{"title":"A Preliminary Investigation of a Semi-Automatic Criminology Intelligence Extraction Method: A Big Data Approach","authors":"M. Trovati, P. Hodgson, C. Hargreaves","doi":"10.1109/INCoS.2015.37","DOIUrl":null,"url":null,"abstract":"The aim of any science is to advance the state-of-the-art knowledge via a rigorous investigation and analysis of empirical observations, as well as the development of new theoretical frameworks. Data acquisition and ultimately the extraction of novel knowledge, is therefore the foundation of any scientific advance. However, with the increasing creation of data in various forms and shapes, identifying relevant information from structured and unstructured data sets raises several challenges, as well as opportunities. In this paper, we discuss a semi-automatic method to identify, analyse and generate knowledge specifically focusing on Criminology. The main motivation is to provide a toolbox to help criminology experts, which would potentially lead to a better understanding and prediction of the properties to facilitate the decision making process. Our initial validation shows the potential of our method providing relevant and accurate results.","PeriodicalId":345650,"journal":{"name":"2015 International Conference on Intelligent Networking and Collaborative Systems","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 International Conference on Intelligent Networking and Collaborative Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INCoS.2015.37","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The aim of any science is to advance the state-of-the-art knowledge via a rigorous investigation and analysis of empirical observations, as well as the development of new theoretical frameworks. Data acquisition and ultimately the extraction of novel knowledge, is therefore the foundation of any scientific advance. However, with the increasing creation of data in various forms and shapes, identifying relevant information from structured and unstructured data sets raises several challenges, as well as opportunities. In this paper, we discuss a semi-automatic method to identify, analyse and generate knowledge specifically focusing on Criminology. The main motivation is to provide a toolbox to help criminology experts, which would potentially lead to a better understanding and prediction of the properties to facilitate the decision making process. Our initial validation shows the potential of our method providing relevant and accurate results.