A Storm in an IoT Cup: The Emergence of Cyber-Physical Social Machines

The concept of social machines is increasingly being used to characterise various socio-cognitive spaces on the Web. Social machines are human collectives using networked digital technology which initiate real-world processes and activities including human communication, interactions and knowledge creation. As such, they continuously emerge and fade on the Web. The relationship between humans and machines is made more complex by the adoption of Internet of Things (IoT) sensors and devices. The scale, automation, continuous sensing, and actuation capabilities of these devices add an extra dimension to the relationship between humans and machines making it difficult to understand their evolution at either the systemic or the conceptual level. This article describes these new socio-technical systems, which we term Cyber-Physical Social Machines, through different exemplars, and considers the associated challenges of security and privacy.Comment: 14 pages, 4 figure

The role of data science in web science

Web science relies on an interdisciplinary approach that seeks to go beyond what any one subject can say about the World Wide Web. By incorporating numerous disciplinary perspectives and relying heavily on domain knowledge and expertise, data science has emerged as an important new area that integrates statistics with computational knowledge, data collection, cleaning and processing, analysis methods, and visualization to produce actionable insights from big data. As a discipline to use within Web science research, data science offers significant opportunities for uncovering trends in large Web-based datasets. A Web science observatory exemplifies this relationship by offering an online platform of tools for carrying out Web science research, allowing users to carry out data science techniques to produce insights into Web science issues such as community development, online behavior, and information propagation. The authors outline the similarities and differences of these two growing subject areas to demonstrate the important relationship developing between them.<br/

Web observations: analysing Web data through automated data extraction

In this thesis, a generic architecture for Web observations is introduced. Beginning with fundamental data aspects and technologies for building Web observations, requirements and architectural designs are outlined. Because Web observations are basic tools to collect information from any Web resource, legal perspectives are discussed in order to give an understanding of recent regulations, e.g. General Data Protection Regulation (GDPR). The general idea of Web observatories, its concepts, and experiments are presented to identify the best solution for Web data collections and based thereon, visualisation from any kind of Web resource. With the help of several Web observation scenarios, data sets were collected, analysed and eventually published in a machine-readable or visual form for users to be interpreted. The main research goal was to create a Web observation based on an architecture that is able to collect information from any given Web resource to make sense of a broad amount of yet untapped information sources. To find this generally applicable architectural structure, several research projects with different designs have been conducted. Eventually, the container based building block architecture emerged from these initial designs as the most flexible architectural structure. Thanks to these considerations and architectural designs, a flexible and easily adaptable architecture was created that is able to collect data from all kinds of Web resources. Thanks to such broad Web data collections, users can get a more comprehensible understanding and insight of real-life problems, the efficiency and profitability of services as well as gaining valuable information on the changes of a Web resource

Building a real-time web observatory

Real-time data streams are becoming primary ways to access data from the Web and from Internet-ready devices. Real-time streams, personal devices, and sensor networks have the potential to contain rich insights for researchers, commerce, and governments. With a vested interest in unlocking the potential benefits hidden within, there has been extensive work conducted on developing technologies to process, integrate, and extract value from the data. However, exposing the value in this data is achieved via sharing the data in a secure and controllable environment. To that end, here the authors present the Web Observatory, a Web platform with an architecture capable of harvesting, querying, and analyzing multiple real-time and historic heterogeneous data, while providing data owners access control to their resources. They consider the current landscape and challenges of using data, analytics, and visualizations, and describe a series of use cases for the Web Observatory