Web Observatory Insights

2019 ◽  
Vol 15 (4) ◽  
pp. 52-68 ◽  
Author(s):  
Naif Radi Aljohani ◽  
Rabeeh Ayaz Abbasi ◽  
Fahad Mohammed Bawakid ◽  
Farrukh Saleem ◽  
Zahid Ullah ◽  
...  
Keyword(s):  
Big Data ◽  
Comparative Analysis ◽  
Deep Understanding ◽  
User Generated Content ◽  
Future Research ◽  
Privacy And Security ◽  
Common Practices ◽  
The Status ◽  
The Common ◽  
The Web

In the present era of Big Data, with continuously increasing amounts of user-generated content, it is becoming a challenge to understand the relation between the content that is available on the Web and the users who are generating that content. Researchers have come up with many ways to understand today's Web better. One of the recently introduced concepts is a Web observatory (WO). This article provides a deep understanding about web observatories. It discusses the status of existing WO systems. The article investigates and gathers the common practices of WOs. This research has implications for researchers and communities in the adoption of the WO concept. The article highlights the challenges of WOs, such as data crawling, privacy and security. It also provides future research and development directions. The article provides a comparative analysis of existing WOs. It discusses the architecture of WOs. It presents components of a WO in a coherent manner and finally provides insights into challenges and limitations of WOs.

Big Data Paradigm: What is the Status of Privacy and Security?

2017 ◽  
Vol 4 (1) ◽  
pp. 1-17 ◽  
Author(s):  
Kenneth David Strang ◽  
Zhaohao Sun
Keyword(s):  
Big Data ◽  
Privacy And Security ◽  
The Status

scholarly journals Big Data Analytics and Firm Performance: A Systematic Review

Information ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 226 ◽  
Author(s):  
Parisa Maroufkhani ◽  
Ralf Wagner ◽  
Wan Khairuzzaman Wan Ismail ◽  
Mas Bambang Baroto ◽  
Mohammad Nourani
Keyword(s):  
Systematic Review ◽  
Big Data ◽  
Firm Performance ◽  
Empirical Research ◽  
Data Analytics ◽  
Web Of Science ◽  
Big Data Analytics ◽  
Future Research ◽  
Science Index ◽  
The Web

The literature on big data analytics and firm performance is still fragmented and lacking in attempts to integrate the current studies’ results. This study aims to provide a systematic review of contributions related to big data analytics and firm performance. The authors assess papers listed in the Web of Science index. This study identifies the factors that may influence the adoption of big data analytics in various parts of an organization and categorizes the diverse types of performance that big data analytics can address. Directions for future research are developed from the results. This systematic review proposes to create avenues for both conceptual and empirical research streams by emphasizing the importance of big data analytics in improving firm performance. In addition, this review offers both scholars and practitioners an increased understanding of the link between big data analytics and firm performance.

Privacy-Preserving Data Mining on the Web

2010 ◽  
pp. 468-481
Author(s):  
Stanley R.M. Oliveira ◽  
Osmar R. Zaïane
Keyword(s):  
Data Mining ◽  
Personal Information ◽  
Privacy Preserving ◽  
Future Research ◽  
Privacy And Security ◽  
Privacy Preserving Data Mining ◽  
The Right To Privacy ◽  
Security Research ◽  
Definition Of ◽  
The Web

Privacy-preserving data mining (PPDM) is one of the newest trends in privacy and security research. It is driven by one of the major policy issues of the information era—the right to privacy. This chapter describes the foundations for further research in PPDM on the Web. In particular, we describe the problems we face in defining what information is private in data mining. We then describe the basis of PPDM including the historical roots, a discussion on how privacy can be violated in data mining, and the definition of privacy preservation in data mining based on users’ personal information and information concerning their collective activities. Subsequently, we introduce a taxonomy of the existing PPDM techniques and a discussion on how these techniques are applicable to Web-based applications. Finally, we suggest some privacy requirements that are related to industrial initiatives and point to some technical challenges as future research trends in PPDM on the Web.

An Orientation to Web 2.0 Tools for Telementoring

2010 ◽  
pp. 206-214
Author(s):  
Robin Hastings
Keyword(s):  
Cloud Computing ◽  
Web 2.0 ◽  
Ease Of Use ◽  
Future Research ◽  
Privacy And Security ◽  
Email Communication ◽  
Web 2.0 Tools ◽  
Stability Data ◽  
Data Portability ◽  
The Web

This chapter gives an overview of Web 2.0 technologies and how they can support telementoring partnerships. Web 2.0 tools offer opportunities for increased networking and social interactivity. Synchronous (chats) and asynchronous (email) communication are possible with these tools. Some of the Web 2.0 capabilities that are introduced in this chapter include cloud computing, Facebook, Ning, and Twitter. FriendFeed and Groupware are also discussed as methods to organize and track a number of Web 2.0 applications for ease of use. Stability, data portability, privacy, and security are issues that are indicated for future research.

Enterprise 2.0 Management Challenges

2013 ◽  
pp. 36-46
Author(s):  
Karen P. Patten ◽  
Lynn B. Keane
Keyword(s):  
User Generated Content ◽  
Future Research ◽  
Second Phase ◽  
Chief Information Officers ◽  
Enterprise 2.0 ◽  
Use Of Services ◽  
It Culture ◽  
The Right ◽  
The Web ◽  
The Way

The nature of the enterprise and the way people work is changing rapidly. The enabling power and competitive advantage of new social and participative technologies will benefit those that recognize the way work is changing. Web 2.0, the “second phase” of the Web, is the foundation of a new and improved Enterprise 2.0. Enterprise 2.0 provides, through a web of interconnected applications, services, and devices, the capabilities for enterprise employees and vendors to be more competitive and productive and for enterprise customers to be more engaged and loyal by accessing the right information from the right people at the right time. This paper describes Enterprise 2.0 management challenges and issues identified by Chief Information Officers, which include the unauthorized use of services and technologies, the integration of a myriad of technologies and capabilities, and the potential compliance and security implications. The authors have proposed a conceptual framework that explores the relationships of three Enterprise 2.0 dimensions – technology, its use, and how resulting user-generated content may lead to business value – with management implications affecting IT culture and policies within the enterprise. This paper provides observations and suggestions for future research.

Big Data for Digital Government

2014 ◽  
Vol 1 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Yu-Che Chen ◽  
Tsui-Chuan Hsieh
Keyword(s):  
Big Data ◽  
Data Management ◽  
Governance Structure ◽  
Future Research ◽  
Business Analytics ◽  
Digital Government ◽  
Privacy And Security ◽  
Critical Issues ◽  
And Performance ◽  
Big Service

“Big data” is one of the emerging and critical issues facing government in the digital age. This study first delineates the defining features of big data (volume, velocity, and variety) and proposes a big data typology that is suitable for the public sector. This study then examines the opportunities of big data in generating business analytics to promote better utilization of information and communication technology (ICT) resources and improved personalization of e-government services. Moreover, it discusses the big data management challenges in building appropriate governance structure, integrating diverse data sources, managing digital privacy and security risks, and acquiring big data talent and tools. An effective big data management strategy to address these challenges should develop a stakeholder-focused and performance-oriented governance structure and build capacity for data management and business analytics as well as leverage and prioritize big data assets for performance. In addition, this study illustrates the opportunities, challenges, and strategy for big service data in government with the E-housekeeper program in Taiwan. This brief case study offers insight into the implementation of big data for improving government information and services. This article concludes with the main findings and topics of future research in big data for public administration.

Leveraging the Web Platform for Ambient Computing

2010 ◽  
Vol 2 (4) ◽  
pp. 33-43 ◽  
Author(s):  
Fabio Mancinelli
Keyword(s):  
Computing System ◽  
Common Practices ◽  
The Common ◽  
Ambient Computing ◽  
Web Platform ◽  
The Web

This paper explores the idea of what can be achieved by using the principles and the technologies of the web platform when they are applied to ambient computing. In this paper, the author presents an experience that realizes some of the goals of an Ambient Computing system by making use of the technologies and the common practices of today’s Web Platform. This paper provides an architecture that lowers the deployment costs by maximizing the reuse of pre-existing components and protocols, while guaranteeing accessibility, interoperability, and extendibility.

Big Data for Digital Transformation of Public Services

2021 ◽  
pp. 250-266
Author(s):  
Deepak Saxena
Keyword(s):  
Big Data ◽  
Public Services ◽  
Open Data ◽  
Regulatory Compliance ◽  
Digital Transformation ◽  
Future Research ◽  
Full Potential ◽  
Privacy And Security ◽  
Big Data Application ◽  
Digital Enterprise

Big data is presently considered integral to the management and strategies for digital enterprise transformation. Beyond being ‘a lot of data', big data can be characterized in terms of seven Vs: volume, velocity, variety, variability, veracity, visualization, and value. Already being applied in private businesses, big data has immense potential for the digital transformation of public services in advancing the e-governance agenda. This chapter explores the nature of big data in public service and discusses its application in areas such as tax administration, transportation, energy, public health, and disaster management. Challenges and concerns are noted in terms of data quality, infrastructure cost, availability of suitable human resources, privacy, and security. Possible solutions such as shared services, cloud computing, open source software, open data framework, and regulatory compliance are noted. The chapter ends by noting future research directions to realize the full potential of Big data application in digital transformation of public services.

Leveraging the Web Platform for Ambient Computing

2011 ◽  
pp. 233-242
Author(s):  
Fabio Mancinelli
Keyword(s):  
Computing System ◽  
Common Practices ◽  
The Common ◽  
Ambient Computing ◽  
Web Platform ◽  
The Web

This paper explores the idea of what can be achieved by using the principles and the technologies of the web platform when they are applied to ambient computing. In this paper, the author presents an experience that realizes some of the goals of an Ambient Computing system by making use of the technologies and the common practices of today’s Web Platform. This paper provides an architecture that lowers the deployment costs by maximizing the reuse of pre-existing components and protocols, while guaranteeing accessibility, interoperability, and extendibility.

Privacy-Preserving Data Mining on the Web

2008 ◽  
pp. 50-63 ◽  
Author(s):  
Stanley R.M. Oliveira ◽  
Osmar R. Zaiane
Keyword(s):  
Data Mining ◽  
Personal Information ◽  
Privacy Preserving ◽  
Future Research ◽  
Privacy And Security ◽  
Privacy Preserving Data Mining ◽  
The Right To Privacy ◽  
Security Research ◽  
Definition Of ◽  
The Web

Privacy-preserving data mining (PPDM) is one of the newest trends in privacy and security research. It is driven by one of the major policy issues of the information era—the right to privacy. This chapter describes the foundations for further research in PPDM on the Web. In particular, we describe the problems we face in defining what information is private in data mining. We then describe the basis of PPDM including the historical roots, a discussion on how privacy can be violated in data mining, and the definition of privacy preservation in data mining based on users’ personal information and information concerning their collective activities. Subsequently, we introduce a taxonomy of the existing PPDM techniques and a discussion on how these techniques are applicable to Web-based applications. Finally, we suggest some privacy requirements that are related to industrial initiatives and point to some technical challenges as future research trends in PPDM on the Web.

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