## Algorithms for Managing, Querying and Processing Big Data in Cloud Environments  
### [PERSON]  
DIA Department, University of Trieste and ICAR-CNR, Trieste 34127, Italy; [EMAIL_ADDRESS]; Tel. : +39-040-558-3419; Fax: +39-040-558-3580  
Academic Editor: [PERSON]  
Received: 12 January 2016; Accepted: 27 January 2016; Published: 1 February 2016  
Big data (e.g., [1; 2; 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. Cloud computing environments (e.g., [4; 5; 6]) represent the \"natural\" context for such data, as they embed 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, which have received a great deal of attention from the research community recently (e.g., [7; 8; 9]), plays a leading role, and algorithmic approaches to these challenges are very promising 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. Furthermore, 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, this special issue \"Algorithms for Managing, Querying and Processing Big Data in Cloud Environments\" of Algorithms focuses the attention on topics related to the theory and practice of algorithms for managing big data in Cloud environments, the design and analysis of algorithms for managing big data in Cloud environments, the tuning and experimental evaluation of algorithms for managing big data in Cloud environments, and so forth. The aim is that of providing a significant milestone on the road of the investigated topic, to be significant for both theory and practice, as well as applications and systems that are founded on such algorithms. The special issue contains four papers which have been accepted after two rigorous review rounds. In the following, we provide an overview on these papers. The first paper [10], entitled \"Multiobjective Cloud Particle Optimization Algorithm Based on Decomposition\", by [PERSON] _et al._, investigates the relevant multi-objective evolutionary paradigm based on decomposition (MOEA/D) that, as the authors correctly state, has received attention from many researchers in recent years. The paper thus presents a novel multi-objective algorithm based on decomposition and the Cloud computing model called multi-objective 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 a 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 verified on 16 well-known multi-objective problems, and experimental results indicate that MOEA/D-CPDE is competitive. The second paper [11], entitled \"Implementation of a Parallel Algorithm Based on a Spark Cloud Computing Platform\", by [PERSON] _et al._, proposes to parallelize the well-known MAX-MIN Ant System(MMAS) algorithm in order to solve the annoying Traveling Salesman Problem (TSP) based on a Spark Cloud computing platform. Indeed, as authors correctly highlight, parallel algorithms, such as the ant colony algorithm, take a long time when solving large-scale problems.