Catálogo de publicaciones - revistas

<jats:p>Describing user activity plays an essential role in ambient intelligence. In this work, we review different methods for human activity recognition, classified as data-driven and knowledge-based techniques. We focus on context ontologies whose ultimate goal is the tracking of human behavior. After studying upper and domain ontologies, both useful for human activity representation and inference, we establish an evaluation criterion to assess the suitability of the different candidate ontologies for this purpose. As a result, any missing features, which are relevant for modeling daily human behaviors, are identified as future challenges.</jats:p>

<jats:p>Real-time systems are one of the most important applications of computers, both in commercial terms and in terms of social impact. Increasingly, real-time computers are used to control life-critical applications and need to meet stringent reliability conditions. Since the reliability of a real-time system is related to the probability of meeting its hard deadlines, these reliability requirements translate to the need to meet critical task deadlines with a very high probability. We survey the problem of how to schedule tasks in such a way that deadlines continue to be met despite processor (permanent or transient) or software failure.</jats:p>

<jats:p>Various research communities have independently arrived at stream processing as a programming model for efficient and parallel computing. These communities include digital signal processing, databases, operating systems, and complex event processing. Since each community faces applications with challenging performance requirements, each of them has developed some of the same optimizations, but often with conflicting terminology and unstated assumptions. This article presents a survey of optimizations for stream processing. It is aimed both at users who need to understand and guide the system’s optimizer and at implementers who need to make engineering tradeoffs. To consolidate terminology, this article is organized as a catalog, in a style similar to catalogs of design patterns or refactorings. To make assumptions explicit and help understand tradeoffs, each optimization is presented with its safety constraints (when does it preserve correctness?) and a profitability experiment (when does it improve performance?). We hope that this survey will help future streaming system builders to stand on the shoulders of giants from not just their own community.</jats:p>

<jats:p>Concept drift primarily refers to an online supervised learning scenario when the relation between the input data and the target variable changes over time. Assuming a general knowledge of supervised learning in this article, we characterize adaptive learning processes; categorize existing strategies for handling concept drift; overview the most representative, distinct, and popular techniques and algorithms; discuss evaluation methodology of adaptive algorithms; and present a set of illustrative applications. The survey covers the different facets of concept drift in an integrated way to reflect on the existing scattered state of the art. Thus, it aims at providing a comprehensive introduction to the concept drift adaptation for researchers, industry analysts, and practitioners.</jats:p>

<jats:p>This survey covers distribution standards oriented to the development of distributed real-time systems. Currently, there are many distribution standards that provide a wide and different set of real-time facilities to control the temporal aspects of applications. Besides giving a general overview of these standards, we describe the real-time mechanisms proposed by each standard to manage both processor and network resources, discuss whether the available facilities are sufficient to guarantee determinism throughout the whole application, and identify a set of features and deployment options that would be desirable in any real-time distribution middleware regardless of its distribution model and standard. The survey identifies open issues and key challenges for future research.</jats:p>

<jats:p>The past two decades have witnessed an explosion in the deployment of large-scale distributed simulations and distributed virtual environments in different domains, including military and academic simulation systems, social media, and commercial applications such as massively multiplayer online games. As these systems become larger, more data intensive, and more latency sensitive, the optimisation of the flow of data, a paradigm referred to as interest management, has become increasingly critical to address the scalability requirements and enable their successful deployment. Numerous interest management schemes have been proposed for different application scenarios. This article provides a comprehensive survey of the state of the art in the design of interest management algorithms and systems. The scope of the survey includes current and historical projects providing a taxonomy of the existing schemes and summarising their key features. Identifying the primary requirements of interest management, the article discusses the trade-offs involved in the design of existing approaches.</jats:p>

<jats:p>Document retrieval is one of the best-established information retrieval activities since the ’60s, pervading all search engines. Its aim is to obtain, from a collection of text documents, those most relevant to a pattern query. Current technology is mostly oriented to “natural language” text collections, where inverted indexes are the preferred solution. As successful as this paradigm has been, it fails to properly handle various East Asian languages and other scenarios where the “natural language” assumptions do not hold. Inthis survey, we cover the recent research in extending the document retrieval techniques to a broader class of sequence collections, which has applications in bioinformatics, data and web mining, chemoinformatics, software engineering, multimedia information retrieval, and many other fields. We focus on the algorithmic aspects of the techniques, uncovering a rich world of relations between document retrieval challenges and fundamental problems on trees, strings, range queries, discrete geometry, and other areas.</jats:p>

<jats:p>Transformation-based learning (TBL) is a machine learning method for, in particular, sequential classification, invented by Eric Brill [Brill 1993b, 1995a]. It is widely used within computational linguistics and natural language processing, but surprisingly little in other areas.</jats:p><jats:p>TBL is a simple yet flexible paradigm, which achieves competitive or even state-of-the-art performance in several areas and does not overtrain easily. It is especially successful at catching local, fixed-distance dependencies and seamlessly exploits information from heterogeneous discrete feature types. The learned representation—an ordered list of transformation rules—is compact and efficient, with clear semantics. Individual rules are interpretable and often meaningful to humans.</jats:p><jats:p>The present article offers a survey of the most important theoretical work on TBL, addressing a perceived gap in the literature. Because the method should be useful also outside the world of computational linguistics and natural language processing, a chief aim is to provide an informal but relatively comprehensive introduction, readable also by people coming from other specialities.</jats:p>

<jats:p>The automatic elimination of duplicate data in a storage system, commonly known as deduplication, is increasingly accepted as an effective technique to reduce storage costs. Thus, it has been applied to different storage types, including archives and backups, primary storage, within solid-state drives, and even to random access memory. Although the general approach to deduplication is shared by all storage types, each poses specific challenges and leads to different trade-offs and solutions. This diversity is often misunderstood, thus underestimating the relevance of new research and development.</jats:p> <jats:p>The first contribution of this article is a classification of deduplication systems according to six criteria that correspond to key design decisions: granularity, locality, timing, indexing, technique, and scope. This classification identifies and describes the different approaches used for each of them. As a second contribution, we describe which combinations of these design decisions have been proposed and found more useful for challenges in each storage type. Finally, outstanding research challenges and unexplored design points are identified and discussed.</jats:p>

<jats:p>A mobility model is used to generate the trajectories of mobile nodes in simulations when developing new algorithms for mobile networks. A model must realistically reflect the scenario in which the technology will be used to reliably validate the algorithm. Considerable progress has been made toward realistic mobility models in the academic literature, and models have become quite complex. A consistent taxonomy has not yet been established for this field. A new multifaceted taxonomy is presented in this work that provides a framework for authors to clearly and consistently describe their models, making them easier to understand and reproduce. By surveying the application field of mobile communication networks, a common nomenclature and a high-level view of existing literature are provided, which are required to reduce duplication of effort and to enable a better sense of the way forward. A tactical scenario demonstrates the application of the taxonomy to model construction.</jats:p>