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

Abstract

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 (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. In the solution proposed by authors, MMAS is combined with Spark MapReduce to execute the path building and the pheromone operation in a distributed computer Cluster. In addition to this, to improve the precision of the proposed solution, the local optimization strategy 2-opt is adapted in MMAS. 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. The third paper [12], entitled "A Data Analytic Algorithm for Managing, Querying, and Processing Uncertain Big Data...

Trajectory Clustering and k-NN for Robust Privacy Preserving Spatiotemporal Databases

“…Thus, deep learning platforms have been designed for mining values in big data [27]. A future direction is to design above methods and experiments in big data environments [28][29][30][31] and investigate their scalability and running time performance under different datasets and parameters k, K combinations.…”

An Application of K-Means Clustering on Tourist Activity in Maui Using Mobile Location Data

“…In the last two decades, the influx of data being collected from a variety of devices such as smart devices, streaming, and app use, has exploded. The size of Big Data may vary depending on the means by which they are collected, but the consistent theme is that the size of this data type is too large to be able to interpret or decipher the way one might a traditional database (Cuzzocrea, 2016;Manyika et al, 2011). Big Data might refer to a database holding several terabytes of information, or may hold several petabytes (Manyika et al 2011).…”

An Application of K-Means Clustering on Tourist Activity in Maui Using Mobile Location Data

“…In the last two decades, the influx of data being collected from a variety of devices such as smart devices, streaming, and app use, has exploded. The size of Big Data may vary depending on the means by which they are collected, but the consistent theme is that the size of this data type is too large to be able to interpret or decipher the way one might a traditional database (Cuzzocrea, 2016;Manyika et al, 2011). Big Data might refer to a database holding several terabytes of information, or may hold several petabytes (Manyika et al 2011).…”