| Summary: | 博士 === 國立成功大學 === 多媒體系統與智慧型運算工程博士學位學程 === 107 === With the advance in mobile devices and various sensors, many events can be noted as spatio-temporal records such as crime data, traffic data, and check-in data, and so on. Spatio-temporal mining has become the emerging research fields that attract a lot of attention. How to extract and analyze in location-aware databases for mining patterns and recommending friends or point-of-interest has become an attractive and challenging issue over the past few years.
In this dissertation, we develop a series of novel and effective data mining frameworks for mining spaito-temporal chaining patterns, actively inferring acquaintance and recommending shielding check-in.
Mining Spatio-Temporal Chaining Patterns with Non-identity Event in Location-aware Databases:
Spatio-temporal pattern mining attempts to discover unknown, potentially interesting and useful event sequences in which events occur within a specific time interval and spatial region. In the literature, mining of spatio-temporal sequential patterns generally relies on the existence of identity information for the accumulation of pattern appearances. For the recent trend of open data, which are mostly released without the specific identity information due to privacy concern, previous work will encounter the challenging difficulty to properly transform such extit{non-identity data into the mining process. In this work, we propose a practical approach, called emph{Top K Spatio-Temporal Chaining Patterns Discovery (abbreviated as emph{TKSTP), to discover frequent spatio-temporal chaining patterns. The emph{TKSTP framework is applied on two real criminal datasets which are released without the identity information. Our experimental studies show that the proposed framework effectively discovers high-quality spatio-temporal chaining patterns. In addition, case studies of crime pattern analysis also demonstrate their applicability and reveal several interestingly hidden phenomenons.
Active Learning-based Approach for Acquaintance Inference with Check-in Event Features in Location-aware Databases:
With the popularity of mobile devices and various sensors, the local geographical activities of human beings can be easily accessed than ever. Yet due to the privacy concern, it is difficult to acquire the social connections among people possessed by services providers, which can benefit applications such as identifying terrorists and recommender systems. In this work, we propose the extit{Location-aware Acquaintance Inference (LAI) problem, which aims at finding the acquaintances for any given query individual based on extit{solely people's extit{local geographical activities, such as geo-tagged posts in Instagram and meeting events in Meetup, within a targeted geo-spatial area. We propose to leverage the concept of extit{active learning to tackle the LAI problem. We develop a novel semi-supervised model, extit{Active Learning-enhanced Random Walk (ARW), which imposes the idea of active learning into the technique of Random Walk with Restart (RWR) in an extit{activity graph. Specifically, we devise a series of extit{Candidate Selection strategies to select unlabeled individuals for labeling, and perform the different extit{Graph Refinement mechanisms that reflect the labeling feedback to guide the RWR random surfer. Experiments conducted on Instagram and Meetup datasets exhibit the promising performance, compared with a set of state-of-the-art methods. With a series of empirical settings, ARW is demonstrated to derive satisfying results of acquaintance inference in different real scenarios.
Shielding Check-in Recommendation against Acquaintance Inference with Check-in Event Features in Location-aware Databases:
Location-based social services such as Foursquare and Facebook Place allow users to perform check-ins at places and interact with each other in geography (e.g. check-in together). While existing studies have exhibited that the adversary can accurately infer social ties based on check-in data, the traditional check-in mechanism cannot protect the acquaintance privacy of users. Therefore, we propose a novel extit{shielding check-in system, whose goal is to guide users to check-in at secure places. We accordingly propose a novel research problem, extit{Check-in Shielding against Acquaintance Inference (CSAI), which aims at recommending a list of secure places when users intend to check-ins so that the potential that the adversary correctly identifies the friends of users can be significantly reduced. We develop the extit{Check-in Shielding Scheme (CSS) framework to solve the CSAI problem. CSS consists of two steps, namely estimating the social strength between users and generating a list of secure places. Experiments conducted on Foursquare and Gowalla check-in datasets show that CSS is able to not only outperform several competing methods under various scenario settings, but also lead to the check-in distance preserving and ensure the usability of the new check-in data in Point-of-Interest (POI) recommendation.
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