The LoST-Validation Straightforward-Naming Authority PoinTeR (S-NAPTR) Application Service Tag

5G attains the role of a GPT for an open set of downstream IoT applications in various network industries and within the app economy more generally. Traditionally, sector coupling has been a rather narrow concept focusing on the horizontal synergies of urban system integration in terms of transport, energy, and waste systems, or else the creation of new intermodal markets. The transition toward 5G has fundamentally changed the framing of sector coupling in network industries by underscoring the relevance of differentiating between horizontal and vertical sector coupling. Due to the fixed mobile convergence and the large open set of complementary use cases, 5G has taken on the characteristics of a generalized purpose technology (GPT) in its role as the enabler of a large variety of smart network applications. Due to this vertical relationship, characterized by pervasiveness and innovational complementarities between upstream 5G networks and downstream application sectors, vertical sector coupling between the provider of an upstream GPT and different downstream application industries has acquired particular relevance. In contrast to horizontal sector coupling among different application sectors, the driver of vertical sector coupling is that each of the heterogeneous application sectors requires a critical input from the upstream 5G network provider and combines this with its own downstream technology. Of particular relevance for vertical sector coupling are the innovational complementarities between upstream GPT and downstream application sectors. The focus on vertical sector coupling also has important policy implications. Although the evolution of 5G networks strongly depends on the entrepreneurial, market-driven activities of broadband network operators and application service providers, the future of 5G as a GPT is heavily contingent on the role of frequency management authorities and European regulatory policy with regard to data privacy and security regulations.

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Big Data Analytic Framework for Organizational Leverage

Applied Sciences ◽

10.3390/app11052340 ◽

2021 ◽

Vol 11 (5) ◽

pp. 2340

Author(s):

Sanjay Mathrani ◽

Xusheng Lai

Keyword(s):

Big Data ◽

Web Sites ◽

Big Data Analytics ◽

Building Blocks ◽

Application Service ◽

Data Application ◽

Other Information ◽

Big Data Application ◽

Diverse Data ◽

Framework System

Web data have grown exponentially to reach zettabyte scales. Mountains of data come from several online applications, such as e-commerce, social media, web and sensor-based devices, business web sites, and other information types posted by users. Big data analytics (BDA) can help to derive new insights from this huge and fast-growing data source. The core advantage of BDA technology is in its ability to mine these data and provide information on underlying trends. BDA, however, faces innate difficulty in optimizing the process and capabilities that require merging of diverse data assets to generate viable information. This paper explores the BDA process and capabilities in leveraging data via three case studies who are prime users of BDA tools. Findings emphasize four key components of the BDA process framework: system coordination, data sourcing, big data application service, and end users. Further building blocks are data security, privacy, and management that represent services for providing functionality to the four components of the BDA process across information and technology value chains.

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