Systems Thinking and Complexity Science and the Relevance of Big Data Analytics, Intelligence Functions, and Simulation Models

2018 ◽  
pp. 297-369
Author(s):  
Simon Elias Bibri
Keyword(s):  
Big Data ◽  
Systems Thinking ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Simulation Models ◽  
Complexity Science

A Systematic Approach to Develop Metaheuristic Traffic Simulation Models from Big Data Analytics on Real-World Data

2021 ◽  
Author(s):  
Ashish Naidu ◽  
Archak Mittal ◽  
Rebecca Kreucher ◽  
Alice Chen Zhang ◽  
Walter Ortmann ◽  
...  
Keyword(s):  
Big Data ◽  
Real World ◽  
Data Analytics ◽  
Systematic Approach ◽  
Traffic Simulation ◽  
Big Data Analytics ◽  
Simulation Models ◽  
Real World Data ◽  
World Data

Managerial Controversies in Artificial Intelligence and Big Data Analytics

2022 ◽  
pp. 1745-1764
Author(s):  
Kenneth David Strang ◽  
Zhaohao Sun
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Action Research ◽  
Literature Review ◽  
Systems Thinking ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Business Services ◽  
Volume Velocity ◽  
Technology Process

This chapter discusses several fundamental and managerial controversies associated with artificial intelligence and big data analytics which will be of interest to quantitative professionals and practitioners in the fields of computing, e-commerce, e-business services, and e-government. The authors utilized the systems thinking technique within an action research framework. They used this approach because their ideology was pragmatic, the problem at hand, was complex and institutional (healthcare discipline), and they needed to understand the problems from both a practitioner and a nonhuman technology process viewpoint. They used the literature review along with practitioner interviews collected at a big data conference. Although they found many problems, they considered these to be already encompassed into the big data five V's (volume, velocity, variety, value, veracity). Interestingly, they uncovered three new insights about the hidden healthcare artificial intelligence and big data analytics risks; then they proposed solutions for each of these problems.

Managerial Controversies in Artificial Intelligence and Big Data Analytics

2019 ◽  
pp. 55-74
Author(s):  
Kenneth David Strang ◽  
Zhaohao Sun
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Action Research ◽  
Literature Review ◽  
Systems Thinking ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Business Services ◽  
Volume Velocity ◽  
Technology Process

This chapter discusses several fundamental and managerial controversies associated with artificial intelligence and big data analytics which will be of interest to quantitative professionals and practitioners in the fields of computing, e-commerce, e-business services, and e-government. The authors utilized the systems thinking technique within an action research framework. They used this approach because their ideology was pragmatic, the problem at hand, was complex and institutional (healthcare discipline), and they needed to understand the problems from both a practitioner and a nonhuman technology process viewpoint. They used the literature review along with practitioner interviews collected at a big data conference. Although they found many problems, they considered these to be already encompassed into the big data five V's (volume, velocity, variety, value, veracity). Interestingly, they uncovered three new insights about the hidden healthcare artificial intelligence and big data analytics risks; then they proposed solutions for each of these problems.

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.

A Review on Big Data Analytics Tools in Context with Scalability

2019 ◽  
Vol 7 (2) ◽  
pp. 273-277
Author(s):  
Ajay Kumar Bharti ◽  
Neha Verma ◽  
Deepak Kumar Verma
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics

Big Data Analytics Solutions and Challenges

2017 ◽  
Vol 49 (004) ◽  
pp. 825--830
Author(s):  
A. AHMED ◽  
R.U. AMIN ◽  
M. R. ANJUM ◽  
I. ULLAH ◽  
I. S. BAJWA
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics

Can 'Big Data' Analytics Predict Policing Practice?

2016 ◽  
Author(s):  
Janet Chan ◽  
Lyria Bennett Moses
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics
Sign in / Sign up

Export Citation Format

Share Document