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Business processes read and write data objects, usually stored in databases. Although data models and activity-oriented business process models originate from different paradigms, they need to work together properly. The data object states are transformed during each process instance by the activities of the process model. It is therefore necessary to verify whether the states of the data objects are correct according to the process model, and to discoverthe states ofthe stored data objects.Thisimplies determining the relation between the data objects stored in the database, the data objects involved in the process, and the activities that within the business processthat create the data objects and modify theirstates. In order to verify the business process annotated with data states and to reduce the existing gap between data model and business process model, we propose a methodology that includes enlarging the capability to describe data states in business processes; verifying the completeness and consistency of the data states described in accordance with their relation to the business process model; and discovering the states of the data objects stored in the database according to the business process model where they are managed. The methodology is supported by a framework that enables a natural-like language to be employed to describe the states, to apply the necessary algorithms to verify the consistency and completeness of the model, and to determine the states of the stored data objects according to the model described. To validate our proposal, an extension of ActivitiT M has been implemented and applied to a real example as an illustration of its applicability Ministerio de Ciencia y Tecnología RTI2018-094283-B-C33 (ECLIPSE) Junta de Andalucía METAMORFOSIS (US-1381375)

Wireless sensor networks (WSNs), as an integral part of most Internet of Things (IoT) devices, are currently proliferating providing a new paradigm of emerging technologies. It is estimated that the number of globally connected products will increase exponentially in the next decade. Therefore, it is not surprising finding applications in different areas such as smart homes, smart cities, industry, e-health, defense, security, vehicle networks, agriculture, and logistics among others. WSNs transmit the information gathered by the existing sensors in the IoT devices to header nodes acting as gateways to reach cloud computing nodes that will process this information. The processing of the data gathered through the sensors has made it possible to provide intelligence in strategical environments such as in the agrifood sector. Securing how this information is transmitted and assuring the integrity of the information is preserved. The use of blockchain technology has proven to be effective for securing the integrity of data transactions among entities. In this paper, we seize the advantage of this circumstance to design a robust mechanism based on smart contracts and blockchain technology that allow the reliable processing of data. Ministerio de Ciencia y Tecnología ECLIPSE RTI2018-094283-B-C33

Business process discovery provides mechanisms to extract the general process behaviour from event observations. However, not always the logs are available and must be extracted from repositories, such as relational databases. Derived from the references that exist between the relational tables, several are the possible combinations of traces of events that can be extracted from a relational database. Dif ferent traces can be extracted depending on which attribute represents the case−id, what are the attributes that represent the execution of an activity, or how to obtain the timestamp to define the order of the events. This paper proposes a method to analyse a wide range of possible traces that could be extracted from a relational database, based on measuring the level of interest of extracting a trace log, later used for a discov ery process. The analysis is done by means of a set of proposed metrics before the traces are generated and the process is discovered. This anal ysis helps to reduce the computational cost of process discovery. For a possible case−id every possible traces are analysed and measured. To validate our proposal, we have used a real relational database, where the detection of processes (most and least promising) are compared to rely on our proposal. Ministerio de Ciencia y Tecnología RTI2018-094283-B-C33

Configuration techniques have been used in several fields, such as the design of business process models. Sometimes these models depend on the data dependencies, being easier to describe what has to be done instead of how. Configuration models enable to use a declarative representation of business processes, deciding the most appropriate work-flow in each case. Unfortunately, data dependencies among the activities and how they can affect the correct execution of the process, has been overlooked in the declarative specifications and configurable systems found in the literature. In order to find the best process configuration for optimizing the execution time of processes according to data dependencies, we propose the use of Constraint Programming paradigm with the aim of obtaining an adaptable imperative model in function of the data dependencies of the activities described declarative. Ministerio de Ciencia y Tecnología TIN2015-63502-C3-2-R Fondo Europeo de Desarrollo Regional

A novel methodology is introduced in this research study to detect seismic precursors. Based on an existing approach, the new methodology searches for patterns in the historical data. Such patterns may contain statistical or soil dynamics information. It improves the original version in several aspects. First, new seismicity indicators have been used to characterize earthquakes. Second, a machine learning clustering algorithm has been applied in a very flexible way, thus allowing the discovery of new data groupings. Third, a novel search strategy is proposed in order to obtain non-overlapped patterns. And, fourth, arbitrary lengths of patterns are searched for, thus discovering long and short-term behaviors that may influence in the occurrence of medium-large earthquakes. The methodology has been applied to seven different datasets, from three different regions, namely the Iberian Peninsula, Chile and Japan. Reported results show a remarkable improvement with respect to the former version, in terms of all evaluated quality measures. In particular, the number of false positives has decreased and the positive predictive values increased, both of them in a very remarkable manner. Ministerio de Ciencia y Tecnología TIN2011-28956-C00 Junta de Andalucía P12-TIC-1728 Instituto Ramón y Cajal (RYC) RYC-2012-11984

Background: Triclustering has shown to be a valuable tool for the analysis of microarray data since its appearance as an improvement of classical clustering and biclustering techniques. The standard for validation of triclustering is based on three different measures: correlation, graphic similarity of the patterns and functional annotations for the genes extracted from the Gene Ontology project (GO). Results: We propose TRIQ, a single evaluation measure that combines the three measures previously described: correlation, graphic validation and functional annotation, providing a single value as result of the validation of a tricluster solution and therefore simplifying the steps inherent to research of comparison and selection of solutions. TRIQ has been applied to three datasets already studied and evaluated with single measures based on correlation, graphic similarity and GO terms. Triclusters have been extracted from this three datasets using two different algorithms: TriGen and OPTricluster. Conclusions: TRIQ has successfully provided the same results as a the three single evaluation measures. Furthermore, we have applied TRIQ to results from another algorithm, OPTRicluster, and we have shown how TRIQ has been a valid tool to compare results from different algorithms in a quantitative straightforward manner. Therefore, it appears as a valid measure to represent and summarize the quality of tricluster solutions. It is also feasible for evaluation of non biological triclusters, due to the parametrization of each component of TRIQ. Ministerio de Ciencia y Tecnología TIN2014-55894-C2-1-R

In the last decade, the interest in microarray technology has exponentially increased due to its ability to monitor the expression of thousands of genes simultaneously. The reconstruction of gene association networks from gene expression profiles is a relevant task and several statistical techniques have been proposed to build them. The problem lies in the process to discover which genes are more relevant and to identify the direct regulatory relationships among them. We developed a multi-objective evolutionary algorithm for mining quantitative association rules to deal with this problem. We applied our methodology named GarNet to a well-known microarray data of yeast cell cycle. The performance analysis of GarNet was organized in three steps similarly to the study performed by Gallo et al. GarNet outperformed the benchmark methods in most cases in terms of quality metrics of the networks, such as accuracy and precision, which were measured using YeastNet database as true network. Furthermore, the results were consistent with previous biological knowledge. Ministerio de Ciencia y Tecnología TIN2011-28956-C02-02 Junta de Andalucía P11-TIC-7528

Microarrays have revolutionized biotechnological research.The analysis of newdata generated represents a computational challenge due to the characteristics of these data. Clustering techniques are applied to create groups of genes that exhibit a similar behavior. Biclustering emerges as a valuable tool for microarray data analysis since it relaxes the constraints for grouping, allowing genes to be evaluated only under a subset of the conditions. However, if a third dimension appears in the data, triclustering is the appropriate tool for the analysis. This occurs in longitudinal experiments in which the genes are evaluated under conditions at several time points. All clustering, biclustering, and triclustering techniques guide their search for solutions by a measure that evaluates the quality of clusters. We present an evaluation measure for triclusters called Mean Square Residue 3D. This measure is based on the classic biclustering measure Mean Square Residue. Mean Square Residue 3D has been applied to both synthetic and real data and it has proved to be capable of extracting groups of genes with homogeneous patterns in subsets of conditions and times, and these groups have shown a high correlation level and they are also related to their functional annotations extracted from the Gene Ontology project. Ministerio de Ciencia y Tecnología TIN2011-28956-C02-02 Junta de Andalucía TIC-7528

n this work, we present the results of applying data mining techniques to hydrogen breath test data. Disposal of H2 gas is of utmost relevance to maintain efficient microbial fermentation processes. Objectives Analyze a set of data of hydrogen breath tests by use of data mining tools. Identify new patterns of H2 production. Methods Hydrogen breath tests data sets as well as k-means clustering as the data mining technique to a dataset of 2571 patients. Results Six different patterns have been extracted upon analysis of the hydrogen breath test data. We have also shown the relevance of each of the samples taken throughout the test. Conclusions Analysis of the hydrogen breath test data sets using data mining techniques has identified new patterns of hydrogen generation upon lactose absorption. We can see the potential of application of data mining techniques to clinical data sets. These results offer promising data for future research on the relations between gut microbiota produced hydrogen and its link to clinical symptoms. Ministerio de Ciencia y Tecnología TIN2014-55894-C2-1-R

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 Comisión Interministerial de Ciencia y Tecnología (CICYT) SETI (TIN2009-07366) Junta de Andalucía P07-TIC-2533 (Isabel) Ministerio de Ciencia y Tecnología TIN2010-21744-C02-1 Ministerio de Ciencia y Tecnología TIN2007-64119 Junta de Andalucía P07-TIC-02602 Junta de Andalucía P08-TIC-4100 Ministerio de Ciencia e Innovación TIN2008-04718-E

The forecasting process of real-world time series has to deal with especially unexpected values, commonly known as outliers. Outliers in time series can lead to unreliable modeling and poor forecasts. Therefore, the identification of future outlier occurrence is an essential task in time series analysis to reduce the average forecasting error. The main goal of this work is to predict the occurrence of outliers in time series, based on the discovery of motifs. In this sense, motifs will be those pattern sequences preceding certain data marked as anomalous by the proposed metaheuristic in a training set. Once the motifs are discovered, if data to be predicted are preceded by any of them, such data are identified as outliers, and treated separately from the rest of regular data. The forecasting of outlier occurrence has been added as an additional step in an existing time series forecasting algorithm (PSF), which was based on pattern sequence similarities. Robust statistical methods have been used to evaluate the accuracy of the proposed approach regarding the forecasting of both occurrence of outliers and their corresponding values. Finally, the methodology has been tested on six electricity-related time series, in which most of the outliers were properly found and forecasted. Ministerio de Ciencia y Tecnología TIN2007- 68084-C-00 Junta de Andalucia P07-TIC- 02611

Earthquakes arrive without previous warning and can destroy a whole city in a few seconds, causing numerous deaths and economical losses. Nowadays, a great effort is being made to develop techniques that forecast these unpredictable natural disasters in order to take precautionary measures. In this paper, clustering techniques are used to obtain patterns which model the behavior of seismic temporal data and can help to predict medium–large earthquakes. First, earthquakes are classified into different groups and the optimal number of groups, a priori unknown, is determined. Then, patterns are discovered when medium–large earthquakes happen. Results from the Spanish seismic temporal data provided by the Spanish Geographical Institute and non-parametric statistical tests are presented and discussed, showing a remarkable performance and the significance of the obtained results. Ministerio de Ciencia y Tecnología BIA2004-01302 Ministerio de Ciencia y Tecnología TIN-68084-C02 Junta de Andalucía P07-TIC-02611

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. Resume. L’apprentissage automatique combine des techniques inductives et automatisees pour la reconnaissance des formes. Ces techniques peuvent etre utilisees avec des ensembles de donnees de teledetection pour cartographier la biomasse aerienne « aboveground biomass » (AGB) avec un degre de precision acceptable pour l’evaluation et la gestion des ecosystemes forestiers. Malheureusement, des comparaisons statistiquement rigoureuses des algorithmes d’apprentissage automatique sont rares. Le but de cette etude etait de comparer les performances des 3 methodes d’apprentissage automatique non parametriques les plus frequemment rapportees dans la litterature, vis., les machines a vecteurs de support « Support Vector Machine » (SVM), les k plus proches voisins « k-nearest neighbor » (kNN) et les forets aleatoires « Random Forest » (RF), avec celle de la regression lineaire multiple parametrique (MLR) pour l’estimation de l’AGB provenant des donnees de reflectance spectrale de Landsat-5 Thematic Mapper (TM), des caracteristiques de texture derivees de l’indice de vegetation par difference normalisee « Normalized Difference Vegetation Index » (NDVI) et des caracteristiques topographiques derivees d’un modele numerique de terrain « digital elevation model » (DEM).Les resultats obtenus pour 99 sites permanents (pour la calibration/validation des modeles) etablis au cours de l’hiver 2011 par l’echantillonnage systematique dans l’Etat de Durango (Mexique), ont montre que les SVM montrent leurs meilleures performances une fois que le parametrage a ete optimise. Par ailleurs, les SVM pourraient etre surpassees par les RF. Cependant, les kNN ont donne les meilleurs resultats globaux par rapport aux mesures d’ajustement. Les resultats confirment que les algorithmes d’apprentissage automatique non parametriques sont des outils puissants pour l’estimation de l’AGB avec des ensembles de donnees provenant de capteurs avec une resolution spatiale moyenne.

This work proposes a methodology to identify genes highly related with cancer. In particular, a multi-objective evolutionary algo rithm named CANGAR is applied to obtain quantitative association rules. This kind of rules are used to identify dependencies between genes and their expression levels. Hierarchical cluster analysis, fold-change and review of the literature have been considered to validate the relevance of the results obtained. The results show that the reported genes are consistent with prior knowledge and able to characterize cancer colon patients. Ministerio de Ciencia y Tecnología TIN2011-28956-C02-02 Ministerio de Economía y Competitividad TIN2014-55894-C2-1-R Junta de Andalucía P11-TIC-7528 Junta de Andalucía P12-TIC-1728

Business Intelligence requires the acquisition and aggrega tion of key pieces of knowledge from multiple sources in order to provide valuable information to customers. The Web is the largest source of infor mation nowadays. Unfortunately, the information it provides is available in semi-structured human-friendly formats, which makes it difficult to be processed by automated business processes. Classical propositional and ILP machine-learning techniques have been applied for this pur pose. However, the former have not enough expressive power, whereas the latter are more expressive but intractable with large datasets. Propo sitionalisation was devised as a means to provide propositional techniques with more expressive power, enabling them to exploit structural infor mation in a propositional way that allows them to be efficient. In this paper, we present a proposal to extract information from semi-structured web documents that uses this approach. It leverages a classical propo sitional machine learning technique and enhances it with the ability to learn from an unbounded context, which helps increase its precision and recall. Our experiments prove that our proposal outperforms other state of-art techniques in the literature. Ministerio de Educación y Ciencia TIN2007-64119 Junta de Andalucía P07-TIC-2602 Junta de Andalucía P08-TIC-4100 Ministerio de Ciencia e Innovación TIN2008-04718-E Ministerio de Ciencia e Innovación TIN2010-21744 Ministerio de Economía, Industria y Competitividad TIN2010-09809-E Ministerio de Ciencia e Innovación TIN2010-10811-E Ministerio de Ciencia e Innovación TIN2010-09988-E Ministerio de Economía y Competitividad TIN2011-15497-E Ministerio de Economía y Competitividad TIN2013-40848-R

Business processes involve data that can be modified and updated by various activities at any time. The data involved in a business process can be associated with flow elements or data stored. These data must satisfy the business compliance rules associated with the process, where business compliance rules are policies or statements that govern the behaviour of a company. To improve and automate the validation and diagnosis of compliance rules based on the description of data semantics (called Business Data Constraints), we propose a framework where dataflow variables and stored data are analyzed. The validation and diagnosis process is automated using Constraint Programming, to permit the detection and identification of possibly unsatisfiable Business Data Constraints, even if the data involved in these constraints are not all instantiated. This implies that the potential errors can be determined in advance. Furthermore, a language to describe Business Data Constraints is proposed, for the improvement of user-oriented aspects of the business process description. This language allows a business expert to write Business Data Constraints that will be automatically validated in run-time, without the support of an information technology expert. HighlightsThis paper proposes an enlargement of the business process model for data values.A language is defined for Business Data Constraints associated to each activity.The validation and diagnosis is developed at runtime according to the data values.It permits an early identification of the non-compliance using Constraint Programming.

Sometimes the business process model is not known completely, but a set of compliance rules can be used to describe the ordering and temporal relations between activities, incompatibilities, and existence dependencies in the process. The analysis of these compliance rules and the temporal events thrown during the execution of an instance, can be used to detect and diagnose a process behaviour that does not satisfy the expected behaviour. We propose to combine model-based diagnosis and constraint programming for the compliance violation analysis. This combination facilitates the diagnosis of discrepancies between the compliance rules and the events that the process generates as well as enables us to propose correct event time intervals to satisfy the compliance rules. Austrian Science Fund (FWF):I743 Ministerio de Ciencia y Tecnología TIN2009-13714

A worked example of polytypism is presented, aimed at assisting undergraduates in the learning and instructors in the teaching of this topic. In particular, this crystallography concept, not necessarily obvious for beginners, is illustrated pedagogically using to that end the model case of the prolific polytypism of silicon carbide (SiC). On the basis of concepts that are easily assimilated by students (i.e. simple topological constraints) this article first presents a unified description of the polytypism phenomenon in SiC that allows one to understand without difficulty the existence of its numerous polytypic variants and how they develop. Then the various notations used to designate these different polytypes are described, and finally the crystal structures of the most common are discussed. This worked example is thus expected to contribute to motivating undergraduates in the study of a crystallography topic that often is not treated in sufficient depth in class. Ministerio de Ciencia y Tecnología MAT2010-16848

This work explores the use of different seismicity indicators as inputs for artificial neural networks. The combination of multiple indicators that have already been successfully used in different seismic zones by the application of feature selection techniques is proposed. These techniques evaluate every input and propose the best combination of them in terms of information gain. Once these sets have been obtained, artificial neural networks are applied to four Chilean zones (the most seismic country in the world) and to two zones of the Iberian Peninsula (a moderate seismicity area). To make the comparison to other models possible, the prediction problem has been turned into one of classification, thus allowing the application of other machine learning classifiers. Comparisons with original sets of inputs and different classifiers are reported to support the degree of success achieved. Statistical tests have also been applied to confirm that the results are significantly different than those of other classifiers. The main novelty of this work stems from the use of feature selection techniques for improving earthquake prediction methods. So, the infor-mation gain of different seismic indicators has been determined. Low ranked or null contribution seismic indicators have been removed, optimizing the method. The optimized prediction method proposed has a high performance. Finally, four Chilean zones and two zones of the Iberian Peninsula have been charac-terized by means of an information gain analysis obtained from different seismic indicators. The results confirm the methodology proposed as the best features in terms of information gain are the same for both regions. Ministerio de Ciencia y Tecnología BIA2004-01302 Ministerio de Ciencia y Tecnología TIN2011-28956-C02-01 Junta de Andalucía P11-TIC-7528

The combination of activities to achieve optimal goals sometimes has a complex solution. Business Process Model and Notation (BPMN) 2.0 facilitates the modelling of business processes by providing new artifacts, such as various types of tasks, source of data and relations between tasks. Sometimes, although the order of the activities can be known, the concrete data values that the activities interchange to optimize their behaviour needs to be found, specially when input parameters of an activity affect to the input parameter of the others. Taking into account the lack of priority and clear sequential relationship between the activities of such combination, a deep analysis of possible models and data input values for the activities is necessary. For that reason, an extension of BPMN 2.0 with a new type of sub-process and its associated marker is proposed. The aim of this new sub-process is to define, in an easy way, a combination of several activities to find out, in an automated way, the concrete values of the data handling that optimize an overall objective. Junta de Andalucía P08-TIC-04095 Ministerio de Ciencia y Tecnología TIN2009-13714

In this study, a novel residue-residue contacts prediction approach based on evolutionary computation is presented. The prediction is based on four amino acids properties. In particular, we consider the hydrophobicity, the polarity, the charge and residues size. The prediction model consists of a set of rules that identi fies contacts between amino acids. Ministerio de Ciencia y Tecnología TIN2007-68084-C02-00 Junta de Andalucía P07-TIC-02611

In this study, we present a residue-residue contact prediction approach based on evolutionary computation. Some amino acid properties are employed according to their importance in the folding process: hydrophobicity, polarity, charge and residue size. Our evolutionary algorithm provides a set of rules which determine different cases where two amino acids are in contact. A rule represents two windows of three amino acids. Each amino acid is characterized by these four properties. We also include a statistical study for the propensities of contacts between each pair of amino acids, according to their types, hydrophobicity and polarity. Different experiments were also performed to determine the best selection of properties for the structure prediction among the cited properties. Junta de Andalucía P07-TIC-02611 Ministerio de Ciencia y Tecnología TIN2007-68084-C02-00

An evolutionary approach for finding existing relationships among several variables of a multidimensional time series is presented in this work. The proposed model to discover these relationships is based on quantitative association rules. This algorithm, called QARGA (Quantitative Association Rules by Genetic Algorithm), uses a particular codification of the individuals that allows solving two basic problems. First, it does not perform a previous attribute discretization and, second, it is not necessary to set which variables belong to the antecedent or consequent. Therefore, it may discover all underlying dependencies among different variables. To evaluate the proposed algorithm three experiments have been carried out. As initial step, several public datasets have been analyzed with the purpose of comparing with other existing evolutionary approaches. Also, the algorithm has been applied to synthetic time series (where the relationships are known) to analyze its potential for discovering rules in time series. Finally, a real-world multidimensional time series composed by several climatological variables has been considered. All the results show a remarkable performance of QARGA. Ministerio de Ciencia y Tecnología TIN2007- 68084-C02-02 Junta de Andalucia P07-TIC- 02611

Enterprise Application Integration (EAI) solutions cope with two kinds of problems within software ecosystems, namely: keeping a number of application’s data in synchrony or creating new functionality on top of them. ESBs provide the technology required to implement a variety of EAI solutions at sensible costs, but they are still far from negligible. It is not surprising then that many authors are working on proposals to endow them with domain-specific tools to help software engineers reduce integration costs. In this article, we introduce a proposal called Guaraná. Its key features are as follows: it provides explicit support to devise EAI solutions using enterprise integration patterns by means of a graphical model; its DSL enables software engineers to have not only the view of a process, but also a view of the whole set of processes of which an EAI solution is composed; both processes and tasks can have multiple inputs and multiple outputs; and, finally, its runtime system provides a task-based execution model that is usually more efficient than the process-based execution models in current use. We have also implemented a graphical editor for our DSL and a set of scripts to transform our models into Java code ready to be compiled and executed. To set up a solution from this code a software engineer only needs to configure a number of adapters to communicate with the applications being integrated. Ministerio de Educación y Ciencia TIN2007-64119 Junta de Andalucía P07-TIC-2602 Junta de Andalucía P08-TIC-4100 Ministerio de Ciencia e Innovación TIN2008-04718-E Ministerio de Ciencia e Innovación TIN2010-21744 Ministerio de Ciencia y Tecnología TIN-2007-67843-C06

The most widespread biclustering algorithms use the Mean Squared Residue (MSR) as measure for assessing the quality of biclusters. MSR can identify correctly shifting patterns, but fails at discovering biclusters presenting scaling patterns. Virtual Error (VE) is a measure which improves the performance of MSR in this sense, since it is effective at recognizing biclusters containing shifting patters or scaling patterns as quality biclusters. However, VE presents some drawbacks when the biclusters present both kind of patterns simultaneously. In this paper, we propose a improvement of VE that can be integrated in any heuristic to discover biclusters with shifting and scaling patterns simultaneously. Ministerio de Ciencia y Tecnología TIN2007-68084-C02-00

A new measure to evaluate the quality of a bicluster is proposed in this paper. This measure is based on correlations among genes. Moreover, a new evolutionary metaheuristic based on Scatter Search, which uses this measure as the fitness function, is presented to obtain biclusters that contain groups de highly-correlated genes. Later, an analysis of the correlation matrix of these biclusters is made to select these groups of genes that define new biclusters with shifting and scaling patterns. Experimental results from human B cell lymphoma are presented. Ministerio de Ciencia e Innovación TIN2007-68084-C02 Junta de Andalucía P07-TIC-02611

Scatter Search is an evolutionary method that combines ex isting solutions to create new offspring as the well–known genetic algo rithms. This paper presents a Scatter Search with the aim of finding biclusters from gene expression data. However, biclusters with certain patterns are more interesting from a biological point of view. Therefore, the proposed Scatter Search uses a measure based on linear correlations among genes to evaluate the quality of biclusters. As it is usual in Scatter Search methodology an improvement method is included which avoids to find biclusters with negatively correlated genes. Experimental results from yeast cell cycle and human B-cell lymphoma datasets are reported showing a remarkable performance of the proposed method and measure Ministerio de Ciencia y Tecnología TIN2007-68084-C00 Junta de Andalucía P07-TIC-02611

Genomes of many organisms have been sequenced over the last few years. However, transforming such raw sequence data into knowledge remains a hard task. A great number of prediction programs have been developed to address part of this problem: the location of genes along a genome and their expression. We propose a multi-objective methodology to combine state-of-the-art algorithms into an aggregation scheme in order to obtain optimal methods’ aggregations. The results obtained show a major improvement in sensitivity when our methodology is compared to the performance of individual methods for gene finding and gene expression problems. The methodology proposed here is an automatic method generator, and a step forward to exploit all already existing methods, by providing alternative optimal methods’ aggregations to answer concrete queries for a certain biological problem with a maximized accuracy of the prediction. As more approaches are integrated for each of the presented problems, de novo accuracy can be expected to improve further., Ministry of Science and Innovation, Spain (MICINN) Spanish Government TIN-2006-12879, Junta de Andalucia TIC-02788, Howard Hughes Medical Institute, European Commission Junta de Andalucia

In this paper a hybrid metaheuristic for biclustering based on Scatter Search and Genetic Algorithms is presented. A general scheme of Scatter Search has been used to obtain high–quality biclusters, but a way of generating the initial population and a method of combination based on Genetic Algorithms have been chosen. Experimental results from yeast cell cycle and human B-cell lymphoma are reported. Finally, the performance of the proposed hybrid algorithm is compared with a genetic algorithm recently published. Ministerio de Ciencia y Tecnología TIN-68084-C02-01 Junta de Andalucía P07-TIC-02611

Parametric software cost estimation models are based on mathematical relations, obtained from the study of historical software projects databases, that intend to be useful to estimate the effort and time required to develop a software product. Those databases often integrate data coming from projects of a heterogeneous nature. This entails that it is difficult to obtain a reasonably reliable single parametric model for the range of diverging project sizes and characteristics. A solution proposed elsewhere for that problem was the use of segmented models in which several models combined into a single one contribute to the estimates depending on the concrete characteristic of the inputs. However, a second problem arises with the use of segmented models, since the belonging of concrete projects to segments or clusters is subject to a degree of fuzziness, i.e. a given project can be considered to belong to several segments with different degrees. This paper reports the first exploration of a possible solution for both problems together, using a segmented model based on fuzzy clusters of the project space. The use of fuzzy clustering allows obtaining different mathematical models for each cluster and also allows the items of a project database to contribute to more than one cluster, while preserving constant time execution of the estimation process. The results of an evaluation of a concrete model using the ISBSG 8 project database are reported, yielding better figures of adjustment than its crisp counterpart. Ministerio de Ciencia y Tecnología TIN2004-06689-C03

Biomedical research has been revolutionized by high throughput techniques and the enormous amount of data they are able to generate. In particular technology has the capacity to monitor changes in RNA abundance for thou sands of genes simultaneously. The interest shown over microarray analysis methods has rapidly raised. Clustering is widely used in the analysis of microarray data to group genes of interest targeted from microarray experiments on the basis of similarity of expression patterns. In this work we apply two clustering algorithms, K-means and Expecta tion Maximization to particular a problem and we compare the groupings obtained on the basis of the cohesiveness of the gene products associated to the genes in each cluster Ministerio de Ciencia y Tecnología TIN-2006-12879 Junta de Andalucía TIC-02788

The standard form for dealing with multi-class classification problems when biclassifiers are used is to consider a two-phase (decomposition, reconstruction) training scheme. The most popular decomposition procedures are pairwise coupling (one versus one, 1-v-1), which considers a learning machine for each Pair of classes, and the one-versus-all scheme (one versus all, 1-v-r), which takes into consideration each class versus the remaining classes. In this article a 1-v-1 tri-class Support Vector Machine (SVM) is presented. The expansion of the architecture of this machine into three categories specifically addresses the decomposition problem of how to prevent the loss of information which occurs in the usual 1-v-1 training procedure. The proposed machine, by means of a third class, allows all the information to be incorporated into the remaining training patterns when a multi-class problem is considered in the form of a 1-v-1 decomposition. Three general structures are presented where each improves some features from the precedent structure. In order to deal with multi-classification problems, it is demonstrated that the final machine proposed allows ordinal regression as a form of decomposition procedure. Examples and experimental results are presented which illustrate the performance of the new tri-class SV machine. Junta de Andalucía ACPAI-2003/014 Ministerio de Ciencia y Tecnología TIC2002-04371-C02-01

In this paper the authors evaluate the timing and power performance of three skew-tolerant clocking schemes. These schemes are the well known master-slave clocking scheme (MS) and two schemes developed by the authors: Parallel alternating latches clocking scheme (PALACS) and four-phase parallel alternating latches clocking scheme (four-phase PALACS). In order to evaluate the timing performance, the authors introduce algorithms to obtain the clock waveforms required by a synchronous sequential circuit. Separated algorithms were developed for every clocking scheme. From these waveforms it is possible to get parameters such as the non-overlapping time and the clock period. They have been implemented in a tool and have been used to compare the timing performance of the clocking schemes applied to a simple circuit. To analyse the power consumption the authors have electrically simulated a simple circuit for several operation frequencies. The most remarkable conclusion is that it is possible to save about 50% of the power consumption of the clock distribution network by using PALACS.

In this paper we investigate how to adapt the TextRank method to make it work in a supervised way. TextRank is a graph based method that applies the ideas of the ranking algorithm used in Google (PageRank) to Natural Language Processing (NLP) tasks. This approach has given very good results in many NLP tasks like text summarization, keyword extraction or word sense disambiguation. In all these tasks Text- Rank operates in an unsupervised way, without using any training corpus. Our main contribution is the definition of a method that allows to apply TextRank to a graph that includes information generated from a training tagged corpus. We have tested our method with the Part of Speech (POS) tagging task, comparing the results with those obtained with tools specialized in this task. The performance of our system is quite near to these tools, improving the results of two of them when the corpus tagset is big and therefore the tagging task more complicated. Ministerio de Ciencia y Tecnología TIN2004-07246-C03-03

In this paper, a system developed for monitoring, assisting and educating people with diabetes, named eDiab, is described. A central node (PDA or mobile phone) is used at the patient’s side for the transmission of medical information, health advices, alarms, reminders, etc. The software is adapted to blind users by using a screen reader called Mobile Speak Pocket/Phone. The glucose sensor is connected to the central node through wireless links (Zigbee/Bluetooth) and the communication between the central node and the server is established with a GPRS/GSM connection. Finally, a subsystem for health education (which sends medical information and advice like treatment reminder), still under development, is briefly described. Ministerio de Ciencia y Tecnología TIC2003- 08164-C03-02

Many papers related to Constraint Databases (CDBs) theories exist, including proposals that present frameworks for the treatment of constraints as a new data type. Our proposal presents a new way of storing and manipulating constraints as a usual data, and of making queries about the constraint variables derived from an Object-Relational Constraint Database (ORCDB). In this work, the constraints stored in an ORCDB are only polynomial equality constraints. The proposal is based on Gr¨obner bases, constraint consistency and constraint optimisation techniques. Most works in CDB use spatial-temporal data as a case study, however this work presents an emergent engineering domain, that of fault diagnosis. Ministerio de Ciencia y Tecnología DPI2003-07146-C02-01

Model-Based Diagnosis allows to determine why a correctly designed system does not work as it was expected. In this paper, we propose a methodology for software diagnosis which is based on the combination of Design by Contract, Model-Based Diagnosis and Constraint Programming. The contracts are specified by assertions embedded in the source code. These assertions and an abstraction of the source code are transformed into constraints, in order to obtain the model of the system. Afterwards, a goal function is created for detecting which assertions or source code statements are incorrect. The application of this methodology is automatic and is based on Constraint Programming techniques. The originality of this work stems from the transformation of contracts and source code into constraints, in order to determine which assertions and source code statements are not consistent with the specification. Ministerio de Ciencia y Tecnología DPI2003-07146-C02-01

In this paper we address the problem of describing the complexity of the evolution of a P system. This issue is is specially hard in the case of P systems with active membranes, where the number of steps of a computation is not sufficient to evaluate the complexity. Sevilla carpets were introduced in [1], and they describe the space-time complexity of P systems. Based on them, we define some new parameters which can be used to compare evolutions of P systems. To illustrate this, we also include two different cellular solutions to the Subset Sum problem and compare them via these new parameters. Ministerio de Ciencia y Tecnología TIC2002-04220-C03-01

In this paper we present an implementation in Prolog of an effective solution to the Knapsack problem via a family of deterministic P systems with active membranes using 2-division. Ministerio de Ciencia y Tecnología TIC2002-04220-C03-01

Model-based Diagnosis allows the identification of the parts which fail in a system. The models are based on the knowledge of the system to diagnose, and can be represented by constraints associated to components. Inputs and outputs of components are represented as variables of those constraints, and they can be observable and non-observable depending on the situation of sensors. In order to obtain the minimal diagnosis in a system, an important issue is to find out the possible minimal conflicts in an efficient way. In this work, we propose a new approach to automate and to improve the determination of possible minimal conflict sets. This approach has two phases. In the first phase, we determine components clusters in the system in order to reduce drastically the number of contexts to consider. In the second phase, we construct a reduced context network with the possible minimal conflicts. In this phase we use Gröbner bases reduction.A novel logical architecture of Constraint Databases is used to store the model, the components clusters and possible minimal conflict sets. The necessary information in each phase is obtained by using a standard query language. Ministerio de Ciencia y Tecnología DPI2003-07146-C02-01

Classical database management can be flawed if the Knowledge database is built within a complex Knowledge Domain. We must then deal withinconsistencies and, in general, withanomalies of several types. In this paper we study computational and cognitive problems in dealing qualitative spatial databases. Ministerio de Ciencia y Tecnología TIC2000-1368-C03-02 Junta de Andalucía TIC-137

Meter tampering is defined as a fraudulent manipulation which implies a service that is not billed by a utility company. It is a lack of consumption control for the utility company and a main problem because they represent an important loss of income. We have developed a methodology consists of a set of three algorithms for the detection of meter tampering in the Emasesa Company (a water distribution company in Seville and one of the most important of the country). The algorithms were generated and programmed after a data mining process from the database of the company and they detect three type of consumption patterns: Progressive drops, sudden drops and abnormally low consumption. The methodology has been tested with in situ inspections of the customers of a village of the province of Seville. Once carried out the inspections by the utility, the inspectors confirmed a good success rate taking into account that the detection of this type of fraud is very difficult because it is a noninvasive technique. Besides, this type of detections is a topic that, if we take a look at the state of the art, there are few references or works. Ministerio de Ciencia y Tecnología TEC2013-40767-R

Metamodel evolution is rarely driven by empirical evidences of metamodel drawbacks. In this paper, the evolution of the use case metamodel used by the publicly available requirements management tool REM is presented. This evolution has been driven by the analysis of empirical data obtained during the assessment of several metrics–based verification heuristics for use cases developed by some of the authors and previously presented in other international fora. The empirical analysis has made evident that some common defects found in use cases developed by software engineering students were caused not only by their lack of experience but also by the expressive limitations imposed by the underlying use case metamodel used in REM. Once these limitations were clearly identified, a number of evolutionary changes were proposed to the REM use case metamodel in order to increase use case quality, i.e. to avoid those situations in which the metamodel were the cause of defects in use case specifications. Ministerio de Ciencia y Tecnología TIC 2003-02737-C02-01

The subsumption relation is crucial in the Machine Learning systems based on a clausal representation. In this paper we present a class of operators for Machine Learning based on clauses which is a characterization of the subsumption relation in the following sense: The clause C 1 subsumes the clause C 2 iff C 1 can be reached from C 2 by applying these operators. In the second part of the paper we give a formalization of the closeness among clauses based on these operators and an algorithm to compute it as well as a bound for a quick estimation. Ministerio de Ciencia y Tecnología TIC 2000-1368-C03-0 Junta de Andalucía TIC-137

Address event representation (AER) is a neuromorphic interchip communication protocol that allows for real-time connectivity between huge number neurons located on different chips. By exploiting high speed digital communication circuits (nano-seconds), synaptic neural connections can be time multiplexed (mili-seconds). When building multi-chip muti-layered AER systems it is absolutely necessary to have a computer interface that allows: (a) to read AER interchip traffic; and (b) inject a sequence of events to the AER structure. This paper presents a PCI to AER interface, that dispatches a sequence of events with timing information. It is able to recovery the possible delays introduced by AER bus. It has been implemented in real time hardware using VHDL and tested in a PCI-AER board, developed by authors, that currently capable to send and receive events at a peak rate of 16 Mev/sec, and a typical rate of 10 Mev/sec European Commission IST-2001-34124 Ministerio de Ciencia y Tecnología TIC-2003-08164-C03-02

In this paper we present a class of operators for Machine Learning based on Logic Programming which represents a characterization of the subsumption relation in the following sense: The clause C 1 subsumes the clause C 2 iff C 1 can be reached from C 2 by applying these operators. We give a formalization of the closeness among clauses based on these operators and an algorithm to compute it as well as a bound for a quick estimation. We extend the operator to programs and we also get a characterization of the subsumption between programs. Finally, a weak metric is presented to compute the closeness among programs based on subsumption. Ministerio de Ciencia y Tecnología TIC 2000-1368-C03-0 Junta de Andalucía TIC-137