Data Farming: The Meanings and Methods Behind the Metaphor

17 USC 105 interim-entered record; under review.The article of record as published may be found at https://doi.org/10.36819/SW21.002Operational Research Society Simulation Workshop 2021Data farming captures the notion of purposeful data generation from simulation models. The ready availability of computing power has fundamentally changed the way simulation and other computational models can be used to provide insights to decision makers. Large-scale designed experiments let us grow the simulation output efficiently and effectively. We can explore massive input spaces, use statistical and visualization techniques to uncover interesting features of complex response surfaces, and explicitly identify cause-and-effect relationships. Nonetheless, there are many opportunities for research methods that could further enhance this process. I will begin with a brief overview of key differences between physical and simulation experiments, as well as current data farming capabilities and their relationship to emerging techniques in data science and analytics. I will then share some thoughts about opportunities and challenges for further improving the state of the art, and transforming the state of the practice, in this domain

Enhancing discrete-event simulation with big data analytics: a review

This article presents a literature review of the use of the OR technique of discrete-event simulation (DES) in conjunction with the big data analytics (BDA) approaches of data mining, machine learning, data farming, visual analytics, and process mining. The two areas are quite distinct. DES represents a mature OR tool using a graphical interface to produce an industry strength process modelling capability. The review reflects this and covers commercial off-the-shelf DES software used in an organisational setting. On the contrary the analytics techniques considered are in the domain of the data scientist and usually involve coding of algorithms to provide outputs derived from big data. Despite this divergence the review identifies a small but emerging literature of use-cases and from this a framework is derived for a DES development methodology that incorporates the use of these analytics techniques. The review finds scope for two new categories of simulation and analytics use: an enhanced capability for DES from the use of BDA at the main stages of the DES methodology as well as the use of DES in a data farming role to drive BDA techniques

Multikonferenz Wirtschaftsinformatik (MKWI) 2016: Technische Universität Ilmenau, 09. - 11. März 2016; Band III

Übersicht der Teilkonferenzen Band III • Service Systems Engineering • Sicherheit, Compliance und Verfügbarkeit von Geschäftsprozessen • Smart Services: Kundeninduzierte Kombination komplexer Dienstleistungen • Strategisches IT-Management • Student Track • Telekommunikations- und Internetwirtschaft • Unternehmenssoftware – quo vadis? • Von der Digitalen Fabrik zu Industrie 4.0 – Methoden und Werkzeuge für die Planung und Steuerung von intelligenten Produktions- und Logistiksystemen • Wissensmanagemen