This tutorial presents the state-of-the-art research that lies at the intersection of two hot topics in the data management community: (1) social networking and (2) mobility. In this tutorial, we give an overview of existing research work, systems, and applications related to both social networking and mobility. In addition, we introduce several resources (i.e., datasets, software tools) as well as a list of promising research directions. Online social networks, such as Facebook and Twitter have become very popular in the past decade. Users register to online social networks in order to keep in touch with their friends and family, learn about their news, get recommendations from them, and engage in online social events. As mobile devices (e.g., smart phones, GPS devices) became ubiquitous, location-based social networking services (e.g., Foursquare and Facebook Places) are getting more and more popular. For instance, as of September 2012, Foursquare claims to have over 25 million people worldwide, and over billions of check-ins with millions more every day. Users, in a location-based social network, are associated with a geo-location, and might alert friends when visiting a venue (e.g., restaurant, bar) by checking-in on their mobile phones (e.g., iPhone, Android). The rise of location-based social networking applications has led to the emergence of both social networking and mobility side by side, which led to the rise of new research challenges and opportunities. This tutorial presents the state-of-the-art research that lies at the intersection of both: Social Networking and Mobility. The merger between social networking and mobility [33, 4, 8, 46] brought together the following data types: (1) Social Networking data: represents the friendship between different users (usually represented by a social graph) as well as all sorts of social interactions between users. (2) Spatial/Spatio-temporal data: represents the users geo-locations, venues (e.g., restaurant, gym, shopping mall) geo-locations and information about users visiting different * This work is supported in part by the National Science Foundation under Grants IIS-0952977 and IIS-1218168. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. places at different times. (3) Users Opinions data: represents how much a user likes the places she visits by expressing (e.g., Alice visited restaurant A and gave it a rating of five over five). We then illustrate how different mixes of this data trilogy has been leveraged to explore new trends and by developers to build novel mobile applications [4, 8, 16, 42] . Social networking data management research is mainly concerned with managing users social interactions and collaboration, storing / retrieving social media (e.g., Microblogs, News Feed), and analyzing users behavior. Mobile data management research focuses on handling user GeoSpatial location and contextual information. This tutorial takes an overarching approach by surveying the research that combines both social networking and mobility from the following perspectives: (1) Microblog search and social news feed queries, (2) Recommendation Services, (3) Analytics, (4) Crowdsourcing, (5) System and Media Visualization, and (6) Risks and Threats. In summary, the tutorial consists of the following parts: In this part, we first describe how both geo-location and social awareness work in concert to answer queries [3, 1, 7, 6, 5, 13, 21, 30, 35, 38, 41] . We present recent studies that show how both spatial and social aspects can be combined to enhance Microblog search and social news feed quality. We also analyze existing GeoSocial Microblog search and Location-aware New feed querying system from a scalability and efficiency perspective. We present recent studies which show that geo-location matters in recommender systems, and we manifest several techniques to incorporate the spatial/spatio-temporal information [17, 34, 37, 39, 43] side-by-side with users opinions data in traditional recommender systems [16, 28, 36, 31] . We also highlight the research works that leverage GeoSocial data points and trajectories for travel and itinerary recommendations [47, 45] . 3. PART III: Analytics: We give an overview of computational techniques that are harnessed to analyze GeoSocial data [9, 23, 20, 44] to learn more about human behavior, the societal and economical consequences of such analysis [19, 2] . 4. PART IV: Crowdsourcing: We summarize research work performed in the Volunteered Geographic Information (VGI) and participatory sensing area. We then survey recent papers that address the crowdsourcing topic [15, 11, 24, 25, 27, 26 ] from a mobility perspective [10, 14] . 5. PART V: Systems and Media Visualization: we highlight systems that aim at visualizing GeoSocial Media [22, 33, 48] (e.g., Geo-Tagged Tweets, Geo-Tagged Videos) and social 1196
Among them, social relationships between moving objects are one of the important types of semantic information and need to be considered by the data management community, especially with respect to the trends of prevalent online social networks or location-based social networks REF .
Mobility and Social Networking: A Data Management Perspective
PVLDB
PVLDB
Computer Science