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Abstract. The rapid deployment of low-cost ubiquitous sensing devices -including RFID tags and readers, global positioning systems, wireless audio, video, and bio sensors -makes it possible to create instrumented environments and to capture the physical and communicative interaction of an individual with these environments in a digital register. One of the grand challenges of current AI research is to process this multimodal and massive data stream, to recognize, classify, and represent its digital content in a context-sensitive way, and finally to integrate behavior understanding with reasoning and learning about the individual's day by day experiences. This augmented personal memory is always accessible to its owner through an Internet-enabled smartphone using high-speed wireless communication technologies. In this contribution, we discuss how such an augmented personal memory can be built and applied for providing the user with context-related reminders and recommendations in a shopping scenario. With the ultimate goal of supporting communication between individuals and learning from the experiences of others, we apply this novel methods as the basis for a specific way of exploiting memories -the sharing of augmented personal memories in a way that doesn't conflict with privacy constraints.

Abstract. Many Web applications provide personalized and adapted services and contents to their users. As these Web applications are becoming increasingly connected, a new interesting challenge in their engineering is to allow the Web applications to exchange, reuse, integrate, interlink, and enrich their data and user models, hence, to allow for user modeling and personalization across application boundaries. In this paper, we present the Grapple User Modeling Framework (GUMF) that facilitates the brokerage of user profile information and user model representations. We show how the existing GUMF is extended with a new method that is based on configurable derivation rules that guide a new knowledge deduction process. Using our method, it is possible not only to integrate data from GUMF dataspaces, but also to incorporate and reuse RDF data published as Linked Data on the Web. Therefore, we introduce the so-called Grapple Derivation Rule (GDR) language as well as the corresponding GDR Engine. Further, we showcase the extended GUMF in the context of a concrete project in the e-learning domain.