scholarly journals Atualização da doutrina de gerenciamento de crises: Incidentes policiais e centros de consciência situacional C5I na quarta revolução industrial

2020 ◽  
Vol 13 (1) ◽  
pp. 49-59
Author(s):  
Paulo Augusto Aguilar
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Business Intelligence

O presente artigo tem como objetivo abordar a atualização de gerenciamento de crises. Trata-se de um tema bastante importante para a atividade policial, pois visa a expandir a possibilidade das polícias brasileiras, seja militar, civil ou federal, de investigar delitos diversos, relacionados à segurança pública, ao fornecer conceitos de Big Data, Data Mining, Data Storytelling e Business Intelligence como forma de gerar melhor consciência situacional e imagem operacional comum de incidentes de todos os tipos e tamanhos, tudo isso com a flexibilidade de aplicativos disponíveis em smartphones, em tempo real, agilizando a capacidade de resposta e de adaptação do Estado diante de cenários VUCA, utilizado para descrever cenários caracterizados por volatilidade (volatility), incerteza (uncertainty), complexidade (complexity) e ambiguidade (ambiguity).

Big Data Quality for Data Mining in Business Intelligence Applications

2021 ◽  
pp. 64-91
Author(s):  
Arun Thotapalli Sundararaman
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Quality ◽  
Business Intelligence ◽  
Future Research ◽  
Quality Factors ◽  
Data Volume ◽  
Mining Algorithms ◽  
Data Mining Application

Study of data quality for data mining application has always been a complex topic; in the recent years, this topic has gained further complexity with the advent of big data as the source for data mining and business intelligence (BI) applications. In a big data environment, data is consumed in various states and various forms serving as input for data mining, and this is the main source of added complexity. These new complexities and challenges arise from the underlying dimensions of big data (volume, variety, velocity, and value) together with the ability to consume data at various stages of transition from raw data to standardized datasets. These have created a need for expanding the traditional data quality (DQ) factors into BDQ (big data quality) factors besides the need for new BDQ assessment and measurement frameworks for data mining and BI applications. However, very limited advancement has been made in research and industry in the topic of BDQ and their relevance and criticality for data mining and BI applications. Data quality in data mining refers to the quality of the patterns or results of the models built using mining algorithms. DQ for data mining in business intelligence applications should be aligned with the objectives of the BI application. Objective measures, training/modeling approaches, and subjective measures are three major approaches that exist to measure DQ for data mining. However, there is no agreement yet on definitions or measurements or interpretations of DQ for data mining. Defining the factors of DQ for data mining and their measurement for a BI system has been one of the major challenges for researchers as well as practitioners. This chapter provides an overview of existing research in the area of BDQ definitions and measurement for data mining for BI, analyzes the gaps therein, and provides a direction for future research and practice in this area.

scholarly journals Research Challenges and Technology Progress of Data Mining with Bigdata

2019 ◽  
pp. 308-315
Author(s):  
Pushpa Mannava
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Business Intelligence ◽  
Elapsed Time ◽  
Research Challenges ◽  
The Common

'Big Data' has spread quickly in the framework of Data Mining as well as Business Intelligence. This brand-new circumstance can be de?ned by means of those troubles that can not be efficiently or ef?ciently resolved making use of the common computing resources that we currently have. We have to highlight that Big Data does not simply imply huge volumes of data but likewise the requirement for scalability, i.e., to make sure a response in an acceptable elapsed time. This paper discusses about the research challenges and technology progress of data mining with big data.

Integration of Data Mining and Business Intelligence in Big Data Analytics

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.

Integration of Data Mining and Business Intelligence in Big Data Analytics

2022 ◽  
pp. 1892-1922
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.

scholarly journals Big data mining and business intelligence trends

2017 ◽  
Vol 7 (1) ◽  
pp. 23-33 ◽  
Author(s):  
Harun Bayer ◽  
Mustafa Aksogan ◽  
Enes Celik ◽  
Adil Kondiloglu
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Business Intelligence ◽  
Big Data Mining

Big Data Mining and Business Intelligence Trends

2017 ◽  
Vol 7 (1) ◽  
pp. 23-33
Author(s):  
Harun Bayer ◽  
Mustafa Aksogan ◽  
Enes Celik ◽  
Adil Kondiloglu
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Business Intelligence ◽  
High Capacity ◽  
Efficient Solutions ◽  
Strategic Decisions ◽  
Business World ◽  
Large Size ◽  
Capacity Data ◽  
Operational Processes

The conventional databases are not capable of coping with the high capacity data due to different forms of these data’s and fast production speed. In this context, The Big Data structure comes into the scene. The Big Data has been stated as the gold of our age by many authorities. Today, large sizes of data can be analyzed and this led to changes in the lives of people, companies, states, and researchers. The companies develop effective and efficient solutions by analyzing large size of data through big data solutions for their strategic decisions, operational processes, campaign management and marketing techniques. In this research, the introduction has been made to the Big Data architecture, along with daily increasing data mining techniques and methods which will be a solution for accumulating data and current advancements in big data solutions have been addressed. In addition, some well-known companies’ tendency to implement business intelligence systems have been examined. The effects of potential threads which are the results of the big data in the business world are analyzed and a couple of suggestions for the future have been presented.

Operations-Intelligence-Strategy (OIS) Process in Healthcare

2020 ◽  
pp. 88-105
Author(s):  
Xue Ning
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Business Intelligence ◽  
Sensor Data ◽  
Strategy Development ◽  
Healthcare Organizations ◽  
Term Care ◽  
Biomedical Image ◽  
Operational Level ◽  
Starting Point

The healthcare industry has generated a huge amount of data in diverse formats. The big data in healthcare is leading the revolution in healthcare. Collecting data at the operational level is the starting point for the big data-driven healthcare revolution. By analyzing the operational level big data, healthcare organizations can gain the business intelligence for further strategy development, for example how to improve the healthcare quality, how to provide better long-term care, and how to empower the patients. This chapter discusses this process as operations-intelligence-strategy (OIS) process in healthcare. Objectives are understanding how to gain business intelligence from sensor data mining in healthcare, biomedical signal analysis, and biomedical image analysis, and exploring the applications and impacts of the OIS process, with a focus on the sensor data mining in healthcare.

Sign in / Sign up

Export Citation Format

Share Document