EditorialScientific Programming Techniques and Algorithms forData-Intensive Engineering Environments

Giner Alor-Hernández ,1 Jezreel Mej-a-Miranda,2 and José Mar-a Álvarez-Rodr-guez 3

1Division of Research and Postgraduate Studies, Instituto Tecnologico de Orizaba, Orizaba, VER, Mexico2Research Center in Mathematics (CIMAT, A. C.), Unit Zacatecas, Zacatecas, ZAC, Mexico3Department of Engineering and Computer Science, Universidad Carlos III de Madrid, Madrid, Spain

Correspondence should be addressed to Giner Alor-Hernandez; galor@itorizaba.edu.mx

Received 18 February 2018; Accepted 19 February 2018; Published 5 November 2018

Copyright © 2018 Giner Alor-Hernandez et al. This is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properlycited.

1. Introduction

In recent years, the development, advancement, and use ofInformation and Communications Technology (ICT) havehad a major impact on the operation, structure, and strategyof organizations around the world. Today, it is unthinkable toconceive an organization without the use of ICT, because itallows for the reduction of communication costs and oper-ation while increasing flexibility, interactivity, performance,and productivity. As a result, digital technology fueled byICT and ICT is currently embedded in any task, activity, andprocess that is done in any organization or even our dailylife activities. This new digital age also implies that science,engineering, and business environments need to reshapetheir strategies and underlying technology to become a keyplayer of the Industrial Revolution 4.0.

Engineering methods such as requirements engineering,systems modeling, complex network analysis, or simulationare currently applied to support the development of criti-cal systems and decision-making processes in operationalenvironments. As an example, cyberphysical systems featuredby mechanical, electrical, and software components are amajor challenge for the industry in which new and integratedscience and engineering techniques are required to developand operate these systems in a collaborative data-intensiveenvironment.

Both the development processes and the operationalenvironments of complex systems need the application ofscientific and engineering methods to fulfill the management

of newmultidisciplinary, data-intensive, and software-centricenvironments. Programming paradigms such as functional,symbolic, logic, linear, or reactive programming in conjunc-tion with development platforms are considered a corner-stone for the proper development of intelligent and federatedprogramming platforms to support continuous and collabo-rative engineering.

More specifically, the availability of huge amounts ofdata that are continuously generated by persons, tools, sen-sors, and any other smart connected device requires newarchitectures to address the challenge of solving complexproblems such as pattern identification, process optimization,discovery of interactions, knowledge inference, execution oflarge simulations, or machine cooperation. This situationimplies the rethinking and application of innovative scientificprogramming techniques for numerical, scientific, and engi-neering computation on top of well-defined hardware andsoftware architectures to support the proper developmentand operation of complex systems.

In this context, the evolution and extension of engi-neering methods through scientific programming techniquesin data-intensive environments are expected to take advan-tage of innovative algorithms implemented using differentprogramming paradigms and execution platforms. The con-junction of scientific programming techniques and engineer-ing techniques will support and enhance existing develop-ment and production environments to provide high qual-ity, economical, reliable, and efficient data-centric softwareproducts and services. This advance in the field of scientific

HindawiScientific ProgrammingVolume 2018, Article ID 1351239, 3 pageshttps://doi.org/10.1155/2018/1351239

2 Scientific Programming

programmingmethods will become a key enabler for the nextwave of software systems and engineering.

Therefore, the main objective of this special issue was tocollect and consolidate innovative and high quality researchcontributions regarding scientific programing techniquesand algorithms applied to the enhancement and improve-ment of engineering methods to develop real and sustainabledata-intensive science and engineering environments.

2. Papers

This special issue aims to provide insights into the recentadvances in the aforementioned topics by soliciting originalscientific contributions in the form of theoretical founda-tions, models, experimental research, surveys, and case stud-ies for scientific programing techniques and algorithms indata-intensive environments. This special issue just containsone type of contribution: regular research papers. Theseworks have been edited according to the norms and guide-lines of the journal. Several call for papers were distributedamong the main mailing lists of the field for researchers tosubmit their works to this issue. We received a total of 20submissions which were subject to a rigorous review processto ensure their clarity, authenticity, and relevance to thisspecial issue. At least three reviewers were assigned to everywork to proceed with the peer review process. Seven paperswere finally accepted for their publication after correctionsrequested by reviewers and editors were addressed.The sevenregular research papers introduce new and interesting resultsin the form of theoretical and experimental research and casestudies under new perspectives on scientific programmingtechniques and algorithms for data-intensive engineeringenvironments.

One of the special issue’s research papers is entitled“Design and Solution of a SurrogateModel for PortfolioOpti-mization Based on Project Ranking,” where E. Fernandez etal. propose a knowledge-based decision support system forthe Portfolio Selection Problem on a Set of Ordered Projects.The results show that the reduction of the dimensionalitysupports the decision-maker in choosing the best portfolio.

In another contribution, entitled “Sentiment Analysis inSpanish for Improvement of Products and Services: A DeepLearning Approach,” M. A. Paredes-Valverde et al. proposea deep-learning-based approach that allows companies andorganizations to detect opportunities for improving the qual-ity of their products or services through sentiment analysis.The approach is based on Convolutional Neural Network(CNN) and word2vec. To determine the effectiveness ofthe approach for classifying tweets, some experiments wereconducted with different sizes of a Twitter corpus composedof 100000 tweets. Results showed a precision of 88.7%, arecall of 88.7%, and an 𝐹-measure of 88.7% considering thecomplete dataset.

In a further paper, entitled Scalable “Parallel DistributedCoprocessor System for Graph Searching Problems withMassive Data,” Y. Sun at al. propose a scalable and novelfield programmable gate array-based heterogeneous multi-core system for scientific programming. Authors designedconsiderable parallelism and relatively low clock frequencies

to achieve high performance and customized memory archi-tecture to deal with irregular memory access pattern.

In an additional contribution, entitled “Analysis of Med-ical Opinions about the Nonrealization of Autopsies in aMexican Hospital Using Association Rules and BayesianNetworks,” E. R. Delgado identifies the factors influencingthe reduction of autopsies in a hospital in Veracruz. Thestudy is based on the application of data mining techniquessuch as association rules and Bayesian networks in datasetscreated from the opinions of physicians. For the explorationand extraction of the knowledge, some algorithms likeApriori, FPGrowth, PredictiveApriori, Tertius, J48, NaiveBayes, MultilayerPerceptron, and BayesNet were analyzed.To generate mining models and present the new knowledgein natural language, a web-based application was developed.The results have been validated by specialists in the field ofpathology.

The fifth paper, entitled “Applying Softcomputing forCopper Recovery in Leaching Process,” C. Leiva et al.present a predictive modelling contrasting; a linear model,a quadratic model, a cubic model, and a model based onthe use of an artificial neural network (ANN) for copperrecovery in leaching process. The ANN was constructedwith 9 input variables, 6 hidden layers, and a neuron inthe output layer corresponding to copper leaching predic-tion. The validation of the models was performed withreal information and these results were used by a miningcompany in northern Chile to improve copper mining pro-cesses.

In a further contribution, entitled “Semantic Annotationof Unstructured Documents Using Concepts Similarity,”F. Pech et al. propose a semantic annotation strategy forunstructured documents as part of a semantic search engine.Ontologies are used to determine the context of the enti-ties specified in the query. The strategy to extracting thecontext is focused on concepts similarity. Each relevantterm of the document is associated with an instance inthe ontology. The similarity between each of the explicitrelationships is measured through the combination of twotypes of associations: the association between each pair ofconcepts and the calculation of the weight of the relation-ships.

Finally, a paper entitled “A Heterogeneous System Basedon Latent Semantic Analysis Using GPU and Multi-CPU”is by G. A. Leon-Paredes et al. They introduce a hetero-geneous Latent Semantic Analysis (hLSA) system, whichhas been developed using general-purpose computing ongraphics processing units (GPGPU), which can solve largeproblems faster through the thousands of concurrent threadson the multiple-core multiprocessors of GPUs and multi-CPU architectures which offer a shared memory program-ming model in a multithreaded environment. The results ofthe experiments show that the acceleration reached by anhLSA system for large matrices with one hundred and fiftythousand million values is around eight times faster thanthe standard LSA version with an accuracy of 88% and arecall of 100%. The performance gain is achieved by usingheterogeneous system architectures for matrix computationand text processing.

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3. Conclusions

As can be seen, all accepted papers are aligned with the scopeof the special issue, and all of them provide quite interestingresearch techniques, models, and studies directly applied tothe area of scientific programming techniques and algorithmsfor data-intensive engineering Environments.

Acknowledgments

The preparation of this special collection has been par-tially supported by the Tecnologico Nacional de Mexico(TECNM), National Council of Science and Technology(CONACYT), and the Public Education Secretary (SEP)through PRODEP. It has also been supported by the ResearchAgreement between the RTVE (the Spanish Radio andTelevision Corporation) and the UC3M to boost research inthe fields of big data, linked data, complex network analysis,and natural language. Last but not least, we would also liketo express our gratitude to the reviewers who kindly acceptedto contribute in the evaluation of papers at all stages of theediting process. We equally and especially wish to thankthe editorial board for the opportunity to edit this specialissue and for providing valuable comments to improve theselection of research works.

Giner Alor-HernandezJezreel Mejıa-Miranda

Jose Marıa Alvarez-Rodrıguez

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