scholarly journals Big Data Analytics for Medication Management in Diabetes Mellitus

2016 ◽  
Vol 1 (1) ◽  
pp. 42 ◽  
Author(s):  
Lidong Wang ◽  
Cheryl Ann Alexander
Keyword(s):  
Diabetes Mellitus ◽  
Big Data ◽  
Data Analytics ◽  
Medication Management ◽  
Big Data Analytics ◽  
Insurance Companies ◽  
Pharmaceutical Companies ◽  
Survey Paper ◽  
Pharmacy Refill ◽  
Record Data

Medication development plays a prominent role in the fight against chronic illness such as hypertension, diabetes mellitus, asthma, etc. Without proper testing and methods for management of drug data, the disease management would fail. Providers rely on pharmaceutical companies to provide research data in widespread formats and pharmaceutical companies rely on hospitals for electronic medical record data (EHR) and for pharmacy refill records from insurance companies. Big Data Analytics (BDA) provides an excellent basis to examine and manage terabytes of data that comprises drug data and can manage all aspects of drug development. This survey paper examines the current literature to determine what is current practice in the area of Big Data analytics and medication management.

scholarly journals A Survey on Big Data Analytics Model

2019 ◽  
Vol 4 (1) ◽  
pp. 1-13
Author(s):  
Candra Kurniawan
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Analytical Model ◽  
Business Strategy ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Survey Paper ◽  
Making Choices ◽  
Data Extracting

Topic about big data analytics have received a lot of attention and interest at this time. There are many topics can be discussed related to the analytical model, tools, and technology used. Big data analytics model involves many processes with various technologies used. Skills in handling big data, extracting mining, and developing insight are needed in applying big data analytics. Suitable analytical hardware and software also needed in decision making. Big data analytics is a key to a business strategy, but only a small portion of big data is currently used to support their business strategy. Big data analitycs can answer many questions about how to manage costs, time, and development or optimization strategies, and other decision making choices. However, there are many challenges in big data analytics technology. This survey paper addresses topics related to the analytical model, tools, and technology used. This paper also discusses the application of big data analytics in various fields.

scholarly journals A Novel Customized Big Data Analytics Framework for Drug Discovery

2018 ◽  
Author(s):  
A. Jainul Fathima ◽  
G. Murugaboopathi
Keyword(s):  
Big Data ◽  
Drug Discovery ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Pharmaceutical Companies ◽  
Biomedical Data ◽  
Analytic Framework ◽  
Computational Tools ◽  
Computational Drug Discovery ◽  
Insight Into

Drug discovery is related to analytics as the method requires a technique to handle the extremely large volume of structured and unstructured biomedical data of multi-dimensional and complexity from pharmaceutical companies. To tackle the complexity of data and to get better insight into the data, big data analytics can be used to integrate the massive amount of pharmaceutical data and computational tools in an analytic framework. This paper presents an overview of big data analytics in the field of drug discovery and outlines an analytic framework which can be applied to computational drug discovery process and briefly discuss the challenges. Hence, big data analytics may contribute to better drug discovery.  

scholarly journals Development of Big Data Analytics Model

2019 ◽  
Vol 4 (1) ◽  
pp. 14-25
Author(s):  
Saiful Rizal
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Big Data ◽  
Data Storage ◽  
Data Structures ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Large Data ◽  
Complex Data ◽  
Survey Paper

The development of information technology produces very large data sizes, with various variations in data and complex data structures. Traditional data storage techniques are not sufficient for storage and analysis with very large volumes of data. Many researchers conducted their research in analyzing big data with various analytics models in big data. Therefore, the purpose of the survey paper is to provide an understanding of analytics models in big data for various uses using algorithms in data mining. Preprocessing big data is the key to turning big data into big value.

Big Data Analytics: The Next Big Thing

2019 ◽  
Vol 54 (5) ◽  
pp. 20
Author(s):  
Dheeraj Kumar Pradhan
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics

Strategy in Discovery Mode - Wie Big Data & Analytics strategisches Denken verdrängen kann

2020 ◽  
Vol 49 (5) ◽  
pp. 11-17
Author(s):  
Thomas Wrona ◽  
Pauline Reinecke
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics

Big Data & Analytics (BDA) ist zu einer kaum hinterfragten Institution für Effizienz und Wettbewerbsvorteil von Unternehmen geworden. Zu viele prominente Beispiele, wie der Erfolg von Google oder Amazon, scheinen die Bedeutung zu bestätigen, die Daten und Algorithmen zur Erlangung von langfristigen Wettbewerbsvorteilen zukommt. Sowohl die Praxis als auch die Wissenschaft scheinen geradezu euphorisch auf den „Datenzug“ aufzuspringen. Wenn Risiken thematisiert werden, dann handelt es sich meist um ethische Fragen. Dabei wird häufig übersehen, dass die diskutierten Vorteile sich primär aus einer operativen Effizienzperspektive ergeben. Strategische Wirkungen werden allenfalls in Bezug auf Geschäftsmodellinnovationen diskutiert, deren tatsächlicher Innovationsgrad noch zu beurteilen ist. Im Folgenden soll gezeigt werden, dass durch BDA zwar Wettbewerbsvorteile erzeugt werden können, dass aber hiermit auch große strategische Risiken verbunden sind, die derzeit kaum beachtet werden.

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