Evolutionary Subject Tagging in the Humanities; Supporting Discovery and Examination in Digital Cultural Landscapes

In this paper, the authors attempt to identify problematic issues for subject tagging in the humanities, particularly those associated with information objects in digital formats. In the third major section, the authors identify a number of assumptions that lie behind the current practice of subject classification that we think should be challenged. We move then to propose features of classification systems that could increase their effectiveness. These emerged as recurrent themes in many of the conversations with scholars, consultants, and colleagues. Finally, we suggest next steps that we believe will help scholars and librarians develop better subject classification systems to support research in the humanities.NEH Office of Digital Humanities: Digital Humanities Start-Up Grant (HD-51166-10

Automated user modeling for personalized digital libraries

Digital libraries (DL) have become one of the most typical ways of accessing any kind of digitalized information. Due to this key role, users welcome any improvements on the services they receive from digital libraries. One trend used to improve digital services is through personalization. Up to now, the most common approach for personalization in digital libraries has been user-driven. Nevertheless, the design of efficient personalized services has to be done, at least in part, in an automatic way. In this context, machine learning techniques automate the process of constructing user models. This paper proposes a new approach to construct digital libraries that satisfy user’s necessity for information: Adaptive Digital Libraries, libraries that automatically learn user preferences and goals and personalize their interaction using this information

Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure

Big data research has attracted great attention in science, technology, industry and society. It is developing with the evolving scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies. However, its nature and fundamental challenge have not been recognized, and its own methodology has not been formed. This paper explores and answers the following questions: What is big data? What are the basic methods for representing, managing and analyzing big data? What is the relationship between big data and knowledge? Can we find a mapping from big data into knowledge space? What kind of infrastructure is required to support not only big data management and analysis but also knowledge discovery, sharing and management? What is the relationship between big data and science paradigm? What is the nature and fundamental challenge of big data computing? A multi-dimensional perspective is presented toward a methodology of big data computing.Comment: 59 page

An architecture for certification-aware service discovery

Service-orientation is an emerging paradigm for building complex systems based on loosely coupled components, deployed and consumed over the network. Despite the original intent of the paradigm, its current instantiations are limited to a single trust domain (e.g., a single organization). Also, some of the key promises of service-orientation - such as the dynamic orchestration of externally provided software services, using runtime service discovery and deployment - are still unachieved. One of the main reasons for this is the trust gap that normally arises when software services, offered by previously unknown providers, are to be selected at run-time, without any human intervention. To close this gap, the concept of machine-readable security certificates (called asserts) has been recently introduced, which paves the way to automated processing about security properties of services. Similarly to current security certification schemes, the assessment of the security properties of a service is delegated to an independent third party (certification authority), who issues a corresponding assert, bound to the service. In this paper, we propose an architecture, which exploits the assert concept to realise a certification-aware service discovery framework. The architecture supports the discovery of single services based on certified security properties (in additional to the usual functional properties), as well as the dynamic synthesis of service compositions, that satisfy the given security properties. The architecture is extensible, thus allowing for a range of domain specific matchmaking components, to cover dimensions related to, e.g., performance, cost and other non-functional characteristics