Web information systems developers are increasingly seeking apply a 'Web as a Platform' based approach in which web based content and web services are integrated to provide the platform and environment feature...
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Web information systems developers are increasingly seeking apply a 'Web as a Platform' based approach in which web based content and web services are integrated to provide the platform and environment features that are typically offered by desktop environments and applications. Taking such an approach, next generation Personalized Web information systems need to be capable of both dynamically personalizing web media and web services to create customized experiences tailored to the needs, tasks and context of the user. This paper discusses the design, implementation of a framework capable of generating and delivering Personalized Educational Activities. This framework is capable of effectively generating adaptive service work flows and adaptively composing multimedia content, seamlessly integrating the adaptive selection, composition and presentation of content and services based on an educationally driven strategy.
While the analysis of online social networks is a prominent research topic, offline real-world networks are still not covered extensively. However, their analysis can provide important insights into human behavior. In...
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While the analysis of online social networks is a prominent research topic, offline real-world networks are still not covered extensively. However, their analysis can provide important insights into human behavior. In this paper, we analyze influence factors for link prediction in human contact networks. Specifically, we consider the prediction of new links, and extend it to the analysis of recurring links. Furthermore, we consider the impact of stronger ties for the prediction. The results and insights of the analysis are a first step onto predictability applications for human contact networks.
This paper presents research carried out in order to elicit user needs for the design and development of a digital library and research platform intended to enhance user engagement with cultural heritage collections. ...
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As a result of the author's need for help in finding a given name for the unborn baby, the name ling, a search engine for given names, based on data from the ``Social Web'' was born. Within less than six m...
As a result of the author's need for help in finding a given name for the unborn baby, the name ling, a search engine for given names, based on data from the ``Social Web'' was born. Within less than six months, more than 35,000 users accessed \name ling with more than 300,000 search requests, underpinning the relevance of the underlying research questions. The present work compares different metrics for calculating similarities among given names, based on co-occurrences within wikipedia. In particular, the task of finding relevant names for a given search query is considered as a ranking task and the performance of different statical measures of relatedness among given names are evaluated with respect to name ling's actual usage data. By publishing the considered usage data, the research community is stipulated for developing advanced recommendation systems and analyzing influencing factors for the choice of a given name.
In this chapter we explain the definition of the term (data) exploration. We refine this definition in the context of browsing, navigating and searching. We provide a definition of bisociative exploration and derive r...
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In the previous chapters of this book quite different approaches to create networks based on existing data collections (Part II) have been discussed and diverse methods for network analysis have been proposed (Part II...
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The connection of ubiquitous and social computing is an emerging research area which is combining two prominent areas of computer science. In this paper, we tackle this topic from different angles: We describe data mi...
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ISBN:
(纸本)9781467351461
The connection of ubiquitous and social computing is an emerging research area which is combining two prominent areas of computer science. In this paper, we tackle this topic from different angles: We describe data mining methods for ubiquitous and social data, specifically focusing on physical and social activities, and provide exemplary analysis results. Furthermore, we give an overview on the Ubicon platform which provides a framework for the creation and hosting of ubiquitous and social applications for diverse tasks and projects. Ubicon features the collection and analysis of both physical and social activities of users for enabling inter-connected applications in ubiquitous and social contexts. We summarize three real-world systems built on top of Ubicon, and exemplarily discuss the according mining and analysis aspects.
After having been a term of reflection in philosophy as well as psychology for ages, the fascination with human wisdom finally reaches the realms of computer science. In comparison to the first philosophical definitio...
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Surprising a user with unexpected and fortunate recommendations is a key challenge for recommender systems. Motivated by the concept of bisociations, we propose ways to create an environment where such serendipitous r...
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When consuming data from federated domains, it is often necessary to identify the relationships that exist between the data schemas used in each domain. Discovering the exact nature of these relationships is difficult...
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When consuming data from federated domains, it is often necessary to identify the relationships that exist between the data schemas used in each domain. Discovering the exact nature of these relationships is difficult due to data set schema heterogeneity. Prior work has focused on inter-domain class equivalence. However it is not always possible to find an equivalent class in both schemas. For example, when instances are modeled as classes in one domain (e.g. router type) but as the attribute values of a single class in the other domain (e.g. router interface). This paper investigates whether when classifying instances in one data set against a second schema, it may be more useful to use some attribute (or attribute group) other than the original class type, to perform this classification. A machine-learning based classification approach to appropriate attribute selection is presented and its operation is evaluated using two large data-sets available on the web as Linked data. The classification problem is compounded by the less formal semantics of Linked data when compared to full ontologies but this also highlights the strength of our approach to dealing with noisy or under-specified data-sets and schemas. The experimental results show that our attribute selection approach is capable of discovering appropriate mappings for cases where the correspondence is conditioned on one attribute and that information gain provides a suitable scoring function for selection of correspondence patterns to describe these complex attribute-based mappings.
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