EditorialData-Driven Computational Intelligence forScientific Programming

Alvaro Rubio-Largo ,1 Juan Carlos Preciado ,2 and Luis Iribarne 3

1Universidade Nova de Lisboa, Lisboa 1099-085, Portugal2University of Extremadura, Caceres 10003, Spain3University of Almerıa, Almerıa 04120, Spain

Correspondence should be addressed to Alvaro Rubio-Largo; arl@unex.es

Received 22 July 2019; Accepted 22 July 2019; Published 19 August 2019

Copyright © 2019 Alvaro Rubio-Largo et al. #is 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 isproperly cited.

In recent years, big data and its potential to shed valuedinsights into enhanced decision-making processes hasattracted an increasing interest from both academia andindustry. #roughout history, there have been eras that havemarked turning points in society. Currently, we are facedwith the beginning of one of these turning points, a new leapin evolution which arises thanks to the advantages oftechnology, but which has recently been revolutionizingtechnical concepts of development and programming. Weare in a new age, the era of Data [1].

Currently, the volume of data generated each day is veryhigh, coming from different multiple sources and in diverseformats, usually designed with different goals, methods,profiles, and production and consumption rhythms. #eamount of data generated by businesses, public adminis-trations, and numerous industrial and scientific researchfacilities has increased immeasurably in the past years,turning traditional systems into complex or supercomplexsystems. #ese data may be structured, semistructured, and/or unstructured, extracted from sources as different asNatural Language Processing [2] (chatbots, comments, andsocial media), multimedia content (videos, images, andaudio), geographic information systems (GIS), or sensors(Internet of #ings/Everything) on a wide variety of plat-forms or environments (e.g., machine-to-machine com-munications, social media sites, sensors networks, or naturalinteraction) [3].

#ese are highly relevant data, so much so that they mustbe taken into account when designing and developing thesolutions of the future (or redesigning the present ones).#e

proper evolution of data concept is of vital importance, dueto the fact that they have a huge impact on the economic andsocial development of the institutions and enterprises theybelong to.

In this new cycle of massive data, our administrations,enterprises, and citizens generate colossal amounts of data,data which on their own do not help us in our everyday lifeor in making decisions, and hence the importance of treatingthese data in order to turn them into information that isuseful and relevant for them [4].

#e evolution of computational intelligence and big datapractices stimulates an increasing interest in Analytics so-lution [5]. Accurate prediction, precise identification oftrends, and discovering behaviour patterns can optimize theresource usage or consumption, generating new knowledgein science and research, and enabling faster and betterdecisions in politics, weather, science, research, real estate,sports, or healthcare, among other social application do-mains [2].

To date, the solutions proposed have been disjointed,and actions put into practice as a result of fashions orthrough the implantation of a certain known technology,but without a perspective of the specific need of the conceptof amount of data. #e challenge is no longer the evolutionand development of the technology, the current issue is notrelated with the production and acquisition of data, andnow we are treating with new challenges and problemsregarding the processing of these data to build information[6]. Based on these concepts and the engineering ofcomplex massive data systems with high availability, we see

HindawiScientific ProgrammingVolume 2019, Article ID 5235706, 4 pageshttps://doi.org/10.1155/2019/5235706

that a computational intelligence environment is config-ured like a system, a smart system. In this context,methodology supporting the design of this system wouldfirstly ask the computational intelligence system’s designer,to identify the requirements for the existing data requiredto continue with the other phases.

#e great data acquisition obliges us to have innovativemassive data storage systems on a scale not yet contem-plated. #is mass of data is growing exponentially, and 90%of data on a worldwide scale has been created only in the lasttwo years [7]. #e management and processing of this datadefines a new knowledge management paradigm which canonly be addressed by means of constant methodological andtechnological innovation regarding the field of computa-tional intelligence applied to scientific programming [8].

#e different data sources and formats result in acomplexity which makes decision-making expensive, and incases where the implementation of Big Data, Business In-telligence, and Business Analytics is not enough, users needmore and better ways of understanding data. In this sce-nario, techniques like Data Visualization come into thescene, which enable the visual simplification of all in-formation built from the collected data. #is informationvisualization supports decision-making and the advancedanalysis of the data collected, by means of report andgraphical representation techniques, with data visualizationcapacities which enable actors to build the necessary in-formation flexibly on the same set of data, based on this dataand placing it or other data at the service of third parties,hence providing an environment of rich collaboration andinnovation. One of the trends given clear importance byComputation Intelligence Systems is the visualization ofinformation in a simple, agile, and powerful way, i.e.,Computation Intelligence Systems makes possible theprocess data and display information quickly and in areasonable time frame to assimilate all possible informationby the people for whom it is intended. Definitively, theadvance in technology and scientific programming we areexperiencing plays an essential role in the evolution ofcomputational intelligence environment [9].

Computational intelligence techniques form a set ofnature-inspired computational methodologies and tech-niques which have been developed to face the aforemen-tioned complex scientific programming, for whichtraditional models are unable to work due to high com-plexity, uncertainty, and the stochastic nature of processes.#ese techniques typically include parallel/distributed pat-tern-recognition techniques, genetic programming, fuzzysystems, or evolutionary computation. #e overall aim ofthis special issue is to collect state-of-the-art researchfindings on the latest developments, as well as up-to-dateissues and challenges in the field of computational in-telligence applied to scientific programming.

#is special issue is configured like a collection of paperson a hot topic of increasing interest within of scientificprogramming, presenting and describing new research orapplications in this field. Several call for papers were sent anddistributed among the main mailing lists of the field forrelevant researchers to submit their research to this special

issue. As an example of the current interest in this field, it isworth mentioning that, for this special issue, we havemanaged a total of 15 high-quality submissions from dif-ferent countries: Spain, China, India, Greece, Australia,United Kingdom, and Vietnam. #ese researches have beenmanaged according to the terms and guidelines of thisjournal. All the papers included in this special issue werereviewed by at least two expert reviewers. Furthermore, allthe papers in the special issue received a minimum of tworeview rounds. Finally, five papers of high quality inemerging research areas were accepted for inclusion in thespecial issue (acceptance rate� 5/15� 33.33%). In summary,we think these papers bring us an international sampling ofsignificant work.

In general, the papers included in this special issue coverdetailed scientific aspects related mainly with the fields ofrecommendation systems, machine learning, pattern rec-ognition, spatial data interaction, and complex events. Re-search advances are applied to different social domains suchas smart cities, scientific digital libraries, urban spatial data,and public health.

#e title of our first paper is “Study of Urban SystemSpatial Interaction Based on Microblog Data: A Case ofHuaihe River Basin, China,” by Y. Fan, J. Yao, Z. He, B. He,and M. Li. #is paper obtains the user microblog in-formation through the sina microblog open platform andstudies the urban spatial pattern and urban interaction bymeans of statistical analysis and spatial analysis. #is papertakes the Huaihe basin as the case area to verify the proposalpresented on it.

#e main research conclusions from our first paper areas follows: (1) the data interface provided by the microblogplatform can study the urban spatial pattern. #e usertrajectory of microblog data can explore the spatial re-lationship of regional cities, and data acquisition and dataquality evaluation can meet the research requirements, and(2) based on microblog data, the spatial and temporalcharacteristics of the urban system spatial pattern in theHuaihe River Basin are analyzed from network connectivityand urban interaction. #e study found that the urbanspatial relation in the Huaihe River Basin has the followingcharacteristics: the spatial difference of urban size distri-bution is obvious; urban layout presents a stratified aggre-gation phenomenon; and the high-grade cities lead the city’sinteraction.

As for the application of microblog data in urban re-search, the current data mainly focus on information text,social relations, and other aspects. #e research is mainlyabout event detection and hot spot exploration. #e com-bination of big data thinking and data mining technologywill have more research findings in the study of urbanproblems.

#e second paper is “Practical Experiences in the Useof Pattern-Recognition Strategies to Transform Soft-ware Project Plans into Software Business Processes ofInformation Technology Companies” by C. Arevalo,I. Ramos, J. Gutierrez, andM. Cruz.#e authors’ proposalprovides a framework to generate software businessprocesses that would otherwise be hidden or wasted in

2 Scientific Programming

databases of non-process-aware information systems(non-PAISs). #is hidden knowledge can be used toimplement the business process management approach ininformation technology companies (ITCs) that will helpthem to become more competitive and reduce costs.Compared to other business process discovery methodsused with non-PAISs, their results are more adjusted tothe reality of processes since they focus on trans-formations among artifacts that are close to executedprocesses that exist at different levels of abstraction (i.e.,platform level and software expert level). Furthermore,business processes may be enriched with data regardingresources and costs that may also be bound to projects inproject management systems. #is way, new data will beavailable to set metrics and study key performance in-dicators of software business processes.

#is paper illustrates the AQUA-WS project case studyto test the developed MDA-based roadmap. In this casestudy, they have shown that generated processes are similarto real processes that a business software expert may de-sign. For this reason, they have delivered a semiautomaticproposal to obtain processes of ITCs. As future work, theauthors say that they will be able to use further sourcesystems, such as other PMSs, ECMs, ERPs, CRMs, SCMs,or tailor-made software. Besides, they will propose road-maps to specific SPMLs or GPMLs targets, used by ITCsthat work with this type of systems. In this case study, theyhave considered the following aspects of source systems,target systems, and heuristics to generate businessprocesses.

Our third paper is “A High-Frequency Data-DrivenMachine Learning Approach for Demand Forecasting inSmart Cities” by J. C. Preciado, A. E. Prieto, R. Benitez, R.Rodrıguez-Echeverrıa, and J. M. Conejero. #is paperpresents an approach based on pattern-similarity techniquesto forecast water demand, referred to as short-term patternsimilarity (STPS). #is work faces two important challengesthat have been traditionally neglected in previous ap-proaches, namely, a high frequency of predictions (based onmeasurements in terms of minutes) and the need for ex-ternal data such as annual seasonality or weather that in-creases the complexity of the approaches. In that sense, onthe one hand, it is based on 1min steps predictions, and, onthe other hand, it does not require estimating annual sea-sonality since it determines this seasonality by constructingthe X and Y patterns in which the series has beennormalized.

In order to validate their approach, the study was appliedover three different sites of a city in northern Spain. #eresults obtained provided interesting insights, such as thebest predictions obtained in high-density population areas,the difficulties for identifying patterns for Sundays in in-dustrial areas, or the higher random behaviour in low-density areas. Additionally, their pattern-similarity ap-proach (STPS) was also compared to other similar tech-niques that have been previously used for water forecasting,i.e., αβ water demand forecast (αβ-WDF) and generalizedregression neural network (GRNN). #e results obtained

evidenced that αβ-WDF was the approach with worst resultswhilst GRNN and STPS behave similarly. As future work,the authors try to manage some weaknesses already iden-tified in the proposed method. Firstly, predictions success islower when anomalous days are taken into account. Sec-ondly, with the aim of improving the results for data siteswhere apparently there is not regularity, such as the low-density population area in our study, other approach like theshorter prediction horizons could be considered, for in-stance, 4–6 hours. Notwithstanding, this is a subject thatremains currently untested. Finally, another interesting lineof further work is the application of the presented methodfor water distribution in different cities with similar waterrequirements.

A systematic mapping study is addressed in the fourthpaper, “Recommendation and Classification Systems: ASystematic Mapping Study,” by J. G. Enrıquez, L. Morales-Trujillo, F. Calle-Alonso, F. J. Domınguez-Mayo, and J. M.Lucas-Rodrıguez. #is study has been performed to facil-itate researchers and practitioners the task of choosing themost appropriate system, technology, or algorithm to in-clude in the ADAGIO project for satisfying their re-quirements. In this sense, this paper presents a systematicmapping study (SMS) that analyzes the current state of theart of the recommendation and classification systems andhow they work together.#en, from the point of view of thesoftware development life cycle, this review also shows thatthe work being done in the ML (for classification andrecommendation) research and industrial environment isfar from earlier stages such as business requirements andanalysis. #is makes it very difficult to find efficient andeffective solutions that support real business needs from anearly stage. #en, this paper suggests the development ofnew ML research lines to facilitate its application in thedifferent domains. As future work, the authors propose avery interesting research line may focus on how to combinethese systems to obtain more efficient and effectivesolutions.

Unlike most SMSs that are focused on the scientific lit-erature, this study has been carried out from two points of viewas discussed throughout the paper: the scientific and the in-dustrial scopes. Within the scientific field, the results showedthat the most studied technique in recommendation systems isrecommendation with the use of collaborative filters, closelyfollowed by those that use content-based filters. Only 14 usedhybrid recommendation systems, whereas 31 used collabora-tive filtering and 29 used content-based methods. #is is aninteresting suggestion for researchers starting to use recom-mender systems, to find which of them are more popular andmore used in the scientific environment. By conductingmarketresearch through systematic industrial mapping, it was foundthat there are many technologies that offer automatic learningsolutions, and most of which are complete systems or libraries.However, the nature of most of them could not be knownbecause the proprietary software did not allow it. Anotherimportant issue that must be highlighted is that not only thecommunities of free software developers are interested in thistopic but also there are large companies that are working on it

Scientific Programming 3

for commercial purposes. #is clearly shows the underlyingeconomic interest, an indicator that it is a branch of long-distance research.

#e fifth paper is entitled “Facilitating the QuantitativeAnalysis of Complex Events through a Computational In-telligenceModel-Driven Tool” by G. Dıaz, H.Macia, V. Valero,J. Boubeta-Puig, andG.Ortiz. Complex event processing (CEP)is a computational intelligence technology capable of analyzingbig data streams for event pattern recognition in real time. Inthis paper, the authors illustrate the use of the MEdit4CEP–CPN approach for the complex event analysis through a casestudy based on the sick building syndrome. #e event patternshave been graphically modeled with MEdit4CEP–CPN andthen automatically transformed into both Event ProcessingLanguages (EPL) and Coloured Petri Nets (CPN) code. Ad-ditionally, CPN Tools has been used to make quantitativeanalysis of events produced for this case study. Given theflexibility provided byMEdit4CEP–CPN, this analysis could beapplied to other cutting-edge real-world case studies, such aseHealth, robotic, and mobile edge, and cloud computingapplications.

#e main advantage of MEdit4CEP-CPN is that sup-ports many functionalities that other approaches do notprovide, such as (1) modeling CEP domains and eventpatterns in a user-friendly way by dragging and droppingelements on a canvas, (2) validating the pattern syntax, (3)automatically transforming the graphical patterns into aCPN model, (4) automatically transforming the CPNmodel to the XML code executable by CPN Tools andvalidating the pattern semantics, (5) automatically gener-ating the Esper EPL code and deploying it in a particularevent-based system, and (6) providing a quantitativeanalysis of complex events through the CPN Tools exe-cutable model automatically generated by the tool. Asfuture work, the authors plan to add additional features andfunctionalities to MEdit4CEP-CPN, such as further EPLoperators and new transformation techniques.

Data and computational intelligence are outstandingresearch issues in the field of computer sciences whichcombined together represent one of the most emergingtopics at present. We sincerely hope that you enjoy thisspecial issue. We also have hopes that paper collection as awhole can pleasantly introduce readers to the composite andchallenging arena of the application of computational in-telligence to the scientific programming field, giving a freshview of several state-of-the-art solutions from diverse per-spectives. All accepted papers are within the scope of thejournal and particularly the special issue, and all of themprovide relevant and interesting research techniques,models, and work directly applied to the area of scientificprogramming. Before concluding, we want to express oursincere gratitude to all the authors who submitted theirpaper to this special issue and the many reviewers whosededicated efforts made this special issue possible.

Conflicts of Interest

#e editors declare that they have no potential conflicts ofinterest.

Acknowledgments

#e editors wish to acknowledge the collaborative fundingsupport from Ministerio de Economıa e Innovacion (Spain)(RTI2018-098652-B-I00 (MINECO/ERDF, EU) project),Ministry of Economy and Competitiveness (Spain) (CoS-mart TIN2017-83964-R project), andConsejerıa de Economıae Infraestructuras/Junta de Extremadura (Spain)–EuropeanRegional Development Fund (ERDF) (GR18112 project andIB16055 project). #e editors would also like to thank thereviewers for their generous time in providing detailed com-ments and suggestions that helped us to improve the quality ofthis special issue.

Alvaro Rubio-LargoJuan Carlos Preciado

Luis Iribarne

References

[1] C. L. P. Chen and C.-Y. Zhang, “Data-intensive applications,challenges, techniques and technologies: a survey on big data,”Information Sciences, vol. 275, pp. 314–347, 2014.

[2] S. Sun, C. Luo, and J. Chen, “A review of natural languageprocessing techniques for opinion mining systems,” In-formation Fusion, vol. 36, pp. 10–25, 2017.

[3] H. Harb, A. Makhoul, and C. Abou Jaoude, “A real-timemassive data processing technique for densely distributedsensor networks,” IEEE Access, vol. 6, pp. 56551–56561, 2018.

[4] L. Cao, “Data science: a comprehensive overview,” ACMComputing Surveys, vol. 50, no. 3, pp. 1–42, 2017.

[5] K. Lepenioti, A. Bousdekis, D. Apostolou, and G. Mentzas,“Prescriptive analytics: literature review and research chal-lenges,” International Journal of Information Management,vol. 50, pp. 57–70, 2020.

[6] A. L’Heureux, K. Grolinger, H. F. Elyamany, andM. A. M. Capretz, “Machine learning with big data: challengesand approaches,” IEEE Access, vol. 5, pp. 7776–7797, 2017.

[7] B. P. L. Lau, S. H. Marakkalage, Y. Zhou et al., “A survey of datafusion in smart city applications,” Information Fusion, vol. 52,pp. 357–374, 2019.

[8] Y. Jin and B. Hammer, “Computational intelligence in big data[guest editorial],” IEEE Computational Intelligence Magazine,vol. 9, no. 3, pp. 12-13, 2014.

[9] Z. Lv, H. Song, P. Basanta-Val, A. Steed, and M. Jo, “Next-generation big data analytics: state of the art, challenges, andfuture research topics,” IEEE Transactions on Industrial In-formatics, vol. 13, no. 4, pp. 1891–1899, 2017.

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