CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Building Blocks for IoT Analytics Internet-of-Things Analytics

Internet-of-Things (IoT) Analytics are an integral element of most IoT applications, as it provides the means to extract knowledge, drive actuation services and optimize decision making. IoT analytics will be a major contributor to IoT business value in the coming years, as it will enable organizations to process and fully leverage large amounts of IoT data, which are nowadays largely underutilized. The Building Blocks of IoT Analytics is devoted to the presentation the main technology building blocks that comprise advanced IoT analytics systems. It introduces IoT analytics as a special case of BigData analytics and accordingly presents leading edge technologies that can be deployed in order to successfully confront the main challenges of IoT analytics applications. Special emphasis is paid in the presentation of technologies for IoT streaming and semantic interoperability across diverse IoT streams. Furthermore, the role of cloud computing and BigData technologies in IoT analytics are presented, along with practical tools for implementing, deploying and operating non-trivial IoT applications. Along with the main building blocks of IoT analytics systems and applications, the book presents a series of practical applications, which illustrate the use of these technologies in the scope of pragmatic applications. Technical topics discussed in the book include: Cloud Computing and BigData for IoT analyticsSearching the Internet of ThingsDevelopment Tools for IoT Analytics ApplicationsIoT Analytics-as-a-ServiceSemantic Modelling and Reasoning for IoT AnalyticsIoT analytics for Smart BuildingsIoT analytics for Smart CitiesOperationalization of IoT analyticsEthical aspects of IoT analyticsThis book contains both research oriented and applied articles on IoT analytics, including several articles reflecting work undertaken in the scope of recent European Commission funded projects in the scope of the FP7 and H2020 programmes. These articles present results of these projects on IoT analytics platforms and applications. Even though several articles have been contributed by different authors, they are structured in a well thought order that facilitates the reader either to follow the evolution of the book or to focus on specific topics depending on his/her background and interest in IoT and IoT analytics technologies. The compilation of these articles in this edited volume has been largely motivated by the close collaboration of the co-authors in the scope of working groups and IoT events organized by the Internet-of-Things Research Cluster (IERC), which is currently a part of EU's Alliance for Internet of Things Innovation (AIOTI)

The Internet of Things Will Thrive by 2025

This report is the latest research report in a sustained effort throughout 2014 by the Pew Research Center Internet Project to mark the 25th anniversary of the creation of the World Wide Web by Sir Tim Berners-LeeThis current report is an analysis of opinions about the likely expansion of the Internet of Things (sometimes called the Cloud of Things), a catchall phrase for the array of devices, appliances, vehicles, wearable material, and sensor-laden parts of the environment that connect to each other and feed data back and forth. It covers the over 1,600 responses that were offered specifically about our question about where the Internet of Things would stand by the year 2025. The report is the next in a series of eight Pew Research and Elon University analyses to be issued this year in which experts will share their expectations about the future of such things as privacy, cybersecurity, and net neutrality. It includes some of the best and most provocative of the predictions survey respondents made when specifically asked to share their views about the evolution of embedded and wearable computing and the Internet of Things

Building Blocks for IoT Analytics Internet-of-Things Analytics

Internet-of-Things (IoT) Analytics are an integral element of most IoT applications, as it provides the means to extract knowledge, drive actuation services and optimize decision making. IoT analytics will be a major contributor to IoT business value in the coming years, as it will enable organizations to process and fully leverage large amounts of IoT data, which are nowadays largely underutilized. The Building Blocks of IoT Analytics is devoted to the presentation the main technology building blocks that comprise advanced IoT analytics systems. It introduces IoT analytics as a special case of BigData analytics and accordingly presents leading edge technologies that can be deployed in order to successfully confront the main challenges of IoT analytics applications. Special emphasis is paid in the presentation of technologies for IoT streaming and semantic interoperability across diverse IoT streams. Furthermore, the role of cloud computing and BigData technologies in IoT analytics are presented, along with practical tools for implementing, deploying and operating non-trivial IoT applications. Along with the main building blocks of IoT analytics systems and applications, the book presents a series of practical applications, which illustrate the use of these technologies in the scope of pragmatic applications. Technical topics discussed in the book include: Cloud Computing and BigData for IoT analyticsSearching the Internet of ThingsDevelopment Tools for IoT Analytics ApplicationsIoT Analytics-as-a-ServiceSemantic Modelling and Reasoning for IoT AnalyticsIoT analytics for Smart BuildingsIoT analytics for Smart CitiesOperationalization of IoT analyticsEthical aspects of IoT analyticsThis book contains both research oriented and applied articles on IoT analytics, including several articles reflecting work undertaken in the scope of recent European Commission funded projects in the scope of the FP7 and H2020 programmes. These articles present results of these projects on IoT analytics platforms and applications. Even though several articles have been contributed by different authors, they are structured in a well thought order that facilitates the reader either to follow the evolution of the book or to focus on specific topics depending on his/her background and interest in IoT and IoT analytics technologies. The compilation of these articles in this edited volume has been largely motivated by the close collaboration of the co-authors in the scope of working groups and IoT events organized by the Internet-of-Things Research Cluster (IERC), which is currently a part of EU's Alliance for Internet of Things Innovation (AIOTI)

Beyond Innovation and Competition: The Need for Qualified Transparency in Internet Intermediaries

Internet service providers and search engines have mapped the web, accelerated e-commerce, and empowered new communities. They also pose new challenges for law. Individuals are rapidly losing the ability to affect their own image on the web - or even to know what data are presented about them. When web users attempt to find information or entertainment, they have little assurance that a carrier or search engine is not biasing the presentation of results in accordance with its own commercial interests. Technology’s impact on privacy and democratic culture needs to be at the center of internet policy-making. Yet before they promulgate substantive rules, key administrators must genuinely understand new developments. While the Federal Trade Commission and the Federal Communications Commission in the U.S. have articulated principles of editorial integrity for search engines and net neutrality for carriers, they have not engaged in the monitoring necessary to enforce these guidelines. This article proposes institutions for “qualified transparency” within each Commission to fill this regulatory gap. Qualified transparency respects legitimate needs for confidentiality while promoting individuals’ and companies\u27 capacity to understand how their reputations - and the online world generally - are shaped by dominant intermediaries

Digital Market Concentration: An Institutional and Social Cost Analysis

In this thesis, I develop an analysis of the industry concentration seen in digital markets today. I begin with a description and argument for the use of institutional economics. This framework allows for the integration of an interdisciplinary approach to economics. My analysis details the socioeconomic and political impacts, as well as the underlying market dynamics that have pushed digital markets towards concentration. I offer novel explanations for the lack of firm behavior that should theoretically increase profit, the existence of barriers to competition, and consumer behavior that focus on the role of social institutions. I also detail many of the social costs of these concentrated markets, such as their impact on democracy, power to influence social institutions, and the impact they have on concentration in other markets. This is done to show that the fears surrounding monopolies do not end with prices. Even in digital markets, where many times prices are very low, if not zero, there are reasons that monopoly is economically inefficient and socially sub-optimal. However, due to the path-dependent nature of the extreme benefits associated with digital markets, policymakers cannot reasonably propose breaking up these companies. Instead, they must use the power of the government to counteract the conglomerations of social power seen in these private companies in search of an optimal outcome

More than "just shopping:" personalization, privacy and the (ab)use of data

Working draft of the Personalization/Customization GroupEmerging technologies often produce unexpected consequences that existing institutions and policies are unable to deal with effectively. Because predicting the consequences of technological change is difficult, responses to emerging technologies tend to be reactive (if not passive), rather than proactive. Improved understanding of the potential consequences of a particular technology would enable policymakers and analysts to implement appropriate measures more quickly and perhaps even act prospectively. This paper proposes a general approach that can be used to identify potential sources of disruption from emerging technologies in order to enable proactive policy actions to limit the negative consequences of these disruptions. New technologies are often characterized through the use of metaphors and/or comparisons to existing technologies. While such comparisons provide an easy way to generate understanding of a new technology they often also neglect important aspects of that technology. As a result, the use of metaphors and comparisons creates a disconnect between what the metaphor suggests is happening and what is actually taking place. The incompleteness of the metaphors leads to a disparity in the appreciation of the benefits, opportunities, and pitfalls of a new technology. This disparity allows certain aspects of the technology to be ignored and/or exploited, with potentially disruptive social consequences. An analysis of the mismatch between metaphorical characterizations and the actual attributes of a new technology can help identify otherwise overlooked issues and determine if existing institutions and policies can adequately respond. This paper uses a study of personalization technologies by online retailers to demonstrate the potential for disruption caused by failures of metaphor to adequately describe new technologies. Online retailing technologies have equipped firms with tools that allow them to move closer to the ``mass market of one" --- satisfying the demands of a mass market through individually-targeted sales strategies (i.e., personalization). While the metaphors of ``shopping" and ``catalog" have been used to describe online retail ``stores," these metaphors fail to capture several key aspects of online retail technologies such as aggregation, replication, persistence, and analysis of the personal data easily collected by such businesses. As a result, the institutions that exist to protect consumers when dealing with traditional, physical stores may no longer be sufficient. Furthermore, the pervasiveness of the metaphor undermines the ability of consumers to understand or debate the negative consequences of personalization, especially in the areas of privacy and identity.National Science Foundatio

Social Feedback: Social Learning from Interaction History to Support Information Seeking on the Web

Information seeking on the Web has become a central part of many daily activities. Even though information seeking is extremely common, there are many times when these tasks are unsuccessful, because the information found is less than ideal or the task could have been completed more efficiently. In unsuccessful information-seeking tasks, there are often other people who have knowledge or experience that could help improve task success. However, information seekers do not typically look for help from others, because tasks can often be completed alone (even if inefficiently). One of the problems is that web tools provide people with few opportunities to learn from one another’s experiences in ways that would allow them to improve their success. This dissertation presents the idea of social feedback. Social feedback is based on the theory of social learning, which describes how people learn from observing others. In social feedback, observational learning is enabled through the mechanism of interaction history – the traces of activity people create as they interact with the Web. Social feedback systems collect and display interaction history to allow information seekers to learn how to complete their tasks more successfully by observing how other people have behaved in similar situations. The dissertation outlines the design of two social-feedback systems, and describes two studies that demonstrate the real world applicability and feasibility of the idea. The first system supports global learning, by allowing people to learn new search skills and techniques that improve information seeking success in many different tasks. The second system supports local learning, in which people learn how to accomplish specific tasks more effectively and more efficiently. Two further studies are conducted to explore potential real-world challenges to the successful deployment of social feedback systems, such as the privacy concerns associated with the collection and sharing of interaction history. These studies show that social feedback systems can be deployed successfully for supporting real world information seeking tasks. Overall, this research shows that social feedback is a valuable new idea for the social use of information systems, an idea that allows people to learn from one another’s experiences and improve their success in many common real-world tasks