Catálogo de publicaciones - revistas

<jats:p> Activities of a clinical staff in healthcare environments must regularly be adapted to new treatment methods, medications, and technologies. This constant evolution requires the monitoring of the workflow, or the sequence of actions from actors involved in a procedure, to ensure quality of medical services. In this context, recent advances in sensing technologies, including Real-time Location Systems and Computer Vision, enable high-precision tracking of actors and equipment. The current state-of-the-art about healthcare workflow monitoring typically focuses on a single technology and does not discuss its integration with others. Such an integration can lead to better solutions to evaluate medical workflows. This study aims to fill the gap regarding the analysis of monitoring technologies with a systematic literature review about sensors for capturing the workflow of healthcare environments. Its main scientific contribution is to identify both current technologies used to track activities in a clinical environment and gaps on their combination to achieve better results. It also proposes a taxonomy to classify work regarding sensing technologies and methods. The literature review does not present proposals that combine data obtained from Real-time Location Systems and Computer Vision sensors. Further analysis shows that a <jats:italic>multimodal</jats:italic> analysis is more flexible and could yield better results. </jats:p>

<jats:p>The rapid development of cloud computing promotes a wide deployment of data and computation outsourcing to cloud service providers by resource-limited entities. Based on a pay-per-use model, a client without enough computational power can easily outsource large-scale computational tasks to a cloud. Nonetheless, the issue of security and privacy becomes a major concern when the customer’s sensitive or confidential data is not processed in a fully trusted cloud environment. Recently, a number of publications have been proposed to investigate and design specific secure outsourcing schemes for different computational tasks. The aim of this survey is to systemize and present the cutting-edge technologies in this area. It starts by presenting security threats and requirements, followed with other factors that should be considered when constructing secure computation outsourcing schemes. In an organized way, we then dwell on the existing secure outsourcing solutions to different computational tasks such as matrix computations, mathematical optimization, and so on, treating data confidentiality as well as computation integrity. Finally, we provide a discussion of the literature and a list of open challenges in the area.</jats:p>

<jats:p>The main achievements of spatio-temporal modelling in the field of Geographic Information Science that spans the past three decades are surveyed. This article offers an overview of: (i) the origins and history of Temporal Geographic Information Systems (T-GIS); (ii) relevant spatio-temporal data models proposed; (iii) the evolution of spatio-temporal modelling trends; and (iv) an analysis of the future trends and developments in T-GIS. It also presents some current theories and concepts that have emerged from the research performed, as well as a summary of the current progress and the upcoming challenges and potential research directions for T-GIS. One relevant result of this survey is the proposed taxonomy of spatio-temporal modelling trends, which classifies 186 modelling proposals surveyed from more than 1,450 articles.</jats:p>

<jats:p>Recent demands for storing and querying big data have revealed various shortcomings of traditional relational database systems. This, in turn, has led to the emergence of a new kind of complementary nonrelational data store, named as NoSQL. This survey mainly aims at elucidating the design decisions of NoSQL stores with regard to the four nonorthogonal design principles of distributed database systems: data model, consistency model, data partitioning, and the CAP theorem. For each principle, its available strategies and corresponding features, strengths, and drawbacks are explained. Furthermore, various implementations of each strategy are exemplified and crystallized through a collection of representative academic and industrial NoSQL technologies. Finally, we disclose some existing challenges in developing effective NoSQL stores, which need attention of the research community, application designers, and architects.</jats:p>

<jats:p>Automatic machine-based Facial Expression Analysis (FEA) has made substantial progress in the past few decades driven by its importance for applications in psychology, security, health, entertainment, and human–computer interaction. The vast majority of completed FEA studies are based on nonoccluded faces collected in a controlled laboratory environment. Automatic expression recognition tolerant to partial occlusion remains less understood, particularly in real-world scenarios. In recent years, efforts investigating techniques to handle partial occlusion for FEA have seen an increase. The context is right for a comprehensive perspective of these developments and the state of the art from this perspective. This survey provides such a comprehensive review of recent advances in dataset creation, algorithm development, and investigations of the effects of occlusion critical for robust performance in FEA systems. It outlines existing challenges in overcoming partial occlusion and discusses possible opportunities in advancing the technology. To the best of our knowledge, it is the first FEA survey dedicated to occlusion and aimed at promoting better-informed and benchmarked future work.</jats:p>

<jats:p>Network-enabled sensing and actuation devices are key enablers to connect real-world objects to the cyber world. The Internet of Things (IoT) consists of the network-enabled devices and communication technologies that allow connectivity and integration of physical objects (Things) into the digital world (Internet). Enormous amounts of dynamic IoT data are collected from Internet-connected devices. IoT data are usually multi-variant streams that are heterogeneous, sporadic, multi-modal, and spatio-temporal. IoT data can be disseminated with different granularities and have diverse structures, types, and qualities. Dealing with the data deluge from heterogeneous IoT resources and services imposes new challenges on indexing, discovery, and ranking mechanisms that will allow building applications that require on-line access and retrieval of ad-hoc IoT data. However, the existing IoT data indexing and discovery approaches are complex or centralised, which hinders their scalability. The primary objective of this article is to provide a holistic overview of the state-of-the-art on indexing, discovery, and ranking of IoT data. The article aims to pave the way for researchers to design, develop, implement, and evaluate techniques and approaches for on-line large-scale distributed IoT applications and services.</jats:p>

<jats:p>Until not long ago, manually capturing and storing provenance from scientific experiments were constant concerns for scientists. With the advent of computational experiments (modeled as scientific workflows) and Scientific Workflow Management Systems, produced and consumed data, as well as the provenance of a given experiment, are automatically managed, so provenance capturing and storing in such a context is no longer a major concern. Similarly to several existing big data problems, the bottom line is now on how to analyze the large amounts of provenance data generated by workflow executions and how to be able to extract useful knowledge of this data. In this context, this article surveys the current state of the art on provenance analytics by presenting the key initiatives that have been taken to support provenance data analysis. We also contribute by proposing a taxonomy to classify elements related to provenance analytics.</jats:p>

<jats:p>This survey presents multidimensional scaling (MDS) methods and their applications in real world. MDS is an exploratory and multivariate data analysis technique becoming more and more popular. MDS is one of the multivariate data analysis techniques, which tries to represent the higher dimensional data into lower space. The input data for MDS analysis is measured by the dissimilarity or similarity of the objects under observation. Once the MDS technique is applied to the measured dissimilarity or similarity, MDS results in a spatial map. In the spatial map, the dissimilar objects are far apart while objects which are similar are placed close to each other. In this survey article, MDS is described in comprehensive fashion by explaining the basic notions of classical MDS and how MDS can be helpful to analyze the multidimensional data. Later on, various special models based on MDS are described in a more mathematical way followed by comparisons of various MDS techniques.</jats:p>

<jats:p>Many networking research activities are dependent on the availability of network captures. Even outside academic research, there is a need for sharing network captures to cooperate on threat assessments or for debugging. However, most network captures cannot be shared due to privacy concerns.</jats:p> <jats:p>Anonymisation of network captures has been a subject of research for quite some time, and many different techniques exist. In this article, we present an overview of the currently available techniques and implementations for network capture anonymisation.</jats:p> <jats:p>There have been many advances in the understanding of anonymisation and cryptographic methods, which have changed the perspective on the effectiveness of many anonymisation techniques. However, these advances, combined with the increase of computational abilities, may have also made it feasible to perform anonymisation in real time. This may make it easier to collect and distribute network captures both for research and for other applications.</jats:p> <jats:p>&lt;?tight?&gt;This article surveys the literature over the period of 1998–2017 on network traffic anonymisation techniques and implementations. The aim is to provide an overview of the current state of the art and to highlight how advances in related fields have shed new light on anonymisation and pseudonimisation methodologies. The few currently maintained implementations are also reviewed. Last, we identify future research directions to enable easier sharing of network traffic, which in turn can enable new insights in network traffic analysis.</jats:p>

<jats:p>Contemporary mobile devices are the result of an evolution process, during which computational and networking capabilities have been continuously pushed to keep pace with the constantly growing workload requirements. This has allowed devices such as smartphones, tablets, and personal digital assistants to perform increasingly complex tasks, up to the point of efficiently replacing traditional options such as desktop computers and notebooks. However, due to their portability and size, these devices are more prone to theft, to become compromised, or to be exploited for attacks and other malicious activity. The need for investigation of the aforementioned incidents resulted in the creation of the Mobile Forensics (MF) discipline. MF, a sub-domain of digital forensics, is specialized in extracting and processing evidence from mobile devices in such a way that attacking entities and actions are identified and traced. Beyond its primary research interest on evidence acquisition from mobile devices, MF has recently expanded its scope to encompass the organized and advanced evidence representation and analysis of future malicious entity behavior. Nonetheless, data acquisition still remains its main focus. While the field is under continuous research activity, new concepts such as the involvement of cloud computing in the MF ecosystem and the evolution of enterprise mobile solutions—particularly mobile device management and bring your own device—bring new opportunities and issues to the discipline. The current article presents the research conducted within the MF ecosystem during the last 7 years, identifies the gaps, and highlights the differences from past research directions, and addresses challenges and open issues in the field.</jats:p>