Catálogo de publicaciones - revistas

<jats:p>Big data processing systems (e.g., Hadoop, Spark, Storm) contain a vast number of configuration parameters controlling parallelism, I/O behavior, memory settings, and compression. Improper parameter settings can cause significant performance degradation and stability issues. However, regular users and even expert administrators grapple with understanding and tuning them to achieve good performance. We investigate existing approaches on parameter tuning for both batch and stream data processing systems and classify them into six categories: rule-based, cost modeling, simulation-based, experiment-driven, machine learning, and adaptive tuning. We summarize the pros and cons of each approach and raise some open research problems for automatic parameter tuning.</jats:p>

<jats:p>In the last decade, the problem of computational metaphor processing has garnered immense attention from the domains of computational linguistics and cognition. A wide panorama of approaches, ranging from a hand-coded rule system to deep learning techniques, have been proposed to automate different aspects of metaphor processing. In this article, we systematically examine the major theoretical views on metaphor and present their classification. We discuss the existing literature to provide a concise yet representative picture of computational metaphor processing. We conclude the article with possible research directions.</jats:p>

<jats:p>When developing peer-to-peer applications on distributed ledger technology (DLT), a crucial decision is the selection of a suitable DLT design (e.g., Ethereum), because it is hard to change the underlying DLT design post hoc. To facilitate the selection of suitable DLT designs, we review DLT characteristics and identify trade-offs between them. Furthermore, we assess how DLT designs account for these trade-offs and we develop archetypes for DLT designs that cater to specific requirements of applications on DLT. The main purpose of our article is to introduce scientific and practical audiences to the intricacies of DLT designs and to support development of viable applications on DLT.</jats:p>

<jats:p>Supervisory Control and Data Acquisition (SCADA) systems play an important role in monitoring industrial processes such as electric power distribution, transport systems, water distribution, and wastewater collection systems. Such systems require a particular attention with regards to security aspects, as they deal with critical infrastructures that are crucial to organizations and countries. Protecting SCADA systems from intrusion is a very challenging task because they do not only inherit traditional IT security threats but they also include additional vulnerabilities related to field components (e.g., cyber-physical attacks). Many of the existing intrusion detection techniques rely on supervised learning that consists of algorithms that are first trained with reference inputs to learn specific information, and then tested on unseen inputs for classification purposes. This article surveys supervised learning from a specific security angle, namely SCADA-based intrusion detection. Based on a systematic review process, existing literature is categorized and evaluated according to SCADA-specific requirements. Additionally, this survey reports on well-known SCADA datasets and testbeds used with machine learning methods. Finally, we present key challenges and our recommendations for using specific supervised methods for SCADA systems.</jats:p>

<jats:p>More than 70% of characters in the source code are used to label identifiers. Consequently, identifiers are one of the most important source for program comprehension. Meaningful identifiers are crucial to understand and maintain programs. However, for reasons like constrained schedule, inexperience, and unplanned evolution, identifiers may fail to convey the semantics of the entities associated with them. As a result, such entities should be renamed to improve software quality. However, manual renaming and recommendation are fastidious, time consuming, and error prone, whereas automating the process of renamings is challenging: (1) It involves complex natural language processing to understand the meaning of identifers; (2) It also involves difficult semantic analysis to determine the role of software entities. Researchers proposed a number of approaches and tools to facilitate renamings. We present a survey on existing approaches and classify them into identification of renaming opportunities, execution of renamings, and detection of renamings. We find that there is an imbalance between the three type of approaches, and most of implementation of approaches and evaluation dataset are not publicly available. We also discuss the challenges and present potential research directions. To the best of our knowledge, this survey is the first comprehensive study on renamings of software entities.</jats:p>

<jats:p>Internet of Things (IoT) and Network Softwarization are fast becoming core technologies of information systems and network management for the next-generation Internet. The deployment and applications of IoT range from smart cities to urban computing and from ubiquitous healthcare to tactile Internet. For this reason, the physical infrastructure of heterogeneous network systems has become more complicated and thus requires efficient and dynamic solutions for management, configuration, and flow scheduling. Network softwarization in the form of Software Defined Networks and Network Function Virtualization has been extensively researched for IoT in the recent past. In this article, we present a systematic and comprehensive review of virtualization techniques explicitly designed for IoT networks. We have classified the literature into software-defined networks designed for IoT, function virtualization for IoT networks, and software-defined IoT networks. These categories are further divided into works that present architectural, security, and management solutions. Besides, the article highlights several short-term and long-term research challenges and open issues related to the adoption of software-defined Internet of Things.</jats:p>

<jats:p>Entity Resolution (ER), a core task of Data Integration, detects different entity profiles that correspond to the same real-world object. Due to its inherently quadratic complexity, a series of techniques accelerate it so that it scales to voluminous data. In this survey, we review a large number of relevant works under two different but related frameworks: Blocking and Filtering. The former restricts comparisons to entity pairs that are more likely to match, while the latter identifies quickly entity pairs that are likely to satisfy predetermined similarity thresholds. We also elaborate on hybrid approaches that combine different characteristics. For each framework we provide a comprehensive list of the relevant works, discussing them in the greater context. We conclude with the most promising directions for future work in the field.</jats:p>

<jats:p> The next generation of supercomputers will break the exascale barrier. Soon we will have systems capable of at least one quintillion (billion billion) floating-point operations per second (10 <jats:sup>18</jats:sup> FLOPS). Tremendous amounts of work have been invested into identifying and overcoming the challenges of the exascale era. In this work, we present an overview of these efforts and provide insight into the important trends, developments, and exciting research opportunities in exascale computing. We use a three-stage approach in which we (1) discuss various exascale landmark studies, (2) use data-driven techniques to analyze the large collection of related literature, and (3) discuss eight research areas in depth based on influential articles. Overall, we observe that great advancements have been made in tackling the two primary exascale challenges: energy efficiency and fault tolerance. However, as we look forward, we still foresee two major concerns: the lack of suitable programming tools and the growing gap between processor performance and data bandwidth (i.e., memory, storage, networks). Although we will certainly reach exascale soon, without additional research, these issues could potentially limit the applicability of exascale computing. </jats:p>

<jats:p>With the development of wireless technology, various emerging mobile applications are attracting significant attention and drastically changing our daily lives. Applications such as augmented reality and object recognition demand stringent delay and powerful processing capability, which exerts enormous pressure on mobile devices with limited resources and energy. In this article, a survey of techniques for mobile device energy optimization is presented in a hierarchy of device design and operation, computation offloading, wireless data transmission, and cloud execution of offloaded computation. Energy management strategies for mobile devices from hardware and software aspects are first discussed, followed by energy-efficient computation offloading frameworks for mobile applications that trade application response time for device energy consumption. Then, techniques for efficient wireless data communication to reduce transmission energy are summarized. Finally, the execution mechanisms of application components or tasks in various clouds are further described to provide energy-saving opportunities for mobile devices. We classify the research works based on key characteristics of devices and applications to emphasize their similarities and differences. We hope that this survey will give insights to researchers into energy management mechanisms on mobile devices, and emphasize the crucial importance of optimizing device energy consumption for more research efforts in this area.</jats:p>

<jats:p>Industrial Internet of Things (IIoT) is present in many participants from the energy, health, manufacturing, transport, and public sectors. Many factors catalyze IIoT, such as robotics, artificial intelligence, and intelligent decentralized manufacturing. However, the convergence between IT, OT, and IoT environments involves the integration of heterogeneous technologies through protocols, standards, and buses. However, this integration brings with it security risks. To avoid the security risks, especially when systems in different environments interact, it is important and urgent to create an early consensus among the stakeholders on the IIoT security. The default Common Vulnerability Scoring System (CVSS) offers a mechanism to measure the severity of an asset's vulnerability and therefore a way to characterize the risk. However, CVSS by default has two drawbacks. On the one hand, to carry out a risk analysis, it is necessary to have additional metrics to the one established by CVSSv3.1. On the other hand, this index has been used mostly in IT environments and although there are numerous efforts to develop a model that suits industrial environments, there is no established proposal. Therefore, we first propose a survey of the main 33 protocols, standards, and buses used in an IIoT environment. This survey will focus on the security of each one. The second part of our study consists of the creation of a framework to characterize risk in industrial environments, i.e., to solve both problems of the CVSS index. To this end, we created the Vulnerability Analysis Framework (VAF), which is a methodology that allows the analysis of 1,363 vulnerabilities to establish a measure to describe the risk in IIoT environments.</jats:p>