{'fraction_non_alphanumeric': 0.02418745275888133, 'fraction_numerical': 0.0, 'mean_word_length': 5.186915887850467, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A framework for combining Business Intelligence & Agent-based Simulation

Integrated environmental modeling approaches, especially agent-based modeling ones, are increasingly used in large-scale decision support systems. A major consequence of this trend is the manipulation and generation of a huge amount of data in simulations, which require to be efficiently managed. In this paper, we present a Combination Framework of Business intelligence solution and Multi-agent platform (CFBM) and its implementation on the GAMA platform. CFBM is a logical framework dedicated to the management of the input and output data of simulations, as well as the corresponding empirical datasets in an integrated way. To this purpose we propose to couple two major methodologies: Multi-Agent Simulation (MAS) on one hand and Business Intelligence (BI) solution on the other. An integrated model of a surveillance network and Brown Plant Hopper (BPH) invasion is presented and used throughout the paper as a case study of managing the data used and generated during the life-cycle of a simulation, from its initialization using real data to the generation of simulation outcomes. We show how the proposed combination framework can turn a model into an effective decision-support system, by helping the modeler to manage what-if scenarios.