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In this paper, we propose and describe a novel recommender system for big data applications that provides recommendations on the base of the interactions among users and generated multimedia contents in one or more social media networks, leveraging a collaborative and user-centered approach. Preliminary experiments using data of several online social networks show how our approach obtains very promising...
We present a new method for detecting descriptive community patterns capturing exceptional (sequential) link trails. For that, we provide a novel problem formalization: We model sequential data as first-order Markov chain models, mapped to an attributed weighted network represented as a graph. Then, we detect subgraphs (communities) using exceptional model mining techniques: We target subsets of sequential...
Location-based sequential event prediction is an interesting problem with many real-world applications. For example, knowing when and where people will use certain kinds of services could enable the development of robust anticipatory systems. A key to this problem is in understanding the nature of the process from which sequential data arises. Usually, human behavior exhibits distinct spatial, temporal,...
Graph Pattern Matching (GPM) plays a significant role in social network analysis, which has been widely used in, for example, experts finding, social community mining and social position detection. Given a pattern graph GQ and a data graph GD, a GPM algorithm finds those subgraphs, GM, that match GQ in GD. However, the existing GPM methods do not consider the multiple constraints on edges in GQ, which...
With increasing complexity of the social systems under surveillance, demand grows for automated tools which are able to support end users in making sense of situational context from the amount of available data and incoming data streams. This paper presents MOSAIC (Multi-Modal Situation Assessment and Analytics Platform), a semantically integrated system which aims at exploiting multi-modal data analysis...
Dataspace applications necessitate the creation of associations among data items over time. For example, once information about people is extracted from sources on the Web, associations among them may emerge as a consequence of different criteria, such as their city of origin or their elected hobbies. In this paper, we advocate a declarative approach to specifying these associations. We propose that...
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