Lenguajes y Sistemas Informáticos

URI permanente para esta comunidadhttps://hdl.handle.net/11441/11391

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1st International Workshop on Maturity of Web Engineering Practices (MATWEP 2018)
(Springer, 2018) Domínguez Mayo, Francisco José; González Enríquez, José; Koch, Nora; Morillo Baro, Esteban; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Ministerio de Economía y Competitividad (MINECO). España
Knowledge transfer and adoption of software engineering approaches by practitioners is always a challenge for both academia and industry. The objective of the workshop MATWEP is to provide an open discussion space that combines solid theory work with practical on-the- field experience in the Web Engineering area. The topics covered are knowledge transfer of Web Engineering approaches, such as methods, techniques and tools in all phases of the development life-cycle of Web applications. We report on the papers presented in the edition 2018 and the fruitful discussion on these topics.
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 bioinspired ensemble approach for multi-horizon reference evapotranspiration forecasting in Portugal
(Association for Computing Machinery, 2023-03) Jiménez Navarro, Manuel Jesús; Martínez Ballesteros, María del Mar; Sofia Brito, Isabel; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Ministerio de Ciencia e Innovación (MICIN). España; Junta de Andalucía
The year 2022 was the driest year in Portugal since 1931 with 97% of territory in severe drought. Water is especially important for the agricultural sector in Portugal, as it represents 78% total consumption according to theWater Footprint report published in 2010. Reference evapotranspiration is essential due to its importance in optimal irrigation planning that reduces water consumption. This study analyzes and proposes a framework to forecast daily reference evapotranspiration at eight stations in Portugal from 2012 to 2022 without relying on public meteorological forecasts. The data include meteorological data obtained from sensors included in the stations. The goal is to perform a multi-horizon forecasting of reference evapotranspiration using the multiple related covariates. The framework combines the data processing and the analysis of several state-of-the-art forecasting methods including classical, linear, tree-based, artificial neural network and ensembles. Then, an ensemble of all trained models is proposed using a recent bioinspired metaheuristic named Coronavirus Optimization Algorithm to weight the predictions. The results in terms of MAE and MSE are reported, indicating that our approach achieved a MAE of 0.658.
A Case Study for Generating Test Cases from Use Cases
(IEEE Computer Society, 2008) Gutiérrez Rodríguez, Javier Jesús; Escalona Cuaresma, María José; Mejías Risoto, Manuel; Torres Valderrama, Jesús; Centeno, Arturo H.; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Ministerio de Educación y Ciencia (MEC). España; Universidad de Sevilla. TIC021: Ingeniería Web y Testing Temprano (IWT2)
The verification of the correct implementation of use eases is a vital task in software development and quality assurance. Although there are many works describing how to generate test eases from use cases, there are very few ease studies and empirical results of their application and effectiveness. This paper introduces a first ease study that test the correct implementation of use cases in a web system and a command line system, analyses the results and exposes that generation of use cases has a successful about 80%.
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 Catalogue of Inter-Parameter Dependencies in RESTful Web APIs
(Springer, 2019) Martín López, Alberto; Segura Rueda, Sergio; Ruiz Cortés, Antonio; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos; Ministerio de Economía y Competitividad (MINECO). España; Ministerio de Ciencia, Innovación y Universidades (MICINN). España; Ministerio de Educación, Cultura y Deporte (MECD). España; Universidad de Sevilla. TIC205: Ingeniería del Software Aplicada
Web services often impose dependency constraints that re strict the way in which two or more input parameters can be combined to form valid calls to the service. Unfortunately, current specification languages for web services like the OpenAPI Specification provide no support for the formal description of such dependencies, which makes it hardly possible to automatically discover and interact with services without human intervention. Researchers and practitioners are openly requesting support for modelling and validating dependencies among in put parameters in web APIs, but this is not possible unless we share a deep understanding of how dependencies emerge in practice—the aim of this work. In this paper, we present a thorough study on the presence of dependency constraints among input parameters in web APIs in in dustry. The study is based on a review of more than 2.5K operations from 40 real-world RESTful APIs from multiple application domains. Overall, our findings show that input dependencies are the norm, rather than the exception, with 85% of the reviewed APIs having some kind of dependency among their input parameters. As the main outcome of our study, we present a catalogue of seven types of dependencies consistently found in RESTful web APIs
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 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 between Two Regression Methods on LiDAR Data: A Case Study
(Springer, 2011) García Gutiérrez, Jorge; González Ferreiro, Eduardo; Mateos García, Daniel; Riquelme Santos, José Cristóbal; Miranda, David; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Airborne LiDAR (Light Detection and Ranging) has become an excellent tool for accurately assessing vegetation characteristics in forest environments. Previous studies showed empirical relationships between LiDAR and field-measured biophysical variables. Multiple linear regression (MLR) with stepwise feature selection is the most common method for building estimation models. Although this technique has provided very interesting results, many other data mining techniques may be applied. The overall goal of this study is to compare different methodologies for assessing biomass fractions at stand level using airborne Li- DAR data in forest settings. In order to choose the best methodology, a comparison between two different feature selection techniques (stepwise selection vs. genetic-based selection) is presented. In addition, classical MLR is also compared with regression trees (M5P). The results when each methodology is applied to estimate stand biomass fractions from an area of northern Spain show that genetically-selected M5P obtains the best results.
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 Comparative Study of Classifier Combination Methods Applied to NLP Tasks
(Springer, 2011) Enríquez de Salamanca Ros, Fernando; Troyano Jiménez, José Antonio; Cruz Mata, Fermín; Ortega Rodríguez, Francisco Javier; Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
There are many classification tools that can be used for various NLP tasks, although none of them can be considered the best of all since each one has a particular list of virtues and defects. The combination methods can serve both to maximize the strengths of the base classifiers and to reduce errors caused by their defects improving the results in terms of accuracy. Here is a comparative study on the most relevant methods that shows that combination seems to be a robust and reliable way of improving our results.
A Comparative Study of Machine Learning Regression Methods on LiDAR Data: A Case Study
(Springer, 2014) 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 vertical information from sensed objects. LiDAR-derived information is nowadays used to develop environmental models for describing fire behaviour or quantifying biomass stocks in forest areas. A multiple linear regression (MLR) with previous stepwise feature selection is the most common method in the literature to develop LiDAR-derived models. MLR defines the relation between the set of field measurements and the statistics extracted from a LiDAR flight. Machine learning has recently been paid an increasing attention to improve classic MLR results. 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 common regression techniques in machine learning (neural networks, regression trees, support vector machines, nearest neighbour, and ensembles such as random forests). The selected techniques are applied to real LiDAR data from two areas in the province of Lugo (Galizia, Spain). The results show that support vector regression statistically outperforms the rest of techniques when feature selection is applied. However, its performance cannot be said statistically different from that of Random Forests when previous feature selection is skipped.
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.

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