Big Data Case Studies

2013 ◽  
pp. 113-126
Author(s):  
Nitin Sawant ◽  
Himanshu Shah
Keyword(s):  
Big Data ◽  
Case Studies

Big Data in Cultural Tourism – Building Sustainability and Enhancing Competitiveness

2021 ◽  
Keyword(s):  
Big Data ◽  
Case Studies ◽  
Value Chain ◽  
New Technologies ◽  
Cultural Tourism ◽  
End Users ◽  
Ethical Implications ◽  
Tourism Sector ◽  
Cultural Experiences ◽  
Selection Of

The use of big data is becoming increasingly important across the tourism sector and the value chain. With this publication, UNWTO intends to provide a baseline research on using big data by tourism and culture stakeholders, in order to improve the competitiveness of cultural tourism and reinforce its sustainability. The study sets the basis to connect tourism, culture and new technologies for mutual benefits, while calling for a reflection on the ethical implications for policymakers, businesses and end-users. The selection of case studies illustrates the most frequent case-scenarios of the use of big data in cultural tourism within destinations, compiled during the research. As the new technologies are facing ever-evolving scenarios, their use will be harnessed by the tourism sector in its endeavour to innovate and provide new cultural experiences.

scholarly journals ELM Meets Urban Big Data Analysis: Case Studies

2016 ◽  
Vol 2016 ◽  
pp. 1-10
Author(s):  
Ningyu Zhang ◽  
Huajun Chen ◽  
Xi Chen ◽  
Jiaoyan Chen
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Case Studies ◽  
Virtual Worlds ◽  
Health Hazard ◽  
Big Data Analysis ◽  
Application Framework ◽  
Rapid Progress ◽  
Urban Computing ◽  
General Application

In the latest years, the rapid progress of urban computing has engendered big issues, which creates both opportunities and challenges. The heterogeneous and big volume of data and the big difference between physical and virtual worlds have resulted in lots of problems in quickly solving practical problems in urban computing. In this paper, we propose a general application framework of ELM for urban computing. We present several real case studies of the framework like smog-related health hazard prediction and optimal retain store placement. Experiments involving urban data in China show the efficiency, accuracy, and flexibility of our proposed framework.

scholarly journals The effectiveness of cartographic visualisations in landscape archaeology

2018 ◽  
Vol 1 ◽  
pp. 1-5
Author(s):  
David Fairbairn
Keyword(s):  
Big Data ◽  
Data Collection ◽  
Case Studies ◽  
Landscape Archaeology ◽  
Archaeological Prospection ◽  
Archaeological Interpretation

The use of maps and other geovisualisation methods has been longstanding in archaeology. Archaeologists employ advanced contemporary tools in their data collection, analysis and presentation. Maps can be used to render the ‘big data’ commonly collected by archaeological prospection techniques, but are also fundamental output instru-ments for the dissemination of archaeological interpretation and modelling. This paper addresses, through case studies, alternate methods of geovisualisation in archaeology and identifies the efficiencies of each.

Big Data Sharing Among Academics

2013 ◽  
pp. 177-194 ◽  
Author(s):  
Jeonghyun Kim
Keyword(s):  
Big Data ◽  
Social Science ◽  
Case Studies ◽  
Data Sharing ◽  
Physical Science ◽  
Life Science ◽  
Social Research ◽  
Data Repositories ◽  
Current Technology ◽  
Oceanographic Data

The goal of this chapter is to explore the practice of big data sharing among academics and issues related to this sharing. The first part of the chapter reviews literature on big data sharing practices using current technology. The second part presents case studies on disciplinary data repositories in terms of their requirements and policies. It describes and compares such requirements and policies at disciplinary repositories in three areas: Dryad for life science, Interuniversity Consortium for Political and Social Research (ICPSR) for social science, and the National Oceanographic Data Center (NODC) for physical science.

Analytics-as-a-Service (AaaS)

Web Services ◽  
2019 ◽  
pp. 1262-1281
Author(s):  
Chitresh Verma ◽  
Rajiv Pandey
Keyword(s):  
Big Data ◽  
Case Studies ◽  
Data Analytics ◽  
Data Science ◽  
Business Processes ◽  
Service Oriented Architecture ◽  
Big Data Analytics ◽  
Major Branch ◽  
Implementation Level ◽  
Industrial Level

Big Data Analytics is a major branch of data science where the huge amount raw data is processed to get insight for relevant business processes. Integration of big data, its analytics along with Service Oriented Architecture (SOA) is need of the hour, such integration shall render reusability and scalability to various business processes. This chapter explains the concept of Big Data and Big Data Analytics at its implementation level. The Chapter further describes Hadoop and its technologies which are one of the popular frameworks for Big Data Analytics and envisage integrating SOA with relevant case studies. The chapter demonstrates the SOA integration with Big Data through, two case studies of two different scenarios are incorporated that integrates real world implementation with theory and enables better understanding of the industrial level processes and practices.

Security Vulnerabilities, Threats, and Attacks in IoT and Big Data

2020 ◽  
pp. 141-167
Author(s):  
Prabha Selvaraj ◽  
Sumathi Doraikannan ◽  
Vijay Kumar Burugari
Keyword(s):  
Big Data ◽  
Case Studies ◽  
Big Data Analytics ◽  
Cloud Services ◽  
Web Interface ◽  
Security Vulnerabilities ◽  
New Techniques ◽  
Iot Security ◽  
Web Interfaces ◽  
The Web

Big data and IoT has its impact on various areas like science, health, engineering, medicine, finance, business, and mainly, the society. Due to the growth in security intelligence, there is a requirement for new techniques which need big data and big data analytics. IoT security does not alone deal with the security of the device, but it also has to care about the web interfaces, cloud services, and other devices that interact with it. There are many techniques used for addressing challenges like privacy of individuals, inference, and aggregation, which makes it possible to re-identify individuals' even though they are removed from a dataset. It is understood that a few security vulnerabilities could lead to insecure web interface. This chapter discusses the challenges in security and how big data can be used for it. It also analyzes the various attacks and threat modeling in detail. Two case studies in two different areas are also discussed.

Analytics-as-a-Service (AaaS)

2017 ◽  
pp. 26-45
Author(s):  
Chitresh Verma ◽  
Rajiv Pandey
Keyword(s):  
Big Data ◽  
Case Studies ◽  
Data Analytics ◽  
Data Science ◽  
Business Processes ◽  
Service Oriented Architecture ◽  
Big Data Analytics ◽  
Major Branch ◽  
Implementation Level ◽  
Industrial Level

Big Data Analytics is a major branch of data science where the huge amount raw data is processed to get insight for relevant business processes. Integration of big data, its analytics along with Service Oriented Architecture (SOA) is need of the hour, such integration shall render reusability and scalability to various business processes. This chapter explains the concept of Big Data and Big Data Analytics at its implementation level. The Chapter further describes Hadoop and its technologies which are one of the popular frameworks for Big Data Analytics and envisage integrating SOA with relevant case studies. The chapter demonstrates the SOA integration with Big Data through, two case studies of two different scenarios are incorporated that integrates real world implementation with theory and enables better understanding of the industrial level processes and practices.

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