https://reunir.unir.net/handle/123456789/10931 2024-11-07T15:56:48Z https://reunir.unir.net/handle/123456789/11218 Text Analytics: the convergence of Big Data and Artificial Intelligence Moreno, Antonio; Redondo, Teófilo The analysis of the text content in emails, blogs, tweets, forums and other forms of textual communication constitutes what we call text analytics. Text analytics is applicable to most industries: it can help analyze millions of emails; you can analyze customers’ comments and questions in forums; you can perform sentiment analysis using text analytics by measuring positive or negative perceptions of a company, brand, or product. Text Analytics has also been called text mining, and is a subcategory of the Natural Language Processing (NLP) field, which is one of the founding branches of Artificial Intelligence, back in the 1950s, when an interest in understanding text originally developed. Currently Text Analytics is often considered as the next step in Big Data analysis. Text Analytics has a number of subdivisions: Information Extraction, Named Entity Recognition, Semantic Web annotated domain’s representation, and many more. Several techniques are currently used and some of them have gained a lot of attention, such as Machine Learning, to show a semisupervised enhancement of systems, but they also present a number of limitations which make them not always the only or the best choice. We conclude with current and near future applications of Text Analytics. Submitted by Ana María Ampudia Alonso (ana.ampudia@unir.net) on 2021-04-21T10:45:28Z No. of bitstreams: 1 ijimai20163_6_9_pdf_38545.pdf: 1031480 bytes, checksum: a726d724412337b545d5f2c20ebee8ae (MD5); Made available in DSpace on 2021-04-21T10:45:28Z (GMT). No. of bitstreams: 1 ijimai20163_6_9_pdf_38545.pdf: 1031480 bytes, checksum: a726d724412337b545d5f2c20ebee8ae (MD5) https://reunir.unir.net/handle/123456789/11166 Editor’s Note Mochón, Francisco; Gonzálvez, Juan Carlos Digital information has redefined the way in which both public and private organizations are faced with the use of data to improve decision making. The importance of Big Data lies in the huge amount of data generated every day, especially following the emergence of online social networks (Facebook, Twitter, Google Plus, etc.) and the exponential growth of devices such as smartphones, smartwatches and other wearables, sensor networks, etc. as well as the possibility of taking into account increasingly updated and more varied information for decision making. [1] With proper Big Data analysis we can spot trends, get models from historical data for predicting future events or extract patterns from user behaviour, and thus be able to tailor services to the needs of users in a better way. Submitted by Ana María Ampudia Alonso (ana.ampudia@unir.net) on 2021-03-31T14:43:26Z No. of bitstreams: 1 ijimai20163_6_0_pdf_37905.pdf: 569978 bytes, checksum: 5bae1c0b5501b8ba6b1e3271bdfd4465 (MD5); Made available in DSpace on 2021-03-31T14:43:27Z (GMT). No. of bitstreams: 1 ijimai20163_6_0_pdf_37905.pdf: 569978 bytes, checksum: 5bae1c0b5501b8ba6b1e3271bdfd4465 (MD5) https://reunir.unir.net/handle/123456789/11165 Real-Time Prediction of Gamers Behavior Using Variable Order Markov and Big Data Technology: A Case of Study Baldominos, Alejandro; Albacete, Esperanza; Marrero, Ignacio; Saez, Yago This paper presents the results and conclusions found when predicting the behavior of gamers in commercial videogames datasets. In particular, it uses Variable-Order Markov (VOM) to build a probabilistic model that is able to use the historic behavior of gamers and to infer what will be their next actions. Being able to predict with accuracy the next user’s actions can be of special interest to learn from the behavior of gamers, to make them more engaged and to reduce churn rate. In order to support a big volume and velocity of data, the system is built on top of the Hadoop ecosystem, using HBase for real-time processing; and the prediction tool is provided as a service (SaaS) and accessible through a RESTful API. The prediction system is evaluated using a case of study with two commercial videogames, attaining promising results with high prediction accuracies. Submitted by Ana María Ampudia Alonso (ana.ampudia@unir.net) on 2021-03-31T14:37:10Z No. of bitstreams: 1 ijimai20163_6_7_pdf_23096.pdf: 1621083 bytes, checksum: c62e8b559c291576e59538de895bbec3 (MD5); Made available in DSpace on 2021-03-31T14:37:10Z (GMT). No. of bitstreams: 1 ijimai20163_6_7_pdf_23096.pdf: 1621083 bytes, checksum: c62e8b559c291576e59538de895bbec3 (MD5) https://reunir.unir.net/handle/123456789/10962 Using GDELT Data to Evaluate the Confidence on the Spanish Government Energy Policy Bodas-Sagi, Diego; Labeaga, José The growing demand for affordable, reliable, domestically sourced, and low-carbon electricity is a matter of concern and it is driven by several causes including public policy priorities. Policy objectives and new technologies are changing wholesale market design. The analysis of different aspects of energy markets is increasingly on the agendas of academics, firms’ managers or policy makers. Some concerns are global and are related to the evolution of climate change phenomena. Others are regional or national and they strongly appear in countries like Spain with a high dependence on foreign energy sources and high potential of domestic renewable energy sources. We can find a relevant case in Spanish solar energy policy. A series of regulatory reforms since 2010 reduce revenues to existing renewable power generators and they end up the previous system of support to new renewable generation. This policy change has altered the composition of the energy market affecting investment decisions. In this paper, we analyze the public opinion about energy policy of the Spanish Government using the Global Database of Events, Language, and Tone (GDELT). The GDELT Project consists of over a quarter-billion event records in over 300 categories covering the entire world from 1979 to present, along with a massive network diagram connecting every person, organization, location, and theme to this event database. Our aim is to build sentiment indicators arising from this source of information and, in a final step, evaluate if positive and negative indexes have any effect on the evolution of key market variables as prices and demand. Submitted by Pedro Cotillas (pedro.cotillas@unir.net) on 2021-02-03T12:42:50Z No. of bitstreams: 1 ijimai20163_6_6_pdf_24130.pdf: 1789873 bytes, checksum: f3947cd24c47b2e15d088763bcba1d83 (MD5); Made available in DSpace on 2021-02-03T12:42:50Z (GMT). No. of bitstreams: 1 ijimai20163_6_6_pdf_24130.pdf: 1789873 bytes, checksum: f3947cd24c47b2e15d088763bcba1d83 (MD5) https://reunir.unir.net/handle/123456789/10211 Social Network Analysis and Big Data tools applied to the Systemic Risk supervision Mochón, Mari-Carmen After the financial crisis initiated in 2008, international market supervisors of the G20 agreed to reinforce their systemic risk supervisory duties. For this purpose, several regulatory reporting obligations were imposed to the market participants. As a consequence, millions of trade details are now available to National Competent Authorities on a daily basis. Traditional monitoring tools may not be capable of analyzing such volumes of data and extracting the relevant information, in order to identify the potential risks hidden behind the market. Big Data solutions currently applied to the Social Network Analysis (SNA), can be successfully applied the systemic risk supervision. This case of study proposes how relations established between the financial market participants could be analyzed, in order to identify risk of propagation and market behavior, without the necessity of expensive and demanding technical architectures. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2020-06-24T11:35:11Z No. of bitstreams: 1 ijimai20163_6_5_pdf_14449.pdf: 1567937 bytes, checksum: 8c19f6197f0288b94ff5642b4560f14a (MD5); Made available in DSpace on 2020-06-24T11:35:11Z (GMT). No. of bitstreams: 1 ijimai20163_6_5_pdf_14449.pdf: 1567937 bytes, checksum: 8c19f6197f0288b94ff5642b4560f14a (MD5) https://reunir.unir.net/handle/123456789/10210 Big Data & eLearning: A Binomial to the Future of the Knowledge Society Alonso Secades, Vidal; Arranz, Olga There is no doubt that in what refers to the educational area, technology is producing a series of changes that will greatly affect our near future. The increase of students experiences in the new educational systems in distance learning makes possible to have information related to the students ‘activities and how these can be dealt with automatic procedures. The implementation of these analytical methods is possible through the use of powerful new technologies such as Data Mining or Big Data. Relevant information is obtained of the use made by the students of the technological tools in a Learning Management System, thus, allowing us to infer a pattern of behavior of the students, to be used in the future. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2020-06-24T10:53:22Z No. of bitstreams: 1 ijimai20163_6_4_pdf_19453.pdf: 737597 bytes, checksum: ebc22f4becd2d764e142448d322e04cd (MD5); Made available in DSpace on 2020-06-24T10:53:22Z (GMT). No. of bitstreams: 1 ijimai20163_6_4_pdf_19453.pdf: 737597 bytes, checksum: ebc22f4becd2d764e142448d322e04cd (MD5) https://reunir.unir.net/handle/123456789/10209 A Fine Grain Sentiment Analysis with Semantics in Tweets Navas-Delgado, Ismael; Aldana-Montes, Jose F.; Barba Gonzalez, Cristobal; García-Nieto, José Social networking is nowadays a major source of new information in the world. Microblogging sites like Twitter have millions of active users (320 million active users on Twitter on the 30th September 2015) who share their opinions in real time, generating huge amounts of data. These data are, in most cases, available to any network user. The opinions of Twitter users have become something that companies and other organisations study to see whether or not their users like the products or services they offer. One way to assess opinions on Twitter is classifying the sentiment of the tweets as positive or negative. However, this process is usually done at a coarse grain level and the tweets are classified as positive or negative. However, tweets can be partially positive and negative at the same time, referring to different entities. As a result, general approaches usually classify these tweets as “neutral”. In this paper, we propose a semantic analysis of tweets, using Natural Language Processing to classify the sentiment with regards to the entities mentioned in each tweet. We offer a combination of Big Data tools (under the Apache Hadoop framework) and sentiment analysis using RDF graphs supporting the study of the tweet’s lexicon. This work has been empirically validated using a sporting event, the 2014 Phillips 66 Big 12 Men’s Basketball Championship. The experimental results show a clear correlation between the predicted sentiments with specific events during the championship. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2020-06-24T09:27:02Z No. of bitstreams: 1 ijimai20163_6_3_pdf_14247.pdf: 1229376 bytes, checksum: eb1c79f224aab1acf6f5b1290b088b82 (MD5); Made available in DSpace on 2020-06-24T09:27:02Z (GMT). No. of bitstreams: 1 ijimai20163_6_3_pdf_14247.pdf: 1229376 bytes, checksum: eb1c79f224aab1acf6f5b1290b088b82 (MD5) https://reunir.unir.net/handle/123456789/10208 PInCom project: SaaS Big Data Platform for and Communication Channels Lombardo, Juan Manuel; López, Miguel Ángel; Mirón, Felipe; Velasco, Susana; Sevilla, Juan Pablo; Mellado, Juan The problem of optimization will be addressed in this article, based on the premise that the successful implementation of Big Data solutions requires as a determining factor not only effective -it is assumed- but the efficiency of the responsiveness of management information get the best value offered by the digital and technological environment for gaining knowledge. In adopting Big Data strategies should be identified storage technologies and appropriate extraction to enable professionals and companies from different sectors to realize the full potential of the data. A success story is the solution PInCom: Intelligent-Communications Platform that aims customer loyalty by sending multimedia communications across heterogeneous transmission channels. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2020-06-24T08:30:00Z No. of bitstreams: 1 ijimai20163_6_2_pdf_29854.pdf: 1482915 bytes, checksum: 49b9ea3359f9781c15f6227cbea8c602 (MD5); Made available in DSpace on 2020-06-24T08:30:00Z (GMT). No. of bitstreams: 1 ijimai20163_6_2_pdf_29854.pdf: 1482915 bytes, checksum: 49b9ea3359f9781c15f6227cbea8c602 (MD5) https://reunir.unir.net/handle/123456789/10199 Operating an Advertising Programmatic Buying Platform: A Case Study Mochón, Francisco; Gonzalvez-Cabañas, J.C. This paper analyses how new technological developments and the possibilities generated by the internet are shaping the online advertising market. More specifically it focuses on a programmatic advertising case study. The origin of the problem is how publishers resort to automated buying and selling when trying to shift unsold inventory. To carry out our case study, we will use a programmatic online advertising sales platform, which identifies the optimal way of promoting a given product. The platform executes, evaluates, manages and optimizes display advertising campaigns, all in real-time. The empirical analysis carried out in the case study reveals that the platform and its exclusion algorithms are suitable mechanisms for analysing the performance and efficiency of the various segments that might be used to promote products. Thanks to Big Data tools and artificial intelligence the platform performs automatically, providing information in a user-friendly and simple manner. Submitted by Susana Figueroa Navarro (susana.figueroa.n@unir.net) on 2020-06-22T08:13:09Z No. of bitstreams: 1 ijimai20163_6_1_pdf_29476.pdf: 1470590 bytes, checksum: 1df7f252b978d019f53ae215d923dda2 (MD5); Made available in DSpace on 2020-06-22T08:13:09Z (GMT). No. of bitstreams: 1 ijimai20163_6_1_pdf_29476.pdf: 1470590 bytes, checksum: 1df7f252b978d019f53ae215d923dda2 (MD5) https://reunir.unir.net/handle/123456789/5817 Detection of Adverse Reaction to Drugs in Elderly Patients through Predictive Modeling San Miguel Carrasco, Rafael Geriatrics Medicine constitutes a clinical research field in which data analytics, particularly predictive modeling, can deliver compelling, reliable and long-lasting benefits, as well as non-intuitive clinical insights and net new knowledge. The research work described in this paper leverages predictive modeling to uncover new insights related to adverse reaction to drugs in elderly patients. The differentiation factor that sets this research exercise apart from traditional clinical research is the fact that it was not designed by formulating a particular hypothesis to be validated. Instead, it was data-centric, with data being mined to discover relationships or correlations among variables. Regression techniques were systematically applied to data through multiple iterations and under different configurations. The obtained results after the process was completed are explained and discussed next. Submitted by Pedro Cotillas (pedro.cotillas@unir.net) on 2017-10-23T16:13:43Z No. of bitstreams: 0; Made available in DSpace on 2017-10-23T16:13:43Z (GMT). No. of bitstreams: 0