Publications

@article{GUTIERREZ-AVILES25,

title = {MetaGen: A framework for metaheuristic development and hyperparameter optimization in machine and deep learning},

author = {D. Gutiérrez-Avilés and M. J. Jiménez-Navarro and J. F. Torres and F. Martínez-Álvarez},

url = {https://www.sciencedirect.com/science/article/pii/S0925231225007180},

doi = {https://doi.org/10.1016/j.neucom.2025.130046},

year  = {2025},

date = {2025-03-15},

urldate = {2025-03-15},

journal = {Neurocomputing},

volume = {637},

pages = {130046},

abstract = {Hyperparameter optimization is a pivotal step in enhancing model performance within machine learning. Traditionally, this challenge is addressed through metaheuristics, which efficiently explore large search spaces to uncover optimal solutions. However, implementing these techniques can be complex without adequate development tools, which is the primary focus of this paper. Hence, we introduce MetaGen, a novel Python package designed to provide a comprehensive framework for developing and evaluating metaheuristic algorithms. MetaGen follows best practices in Python design, ensuring minimalistic code implementation, intuitive comprehension, and full flexibility in solution representation. The package defines two distinct user roles: Developers, responsible for algorithm implementation for hyperparameter optimization, and Solvers, who leverage pre-implemented metaheuristics to address optimization problems. Beyond algorithm implementation, MetaGen facilitates benchmarking through built-in test functions, ensuring standardized performance comparisons. It also provides automated reporting and visualization tools to analyze optimization progress and outcomes effectively. Furthermore, its modular design allows distribution

and integration into existing machine learning workflows. Several illustrative use cases are presented to demonstrate its adaptability and efficacy. The package, along with code, a user manual, and supplementary materials, is available at: https://github.com/Data-Science-Big-Data-Research-Lab/MetaGen.},

keywords = {big data, deep learning},

pubstate = {published},

tppubtype = {article}

}

@article{vanhaeren2025enhancing,

title = {Enhancing R-loop prediction with high-throughput sequencing data},

author = {T. Vanhaeren and L. Cataneo and F. Divina and P. M. Martínez-García},

url = {https://academic.oup.com/nargab/article/7/2/lqaf077/8160316},

doi = {10.1093/nargab/lqaf077},

year  = {2025},

date = {2025-01-01},

journal = {NAR Genomics and Bioinformatics},

volume = {7},

number = {2},

pages = {lqaf077},

abstract = {R-loops are three-stranded RNA and DNA hybrid structures that often occur in the genome and play important roles in a variety of cellular processes from bacteria to mammals. Sequencing methods profiling R-loops genome-wide have revealed that they can form co-transcriptionally at cell type specific genes and associate with specific chromatin states during cell differentiation and reprogramming. However, current computational methods for the prediction of R-loops rely solely on their DNA sequence properties, which precludes detection across cell types, tissues or developmental stages. Here, we conduct a machine learning approach that allows the prediction of mammalian cell type-specific R-loops using sequence information and high-throughput sequencing signals. Our predictive models are induced from human samples and achieve highly accurate predictions, with transcriptomics, DNA features, chromatin accessibility and the active gene body H3K36me3 epigenomic mark being the most informative datasets. We generate de novo virtual R-loop maps that show high concordance with experimental ones and capture cell type specificity. Our approach compares favorably to sequence-based methods and can be generalized to mouse datasets. Based on this, we generate virtual R-loop maps in 51 mammalian systems that are freely accessible to the scientific community.},

keywords = {big data, bioinformatics, pattern recognition},

pubstate = {published},

tppubtype = {article}

}

@article{vanhaeren2025application,

title = {Application of XAI to the prediction of CTCF binding sites},

author = {T. Vanhaeren and A. R. Troncoso-García and J. F. Torres and F. Divina and P. M. Martínez-García},

url = {https://www.sciencedirect.com/science/article/pii/S259012302402019X},

doi = {10.1016/j.rineng.2024.103776},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {Results in Engineering},

volume = {25},

pages = {103776},

abstract = {The inherent ‘black box’ nature of deep learning models has hindered their widespread adoption in certain fields, as they provide limited transparency into the reasoning behind their predictions. In the last years, Explainable Artificial Intelligence (XAI) techniques have proven to be effective not only in prediction itself but also in the extraction of meaningful knowledge from deep learning models by means of feature interpretation. In this study, Local Interpretable Model-agnostic Explanations are applied to the prediction of CTCF binding sites, a common task in the field of genomics. Good prediction performances and inferred explanations are obtained that highlight the most informative features that contribute to predictions such as chromatin accessibility and cis-regulatory elements which align well with previously reported data. This work represents a proof of concept showing that XAI are suitable for the extraction of molecular insights from complex biological problems like CTCF binding prediction.},

keywords = {big data, bioinformatics, deep learning},

pubstate = {published},

tppubtype = {article}

}

@article{PEREZ24,

title = {Pattern sequence-based algorithm for multivariate big data time series forecasting: Application to electricity consumption},

author = {R. Pérez-Chacón and G. Asencio-Cortés and A. Troncoso and F. Martínez-Álvarez},

url = {https://www.sciencedirect.com/science/article/pii/S0167739X23004752},

doi = {https://doi.org/10.1016/j.future.2023.12.021},

year  = {2024},

date = {2024-01-29},

journal = {Future Generation Computer Systems},

volume = {154},

pages = {397-412},

abstract = {Several interrelated variables typically characterize real-world processes, and a time series cannot be predicted without considering the influence that other time series might have on the target time series. This work proposes a novel algorithm to forecast multivariate big data time series. This new general-purpose approach consists first of a previous pattern recognition performed jointly using all time series that form the multivariate time series and then predicts the target time series by searching for similarities between pattern sequences. The proposed algorithm is designed to tackle multivariate time series forecasting problems within the context of big data. In particular, the algorithm has been developed with a distributed nature to enhance its efficiency in analyzing and processing large volumes of data. Moreover, the algorithm is straightforward to use, with only two parameters needing adjustment. Another advantage of the MV-bigPSF algorithm is its ability to perform multi-step forecasting, which is particularly useful in many practical applications. To evaluate the algorithm’s performance, real-world data from Uruguay’s power consumption has been utilized. Specifically, MV-bigPSF has been compared with both univariate and multivariate methods. Regarding the univariate ones, MV-bigPSF improved 12.8% in MAPE compared to the second-best method. Regarding the multivariate comparison, MV-bigPSF improved 44.8% in MAPE with respect to the second most accurate method. Regarding efficiency, the execution time of MV-bigPSF was 1.83 times faster than the second-fastest multivariate method, both in a single-core environment. Therefore, the proposed algorithm can be a valuable tool for practitioners and researchers working in multivariate time series forecasting, particularly in big data applications.},

keywords = {big data, energy, forecasting, time series},

pubstate = {published},

tppubtype = {article}

}

@article{MARTINEZ24,

title = {Emerging trends in big data analytics and natural disasters (Editorial)},

author = {F. Martínez-Álvarez and R. Scitovski and C. Rubio-Escudero and A. Morales-Esteban},

url = {https://www.sciencedirect.com/science/article/pii/S0098300423001693},

doi = {https://doi.org/10.1016/j.cageo.2023.105465},

year  = {2024},

date = {2024-01-01},

journal = {Computers and Geosciences},

volume = {182},

pages = {105465},

keywords = {big data, natural disasters, time series},

pubstate = {published},

tppubtype = {article}

}

@article{FERNANDEZ23,

title = {A new Apache Spark-based framework for big data streaming forecasting in IoT networks},

author = {A. M. Fernández and D. Gutiérrez-Avilés and A. Troncoso and F. Martínez-Álvarez},

url = {https://link.springer.com/article/10.1007/s11227-023-05100-x},

doi = {https://doi.org/10.1007/s11227-023-05100-x},

year  = {2023},

date = {2023-02-02},

journal = {Journal of Supercomputing},

volume = {79},

pages = {11078–11100},

abstract = {Analyzing time-dependent data acquired in a continuous flow is a major challenge for various fields, such as big data and machine learning. Being able to analyze a large volume of data from various sources, such as sensors, networks, and the internet, is essential for improving the efficiency of our society's production processes. Additionally, this vast amount of data is collected dynamically in a continuous stream. The goal of this research is to provide a comprehensive framework for forecasting big data streams from Internet of Things networks and serve as a guide for designing and deploying other third-party solutions. Hence, a new framework for time series forecasting in a big data streaming scenario, using data collected from Internet of Things networks, is presented. This framework comprises of five main modules: Internet of Things network design and deployment, big data streaming architecture, stream data modeling method, big data forecasting method, and a comprehensive real-world application scenario, consisting of a physical Internet of Things network feeding the big data streaming architecture, being the linear regression the algorithm used for illustrative purposes. Comparison with other frameworks reveals that this is the first framework that incorporates and integrates all the aforementioned modules.},

keywords = {big data, IoT},

pubstate = {published},

tppubtype = {article}

}

@article{garcia2023evolutionary,

title = {Evolutionary feature selection on high dimensional data using a search space reduction approach},

author = {M. García-Torres and R. Ruiz and F. Divina},

url = {https://www.sciencedirect.com/science/article/pii/S0952197622005462},

doi = {10.1016/j.engappai.2022.105556},

year  = {2023},

date = {2023-01-01},

journal = {Engineering Applications of Artificial Intelligence},

volume = {117},

pages = {105556},

publisher = {Elsevier},

abstract = {Feature selection is becoming more and more a challenging task due to the increase of the dimensionality of the data. The complexity of the interactions among features and the size of the search space make it unfeasible to find the optimal subset of features. In order to reduce the search space, feature grouping has arisen as an approach that allows to cluster feature according to the shared information about the class. On the other hand, metaheuristic algorithms have proven to achieve sub-optimal solutions within a reasonable time. In this work we propose a Scatter Search (SS) strategy that uses feature grouping to generate an initial population comprised of diverse and high quality solutions. Solutions are then evolved by applying random mechanisms in combination with the feature group structure, with the objective of maintaining during the search a population of good and, at the same time, as diverse as possible solutions. Not only does the proposed strategy provide the best subset of features found but it also reduces the redundancy structure of the data. We test the strategy on high dimensional data from biomedical and text-mining domains. The results are compared with those obtained by other adaptations of SS and other popular strategies. Results show that the proposed strategy can find, on average, the smallest subsets of features without degrading the performance of the classifier.},

keywords = {big data, feature selection},

pubstate = {published},

tppubtype = {article}

}

@article{MELGAR21_NEUCOMb,

title = {A new big data triclustering approach for extracting three-dimensional patterns in precision agriculture},

author = {L. Melgar-García and D. Gutiérrez-Avilés and M. T. Godinho and R. Espada and I. S. Brito and F. Martínez-Álvarez and A. Troncoso and C. Rubio-Escudero},

url = {https://www.sciencedirect.com/science/article/abs/pii/S0925231222006415},

doi = {https://doi.org/10.1016/j.neucom.2021.06.101},

year  = {2022},

date = {2022-01-01},

journal = {Neurocomputing},

volume = {500},

pages = {268-278},

abstract = {Precision agriculture focuses on the development of site-specific harvest considering the variability of each crop area. Vegetation indices allow the study and delineation of different characteristics of each field zone, generally invisible to the naked-eye. This paper introduces a new big data triclustering approach based on evolutionary algorithms. The algorithm shows its capability to discover three-dimensional patterns on the basis of vegetation indices from vine crops. Different vegetation indices have been tested to find different patterns in the crops. The results reported using a vineyard crop located in Portugal depicts four areas with different moisture stress particularities that can lead to changes in the management of the vineyard. Furthermore, scalability studies have been performed, showing that the proposed algorithm is suitable for dealing with big datasets.},

keywords = {big data, pattern recognition},

pubstate = {published},

tppubtype = {article}

}

@article{morales2022analysisb,

title = {Analysis of Electric Energy Consumption Profiles Using a Machine Learning Approach: A Paraguayan Case Study},

author = {F. Morales and M. García-Torres and G. Velázquez and F. Daumas-Ladouce and P. Gardel-Sotomayor and F. Gómez-Vela and F. Divina and J. L. Vázquez Noguera and C. Sauer Ayala and D. Pinto-Roa},

url = {https://www.mdpi.com/2079-9292/11/2/267},

doi = {10.3390/electronics11020267},

year  = {2022},

date = {2022-01-01},

journal = {Electronics},

volume = {11},

number = {2},

pages = {267},

abstract = {Correctly defining and grouping electrical feeders is of great importance for electrical system operators. In this paper, we compare two different clustering techniques, K-means and hierarchical agglomerative clustering, applied to real data from the east region of Paraguay. The raw data were pre-processed, resulting in four data sets, namely, (i) a weekly feeder demand, (ii) a monthly feeder demand, (iii) a statistical feature set extracted from the original data and (iv) a seasonal and daily consumption feature set obtained considering the characteristics of the Paraguayan load curve. Considering the four data sets, two clustering algorithms, two distance metrics and five linkage criteria a total of 36 models with the Silhouette, Davies–Bouldin and Calinski–Harabasz index scores was assessed. The K-means algorithms with the seasonal feature data sets showed the best performance considering the Silhouette, Calinski–Harabasz and Davies–Bouldin validation index scores with a configuration of six clusters.},

keywords = {big data, energy, time series},

pubstate = {published},

tppubtype = {article}

}

@article{TORRES21,

title = {Deep Learning for Time Series Forecasting: A Survey},

author = {J. F. Torres and D. Hadjout and A. Sebaa and F. Martínez-Álvarez and A. Troncoso},

url = {https://www.liebertpub.com/doi/10.1089/big.2020.0159},

doi = {10.1089/big.2020.0159},

year  = {2021},

date = {2021-02-05},

journal = {Big Data},

volume = {9},

number = {1},

pages = {3-21},

abstract = {Deep learning, one of the most remarkable techniques of machine learning, has been a major success in many fields, including image processing, speech recognition, and text understanding. It is powerful engines capable of learning arbitrary mapping functions, not require a scaled or stationary time series as input, support multivariate inputs, and support multi-step outputs. All of these features together make deep learning useful tools when dealing with more complex time series prediction problems involving large amounts of data, and multiple variables with complex relationships. This paper provides an overview of the most common Deep Learning types for time series forecasting, Explain the relationships between deep learning models and classical approaches to time series forecasting. A brief background of the particular challenges presents in time-series data and the most common deep learning techniques that are often used for time series forecasting is provided. Previous studies that applied deep learning to time series are reviewed.},

keywords = {big data, deep learning, time series},

pubstate = {published},

tppubtype = {article}

}

@article{Melgar21_IS,

title = {Discovering three-dimensional patterns in real-time from data streams: An online triclustering approach},

author = {L. Melgar-García and D. Gutiérrez-Avilés and C. Rubio-Escudero and A. Troncoso},

url = {https://www.sciencedirect.com/science/article/pii/S0020025521000220},

doi = {10.1016/j.ins.2020.12.089},

year  = {2021},

date = {2021-01-01},

journal = {Information Sciences},

volume = {558},

pages = {174-193},

abstract = {Triclustering algorithms group sets of coordinates of 3-dimensional datasets. In this paper, a new triclustering approach for data streams is introduced. It follows a streaming scheme of learning in two steps: offline and online phases. First, the offline phase provides a summary model with the components of the triclusters. Then, the second stage is the online phase to deal with data in streaming. This online phase consists in using the summary model obtained in the offline stage to update the triclusters as fast as possible with genetic operators. Results using three types of synthetic datasets and a real-world environmental sensor dataset are reported. The performance of the proposed triclustering streaming algorithm is compared to a batch triclustering algorithm, showing an accurate performance both in terms of quality and running times. 

},

keywords = {big data, IoT, pattern recognition},

pubstate = {published},

tppubtype = {article}

}

@article{DIVINA2020,

title = {Hybridizing deep learning and neuroevolution: Application to the Spanish short-term electric energy consumption forecasting},

author = {F. Divina and J. F. Torres and M. García-Torres and F. Martínez-Álvarez and A. Troncoso},

url = {https://www.mdpi.com/2076-3417/10/16/5487},

doi = {https://doi.org/10.3390/app10165487},

year  = {2020},

date = {2020-07-30},

journal = {Applied Sciences},

volume = {10},

number = {16},

pages = {5487},

abstract = {The electric energy production would be much more efficient if accurate estimations of the future demand were available, since these would allow allocating only the resources needed for the production of the right amount of energy required. With this motivation in mind, we propose a strategy, based on neuroevolution, that can be used to this aim. Our proposal uses a genetic algorithm in order to find a sub-optimal set of hyper-parameters for configuring a deep neural network, which can then be used for obtaining the forecasting. Such a strategy is justified by the observation that the performances achieved by deep neural networks are strongly dependent on the right setting of the hyper-parameters, and genetic algorithms have shown excellent search capabilities in huge search spaces. Moreover, we base our proposal on a distributed computing platform, which allows its use on a large time-series. In order to assess the performances of our approach, we have applied it to a large dataset, related to the electric energy consumption registered in Spain over almost 10 years. Experimental results confirm the validity of our proposal since it outperforms all other forecasting techniques to which it has been compared.},

keywords = {big data, deep learning, energy, time series},

pubstate = {published},

tppubtype = {article}

}

@article{MARTINEZ-ALVAREZ20,

title = {Coronavirus Optimization Algorithm: A bioinspired metaheuristic based on the COVID-19 propagation model},

author = {F. Martínez-Álvarez and G. Asencio-Cortés and J. F. Torres and D. Gutiérrez-Avilés and L. Melgar-García and R. Pérez-Chacón and C. Rubio-Escudero and A. Troncoso and J. C. Riquelme},

url = {https://www.liebertpub.com/doi/full/10.1089/big.2020.0051},

doi = {10.1089/big.2020.0051},

year  = {2020},

date = {2020-07-22},

journal = {Big Data},

volume = {8},

number = {4},

pages = {308-322},

abstract = {This work proposes a novel bioinspired metaheuristic, simulating how the coronavirus spreads and infects healthy people. From a primary infected individual (patient zero), the coronavirus rapidly infects new victims, creating large populations of infected people who will either die or spread infection. Relevant terms such as reinfection probability, super-spreading rate, social distancing measures or traveling rate are introduced into the model in order to simulate the coronavirus activity as accurately as possible. The infected population initially grows exponentially over time, but taking into consideration social isolation measures, the mortality rate and number of recoveries, the infected population gradually decreases. The Coronavirus Optimization Algorithm has two major advantages when compared to other similar strategies. Firstly, the input parameters are already set according to the disease statistics, preventing researchers from initializing them with arbitrary values. Secondly, the approach has the ability to end after several iterations, without setting this value either. Furthermore, a parallel multi-virus version is proposed, where several coronavirus strains evolve over time and explore wider search space areas in less iterations. Finally, the metaheuristic has been combined with deep learning models, in order to find optimal hyperparameters during the training phase. As application case, the problem of electricity load time series forecasting has been addressed, showing quite remarkable performance.},

keywords = {big data, deep learning, energy, time series},

pubstate = {published},

tppubtype = {article}

}

@article{PEREZ20,

title = {Big data time series forecasting based on pattern sequence similarity and its application to the electricity demand},

author = {R. Pérez-Chacón and G. Asencio-Cortés and F. Martínez-Álvarez and A. Troncoso},

url = {https://www.sciencedirect.com/science/article/pii/S0020025520306010},

doi = {10.1016/j.ins.2020.06.014},

year  = {2020},

date = {2020-06-06},

journal = {Information Sciences},

volume = {540},

pages = {160-174},

abstract = {This work proposes a novel algorithm to forecast big data time series. Based on the well-established Pattern Sequence Forecasting algorithm, this new approach has two major contributions to the literature. First, the improvement of the aforementioned algorithm with respect to the accuracy of predictions, and second, its transformation into the big data context, having reached meaningful results in terms of 

scalability. The algorithm uses the Apache Spark distributed computation framework and it is a ready-to-use application with few parameters to adjust. Physical and cloud clusters have been used to carry out the experimentation, which consisted in applying the algorithm to real-world data from Uruguay electricity demand.},

keywords = {big data, energy, time series},

pubstate = {published},

tppubtype = {article}

}

@article{FERNANDEZ20,

title = {Automated Deployment of a Spark Cluster with Machine Learning Algorithm Integration},

author = {A. M. Fernández and D. Gutiérrez-Avilés and A. Troncoso and F. Martínez-Álvarez},

url = {https://www.sciencedirect.com/science/article/pii/S2214579620300034},

doi = {10.1016/j.bdr.2020.100135},

year  = {2020},

date = {2020-05-12},

journal = {Big Data Research},

volume = {19-20},

pages = {100135},

abstract = {The vast amount of data stored nowadays has turned big data analytics into a very trendy research field. The Spark distributed computing platform has emerged as a dominant and widely used paradigm for cluster deployment and big data analytics. However, to get started up is still a task that may take much time when manually done, due to the requisites that all nodes must fulfill. This work introduces LadonSpark, an open-source and non-commercial solution to configure and deploy a Spark cluster automatically. It has been specially designed for easy and efficient management of a Spark cluster with a friendly graphical user interface to automate the deployment of a cluster and to start up the distributed file system of Hadoop quickly. Moreover, LadonSpark includes the functionality of integrating any algorithm into the system. That is, the user only needs to provide the executable file and the number of required inputs for proper parametrization. Source codes developed in Scala, R, Python, or Java can be supported on LadonSpark. Besides, clustering, regression, classification, and association rules algorithms are already integrated so that users can test its usability from its initial installation.},

keywords = {big data, time series},

pubstate = {published},

tppubtype = {article}

}

@article{MARTINEZ20c,

title = {Advanced Machine Learning and Big Data Analytics in Remote Sensing for Natural Hazards Management (Editorial)},

author = {F. Martínez-Álvarez and D. T. Bui},

url = {https://www.mdpi.com/2072-4292/12/2/301},

doi = {10.3390/rs12020301},

issn = {2072-4292},

year  = {2020},

date = {2020-01-01},

journal = {Remote Sensing},

volume = {12},

number = {2},

pages = {301},

abstract = {This editorial summarizes the performance of the special issue entitled Advanced Machine Learning and Big Data Analytics in Remote Sensing for Natural Hazards Management, which was published at MDPI’s Remote Sensing journal. The special issue took place in years 2018 and 2019 and accepted a total of nine papers from authors of thirteen different countries. So far, these papers have dealt with 116 cites. Earthquakes, landslides, floods, wildfire and soil salinity were the topics analyzed. New methods were introduced, with applications of the utmost relevance},

keywords = {big data, natural disasters},

pubstate = {published},

tppubtype = {article}

}

@article{MARTINEZ19,

title = {Big data and natural disasters: New approaches for temporal and spatial massive data analysis (Editorial)},

author = {F. Martínez-Álvarez and A. Morales-Esteban},

url = {https://www.sciencedirect.com/science/article/pii/S009830041930411X?dgcid=rss_sd_all},

doi = {https://doi.org/10.1016/j.cageo.2019.04.012},

year  = {2019},

date = {2019-08-01},

journal = {Computers and Geosciences},

volume = {129},

pages = {38-39},

keywords = {big data, natural disasters, time series},

pubstate = {published},

tppubtype = {article}

}

@article{NEUCOM2019,

title = {MV-kWNN: A novel multivariate and multi-output weighted nearest neighbors algorithm for big data time series forecasting},

author = {R. Talavera-Llames and R. Pérez-Chacón and A. Troncoso and F. Martínez-Álvarez},

url = {https://www.sciencedirect.com/science/article/pii/S0925231219303236?via%3Dihub},

doi = {10.1016/j.neucom.2018.07.092},

year  = {2019},

date = {2019-01-01},

journal = {Neurocomputing},

volume = {353},

pages = {56-73},

abstract = {This paper introduces a novel algorithm for big data time series forecasting. Its main novelty lies in its ability to deal with multivariate data, i.e. to consider multiple time series simultaneously, in order to make multi-output predictions. Real-world processes are typically characterised by several interrelated variables, and the future occurrence of certain time series cannot be explained without understanding the influence that other time series might have on the target time series. One key issue in the context of the multivariate analysis is to determine a priori whether exogenous variables must be included in the model or not. To deal with this, a correlation analysis is used to find a minimum correlation threshold that an exogenous time series must exhibit, in order to be beneficial. Furthermore, the proposed approach has been specifically designed to be used in the context of big data, thus making it possible to efficiently process very large time series. To evaluate the performance of the proposed approach we use data from Spanish electricity prices. Results have been compared to other multivariate approaches showing remarkable improvements both in terms of accuracy and execution time.},

keywords = {big data, energy, time series},

pubstate = {published},

tppubtype = {article}

}

@article{GALICIA19,

title = {Multi-step forecasting for big data time series based on ensemble learning},

author = {A. Galicia and R. Talavera-Llames and A. Troncoso and I. Koprinska and F. Martínez-Álvarez},

url = {https://www.sciencedirect.com/science/article/abs/pii/S0950705118304957},

doi = {https://doi.org/10.1016/j.knosys.2018.10.009},

year  = {2019},

date = {2019-01-01},

journal = {Knowledge Based-Systems},

volume = {163},

pages = {830-841},

keywords = {big data, time series},

pubstate = {published},

tppubtype = {article}

}

@article{Energies2018,

title = {Big data analytics for discovering electricity consumption patterns in smart cities},

author = {R. Pérez-Chacón and J. M. Luna and A. Troncoso and F. Martínez-Álvarez and J. C. Riquelme},

url = {http://www.mdpi.com/1996-1073/11/3/683

},

doi = {10.3390/en11030683 },

year  = {2018},

date = {2018-01-01},

journal = {Energies},

volume = {11},

number = {3},

pages = {683},

abstract = {New technologies such as sensor networks have been incorporated into the management of buildings for organizations and cities. Sensor networks have led to an exponential increase in the volume of data available in recent years, which can be used to extract consumption patterns for the purposes of energy and monetary savings. For this reason, new approaches and strategies are needed to analyze information in big data environments. This paper proposes a methodology to extract electric energy consumption patterns in big data time series, so that very valuable conclusions can be made for managers and governments. The methodology is based on the study of four clustering validity indices in their parallelized versions along with the application of a clustering technique. In particular, this work uses a voting system to choose an optimal number of clusters from the results of the indices, as well as the application of the distributed version of the k-means algorithm included in Apache Spark’s Machine Learning Library. The results, using electricity consumption for the years 2011–2017 for eight buildings of a public university, are presented and discussed. In addition, the performance of the proposed methodology is evaluated using synthetic big data, which cab represent thousands of buildings in a smart city. Finally, policies derived from the patterns discovered are proposed to optimize energy usage across the university campus.},

keywords = {big data, energy, time series},

pubstate = {published},

tppubtype = {article}

}

@article{INFSCI2018,

title = {A novel Spark-based multi-step forecasting algorithm for big data time series},

author = {A. Galicia and J. F. Torres and F. Martínez-Álvarez and A. Troncoso},

url = {https://www.sciencedirect.com/science/article/pii/S0020025518304493},

doi = {10.1016/j.ins.2018.06.010},

year  = {2018},

date = {2018-01-01},

journal = {Information Sciences},

volume = {467},

pages = {800-818},

abstract = {This paper presents different scalable methods for predicting big time series, namely time series with a high frequency measurement. Methods are also developed to deal with arbitrary prediction horizons. The Apache Spark framework is proposed for distributed computing in order to achieve the scalability of the methods. Prediction methods have been developed using Spark’s MLlib library for machine learning. Since the library does not support multivariate regression, the prediction problem is formulated as h prediction sub-problems, where h is the number of future values to predict, that is, the prediction horizon. Furthermore, different kinds of representative methods have been chosen, such as decision trees, two tree-based ensemble techniques (Gradient-Boosted and Random Forest) and a linear regression method as a reference method for comparisons. Finally, the methodology has been tested in a real time series of electrical demand in Spain, with a time interval of ten minutes between measurements.},

keywords = {big data, energy, time series},

pubstate = {published},

tppubtype = {article}

}

@article{KNOSYS2018b,

title = {Big data time series forecasting based on nearest neighbors distributed computing with Spark},

author = {R. Talavera-Llames and R. Pérez-Chacón and A. Troncoso and F. Martínez-Álvarez},

url = {https://www.sciencedirect.com/science/article/pii/S0950705118303770},

doi = {10.1016/j.knosys.2018.07.026},

year  = {2018},

date = {2018-01-01},

journal = {Knowledge-Based Systems},

volume = {161},

number = {1},

pages = {12-25},

abstract = {A new approach for big data forecasting based on the k-weighted nearest neighbours algorithm is introduced in this work. Such an algorithm has been developed for distributed computing under the Apache Spark framework. Every phase of the algorithm is explained in this work, along with how the optimal values of the input parameters required for the algorithm are obtained. In order to test the developed algorithm, a Spanish energy consumption big data time series has been used. The accuracy of the prediction has been assessed showing remarkable results. Additionally, the optimal configuration of a Spark cluster has been discussed. Finally, a scalability analysis of the algorithm has been conducted leading to the conclusion that the proposed algorithm is highly suitable for big data environments.},

keywords = {big data, energy, time series},

pubstate = {published},

tppubtype = {article}

}

@article{Asencio-Cortes2018b,

title = {Earthquake prediction in California using regression algorithms and cloud-based big data infrastructure},

author = {G. Asencio-Cortes and A. Morales-Esteban and X. Shang and F. Martinez-Alvarez},

doi = {10.1016/j.cageo.2017.10.011},

issn = {0098-3004},

year  = {2018},

date = {2018-01-01},

journal = {Computers and Geosciences},

number = {115},

pages = {198-210},

abstract = {Earthquake magnitude prediction is a challenging problem that has been widely studied during the last decades. Statistical, geophysical and machine learning approaches can be found in literature, with no particularly satisfactory results. In recent years, powerful computational techniques to analyze big data have emerged, making possible the analysis of massive datasets. These new methods make use of physical resources like cloud based architectures. California is known for being one of the regions with highest seismic activity in the world and many data are available. In this work, the use of several regression algorithms combined with ensemble learning is explored in the context of big data (1 GB catalog is used), in order to predict earthquakes magnitude within the next seven days. Apache Spark framework, H2O library in R language and Amazon cloud infrastructure were been used, reporting very promising results.},

keywords = {big data, natural disasters, time series},

pubstate = {published},

tppubtype = {article}

}

@article{Martinez18,

title = {Data Science and Big Data in Energy Forecasting},

author = {F. Martínez-Álvarez and A. Troncoso and J. C. Riquelme},

doi = {10.3390/en11113224},

year  = {2018},

date = {2018-01-01},

journal = {Energies},

volume = {11},

number = {11},

pages = {3224},

keywords = {big data, energy},

pubstate = {published},

tppubtype = {article}

}

@article{KNOSYS2017,

title = {Content-based methods in peer assessment of open-response questions to grade students as authors and as graders},

author = {O. Luaces and J. Díez and A. Alonso-Betanzos and A. Troncoso and A. Bahamonde},

url = {http://www.sciencedirect.com/science/article/pii/S0950705116301964},

doi = {10.1016/j.knosys.2016.06.024},

year  = {2017},

date = {2017-01-01},

journal = {Knowledge-Based Systems},

volume = {117},

pages = {79-87},

abstract = {Massive Open Online Courses (MOOCs) use different types of assignments in order to evaluate student knowledge. Multiple-choice tests are particularly apt given the possibility for automatic assessment of large numbers of assignments. However, certain skills require open responses that cannot be assessed automatically yet their evaluation by instructors or teaching assistants is unfeasible given the large number of students. A potentially effective solution is peer assessment whereby students grade the answers of other students. However, to avoid bias due to inexperience, such grades must be filtered. We describe a factorization approach to grading, as a scalable method capable of dealing with very high volumes of data. Our method is also capable of representing open-response content using a vector space model of the answers. Since reliable peer assessment requires students to make coherent assessments, students can be motivated by their assessments reflecting not only their own answers but also their efforts as graders. The method described is able to tackle both these aspects simultaneously. Finally, for a real-world university setting in Spain, we compared grades obtained by our method and grades awarded by university instructors, with results indicating a notable improvement from using a content-based approach. There was no evidence that instructor grading would have led to more accurate grading outcomes than the assessment produced by our models.},

keywords = {big data},

pubstate = {published},

tppubtype = {article}

}

@article{KNOSYS2015,

title = {A factorization approach to evaluate open-response assignments in MOOCs using preference learning on peer assessments},

author = {O. Luaces and J. Díez and A. Alonso-Betanzos and A. Troncoso and A. Bahamonde},

url = {https://www.sciencedirect.com/science/article/abs/pii/S0950705115002051},

doi = {10.1016/j.knosys.2015.05.019},

year  = {2015},

date = {2015-00-00},

journal = {Knowledge-Based Systems},

volume = {85},

pages = {322-328},

abstract = {Evaluating open-response assignments in Massive Open Online Courses is a difficult task because of the huge number of students involved. Peer grading is an effective method to address this problem. There are two basic approaches in the literature: cardinal and ordinal. The first case uses grades assigned by student-graders to a set of assignments of other colleagues. In the ordinal approach, the raw materials used by grading systems are the relative orders that graders appreciate in the assignments that they evaluate. In this paper we present a factorization method that seeks a trade-off between cardinal and ordinal approaches. The algorithm learns from preference judgments to avoid the subjectivity of the numeric grades. But in addition to preferences expressed by student-graders, we include other preferences: those induced from assignments with significantly different average grades. The paper includes a report of the results obtained using this approach in a real world dataset collected in 3 Universities of Spain, A Coruña, Pablo de Olavide at Sevilla, and Oviedo at Gijón. Additionally, we studied the sensitivity of the method with respect to the number of assignments graded by each student. Our method achieves similar or better scores than staff instructors when we measure the discrepancies with other instructor’s grades.},

keywords = {big data},

pubstate = {published},

tppubtype = {article}

}