As pervasive computing meets social networks, there is a fast growing research field called Pervasive Social Computing. Applications in this area exploit the richness of information arising out of people using sensor-equipped pervasive devices in their everyday life combined with intense use of different Social Networking Services. We call this set of information Pervasive Social Context. We provide a taxonomy to classify Pervasive Social Context along the dimensions space, time, people, and information source (STiPI) as well as commenting on the type and reason for creating such context. A survey of recent research work shows the applicability and usefulness of the taxonomy in classifying and assessing applications and systems in the area of Pervasive Social Computing. Finally, we present some research challenges in this area and illustrate how they affect the systems being surveyed.

Abstract—Any middleware for pervasive computing services has to effectively support both spatially-situated activities and social models of interactions. In this paper, we present the solution integrated in the tuple-based SAPERE middleware to tackle this problem. The idea is to exploit the graph of a social network along with relations deriving from spatial proximity to rule the actual topology of interactions among devices, users and services. The proposed approach can facilitate the autonomous and adaptive activities of pervasive services while accounting for both social and spatial issues, can support effective service discovery and orchestration, and can enable tackling critical privacy issues. I.

Abstract—Middleware infrastructures for pervasive computing, in order to be able to support services and users activities, have to deal with both spatially-situated and socially-situated interactions. In this paper we present the solution adopted in the SAPERE middleware that exploits the graph of a social networks, and combines it with relations deriving from spatial proximity, to drive the topology of interactions among users, devices and services. This results in a middleware that facilitates the development and management of services that are adaptive to both spatial and social concerns, and can support effective service discovery and orchestration, and naturally tackles privacy issues. Keywords-pervasive middleware; social interaction; proximity I.

The explosion of social and sensor data available on the Web provides both challenges and opportunities for their exploitation in contemporary decision support systems. In this paper, we propose a framework for aggregating and linking heterogeneous data from various sources and transforming them to Linked Data. This allows reuse and integration of the produced data with other data resources enabling spatial business intelligence for various domain-specific applications. Our framework can be easily applied to aggregate and interlink data from various types of sources: legacy systems, citizen sensor data, sensor data, and open web data. This paper outlines a number of possible applications of the framework and discusses in detail an example use case where the proposed methodology facilitates identification of business opportunities in London City through analysis of various information facets including property pricing, population spending, sensor, and social data.

Abstract—As mobile cloud computing facilitates a wide spec-trum of smart applications, the need for fusing various types of data available in the cloud grows rapidly. In particular, social and sensor data lie at the core in such applications, but typically processed separately. This paper explores the potential of fusing social and sensor data in the cloud, presenting a practice—a travel recommendation system that offers the predicted mood information of people on where and when users wish to travel. The system is built upon a conceptual framework that allows to blend the heterogeneous social and sensor data for integrated analysis, extracting weather-dependent people’s mood informa-tion from Twitter and meteorological sensor data streams. In order to handle massively streaming data, the system employs various cloud-serving systems, such as Hadoop, HBase, and GSN. Using this scalable system, we performed heavy ETL as well as filtering jobs, resulting in 12 million tweets over four months. We then derived a rich set of interesting findings through the data fusion, proving that our approach is effective and scalable, which can serve as an important basis in fusing social and sensor data in the cloud. I.

With the advances of Internet technologies and an explosive growth in the popularity of social media, an increasingly large part of human life is getting digitized and becoming available on the web. This phenomenon brings opportunities and motivates us to infer users ’ situations by exploiting their interaction events in various social media such as online social networks, blogs and email. One of the key requirements of inferring situations from interaction events is to consider both the semantic and temporal aspects of events in the situation inference process. In this paper, we address this issue and propose a novel approach to exploiting users ’ interaction events in social media to infer their situations. We present an ontology-based interaction event model that captures the properties of users ’ interaction activities in social media. We further provide a rule-based situation specification technique that integrates the interaction event ontology (for semantically matching interaction events) with temporal event relationships (for correlating historical interaction events). We also provide a platform to realize the situation reasoning/inference process, which combines semantic matching and complex event processing. We conduct a performance evaluation of the platform to quantify its efficacy. The feasibility and applicability of our approach is demonstrated by developing a socially aware phone call application as a case study.

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This paper presents models and algorithms for interactive sensing in social networks where individuals act as sensors and the information exchange between individuals is exploited to optimize sensing. Social learning is used to model the interaction between individuals that aim to estimate an underlying state of nature. In this context the following questions are addressed: How can self-interested agents that interact via social learning achieve a tradeoff between individual privacy and reputation of the social group? How can protocols be designed to prevent data incest in online reputation blogs where individuals make recommendations? How can sensing by individuals that interact with each other be used by a global decision maker to detect changes in the underlying state of nature? When individual agents possess limited sensing, compu-tation and communication capabilities, can a network of agents achieve sophisticated global behavior? Social and game theoretic learning are natural settings for addressing these questions. This article presents an overview, insights and discussion of social learning models in the context of data incest propagation, change detection and coordination of decision making.

Abstract—The possibility to have millions of computational devices interconnected across urban environments opens up novel application areas. In such highly distributed scenarios, applications must gain awareness as a result of opportunistic encounters with co-located devices, a departure from traditional reasoning approaches. We envision situated awareness as an emergent property of such networks, where bio-inspired algorithms are employed to coordinate interactions between devices through managing the lifecycle, distribution, and content of data. A congestion-aware, crowd-steering example illustrates this vision. I.