An Approach to Efficiency Evaluation of Services with Smart Attributes

2017 ◽  
Vol 8 (1) ◽  
pp. 64-83 ◽  
Author(s):  
Kirill Kulakov
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Efficiency Evaluation ◽  
Trip Planning ◽  
Actual Problem ◽  
Smart Services ◽  
Time Utilization ◽  
Smart Service ◽  
Services Implementation

Nowadays amount of “smart” services in e-Tourism is growing rapidly. This is due to widespread use of mobile devices with new input methods and large amount of digitized data. In addition, Internet of Things and Big Data analytics has a major impact on development of e-Tourism services and cultural heritage services. At the same time the smart services implementation requires complex methods and high cost of their creation. Thereby there is an actual problem to estimate efficiency of smart services. This paper presents an approach to efficiency evaluation of services with smart attributes. The evaluation is based on service's work time utilization and required manual work. For each used attribute the execution scenario, ordinary (non-smart) service for comparison and used estimates are defined. Each estimate is calculated by taking into account the user's experience. Presented approach is relevant for smart services with big data analytics. The demonstration of the approach was carried out using Cultural trip planning service with possible ordinary services.

An Approach to Efficiency Evaluation of Services With Smart Attributes

2019 ◽  
pp. 1305-1326
Author(s):  
Kirill Kulakov
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Efficiency Evaluation ◽  
Trip Planning ◽  
Smart Services ◽  
Time Utilization ◽  
Smart Service ◽  
Services Implementation ◽  
Tourism Services

Nowadays amount of “smart” services in e-Tourism is growing rapidly. This is due to widespread use of mobile devices with new input methods and large amount of digitized data. In addition, Internet of Things and Big Data analytics has a major impact on development of e-Tourism services and cultural heritage services. At the same time the smart services implementation requires complex methods and high cost of their creation. Thereby there is an actual problem to estimate efficiency of smart services. This paper presents an approach to efficiency evaluation of services with smart attributes. The evaluation is based on service's work time utilization and required manual work. For each used attribute the execution scenario, ordinary (non-smart) service for comparison and used estimates are defined. Each estimate is calculated by taking into account the user's experience. Presented approach is relevant for smart services with big data analytics. The demonstration of the approach was carried out using Cultural trip planning service with possible ordinary services.

RELATIONSHIP IN IOT AND BIG DATA ANALYTICS: THE OPPORTUNITIES AND OPEN CHALLENGES

2021 ◽  
Vol 83 (4) ◽  
pp. 100-111
Author(s):  
Ahmad Anwar Zainuddin ◽  
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Physical World ◽  
Future Directions ◽  
Wide Range ◽  
Time Utilization ◽  
Relationship Of ◽  
The Internet Of Things

Internet of Things (IoT) is an up-and-coming technology that has a wide variety of applications. It empowers physical objects to be organized in a specialized framework to grow its convenience in terms of ease and time utilization. It is to convert the thought of bridging the crevice between the physical world and the machine world. It is also being use in the wide range of the technology in this current situation. One of its applications is to monitor and store data over time from numerous devices allows for easy analysis of the dataset. This analysis can then be the basis of decisions made on the same. In this study, the concept, architecture, and relationship of IoT and Big Data are described. Next, several use cases in IoT and big data in the research methodology are studied. The opportunities and open challenges which including the future directions are described. Furthermore, by proposing a new architecture for big data analytics in the Internet of Things, this paper adds value. Overall, the various types of big IoT data analytics, their methods, and associated big data mining technologies are discussed.

Using Big Data Analytics to Assist a Smart City to Prevent Cyber Security Threats

2021 ◽  
pp. 107-124
Author(s):  
Fenio Annansingh
Keyword(s):  
Big Data ◽  
Cyber Security ◽  
Smart City ◽  
Data Analytics ◽  
Life Quality ◽  
Big Data Analytics ◽  
Review Of The Literature ◽  
Smart Services ◽  
Prevention Model

The concept of a smart city as a means to enhance the life quality of citizens has been gaining increasing importance in recent years globally. A smart city consists of city infrastructure, which includes smart services, devices, and institutions. Every second, these components of the smart city infrastructure are generating data. The vast amount of data is called big data. This chapter explores the possibilities of using big data analytics to prevent cybersecurity threats in a smart city. It also analyzed how big data tools and concepts can solve cybersecurity challenges and detect and prevent attacks. Using interviews and an extensive review of the literature have developed the data analytics and cyber prevention model. The chapter concludes by indicating that big data analytics allow a smart city to identify and solve cybersecurity challenges quickly and efficiently.

scholarly journals Innovation and Big Data in Smart Service Systems

2016 ◽  
Vol 4 (1) ◽  
pp. 11-21 ◽  
Author(s):  
Paul P. Maglio ◽  
Chie-Hyeon Lim
Keyword(s):  
Big Data ◽  
Value Creation ◽  
Economic Activity ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Service Systems ◽  
Research Issues ◽  
Modern View ◽  
Smart Service ◽  
Over Time

As traditionally measured, services, which include everything from transportation to retail to healthcare to entertainment to hospitality and more, account for most economic activity. Taking a more modern view, we define service as value creation that occurs within systems of interacting economic actors. Service systems have been getting smarter over time, as big data analytics have been used to generate information and automate operations that create ever more value for people in the service systems. In this short letter, we describe some of our perspective on the use of big data analytics in smart service systems, suggesting one framework for thinking about big data in this context and outlining a set of research issues.

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
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