scholarly journals ВЕРИФИКАЦИЯ ГЕОМАРКЕТИНГОВЫХ ГИПОТЕЗ ОПТИМАЛЬНОГО РЕШЕНИЯ ВЫБОРА МЕСТОПОЛОЖЕНИЯ (НА ПРИМЕРЕ МАГАЗИНОВ ХОББИ Г. БЕЛГОРОДА)

2022 ◽  
pp. 137-147
Author(s):  
Ольга Герасименко
Keyword(s):  
Data Mining ◽  
Big Data

В статье рассмотрены вопросы встраивания концепции геомаркетинга в систему стратегического маркетингового планирования с целью повышения конкурентоспособности компании. Описаны отдельные результаты контент-анализ термина «геомаркетинг» (в англоязычной версии поисковой системы Academia.edu), предложено авторское определение геомаркетинга. Приведены атрибуты геомаркетинговой концепции 4G: Geomodeling Intelligence, Geo Product, Geotake Value, Geo Data Mining. Методологической основой исследования выступили группы следующих методов: маркетинговые (экспертный опрос, маркетинговые исследования, геомаркетинговый анализ, опрос, социологические исследования), географические (картографические, ГИС), цифровые (обработка пространственных данных, Big Data, программное моделирование). Разработаны концептуальные представления геомаркетинга в системе стратегического маркетингового управления. Сделаны выводы о возможностях применения геомаркетинговых исследований на примере магазинов рыбалки г. Белгорода. Верификация авторских гипотез подтверждена выбором оптимального местоположения для открытия магазина по адресу: г. Белгород, ул. Есенина, д. 9, корпус 3. Инструментом проведения геомаркетинговых исследований является авторское программное обеспечение, оформленное в виде патента.

Challenges and Cloud Computing Environments Towards Big Data

2014 ◽  
pp. 203-208
Author(s):  
Kiran Kumar S V N Madupu
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Technology Development ◽  
Computing Environments ◽  
Modern Technologies

Big Data has terrific influence on scientific discoveries and also value development. This paper presents approaches in data mining and modern technologies in Big Data. Difficulties of data mining as well as data mining with big data are discussed. Some technology development of data mining as well as data mining with big data are additionally presented.

Trends and Opportunities in Health Analytics as a Service and Implications for Use in Low Resource Settings: A Literature Review Abstract (Preprint)

2019 ◽  
Author(s):  
Meghana Bastwadkar ◽  
Carolyn McGregor ◽  
S Balaji
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Intensive Care ◽  
Literature Review ◽  
Health Monitoring ◽  
Data Analytics ◽  
Neonatal Intensive Care ◽  
Big Data Analytics ◽  
Healthcare Facilities

BACKGROUND This paper presents a systematic literature review of existing remote health monitoring systems with special reference to neonatal intensive care (NICU). Articles on NICU clinical decision support systems (CDSSs) which used cloud computing and big data analytics were surveyed. OBJECTIVE The aim of this study is to review technologies used to provide NICU CDSS. The literature review highlights the gaps within frameworks providing HAaaS paradigm for big data analytics METHODS Literature searches were performed in Google Scholar, IEEE Digital Library, JMIR Medical Informatics, JMIR Human Factors and JMIR mHealth and only English articles published on and after 2015 were included. The overall search strategy was to retrieve articles that included terms that were related to “health analytics” and “as a service” or “internet of things” / ”IoT” and “neonatal intensive care unit” / ”NICU”. Title and abstracts were reviewed to assess relevance. RESULTS In total, 17 full papers met all criteria and were selected for full review. Results showed that in most cases bedside medical devices like pulse oximeters have been used as the sensor device. Results revealed a great diversity in data acquisition techniques used however in most cases the same physiological data (heart rate, respiratory rate, blood pressure, blood oxygen saturation) was acquired. Results obtained have shown that in most cases data analytics involved data mining classification techniques, fuzzy logic-NICU decision support systems (DSS) etc where as big data analytics involving Artemis cloud data analysis have used CRISP-TDM and STDM temporal data mining technique to support clinical research studies. In most scenarios both real-time and retrospective analytics have been performed. Results reveal that most of the research study has been performed within small and medium sized urban hospitals so there is wide scope for research within rural and remote hospitals with NICU set ups. Results have shown creating a HAaaS approach where data acquisition and data analytics are not tightly coupled remains an open research area. Reviewed articles have described architecture and base technologies for neonatal health monitoring with an IoT approach. CONCLUSIONS The current work supports implementation of the expanded Artemis cloud as a commercial offering to healthcare facilities in Canada and worldwide to provide cloud computing services to critical care. However, no work till date has been completed for low resource setting environment within healthcare facilities in India which results in scope for research. It is observed that all the big data analytics frameworks which have been reviewed in this study have tight coupling of components within the framework, so there is a need for a framework with functional decoupling of components.

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