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Data Provenance for Agent-Based Models in a Distributed Memory

School of Science, Technology, Engineering & Mathematics, University of Washington Bothell, Bothell, WA 98011, USA
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Informatics 2018, 5(2), 18; https://doi.org/10.3390/informatics5020018
Received: 12 December 2017 / Revised: 4 April 2018 / Accepted: 4 April 2018 / Published: 9 April 2018
(This article belongs to the Special Issue Using Computational Provenance)
Agent-Based Models (ABMs) assist with studying emergent collective behavior of individual entities in social, biological, economic, network, and physical systems. Data provenance can support ABM by explaining individual agent behavior. However, there is no provenance support for ABMs in a distributed setting. The Multi-Agent Spatial Simulation (MASS) library provides a framework for simulating ABMs at fine granularity, where agents and spatial data are shared application resources in a distributed memory. We introduce a novel approach to capture ABM provenance in a distributed memory, called ProvMASS. We evaluate our technique with traditional data provenance queries and performance measures. Our results indicate that a configurable approach can capture provenance that explains coordination of distributed shared resources, simulation logic, and agent behavior while limiting performance overhead. We also show the ability to support practical analyses (e.g., agent tracking) and storage requirements for different capture configurations. View Full-Text
Keywords: data provenance; agent-based systems; distributed parallel computing data provenance; agent-based systems; distributed parallel computing
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Davis, D.B.; Featherston, J.; Vo, H.N.; Fukuda, M.; Asuncion, H.U. Data Provenance for Agent-Based Models in a Distributed Memory. Informatics 2018, 5, 18.

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