Um Survey sobre a utilização de técnicas de Data Mining e Data Analytics por agências de investigação criminal do Brasil

Em investigações criminais complexas, os envolvidos lidam com uma quantidade enorme e complexa de dados que necessitam de recursos computacionais especializados na extração de informações e correlações relevantes para o processo investigativo. Neste cenário, é necessário que haja apoio computacional, desde a etapa de armazenamento e integração entre diferentes bases de dados, até a etapa de análise estatística e descoberta de padrões. Este artigo discute os resultados de um Survey aplicado aos principais órgãos de combate ao crime organizado, tais como as agências de Inteligência de Segurança Pública – ISP, os Laboratórios de Tecnologia de Combate à Lavagem de Dinheiro – LABLDs e os Grupos de Atuação Especial de Repressão ao Crime Organizado – GAECO. O objetivo principal foi o de conhecer o cenário atual da utilização de ferramentas de análise de dados nessas agências, projetando as necessidades de pesquisa e investimentos nesta área. Entre os resultados encontrados, observou-se que 40% dos pesquisados não conhecem e 15% não utilizam soluções de ETL (Extract, Transform and Load), apesar de todos (100%) declararem possuir pelo menos uma ferramenta de Data Mining no seu local de trabalho, bem como também declararem (100%) possuir pelo menos uma ferramenta de OLAP/BI (Online Analytical Processing/Business Intelligence). Por fim e com proeminente destaque, apenas 2,77% dos pesquisados utilizam diretamente algum algoritmo de Mineração de Dados para extração de conhecimento. Este cenário evidencia, inicialmente, que a maior parte dos órgãos especializados em investigação do Brasil ainda não aplica efetivamente as técnicas de Data Mining e de Data Analytics em suas atividades.

Data Mining in Franchise Organizations

Data Warehousing and Mining ◽

10.4018/978-1-59904-951-9.ch169 ◽

2008 ◽

pp. 2722-2733 ◽

Cited By ~ 18

Author(s):

Ye-Sho Chen ◽

Robert Justis ◽

P. Pete Chong

Keyword(s):

Data Mining ◽

Growth Strategy ◽

Pertinent Data ◽

Analytical Processing ◽

Customer Services ◽

Internal Operations ◽

Comprehensive Framework ◽

Franchise Organizations

Franchising has been used by businesses as a growth strategy. Based on the authors’ cumulative research and experience in the industry, this paper describes a comprehensive framework that describes both the franchise environment — from customer services to internal operations — and the pertinent data items in the system. The authors identify the most important aspects of a franchising business, the role of online analytical processing (OLAP) and data mining play and the data items that data mining should focus on to ensure its success.

Online Data Mining

Data Warehousing and Mining ◽

10.4018/978-1-59904-951-9.ch007 ◽

2008 ◽

pp. 75-83

Author(s):

He´ctor Oscar Nigro ◽

Sandra Elizabeth González Císaro

Keyword(s):

Data Mining ◽

Data Analysis ◽

Intelligent Data Analysis ◽

Online Data ◽

Several approaches for intelligent data analysis are not only available but also tried and tested. Online analytical processing (OLAP) and data mining represent two of the most important approaches. They mainly emphasize different aspects of the data and allow deriving of different kinds of information. So far, these approaches have mainly been used in isolation (Schwarz, 2002).

Data Warehouse and Business Intelligence Systems in the Context of E-HRM

Encyclopedia of Human Resources Information Systems ◽

10.4018/978-1-59904-883-3.ch034 ◽

2009 ◽

pp. 223-229

Author(s):

Martin Burgard ◽

Franca Piazza

Keyword(s):

Data Mining ◽

Information Technology ◽

Resource Management ◽

Human Resource Management ◽

Data Warehouse ◽

Human Resource ◽

Business Intelligence ◽

Multidimensional Data ◽

Business Intelligence Systems ◽

The increased use of information technology leads to the generation of huge amounts of data which have to be stored and analyzed by appropriate systems. Data warehouse systems allow the storage of these data in a special multidimensional data base. Based on a data warehouse, business intelligence systems provide different analysis methods such as online analytical processing (OLAP) and data mining to analyze these data. Although these systems are already widely used and the usage is still growing, their application in the area of electronic human resource management (e-HRM) is rather scarce. Therefore, the objective of this article is to depict the components and functionality of these systems and to illustrate the application possibilities and benefits of these systems by selected application examples in the context of e-HRM.

Data Management, Analytics and Innovation - Advances in Intelligent Systems and Computing ◽

Business Intelligence Through Big Data Analytics, Data Mining and Machine Learning

10.1007/978-981-13-9364-8_17 ◽

2019 ◽

pp. 217-230

Author(s):

Wael M. S. Yafooz ◽

Zainab Binti Abu Bakar ◽

S. K. Ahammad Fahad ◽

Ahamed. M Mithun

Keyword(s):

Machine Learning ◽

Data Mining ◽

Big Data ◽

Business Intelligence ◽

Data Analytics ◽

Big Data Analytics

Online Data Mining

Encyclopedia of Database Technologies and Applications ◽

10.4018/978-1-59140-560-3.ch072 ◽

2005 ◽

pp. 427-432

Author(s):

Héctor Oscar Nigro ◽

Sandra Elizabeth González Císaro

Keyword(s):

Data Mining ◽

Data Analysis ◽

Intelligent Data Analysis ◽

Online Data ◽

Analysis on Ship Equipment Consumption Data Based on Data Mining

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.846-847.1141 ◽

2013 ◽

Vol 846-847 ◽

pp. 1141-1144

Author(s):

Dan Dan Chen ◽

Zhi Gang Yao

Keyword(s):

Data Mining ◽

Cluster Analysis ◽

Regression Analysis ◽

Data Warehouse ◽

Comprehensive Analysis ◽

Consumption Data ◽

Ship Maintenance ◽

A comprehensive analysis on a large amount of ship equipment consumption data accumulated over the years is achieved through the establishment of data warehouse, online analytical processing, regression analysis, cluster analysis, etc. by means of data mining. The analysis results present important references for equipment guarantee department in terms of equipment preparation and carrying, etc. and provide the comprehensive analysis and utilization on massive ship maintenance support data with technical means.

Analisa In Depth Online Analytical Processing (OLAP) Business Intelligence (BI) dengan Filter Inheritance pada Distribusi Sosial di Kecamatan Setiabudi

Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) ◽

10.35870/jtik.v5i3.184 ◽

2020 ◽

Vol 5 (3) ◽

pp. 300

Author(s):

Alhadi Alhadi ◽

Iskandar Fitri ◽

Andrianingsih Andrianingsih

Keyword(s):

Business Intelligence ◽

Social Service ◽

Census Data ◽

Social Assistance ◽

Government Agencies ◽

Intelligence System ◽

The Social ◽

The Family ◽

A lot of census data in the sub-district is very useful and helps the social service to provide social assistance in a sub-district. With this Business Intelligence system, it can help analyze information on providing social assistance with the help of using the Tableau Tools so that the information is more detailed and displays a graphic / dashboard visualization. This research is to analyze how certain people who receive social assistance for residents of Setiabudi sub-district, and each provision of social assistance will be collected from the sub-district and submitted to each sub-district to be able to data with certainty, using the number of data on the head of the family registered in Setiabudi District.Keywords:Business Intelligence, Tableau Tools, OLAP, Government Agencies.

An Intelligent Support System Integrating Data Mining and Online Analytical Processing

Organizational Data Mining ◽

10.4018/978-1-59140-134-6.ch010 ◽

2011 ◽

pp. 141-156

Author(s):

Rahul Singh ◽

Richard T. Redmond ◽

Victoria Yoon

Keyword(s):

Data Mining ◽

Decision Making ◽

Decision Support ◽

Decision Makers ◽

Intelligent Decision Making ◽

Course Of Action ◽

Intelligent Decision ◽

Analytical Processing ◽

Intelligent Decision Support

Intelligent decision support requires flexible, knowledge-driven analysis of data to solve complex decision problems faced by contemporary decision makers. Recently, online analytical processing (OLAP) and data mining have received much attention from researchers and practitioner alike, as components of an intelligent decision support environment. Little that has been done in developing models to integrate the capabilities of data mining and online analytical processing to provide a systematic model for intelligent decision making that allows users to examine multiple views of the data that are generated using knowledge about the environment and the decision problem domain. This paper presents an integrated model in which data mining and online analytical processing complement each other to support intelligent decision making for data rich environments. The integrated approach models system behaviors that are of interest to decision makers; predicts the occurrence of such behaviors; provides support to explain the occurrence of such behaviors and supports decision making to identify a course of action to manage these behaviors.

Strategic Utilization of Data Mining

Encyclopedia of Information Science and Technology, First Edition ◽

10.4018/978-1-59140-553-5.ch468 ◽

2005 ◽

pp. 2638-2642

Author(s):

Chandra S. Amaravadi

Keyword(s):

Artificial Intelligence ◽

Data Mining ◽

Decision Support ◽

Information Systems ◽

Relational Databases ◽

Artificial Intelligence Techniques ◽

The Past ◽

In the past decade, a new and exciting technology has unfolded on the shores of the information systems area. Based on a combination of statistical and artificial intelligence techniques, data mining has emerged from relational databases and Online Analytical Processing as a powerful tool for organizational decision support (Shim et al., 2002).

Advances in Business Information Systems and Analytics - Integration Challenges for Analytics, Business Intelligence, and Data Mining ◽

Integration of Data Mining and Business Intelligence in Big Data Analytics

10.4018/978-1-7998-5781-5.ch002 ◽

2021 ◽

pp. 13-43

Author(s):

Atik Kulakli

Keyword(s):

Data Mining ◽

Big Data ◽

Business Intelligence ◽

Data Analytics ◽

Big Data Analytics ◽

Impact Factors ◽

Publication Trends ◽

Author Productivity ◽

Research Questions ◽

Research Domains

The purpose of this chapter is to analyze and explore the research studies for scholarly publication trends and patterns related to the integration of data mining in particular business intelligence in big data analytics domains published in the period of 2010-2019. Research patterns explore in highly prestigious sources that have high impact factors and citations counted in the ISI Web of Science Core Collection database (indexes included SCI-Exp and SSCI). Bibliometric analysis methods applied for this study under the research limitations. Research questions formed based on bibliometric principles concentrating fields such as descriptive of publication, author productivity, country-regions distribution, keyword analysis with contribution among researchers, citation analysis, co-citation patterns searched. Findings showed strong relations and patterns on these important research domains. Besides this chapter would useful for researchers to obtain an overview of publication trends on research domains to be concerned for further studies and shows the potential gaps in those fields.