed (30 April 2015) | Viewed by 35096

Special Issue Editor


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Guest Editor
1. DISPES Department, University of Calabria, 87036 Rende, Italy
2. Institute of High Performance Computing and Networking, Italian National Research Council, Via P. Bucci, 7/11C, 87036 Rende, Italy
Interests: database; data mining; data warehousing; distributed computing; artificial intelligence

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

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Published Papers (5 papers)

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Editorial

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151 KiB  
Editorial
Algorithms for Managing, Querying and Processing Big Data in Cloud Environments
by Alfredo Cuzzocrea
Algorithms 2016, 9(1), 13; https://doi.org/10.3390/a9010013 - 01 Feb 2016
Cited by 1 | Viewed by 4558
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

Jump to: Editorial

2170 KiB  
Article
An Effective and Efficient MapReduce Algorithm for Computing BFS-Based Traversals of Large-Scale RDF Graphs
by Alfredo Cuzzocrea, Mirel Cosulschi and Roberto De Virgilio
Algorithms 2016, 9(1), 7; https://doi.org/10.3390/a9010007 - 11 Jan 2016
Cited by 3 | Viewed by 7892
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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374 KiB  
Article
A Data Analytic Algorithm for Managing, Querying, and Processing Uncertain Big Data in Cloud Environments
by Fan Jiang and Carson K. Leung
Algorithms 2015, 8(4), 1175-1194; https://doi.org/10.3390/a8041175 - 11 Dec 2015
Cited by 61 | Viewed by 6443
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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741 KiB  
Article
Implementation of a Parallel Algorithm Based on a Spark Cloud Computing Platform
by Longhui Wang, Yong Wang and Yudong Xie
Algorithms 2015, 8(3), 407-414; https://doi.org/10.3390/a8030407 - 03 Jul 2015
Cited by 15 | Viewed by 9123
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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1114 KiB  
Article
Multiobjective Cloud Particle Optimization Algorithm Based on Decomposition
by Wei Li, Lei Wang, Qiaoyong Jiang, Xinhong Hei and Bin Wang
Algorithms 2015, 8(2), 157-176; https://doi.org/10.3390/a8020157 - 23 Apr 2015
Cited by 12 | Viewed by 6258
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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