Catálogo de publicaciones - revistas

<jats:p>Large-scale software systems are currently designed as distributed entities and deployed in cloud data centers. To overcome the limitations inherent to this type of deployment, applications are increasingly being supplemented with components instantiated closer to the edges of networks—a paradigm known as edge computing. The problem of how to efficiently orchestrate combined edge-cloud applications is, however, incompletely understood, and a wide range of techniques for resource and application management are currently in use.</jats:p> <jats:p>This article investigates the problem of reliable resource provisioning in joint edge-cloud environments, and surveys technologies, mechanisms, and methods that can be used to improve the reliability of distributed applications in diverse and heterogeneous network environments. Due to the complexity of the problem, special emphasis is placed on solutions to the characterization, management, and control of complex distributed applications using machine learning approaches. The survey is structured around a decomposition of the reliable resource provisioning problem into three categories of techniques: workload characterization and prediction, component placement and system consolidation, and application elasticity and remediation. Survey results are presented along with a problem-oriented discussion of the state-of-the-art. A summary of identified challenges and an outline of future research directions are presented to conclude the article.</jats:p>

<jats:p>A large number of published datasets (or sources) that follow Linked Data principles is currently available and this number grows rapidly. However, the major target of Linked Data, i.e., linking and integration, is not easy to achieve. In general, information integration is difficult, because (a) datasets are produced, kept, or managed by different organizations using different models, schemas, or formats, (b) the same real-world entities or relationships are referred with different URIs or names and in different natural languages,&lt;?brk?&gt;(c) datasets usually contain complementary information, (d) datasets can contain data that are erroneous, out-of-date, or conflicting, (e) datasets even about the same domain may follow different conceptualizations of the domain, (f) everything can change (e.g., schemas, data) as time passes. This article surveys the work that has been done in the area of Linked Data integration, it identifies the main actors and use cases, it analyzes and factorizes the integration process according to various dimensions, and it discusses the methods that are used in each step. Emphasis is given on methods that can be used for integrating several datasets. Based on this analysis, the article concludes with directions that are worth further research.</jats:p>

<jats:p> A <jats:italic>character network</jats:italic> is a graph extracted from a narrative in which vertices represent characters and edges correspond to interactions between them. A number of narrative-related problems can be addressed automatically through the analysis of character networks, such as summarization, classification, or role detection. Character networks are particularly relevant when considering <jats:italic>works of fiction</jats:italic> (e.g., novels, plays, movies, TV series), as their exploitation allows developing information retrieval and recommendation systems. However, works of fiction possess specific properties that make these tasks harder. </jats:p> <jats:p>This survey aims at presenting and organizing the scientific literature related to the extraction of character networks from works of fiction, as well as their analysis. We first describe the extraction process in a generic way and explain how its constituting steps are implemented in practice, depending on the medium of the narrative, the goal of the network analysis, and other factors. We then review the descriptive tools used to characterize character networks, with a focus on the way they are interpreted in this context. We illustrate the relevance of character networks by also providing a review of applications derived from their analysis. Finally, we identify the limitations of the existing approaches and the most promising perspectives.</jats:p>

<jats:p>Autonomous robotic systems are complex, hybrid, and often safety critical; this makes their formal specification and verification uniquely challenging. Though commonly used, testing and simulation alone are insufficient to ensure the correctness of, or provide sufficient evidence for the certification of, autonomous robotics. Formal methods for autonomous robotics have received some attention in the literature, but no resource provides a current overview. This article systematically surveys the state of the art in formal specification and verification for autonomous robotics. Specially, it identifies and categorizes the challenges posed by, the formalisms aimed at, and the formal approaches for the specification and verification of autonomous robotics.</jats:p>

<jats:p>Interest in processing big data has increased rapidly to gain insights that can transform businesses, government policies, and research outcomes. This has led to advancement in communication, programming, and processing technologies, including cloud computing services and technologies such as Hadoop, Spark, and Storm. This trend also affects the needs of analytical applications, which are no longer monolithic but composed of several individual analytical steps running in the form of a workflow. These big data workflows are vastly different in nature from traditional workflows. Researchers are currently facing the challenge of how to orchestrate and manage the execution of such workflows. In this article, we discuss in detail orchestration requirements of these workflows as well as the challenges in achieving these requirements. We also survey current trends and research that supports orchestration of big data workflows and identify open research challenges to guide future developments in this area.</jats:p>

<jats:p>A systematic literature review is presented that surveyed the topic of cloud testing over the period 2012--2017. Cloud testing can refer either to testing cloud-based systems (testing of the cloud) or to leveraging the cloud for testing purposes (testing in the cloud): both approaches (and their combination into testing of the cloud in the cloud) have drawn research interest. An extensive paper search was conducted by both automated query of popular digital libraries and snowballing, which resulted in the final selection of 147 primary studies. Along the survey, a framework has been incrementally derived that classifies cloud testing research among six main areas and their topics. The article includes a detailed analysis of the selected primary studies to identify trends and gaps, as well as an extensive report of the state-of-the-art as it emerges by answering the identified Research Questions. We find that cloud testing is an active research field, although not all topics have received enough attention and conclude by presenting the most relevant open research challenges for each area of the classification framework.</jats:p>

<jats:p>Although malicious software (malware) has been around since the early days of computers, the sophistication and innovation of malware has increased over the years. In particular, the latest crop of ransomware has drawn attention to the dangers of malicious software, which can cause harm to private users as well as corporations, public services (hospitals and transportation systems), governments, and security institutions. To protect these institutions and the public from malware attacks, malicious activity must be detected as early as possible, preferably before it conducts its harmful acts. However, it is not always easy to know what to look for—especially when dealing with new and unknown malware that has never been seen. Analyzing a suspicious file by static or dynamic analysis methods can provide relevant and valuable information regarding a file's impact on the hosting system and help determine whether the file is malicious or not, based on the method's predefined rules. While various techniques (e.g., code obfuscation, dynamic code loading, encryption, and packing) can be used by malware writers to evade static analysis (including signature-based anti-virus tools), dynamic analysis is robust to these techniques and can provide greater understanding regarding the analyzed file and consequently can lead to better detection capabilities. Although dynamic analysis is more robust than static analysis, existing dynamic analysis tools and techniques are imperfect, and there is no single tool that can cover all aspects of malware behavior. The most recent comprehensive survey performed in this area was published in 2012. Since that time, the computing environment has changed dramatically with new types of malware (ransomware, cryptominers), new analysis methods (volatile memory forensics, side-channel analysis), new computing environments (cloud computing, IoT devices), new machine-learning algorithms, and more. The goal of this survey is to provide a comprehensive and up-to-date overview of existing methods used to dynamically analyze malware, which includes a description of each method, its strengths and weaknesses, and its resilience against malware evasion techniques. In addition, we include an overview of prominent studies presenting the usage of machine-learning methods to enhance dynamic malware analysis capabilities aimed at detection, classification, and categorization.</jats:p>

<jats:p>Data mining is used for finding meaningful information out of a vast expanse of data. With the advent of Big Data concept, data mining has come to much more prominence. Discovering knowledge out of a gigantic volume of data efficiently is a major concern as the resources are limited. Cloud computing plays a major role in such a situation. Cloud data mining fuses the applicability of classical data mining with the promises of cloud computing. This allows it to perform knowledge discovery out of huge volumes of data with efficiency. This article presents the existing frameworks, services, platforms, and algorithms for cloud data mining. The frameworks and platforms are compared among each other based on similarity, data mining task support, parallelism, distribution, streaming data processing support, fault tolerance, security, memory types, storage systems, and others. Similarly, the algorithms are grouped on the basis of parallelism type, scalability, streaming data mining support, and types of data managed. We have also provided taxonomies on the basis of data mining techniques such as clustering, classification, and association rule mining. We also have attempted to discuss and identify the major applications of cloud data mining. The various taxonomies for cloud data mining frameworks, platforms, and algorithms have been identified. This article aims at gaining better insight into the present research realm and directing the future research toward efficient cloud data mining in future cloud systems.</jats:p>

<jats:p>Autonomous robots are playing important roles in academic, technological, and scientific activities. Thus, their behavior is getting more complex, particularly, in tasks related to mapping an environment and localizing themselves. These tasks comprise the Simultaneous Localization and Mapping (SLAM) problem. Representation of knowledge related to the SLAM problem with a standard, flexible, and well-defined model, provides the base to develop efficient and interoperable solutions. As many existing works demonstrate, Semantic Web seems to be a clear approach, since they have formulated ontologies, as the base data model to represent such knowledge. In this article, we survey the most popular and recent SLAM ontologies with our aim being threefold: (i) propose a classification of SLAM ontologies according to the main knowledge needed to model the SLAM problem; (ii) identify existing ontologies for classifying, comparing, and contrasting them, in order to conceptualize SLAM domain for mobile robots; and (iii) pin-down lessons to learn from existing solutions in order to design better solutions and identify new research directions and further improvements. We compare the identified SLAM ontologies according to the proposed classification and, finally, we explore new data fields to enrich existing ontologies and highlight new possibilities in terms of performance and efficiency for SLAM solutions.</jats:p>

<jats:p>This is a consolidated look at computational techniques for sustainability, and their limits and possibilities. Sustainability is already well established as a concern and a topic of study and practice, given the alarming increase of environmental degradation, pollution, and other adverse effects of industrialization and urbanization. Computational sustainability, which focuses on the use of effective computational models and computational approaches to help achieve the goal of sustainability, has attracted interest from computer science researchers worldwide. We review recent work on computational techniques applied to a range of domains related to sustainability, from bio-surveillance to poverty mapping, from renewable energy production forecasting to crop disease monitoring, and from agent-based modeling to stochastic network design. In sustainable computing, we discuss some directions that have recently been explored. Finally, we analyze research directions that could be explored in the future to achieve the goal of long-term environmental sustainability.</jats:p>