Using Agent Based Modeling and Simulation for Data Mining

Kugu, Emin; Altay, Levent; Şahingöz, Özgür Koray

doi:10.1007/978-3-642-34481-7_32

Cited by 3 publications

(3 citation statements)

References 10 publications

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“…As postulated by Kugu et al [18], data mining methods allow information to be extracted from data sets. The authors developed a simulation model, which aimed to, using intelligent learning capabilities, generate data for the situations in which there is not enough data stored in the DW to allow such patterns to be discovered.…”

Section: Simulation and Structures To Store And Integrate Datamentioning

confidence: 99%

On the use of simulation as a Big Data semantic validator for supply chain management

Vieira

Dias

Santos

et al. 2020

Simulation Modelling Practice and Theory

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Simulation stands out as an appropriate method for the Supply Chain Management (SCM) field. Nevertheless, to produce accurate simulations of Supply Chains (SCs), several business processes must be considered. Thus, when using real data in these simulation models, Big Data concepts and technologies become necessary, as the involved data sources generate data at increasing volume, velocity and variety, in what is known as a Big Data context. While developing such solution, several data issues were found, with simulation proving to be more efficient than traditional data profiling techniques in identifying them. Thus, this paper proposes the use of simulation as a semantic validator of the data, proposed a classification for such issues and quantified their impact in the volume of data used in the final achieved solution. This paper concluded that, while SC simulations using Big Data concepts and technologies are within the grasp of organizations, their data models still require considerable improvements, in order to produce perfect mimics of their SCs. In fact, it was also found that simulation can help in identifying and bypassing some of these issues.

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Section: Simulation and Structures To Store And Integrate Datamentioning

confidence: 99%

On the use of simulation as a Big Data semantic validator for supply chain management

Vieira

Dias

Santos

et al. 2020

Simulation Modelling Practice and Theory

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“…As postulated by Kugu et al (2012), data mining methods allow information to be extracted from data sets. However, when a DW is new, there may not be enough data stored.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Supply Chain Simulation: A Big Data-Driven Approach

Vieira

Dias

Santos

et al. 2019

2019 Winter Simulation Conference (WSC)

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Simulation of Supply Chains comprises huge amounts of data, resulting in numerous entities flowing in the model. These networks are highly dynamic systems, where entities' relationships and other elements evolve with time, paving the way for real-time Supply Chain decision-support tools capable of using real data. In light of this, a solution comprising of a Big Data Warehouse to store relevant data and a simulation model of an automotive plant, are being developed. The purpose of this paper is to address the modelling approach, which allowed the simulation model to automatically adapt to the data stored in a Big Data Warehouse and thus adapt to new scenarios without manual intervention. The main characteristics of the conceived solution were demonstrated, with emphasis to the real-time and the ability to allow the model to load the state of the system from the Big Data Warehouse.

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“…Once an ABM has been calibrated and validated, it can be used to generate a set of quasi-real data of the appropriate size and quality for specific data mining tasks [87]. This is particularly valuable for organisations that may have only recently begun to gather datasets concerning their processes and so do not have a wealth of historical data to rely on [95].…”

Section: Integration With Abmsmentioning

confidence: 99%

Case Study for the Return on Investment of Internet of Things Using Agent-Based Modelling and Data Science

Houston

Gooberman-Hill

Mathie

et al. 2017

Systems

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Abstract:As technology advances towards new paradigms such as the Internet of Things, there is a desire among business leaders for a reliable method to determine the value of supporting these ventures. Traditional simulation and analysis techniques cannot model the complex systems inherent in fields such as infrastructure asset management, or suffer from a lack of data on which to build a prediction. Agent-based modelling, through an integration with data science, presents an attractive simulation method to capture these underlying complexities and provide a solution. The aim of this work is to investigate this integration as a refined process for answering practical business questions. A specific case study is addressed to assess the return on investment of installing condition monitoring sensors on lift assets in a London Underground station. An agent-based model is developed for this purpose, supported by analysis from historical data. The simulation results demonstrate how returns can be achieved and highlight features induced as a result of stochasticity in the model. Suggestions of future research paths are additionally outlined.

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Using Agent Based Modeling and Simulation for Data Mining

Cited by 3 publications

References 10 publications

On the use of simulation as a Big Data semantic validator for supply chain management

On the use of simulation as a Big Data semantic validator for supply chain management

Real-Time Supply Chain Simulation: A Big Data-Driven Approach

Case Study for the Return on Investment of Internet of Things Using Agent-Based Modelling and Data Science

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