Browsing by Keyword "Big data"
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Item Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions: state of the art and future directions(2021-08-03) Torre-Bastida, Ana I.; Díaz-de-Arcaya, Josu; Osaba, Eneko; Muhammad, Khan; Camacho, David; Del Ser, Javier; HPA; QuantumThis overview gravitates on research achievements that have recently emerged from the confluence between Big Data technologies and bio-inspired computation. A manifold of reasons can be identified for the profitable synergy between these two paradigms, all rooted on the adaptability, intelligence and robustness that biologically inspired principles can provide to technologies aimed to manage, retrieve, fuse and process Big Data efficiently. We delve into this research field by first analyzing in depth the existing literature, with a focus on advances reported in the last few years. This prior literature analysis is complemented by an identification of the new trends and open challenges in Big Data that remain unsolved to date, and that can be effectively addressed by bio-inspired algorithms. As a second contribution, this work elaborates on how bio-inspired algorithms need to be adapted for their use in a Big Data context, in which data fusion becomes crucial as a previous step to allow processing and mining several and potentially heterogeneous data sources. This analysis allows exploring and comparing the scope and efficiency of existing approaches across different problems and domains, with the purpose of identifying new potential applications and research niches. Finally, this survey highlights open issues that remain unsolved to date in this research avenue, alongside a prescription of recommendations for future research.Item Design and implementation of an extended corporate crm database system with big data analytical functionalities(2015-07-25) Torre-Bastida, Ana I.; Villar-Rodriguez, Esther; Gil-Lopez, Sergio; Del Ser, Javier; HPA; Quantum; IAThe amount of open information available on-line from heterogeneous sources and domains is growing at an extremely fast pace, and constitutes an important knowledge base for the consideration of industries and companies. In this context, two relevant data providers can be highlighted: the “Linked Open Data” (LOD) and “Social Media” (SM) paradigms. The fusion of these data sources – structured the former, and raw data the latter –, along with the information contained in structured corporate databases within the organizations themselves, may unveil significant business opportunities and competitive advantage to those who are able to understand and leverage their value. In this paper, we present two complementary use cases, illustrating the potential of using the open data in the business domain. The first represents the creation of an existing and potential customer knowledge base, exploiting social and linked open data based on which any given organization might infer valuable information as a support for decision making. The second focuses on the classification of organizations and enterprises aiming at detecting potential competitors and/or allies via the analysis of the conceptual similarity between their participated projects. To this end, a solution based on the synergy of Big Data and semantic technologies will be designed and developed. The first will be used to implement the tasks of collection, data fusion and classification supported by natural language processing (NLP) techniques, whereas the latter will deal with semantic aggregation, persistence, reasoning and information retrieval, as well as with the triggering of alerts based on the semantized information.Item The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain–behavior relationships after stroke: Big data neuroimaging to study brain–behavior relationships after stroke(2020) Liew, Sook‐Lei; Zavaliangos‐Petropulu, Artemis; Jahanshad, Neda; Lang, Catherine E.; Hayward, Kathryn S.; Lohse, Keith R.; Juliano, Julia M.; Assogna, Francesca; Baugh, Lee A.; Bhattacharya, Anup K.; Bigjahan, Bavrina; Borich, Michael R.; Boyd, Lara A.; Brodtmann, Amy; Buetefisch, Cathrin M.; Byblow, Winston D.; Cassidy, Jessica M.; Conforto, Adriana B.; Craddock, R. Cameron; Dimyan, Michael A.; Dula, Adrienne N.; Ermer, Elsa; Etherton, Mark R.; Fercho, Kelene A.; Gregory, Chris M.; Hadidchi, Shahram; Holguin, Jess A.; Hwang, Darryl H.; Jung, Simon; Kautz, Steven A.; Khlif, Mohamed Salah; Khoshab, Nima; Kim, Bokkyu; Kim, Hosung; Kuceyeski, Amy; Lotze, Martin; MacIntosh, Bradley J.; Margetis, John L.; Mohamed, Feroze B.; Piras, Fabrizio; Ramos‐Murguialday, Ander; Richard, Geneviève; Roberts, Pamela; Robertson, Andrew D.; Rondina, Jane M.; Rost, Natalia S.; Sanossian, Nerses; Schweighofer, Nicolas; Seo, Na Jin; Shiroishi, Mark S.; Soekadar, Surjo R.; Spalletta, Gianfranco; Stinear, Cathy M.; Suri, Anisha; Tang, Wai Kwong W.; Thielman, Gregory T.; Vecchio, Daniela; Villringer, Arno; Ward, Nick S.; Werden, Emilio; Westlye, Lars T.; Winstein, Carolee; Wittenberg, George F.; Wong, Kristin A.; Yu, Chunshui; Cramer, Steven C.; Thompson, Paul M.; Zavaliangos-Petropulu, Artemis; Ramos-Murguialday, Ander; Medical TechnologiesThe goal of the Enhancing Neuroimaging Genetics through Meta‐Analysis (ENIGMA) Stroke Recovery working group is to understand brain and behavior relationships using well‐powered meta‐ and mega‐analytic approaches. ENIGMA Stroke Recovery has data from over 2,100 stroke patients collected across 39 research studies and 10 countries around the world, comprising the largest multisite retrospective stroke data collaboration to date. This article outlines the efforts taken by the ENIGMA Stroke Recovery working group to develop neuroinformatics protocols and methods to manage multisite stroke brain magnetic resonance imaging, behavioral and demographics data. Specifically, the processes for scalable data intake and preprocessing, multisite data harmonization, and large‐scale stroke lesion analysis are described, and challenges unique to this type of big data collaboration in stroke research are discussed. Finally, future directions and limitations, as well as recommendations for improved data harmonization through prospective data collection and data management, are provided.Item Implementation of a Large-Scale Platform for Cyber-Physical System Real-Time Monitoring(2019) Canizo, Mikel; Conde, Angel; Charramendieta, Santiago; Minon, Raul; Cid-Fuentes, Raul G.; Onieva, Enrique; HPAThe emergence of Industry 4.0 and the Internet of Things (IoT) has meant that the manufacturing industry has evolved from embedded systems to cyber-physical systems (CPSs). This transformation has provided manufacturers with the ability to measure the performance of industrial equipment by means of data gathered from on-board sensors. This allows the status of industrial systems to be monitored and can detect anomalies. However, the increased amount of measured data has prompted many companies to investigate innovative ways to manage these volumes of data. In recent years, cloud computing and big data technologies have emerged among the scientific communities as key enabling technologies to address the current needs of CPSs. This paper presents a large-scale platform for CPS real-time monitoring based on big data technologies, which aims to perform real-time analysis that targets the monitoring of industrial machines in a real work environment. This paper is validated by implementing the proposed solution on a real industrial use case that includes several industrial press machines. The formal experiments in a real scenario are conducted to demonstrate the effectiveness of this solution and also its adequacy and scalability for future demand requirements. As a result of the implantation of this solution, the overall equipment effectiveness has been improved.Item An Intelligent Procedure for the Methodology of Energy Consumption in Industrial Environments(Springer, 2020-10-27) Mendia, Izaskun; Gil-Lopez, Sergio; Del Ser, Javier; Grau, Iñaki; Lejarazu, Adelaida; Maqueda, Erik; Perea, Eugenio; Analide, Cesar; Novais, Paulo; Camacho, David; Yin, Hujun; Tecnalia Research & Innovation; IA; DIGITAL ENERGYThe concern of the industrial sector about the increase of energy costs has stimulated the development of new strategies for the effective management of energy consumption in industrial setups. Along with this growth, the irruption and continuous development of digital technologies have generated increasingly complex industrial ecosystems. These ecosystems are supported by a large number of variables and procedures for the operation and control of industrial processes and assets. This heterogeneous technological scenario has made industries difficult to manage by traditional means. In this context, the disruptive potential of cyber physical systems is beginning to be considered in the automation and improvement of industrial services. Particularly, intelligent data-driven approaches relying on the combination of Energy Management Systems (EMS), Manufacturing Execution Systems (MES), Internet of Things (IoT) and Data Analytics provide the intelligence needed to optimally operate these complex industrial environments. The work presented in this manuscript contributes to the definition of the aforementioned intelligent data-driven approaches, defining a systematic, intelligent procedure for the energy efficiency diagnosis and improvement of industrial plants. This data-based diagnostic procedure hinges on the analysis of data collected from industrial plants, aimed at minimizing energy costs through the continuous assessment of the production-consumption ratio of the plant (i.e. energy per piece or kg produced). The proposed methodology aims to support managers and energy-efficiency technicians to minimize the plant’s energy consumption without affecting the production and therefore, increase its competitiveness. The data used in the design of this methodology are real data from a company dedicated to the design and manufacture of automotive components and one of the main manufacturers in the automotive sector worldwide. The present methodology is under the pending patent application EU19382002.4-120.Item Using mobility information to perform a feasibility study and the evaluation of spatio-temporal energy demanded by an electric taxi fleet(2018-02-01) Fraile-Ardanuy, Jesús; Castano-Solis, Sandra; Álvaro-Hermana, Roberto; Merino, Julia; Castillo, Ángela; Tecnalia Research & InnovationHalf of the global population already lives in urban areas, facing to the problem of air pollution mainly caused by the transportation system. The recently worsening of urban air quality has a direct impact on the human health. Replacing today’s internal combustion engine vehicles with electric ones in public fleets could provide a deep impact on the air quality in the cities. In this paper, real mobility information is used as decision support for the taxi fleet manager to promote the adoption of electric taxi cabs in the city of San Francisco, USA. Firstly, mobility characteristics and energy requirements of a single taxi are analyzed. Then, the results are generalized to all vehicles from the taxi fleet. An electrificability rate of the taxi fleet is generated, providing information about the number of current trips that could be performed by electric taxis without modifying the current driver mobility patterns. The analysis results reveal that 75.2% of the current taxis could be replaced by electric vehicles, considering a current standard battery capacity (24–30 kWh). This value can increase significantly (to 100%), taking into account the evolution of the price and capacity of the batteries installed in the last models of electric vehicles that are coming to the market. The economic analysis shows that the purchasing costs of an electric taxi are bigger than conventional one. However, fuel, maintenance and repair costs are much lower. Using the expected energy consumption information evaluated in this study, the total spatio-temporal demand of electric energy required to recharge the electric fleet is also calculated, allowing identifying optimal location of charging infrastructure based on realistic routing patterns. This information could also be used by the distribution system operator to identify possible reinforcement actions in the electric grid in order to promote introducing electric vehicles.