Towards the Integration of Multiagent Applications and Data Mining

Ralha, Célia Ghedini

doi:10.1007/978-1-4419-0522-2_2

Cited by 4 publications

(3 citation statements)

References 32 publications

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“…A few years ago, researchers have studied means of joining forces between agent and data mining technologies. These studies have given rise to a new research field which became known as agent mining [44][45][46][47][48][49] or agent-mining interaction and integration [43,[50][51][52]. Agent mining is the most popular term.…”

Section: Multiagent System For Image Miningmentioning

confidence: 99%

Multiagent System for Image Mining

Freitas¹,

SantosSilva²

2017

Multi-Agent Systems

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The overdone growth, wide availability, and demands for remote sensing databases combined with human limits to analyze such huge datasets lead to a need to investigate tools, techniques, methodologies, and theories capable of assisting humans at extracting knowledge. Image mining arises as a solution to extract implicit knowledge intelligently and semiautomatically or other patterns not explicitly stored in the huge image databases. However, spatial databases are among the ones with the fastest growth due to the volume of spatial information produced many times a day, demanding the investigation of other means for knowledge extraction. Multiagent systems are composed of multiple computing elements known as agents that interact to pursuit their goals. Agents have been used to explore information in the distributed, open, large, and heterogeneous platforms. Agent mining is a potential technology that studies ways of interaction and integration between data mining and agents. This area brought advances to the technologies involved such as theories, methodologies, and solutions to solve relevant issues more precisely, accurately and faster. AgentGeo is evidence of this, a multiagent system of satellite image mining that, promotes advances in the state of the art of agent mining, since it relevant functions to extract knowledge from spatial databases.

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Section: Multiagent System For Image Miningmentioning

confidence: 99%

Multiagent System for Image Mining

Freitas¹,

SantosSilva²

2017

Multi-Agent Systems

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“…Em trabalhos futuros pretendemos avançar nossas pesquisas na integração entre as áreas de MD e de SMA como suporte ao trabalho de auditoria governamental, seguindo a linha de pesquisa de AMII (RALHA, 2009). A continuação do trabalho levará à definição de um modelo arquitetural de SMA, com o desenvolvimento de um protótipo para aplicação em problemas tanto de detecção de formação de cartéis em licitações públicas, como em outras possibilidades de auditoria no âmbito da CGU (SILVA e RALHA, 2011).…”

Section: Conclusãounclassified

Detecção De Cartéis Em Licitações Públicas Com Agentes De Mineração De Dados

Silva¹,

Ralha

2011

RESI

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The Office of the Comptroller General (CGU), as the central agency of Brazil's Federal Government Internal Control is responsible for the fiscalization and auditing to fight and prevent corruption. However, some activities such as government purchasing fraud detection are limited by the difficulty of finding effective solutions, considering the huge volume of data, with millions of finantial registers. In such a context, the proccess of knowledge discovery may take advantage of Data Mining techniques, including classification, clusterization and association rules; which associated to multiagent system enrich the processing power through the interation and distribuiton of data mining agents. Thus, this research work used data mining agents with association rules and clusterization techniques to identify cartels, acting in fraud detection. As a research result, more than one hundred association rules were discovered, of which ten have strong evidence of cartelization, proving the usefulness of the approach to support the work of government auditing.

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“…Considering an ongoing research work, BioAgents was described to illustrate the recent research field of Agent-Mining Interaction and Integration (AMII) (Ralha, 2009). In another perspective, we have implemented a reinforcement learning (RL) method to BioAgents, where we have used together with the genome sequencing projects previously experimented -Paracoccidioides brasiliensis (Pb fungus) and Paullinia cupana (Guaraná plant), together with two reference genomes -Caenorhabditis elegans and Arabidopsis thaliana, respectively, for Pb and Guaraná.…”

Section: Previous Workmentioning

confidence: 99%

A Multi-Agent Tool to Annotate Biological Sequences

Ralha

Schneider

Walter

et al. 2011

Proceedings of the 3rd International Conference on Agents and Artificial Intelligence

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Nowadays, great challenges are imposed by the existence of enormous volume of DNA and RNA sequences, which are continuously being discovered by genome sequencing projects, through the automatic sequencers based on massively parallel sequencing technologies. Thus, the task of identifying biological function for these sequences is a key activity in these high-throuput sequencing projects, where the automatic annotation must be significantly improved. In this context, this paper presents a multi-agent approach to address the important issue of automatic annotation in genome projects. We developed a sophisticated prototype named BioAgents, which simulates biologists knowledge and experience to annotate DNA or RNA sequences in genome sequencing projects, where different specialized intelligent agents work together to accomplish the annotation process.

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Towards the Integration of Multiagent Applications and Data Mining

Cited by 4 publications

References 32 publications

Multiagent System for Image Mining

Multiagent System for Image Mining

Detecção De Cartéis Em Licitações Públicas Com Agentes De Mineração De Dados

A Multi-Agent Tool to Annotate Biological Sequences

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