A Strategy to Identify Data from the Same Object Based on MIP Data Model

Souza, Luciene Carvalho Corrêa de; Pinheiro, Wallace A.

doi:10.1109/brics-cci-cbic.2013.118

Cited by 1 publication

(1 citation statement)

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“…The thematic grouping proposed in this work evolves some concepts from Souza and Pinheiro [7], [8] for multi-criteria clustering of data from different sources. Specifically, we propose new ideas for the architecture, the multi-criteria clustering, the sequential filtering based on attributes and the configuration techniques for the operators.…”

Section: Introductionmentioning

confidence: 99%

Thematic grouping for messages in major events

Pinheiro¹,

Fernandes²

2022

IJISPM

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The process of information evaluation may compete with the decision making process by requiring the limited cognitive resources. In the case of Major Events, such as the Football World Cup or the Olympic Games, the thematic grouping of every information may be overwhelming. The theme switching caused by information associated with a different thematic group may be modelled as an interruption in multitasking set-ups. Thus, automating the thematic grouping of information may facilitate the decision making process by reducing the theme switching for a decision maker when he reads a set of messages. In this paper were used clustering techniques with multi-criteria to group the messages in themes. These criteria were implemented as configurable operators. To achieve a better comprehension of those parameters, we introduced the concepts of Thematic Strength (TS) and Thematic Density (TD). The evaluation of the strategy was made over a set of operational messages available in the Pacificador system during the 2014 FIFA World Cup.

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Section: Introductionmentioning

confidence: 99%