Artículos (Lenguajes y Sistemas Informáticos)

URI permanente para esta colecciónhttps://hdl.handle.net/11441/11392

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2D Triangulation of Signals Source by Pole-Polar Geometric Models
(MDPI, 2019-03-01) Montanha, Aleksandro; Polidorio, Airton M.; Domínguez Mayo, Francisco José; Escalona Cuaresma, María José; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Universidad de Sevilla. TIC021: Ingeniería Web y Testing Temprano
The 2D point location problem has applications in several areas, such as geographic information systems, navigation systems, motion planning, mapping, military strategy, location and tracking moves. We aim to present a new approach that expands upon current techniques and methods to locate the 2D position of a signal source sent by an emitter device. This new approach is based only on the geometric relationship between an emitter device and a system composed of m ≥ 2 signal receiving devices. Current approaches applied to locate an emitter can be deterministic, statistical or machine-learning methods. We propose to perform this triangulation by geometric models that exploit elements of pole-polar geometry. For this purpose, we are presenting five geometric models to solve the point location problem: (1) based on centroid of points of pole-polar geometry, PPC; (2) based on convex hull region among pole-points, CHC; (3) based on centroid of points obtained by polar-lines intersections, PLI; (4) based on centroid of points obtained by tangent lines intersections, TLI; (5) based on centroid of points obtained by tangent lines intersections with minimal angles, MAI. The first one has computational cost O(n) and whereas has the computational cost O(n log n)where n is the number of points of interest. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.
A bargaining-specific architecture for supporting automated service agreement negotiation systems
(Elsevier, 2012) Resinas Arias de Reyna, Manuel; Fernández Montes, Pablo; Corchuelo Gil, Rafael; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Comisión Interministerial de Ciencia y Tecnología (CICYT). España; Junta de Andalucía; Ministerio de Ciencia Y Tecnología (MCYT). España; Ministerio de Ciencia e Innovación (MICIN). España; Universidad de Sevilla. TIC205: Ingeniería del Software Aplicada
The provision of services is often regulated by means of agreements that must be negotiated beforehand. Automating such negotiations is appealing insofar as it overcomes one of the most often cited shortcomings of human negotiation: slowness. Our analysis of the requirements of automated negotiation systems in open environments suggests that some of them cannot be tackled in a protocol-independent manner, which motivates the need for a protocol-specific architecture. However, current state-of-the-art bargaining architectures fail to address all of these requirements together. Our key contribution is a bargaining architecture that addresses all of the requirements we have identified. The definition of the architecture includes a logical view that identifies the key architectural elements and their interactions, a process view that identifies how the architectural elements can be grouped together into processes, a development view that includes a software framework that provides a reference implementation developers can use to build their own negotiation systems, and a scenarios view by means of which the architecture is illustrated and validated
A Bayesian Optimization-Based LSTM Model for Wind Power Forecasting in the Adama District, Ethiopia
(MDPI, 2023-02) Tefera Habtemariam, Ejigu; Kekeba, Kula; Martínez Ballesteros, María del Mar; Martínez Álvarez, Francisco; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Ministerio de Ciencia e Innovación (MICIN). España; Junta de Andalucía
Renewable energies, such as solar and wind power, have become promising sources of energy to address the increase in greenhouse gases caused by the use of fossil fuels and to resolve the current energy crisis. Integrating wind energy into a large-scale electric grid presents a significant challenge due to the high intermittency and nonlinear behavior of wind power. Accurate wind power forecasting is essential for safe and efficient integration into the grid system. Many prediction models have been developed to predict the uncertain and nonlinear time series of wind power, but most neglect the use of Bayesian optimization to optimize the hyperparameters while training deep learning algorithms. The efficiency of grid search strategies decreases as the number of hyperparameters increases, and computation time complexity becomes an issue. This paper presents a robust and optimized long-short term memory network for forecasting wind power generation in the day ahead in the context of Ethiopia’s renewable energy sector. The proposal uses Bayesian optimization to find the best hyperparameter combination in a reasonable computation time. The results indicate that tuning hyperparameters using this metaheuristic prior to building deep learning models significantly improves the predictive performances of the models. The proposed models were evaluated using MAE, RMSE, and MAPE metrics, and outperformed both the baseline models and the optimized gated recurrent unit architecture.
A Benchmark for ASP Systems: Resource Allocation in Business Processes
(Department für Informationsverarbeitung und Prozessmanagement, 2019) Havur, Giray; Cabanillas Macías, Cristina; Polleres, Axel; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Austrian Research Promotion Agency (FFG)
The goal of this paper is to benchmark Answer Set Programming (ASP) systems to test their performance when dealing with a complex optimization problem. In particular, the problem tackled is resource allocation in the area of Business Process Management (BPM). Like many other scheduling problems, the allocation of resources and starting times to business process activities is a challenging optimization problem for ASP solvers. Our problem encoding is ASP Core-2 standard compliant and it is realized in a declarative and compact fashion. We develop an instance generator that produces problem instances of different size and hardness with respect to adjustable parameters. By using the baseline encoding and the instance generator, we provide a comparison between the two award-winning ASP solvers CLASP and WASP and report the grounding performance of GRINGO and I-DLV. The benchmark suggests that there is room for improvement concerning both the grounders and the solvers. Fostered by the relevance of the problem addressed, of which several variants have been described in different domains, we believe this is a solid application-oriented benchmark for the ASP community.
A case study of qualitative change in system dynamics
(Taylor and Francis, 1984) Aracil Santonja, Javier; Toro Bonilla, Miguel; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Universidad de Sevilla. TIC-134: Sistemas Informáticos
The application of dynamical systems qualitative analysis techniques to the study of models of socio-economical systems showing abrupt changes in its qualitative behaviour is proposed. The approach is based on the multiple time-scale properties of a class of non-linear perturbed systems. The change of qualitative behaviour produced in the system can be explained through the singularities of a properly defined surface. The proposed technique is applied to a system dynamics model which describes the collapse of the Maya civilization. The relatively complex model constitutes an excellent illustration of the possibilities of the proposed method. In addition, the example studied in this paper shows the essential role of the non-linearities in modelling the qualitative change, thus pointing out the inherent limitations of the linear models (traditionally used in econometrics) in this sort of problem.
A case study of transactional workload running in virtual machines: the performance evaluation of a flight seats availability service
(Institute of Electrical and Electronics Engineers (IEEE), 2023) Juiz, Carlos; Capo, Bartomeu; Bermejo, Belén; Fernández Montes González, Alejandro; Fernández Cerero, Damián; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; European Union ‘‘NextGenerationEU’’/PRTR
Much of the research has focused on performance evaluation and, particularly, the response time of clusters in cloud computing. However, one important topic has hardly been addressed: the impact of virtual machine consolidation on real business cases, on companies driven by requirements for high performance in transaction response time, specifically on intermediation trip companies. The ability to provide quality service, guaranteed within several milliseconds, is crucial to the business success of these cluster platforms. We present a case study for evaluating the performance of the seat availability service used by a flight carrier. The case study is the application of the performance evaluation methodology that ranges from monitoring to tuning options of a real-world service running on virtual machines, to understand capacity planning or possible substitution by other configurations of virtualization or containerization of the architecture of the cloud platform. This case study also proposes a workload characterisation using data clusters, allowing the architecture to be modeled as a simple network of multiclass queues of any virtual machine on the platform. Additionally, we estimate the new transaction response time by the possibility of either reducing or incrementing the number of virtual machines and their replacement by containers.
A class of neural-network-based transducers for web information extraction
(ScienceDirect, 2013-05) Sleiman, Hassan A.; Corchuelo Gil, Rafael; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Ministerio de Educación y Ciencia (MEC). España; Junta de Andalucía; Ministerio de Ciencia e Innovación (MICIN). España; Ministerio de Economía, Industria y Competitividad; Ministerio de Economía y Competitividad (MINECO). España
The Web is a huge and still growing information repository that has attracted the attention of many companies. Many such companies rely on information extractors to integrate information that is buried into semi-structured web documents into automatic business processes. Many information extractors build on extraction rules,which can be hand crafted or learned using supervised or unsupervised techniques. The literature provides a variety of techniques to learn information extraction rules that build on ad hoc machine learning techniques. In this paper, we propose a hybrid approach that explores the use of standard machine-learning techniques to extract web information. We have specifically explored using neural networks; our results show that our proposal out performs three state-of-the-arttechniques in the literature, which opens up quite a new approach to information extraction.
A classification and systematic review of product line feature model defects
(Springer, 2020) Bhushan, Megha; Negi, A.; Samant, P.; Goel, S.; Kumar, A.; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Product line (PL)-based development is a thriving research area to develop softwareintensive systems. Feature models (FMs) facilitate derivation of valid products from a PL by managing commonalities and variabilities among software products. However, the researchers in academia as well as in the industries experience difficulties in quality assessment of FMs. The increasing complexity and size of FMs may lead to defects, which outweigh the benefits of PL. This paper provides a systematic literature review and key research issues related to the FM defects in PL. We derive a typology of FM defects according to their level of importance. The information on defects’ identification and explanations are provided with formalization. Further, corrective explanations are pre-sented which incorporates various techniques used to fix defects with their implementa-tion. This information would help software engineering community by enabling developers or modelers to find the types of defects and their causes and to choose an appropriate technique to fix defects in order to produce defect-free products from FMs, thereby enhancing the overall quality of PL-based development.
A cloud-based integration platform for enterprise application integration: A Model-Driven Engineering approach
(John Wiley & Sons, 2020-10) Frantz, Rafael Z.; Corchuelo Gil, Rafael; Basto Fernandes, Vitor; Rosa Sequeira, Fernando; Roos Frantz, Fabricia; Arjona, José L.; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
This article addresses major information systems integration problems, approaches, technologies, and tools within the context of Model-Driven Software Engineering. The Guaraná integration platform is introduced as an innovative platform amongst state-of-the-art technologies available for enterprises to design and implement integration solutions. In this article, we present its domain-specificmodeling language and its industrial cloud-based web development platform, which supports the design and implementation of integration solutions. A real-world case study is described and analyzed; then, we delve into its design and implementation, to finally disclose ten measures that empirically help estimating the amount of effort involved in the development of integration solutions.
A clustering approach to extract data from HTML tables
(Elsevier, 2021) Jiménez Aguirre, Patricia; Roldán Salvador, Juan Carlos; Corchuelo Gil, Rafael; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Ministerio de Ciencia e Innovación (MICIN). España; Ministerio de Economía y Competitividad (MINECO). España; Junta de Andalucía; Universidad de Sevilla. TIC258: Data-centric Computing Research Hub
HTML tables have become pervasive on the Web. Extracting their data automatically is difficult because finding the relationships between their cells is not trivial due to the many different layouts, encodings, and formats available. In this article, we introduce Melva, which is an unsupervised domain-agnostic proposal to extract data from HTML tables without requiring any external knowledge bases. It relies on a clustering approach that helps make label cells apart from value cells and establish their relationships. We compared Melva to four competitors on more than 3 000 HTML tables from the Wikipedia and the Dresden Web Table Corpus. The conclusion is that our proposal is 21.70% better than the best unsupervised competitor and equals the best supervised competitor regarding effectiveness, but it is 99.14% better regarding efficiency
A comparative study of classifier combination applied to NLP tasks
(Elsevier, 2013) Enríquez de Salamanca Ros, Fernando; Cruz Mata, Fermín; Ortega Rodríguez, Francisco Javier; García Vallejo, Carlos Antonio; Troyano Jiménez, José Antonio; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
The paper is devoted to a comparative study of classifier combination methods, which have been successfully applied to multiple tasks including Natural Language Processing (NLP) tasks. There is variety of classifier combination techniques and the major difficulty is to choose one that is the best fit for a particular task. In our study we explored the performance of a number of combination methods such as voting, Bayesian merging, behavior knowledge space, bagging, stacking, feature sub-spacing and cascading, for the part-of-speech tagging task using nine corpora in five languages. The results show that some methods that, currently, are not very popular could demonstrate much better performance. In addition, we learned how the corpus size and quality influence the combination methods performance. We also provide the results of applying the classifier combination methods to the other NLP tasks, such as name entity recognition and chunking. We believe that our study is the most exhaustive comparison made with combination methods applied to NLP tasks so far.
A comparison of effort estimation methods for 4GL programs: experiences with Statistics and Data Mining
(World Scientific Publishing Company, 2006) Riquelme Santos, José Cristóbal; Polo, Macario; Aguilar Ruiz, Jesús Salvador; Piattini Velthuis, Mario; Ferrer Troyano, Francisco Javier; Ruiz, Francisco; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
This paper presents an empirical study analysing the relationship between a set of metrics for Fourth–Generation Languages (4GL) programs and their maintainability. An analysis has been made using historical data of several industrial projects and three different approaches: the first one relates metrics and maintainability based on techniques of descriptive statistics, and the other two are based on Data Mining techniques. A discussion on the results obtained with the three techniques is also presented, as well as a set of equations and rules for predicting the maintenance effort in this kind of programs. Finally, we have done experiments about the prediction accuracy of these methods by using new unseen data, which were not used to build the knowledge model. The results were satisfactory as the application of each technique separately provides useful perspective for the manager in order to get a complementary insight from data.
A comparison of machine learning regression techniques for LiDAR-derived estimation of forest variables
(Elsevier, 2015) García Gutiérrez, Jorge; Martínez Álvarez, Francisco; Troncoso Lora, Alicia; Riquelme Santos, José Cristóbal; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Light Detection and Ranging (LiDAR) is a remote sensor able to extract three-dimensional information. Environmental models in forest areas have been benefited by the use of LiDAR-derived information in the last years. A multiple linear regression (MLR) with previous stepwise feature selection is the most common method in the literature to develop those models. MLR defines the relation between the set of field measurements and the statistics extracted from a LiDAR flight. Machine learning has emerged as a suitable tool to improve classic stepwise MLR results on LiDAR. Unfortunately, few studies have been proposed to compare the quality of the multiple machine learning approaches. This paper presents a comparison between the classic MLR-based methodology and regression techniques in machine learning (neural networks, support vector machines, nearest neighbour, ensembles such as random forests) with special emphasis on regression trees. The selected techniques are applied to real LiDAR data from two areas in the province of Lugo (Galizia, Spain). The results confirm that classic MLR is outperformed by machine learning techniques and concretely, our experiments suggest that Support Vector Regression with Gaussian kernels statistically outperforms the rest of the techniques.
A Comparison of Machine Learning Techniques Applied to Landsat-5 TM Spectral Data for Biomass Estimation
(Taylor and Francis, 2016) López Serrano, Pablito M.; López Sánchez, Carlos A.; Álvarez González, Juan G.; García Gutiérrez, Jorge; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Ministerio de Ciencia Y Tecnología (MCYT). España; Universidad de Sevilla. TIC-134: Sistemas Informáticos
Machine learning combines inductive and automated techniques for recognizing patterns. These techniques can be used with remote sensing datasets to map aboveground biomass (AGB) with an acceptable degree of accuracy for evaluation and management of forest ecosystems. Unfortunately, statistically rigorous comparisons of machine learning algorithms are scarce. The aim of this study was to compare the performance of the 3 most common nonparametric machine learning techniques reported in the literature, vis., Support Vector Machine (SVM), k-nearest neighbor (kNN) and Random Forest (RF), with that of the parametric multiple linear regression (MLR) for estimating AGB from Landsat-5 Thematic Mapper (TM) spectral reflectance data, texture features derived from the Normalized Difference Vegetation Index (NDVI), and topographical features derived from a digital elevation model (DEM). The results obtained for 99 permanent sites (for calibration/validation of the models) established during the winter of 2011 by systematic sampling in the state of Durango (Mexico), showed that SVM performed best once the parameterization had been optimized. Otherwise, SVM could be outperformed by RF. However, the kNN yielded the best overall results in relation to the goodness-of-fit measures. The findings confirm that nonparametric machine learning algorithms are powerful tools for estimating AGB with datasets derived from sensors with medium spatial resolution.
A comparison of time series lags and non-lags in Spanish electricity price forecasting using data science models
(Oxford University Press, 2024-03-22) Vega Márquez, Belén; Solís García, Javier; Nepomuceno Chamorro, Isabel de los Ángeles; Rubio Escudero, Cristina; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Ministerio de Ciencia e Innovación; Junta de Andalucía
Electricity is an indicator that shows the progress of a civilization; it is a product that has greatly changed the way we think about the world. Electricity price forecasting became a fundamental task in all countries due to the deregulation of the electricity market in the 1990s. This work examines the effectiveness of using multiple variables for price prediction given the large number of factors that could influence the price of the electricity market. The tests were carried out over four periods using data from Spain and deep learning models. Two different attribute selection methods based on Pearson’s correlation coefficient have been used to improve the efficiency of the training process. The variables used as input to the different prediction models were chosen, considering those most commonly used previously in the literature. This study attempts to test whether using time series lags improves the non-use of lags. The results obtained have shown that lags improve the results compared to a previous work in which no lags were used.
A Conceptual Architecture for an Event-based Information Aggregation Engine in Smart Logistics
(Gesellschaft für Informatik e.V., 2015) Baumgrass, Anne; Cabanillas Macías, Cristina; Di Ciccio, Claudio; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; European Union (UE)
The field of Smart Logistics is attracting interest in several areas of research, including Business Process Management. Awide range of research works are carried out to enhance the capability of monitoring the execution of ongoing logistics processes and predict their likely evolvement. In order to do this, it is crucial to have in place an IT infrastructure that provides the capability of automatically intercepting the digitalised transportation-related events stemming from widespread sources, along with their elaboration, interpretation and dispatching. In this context, we present here the service-oriented software architecture of such an event-based information engine. In particular, we describe the requisites that it must meet. Thereafter, we present the interfaces and subsequently the service-oriented components that are in charge of realising them. The outlined architecture is being utilised as the reference model for an ongoing European research project on Smart Logistics, namely GET Service.
A Constraint-based Approach for a Declarative Temporal Business Process Modeling Language
(2020-07-01) Jiménez Ramírez, Andrés; Barba Rodríguez, Irene; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
We propose the mapping of TConDec-R process models to CSPs, resulting in CSP-TConDec-R problems. In the current section, we provide a definition of the latter in terms of high-level objects and global constraints from CP. Based on this, TConDec-R process models can be implemented in any constraint-based system being able to deal with the high-level objects and constraints considered in such definition. Consequently, the wide variety of existing algorithms provided by CP becomes applicable and we can use them for different purposes, e.g., checking the consistency of a given TConDec-R process model.
A coral-reef approach to extract information from HTML tables
(Elsevier, 2022) Jiménez Aguirre, Patricia; Roldán Salvador, Juan Carlos; Corchuelo Gil, Rafael; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Ministerio de Ciencia e Innovación (MICIN). España; Junta de Andalucía; Universidad de Sevilla. TIC258: Data-centric Computing Research Hub
his article presents Coraline, which is a new table-understanding proposal. Its novelty lies in a coral-reef optimisation algorithm that addresses the problem of feature selection in synchrony with a clustering technique and some custom heuristics that help extract information in a totally unsupervised manner. Our experimental analysis was performed on a large collection of tables with a variety of layouts, encoding problems, and formatting alternatives. Coraline could achieve an F1 score as high as 0.90 and took 7.07 CPU seconds per table, which improves on the best supervised proposal by 6.67% regarding effectiveness and 40.54% regarding efficiency; it also improves on the best unsupervised proposal by 11.11% regarding effectiveness while it remains very competitive regarding efficiency
A CSP model for simple non-reversible and parallel repair plans
(Springer, 2010) Valle Sevillano, Carmelo del; Márquez, Antonio; Barba Rodríguez, Irene; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Thiswork presents a constraint satisfaction problem (CSP) model for the planning and scheduling of disassembly and assembly tasks when repairing or substituting faulty parts. The problem involves not only the ordering of assembly and disassembly tasks, but also the selection of them from a set of alternatives. The goal of the plan is the minimization of the total repairing time, and the model considers, apart from the durations and resources used for the assembly and disassembly tasks, the necessary delays due to the change of configuration in the machines, and to the transportation of intermediate subassemblies between different machines. The problem considers that sub-assemblies that do not contain the faulty part are nor further disassembled, but allows non-reversible and parallel repair plans. The set of all feasible repair plans are represented by an extended And/Or graph. This extended representation embodies all of the constraints of the problem, such as temporal and resource constraints and those related to the selection of tasks for obtaining a correct plan.
A data mining based clinical decision support system for survival in lung cancer
(Via Médica Journals, 2021) Pontes Balanza, Beatriz; Núñez, Francisco; Rubio Escudero, Cristina; Moreno, Alberto; Nepomuceno Chamorro, Isabel de los Ángeles; Moreno León, Jesús; Cacicedo, Jon; Praena Fernández, Juan Manuel; Escobar Rodríguez, Germán Antonio; Parra, Carlos; Delgado León, Blas David; Rivin del Campo, Eleonor; Couñago, Felipe; Riquelme Santos, José Cristóbal; López Guerra, José Luis; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Instituto de Salud Carlos III; Junta de Andalucía; Ministerio de Economía y Competitividad (MINECO). España
Background: A clinical decision support system (CDSS) has been designed to predict the outcome (overall survival) by extracting and analyzing information from routine clinical activity as a complement to clinical guidelines in lung cancer patients. Materials and methods: Prospective multicenter data from 543 consecutive (2013–2017) lung cancer patients with 1167 variables were used for development of the CDSS. Data Mining analyses were based on the XGBoost and Generalized Linear Models algorithms. The predictions from guidelines and the CDSS proposed were compared. Results: Overall, the highest (> 0.90) areas under the receiver-operating characteristics curve AUCs for predicting survival were obtained for small cell lung cancer patients. The AUCs for predicting survival using basic items included in the guidelines were mostly below 0.70 while those obtained using the CDSS were mostly above 0.70. The vast majority of comparisons between the guideline and CDSS AUCs were statistically significant (p < 0.05). For instance, using the guidelines, the AUC for predicting survival was 0.60 while the predictive power of the CDSS enhanced the AUC up to 0.84 (p = 0.0009). In terms of histology, there was only a statistically significant difference when comparing the AUCs of small cell lung cancer patients (0.96) and all lung cancer patients with longer (≥ 18 months) follow up (0.80; p < 0.001). Conclusions: The CDSS successfully showed potential for enhancing prediction of survival. The CDSS could assist physicians in formulating evidence-based management advice in patients with lung cancer, guiding an individualized discussion according to prognosis.

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