Exploring Unconventional Sources in Big Data: A Data Lifecycle Approach for Social and Economic Analysis with Machine Learning

This study delves into the realm of leveraging unconventional sources within the domain of Big Data for conducting insightful social and economic analyses. Employing a Data Lifecycle Approach, the research focuses on harnessing the potential of linear regression, random forest, and XGBoost techniques to extract meaningful insights from unconventional data sources. The study encompasses a structured methodology involving data collection, preprocessing, feature engineering, model selection, and iterative refinement. By applying these techniques to diverse datasets, encompassing sources like social media content, sensor data, and satellite imagery, the study aims to provide a comprehensive understanding of social and economic trends. The results obtained through these methods contribute to an enhanced comprehension of the intricate relationships within societal and economic systems, further highlighting the importance of unconventional data sources in driving valuable insights for decision-makers and researchers alike

Exploring the benefits of scheduling with advanced and real-time information integration in Industry 4.0: A computational study

The technological advances recently brought to the manufacturing arena (collectively known as Industry 4.0) offer huge possibilities to improve decision-making processes in the shop floor by enabling the integration of information in real-time. Among these processes, scheduling is often cited as one of the main beneficiaries, given its data-intensive and dynamic nature. However, in view of the extremely high implementation costs of Industry 4.0, these potential benefits should be properly assessed, also taking into account that there are different approaches and solution procedures that can be employed in the scheduling decision-making process, as well as several information sources (i.e. not only shop floor status data, but also data from upstream/downstream processes). In this paper, we model various decision-making scenarios in a shop floor with different degrees of uncertainty and diverse efficiency measures, and carry out a computational experience to assess how real-time and advance information can be advantageously integrated in the Industry 4.0 context. The extensive computational experiments (equivalent to 6.3 years of CPU time) show that the benefits of using real-time, integrated shop floor data and advance information heavily depend on the proper choice of both the scheduling approach and the solution procedures, and that there are scenarios where this usage is even counterproductive. The results of the paper provide some starting points for future research regarding the design of approaches and solution procedures that allow fully exploiting the technological advances of Industry 4.0 for decision-making in scheduling.Ministerio de Ciencia e Innovación PID2019-108756RB-I0Junta de Andalucía P18-FR-1149, 5835 and US-12645

Methoden zur Identifikation relevanter Datenquellen: Eine Literaturanalyse

Die zunehmende Anzahl an zu verarbeitenden Unternehmensdaten und die steigende strategische Relevanz des Informationsmanagements formulieren einen Bedarf zur Systematisierung des Prozesses zur Identifikation und Selektion relevanter Datenquellen. Obwohl die Datenquellenauswahl eine zentrale Aufgabe im Management der Informationswirtschaft (im Rahmen des strategischen Informationsmanagements) darstellt, fehlt es einer aktuellen Betrachtung zum Stand der Wissenschaft im Hinblick vorhandener Methoden zur Selektion relevanter Datenquellen. Der vorliegende Beitrag schließt diese Forschungslücke und stellt den aktuellen Stand der Wissenschaft zu vorhandenen Methoden zur Identifikation und Selektion relevanter Datenquellen dar. Mittels einer Literaturanalyse wurden insgesamt 37 wissenschaftliche Beiträge identifiziert, welche acht Methoden zur Datenquellenauswahl beschreiben. Die identifizierten Methoden wurden anschließend den Kategorien automatisierte, semi-automatisierte und manuelle Verfahren zugeordnet. Dabei konnte ebenfalls eine zunehmende Tendenz zu automatisierten Methoden zur Datenquellenauswahl beobachtet werden

Article de synthèse : Les outils du Big Data dans la fonction des ressources humaines : quelles transformations pour quels enjeux ?

On entend souvent le terme « Big Data » de nos jours. Cette notion fait l’actualité et irrigue plusieurs domaines aussi bien dans les entreprises et ses fonctions que dans la vie de tous les jours. Dans les organisations elle concerne principalement le marketing et la fonction commerciale, et, plus récemment la fonction ressources humaines dans ses différents aspects notamment le recrutement, la formation, la gestion des carrières, l’évaluation, la mobilité. Ce travail est une revue de littérature transversale des questions les plus posées par les professionnels sur le Big Data RH et leurs réponses éventuelles. Il a pour but de récapituler les travaux antérieurs permettant ainsi une lecture simple de ce qu’est le Big Data, ses usages dans la fonction RH, sa valeur ajoutée dans la quantification RH et enfin les enjeux de son adoption. L’objectif étant de présenter aux praticiens RH une vue d’ensemble sur le sujet Big Data ainsi que les tenants et aboutissants de son adoption dans l’entreprise