Mobile information search for location-based information

This study investigated mobile searching for location-based information by carrying out two experiments in an airport. The independent variables were user context, information type, information requirement pressure, and location-based information type. Experiment 1 compared users’ search performance in different user contexts while searching for different types of information. The results indicated that when users searched for location-based information, the average number of clicks decreased, the importance of the first search result increased, and free recall was better compared with non-location-based information searching. Experiment 2 further investigated the users’ mobile search performance under different levels of information requirement pressure. The results indicated that users under low pressure clicked more search results compared with users under high information requirement pressure. Compared to transactional query searching, when users engaged in informational and navigational queries, the average number of clicks increased, the importance of the first search result decreased, and free recall was worse. There was no significant difference in the number of clicks when users chose the first two search results during a mobile searching process for location-based information.     

Preference-oriented mining techniques for location-based store search

With the development of wireless telecommunication technologies, a number of studies have been done on the issues of location-based services due to wide applications. Among them, one of the active topics is the location-based search. Most of previous studies focused on the search of nearby stores, such as restaurants, hotels, or shopping malls, based on the user’s location. However, such search results may not satisfy the users well for their preferences. In this paper, we propose a novel data mining-based approach, named preference-oriented location-based search (POLS), to efficiently search for k nearby stores that are most preferred by the user based on the user’s location, preference, and query time. In POLS, we propose two preference learning algorithms to automatically learn user’s preference. In addition, we propose a ranking algorithm to rank the nearby stores based on user’s location, preference, and query time. To the best of our knowledge, this is the first work on taking temporal location-based search with automatic user preference learning into account simultaneously. Through experimental evaluations on the real dataset, the proposed approach is shown to deliver excellent performance.     

Examining Factors Influencing the Disruptiveness of Notifications in a Mobile Museum Context

Ubiquitous computing environments continuously infer our context and proactively offer us context aware services and information, suggested by notifications on our mobile devices. However, notifications come with a cost. They may interrupt the user in the current task and be annoying in the wrong context. The challenge becomes how to notify the user about the availability of relevant services while minimizing the level of disruptiveness. Thus, an understanding of what affects the subjective perception of the disruptiveness of the notification is needed. As yet, most of the research on disruptiveness of notifications focused on stationary, task-oriented environments. In this study, we examine the effect of notifications in a special leisure scenario—a museum visit. In two user studies conducted in a museum setting, participants used a context-aware mobile museum guide to receive information on various museum exhibits while periodically receiving notifications. We examined how the user’s activity, the modality of the notification, and the message content affected the perceived level of disruption that the notifications created. We discuss our results in light of existing work in the desktop and mobile domains and provide a framework and recommendations for designing notifications for a mobile museum guide system.     

Towards understanding the gamification upon users’ scores in a location-based social network

Online social platform, such as Wikipedia and Foursquare, has been increasingly exploded due to not only various useful services provided but also social gaming mechanisms that can keep users actively engaged. For example, users are awarded ”virtual goods” like badges and points when they contribute to the community in the network by voluntarily sharing ideas and other information. In this paper, we aim to examine the effectiveness of a social gamification mechanism, named user scores, designed in Foursquare which is one of most popular location-based social networks. A user’s score in Foursquare is an aggregate measure based on recent check-in activities of the user, which reflects a snapshot summary of the user’s temporal and spatial behaviors. Whenever a user checks in to a venue, a list of scores of the user’s friends are visible to the user via a ”leaderboard” which ranks these users’ scores in a descending order. Given a pair of friends who participate in a score competition in such a gimification mechanism, we identify if one user’s scores have significant influence on the other user’s scores by utilizing the Granger Causality Test. To understand what types of users and what types of friends tend to participate in the score competition (i.e., their check-ins are more likely driven by such a gamification mechanism), we extract users’ features (e.g. user’s degree) as well as the features of pairs of friends (e.g., number of common friends, score similarity and ranking difference) to examine whether these features have correlations with those pairs of users who are identified as being involved in the score game. The identified influence on user scores has the important implication on applications including friend and venue recommendations in location-based social networks.     

A context-aware scheme for privacy-preserving location-based services

We address issues related to privacy protection in location-based services (LBSs). Most existing privacy-preserving LBS techniques either require a trusted third-party (anonymizer) or use cryptographic protocols that are computationally and communicationally expensive. Our design of privacy-preserving techniques is principled on not requiring a trusted third-party while being highly efficient in terms of time and space complexities. The problem has two interesting and challenging characteristics: First, the degree of privacy protection and LBS accuracy depends on the context, such as population and road density, around a user’s location. Second, an adversary may violate a user’s location privacy in two ways: (i) based on the user’s location information contained in the LBS query payload and (ii) by inferring a user’s geographical location based on the device’s IP address. To address these challenges, we introduce CAP, a context-aware privacy-preserving LBS system with integrated protection for both data privacy and communication anonymity. We have implemented CAP and integrated it with Google Maps, a popular LBS system. Theoretical analysis and experimental results validate CAP’s effectiveness on privacy protection, LBS accuracy, and communication QoS (Quality-of-Service).     

Adaptive content recommendation for mobile users: Ordering recommendations using a hierarchical context model with granularity

Retrieving timely and relevant information on-site is an important task for mobile users. A context-aware system can understand a user’s information needs and thus select contents according to relevance. We propose a context-dependent search engine that represents user context in a knowledge-based context model, implemented in a hierarchical structure with granularity information. Search results are ordered based on semantic relevance computed as similarity between the current context and tags of search results. Compared against baseline algorithms, the proposed approach enhances precision by 22% and pooled recall by 17%. The use of size-based granularity to compute similarity makes the approach more robust against changes in the context model in comparison to graph-based methods, facilitating import of existing knowledge repositories and end-user defined vocabularies (folksonomies). The reasoning engine being light-weight, privacy protection is ensured, as all user information is processed locally on the user’s phone without requiring communication with an external server.     

Context-aware recommendations through context and activity recognition in a mobile environment

The mobile Internet introduces new opportunities to gain insight in the user’s environment, behavior, and activity. This contextual information can be used as an additional information source to improve traditional recommendation algorithms. This paper describes a framework to detect the current context and activity of the user by analyzing data retrieved from different sensors available on mobile devices. The framework can easily be extended to detect custom activities and is built in a generic way to ensure easy integration with other applications. On top of this framework, a recommender system is built to provide users a personalized content offer, consisting of relevant information such as points-of-interest, train schedules, and touristic info, based on the user’s current context. An evaluation of the recommender system and the underlying context recognition framework shows that power consumption and data traffic is still within an acceptable range. Users who tested the recommender system via the mobile application confirmed the usability and liked to use it. The recommendations are assessed as effective and help them to discover new places and interesting information.     

Modeling context-aware and intention-aware in-car infotainment systems

It is fundamental to understand users’ intentions to support them when operating a computer system with a dynamically varying set of functions, e.g., within an in-car infotainment system. The system needs to have sufficient information about its own and the user’s context to predict those intentions. Although the development of current in-car infotainment systems is already model-based, explicitly gathering and modeling contextual information and user intentions is currently not supported. However, manually creating software that understands the current context and predicts user intentions is complex, error-prone and expensive. Model-based development can help in overcoming these issues. In this paper, we present an approach for modeling a user’s intention based on Bayesian networks. We support developers of in-car infotainment systems by providing means to model possible user intentions according to the current context. We further allow modeling of user preferences and show how the modeled intentions may change during run-time as a result of the user’s behavior. We demonstrate feasibility of our approach using an industrial case study of an intention-aware in-car infotainment system. Finally, we show how modeling of contextual information and modeling user intentions can be combined by using model transformation.     

Countering overlapping rectangle privacy attack for moving kNN queries

An important class of LBSs is supported by the moving k nearest neighbor (MkNN) query, which continuously returns the k nearest data objects for a moving user. For example, a tourist may want to observe the five nearest restaurants continuously while exploring a city so that she can drop in to one of them anytime. Using this kind of services requires the user to disclose her location continuously and therefore may cause privacy leaks derived from the user's locations. A common approach to protecting a user's location privacy is the use of imprecise locations (e.g., regions) instead of exact positions when requesting LBSs. However, simply updating a user's imprecise location to a location-based service provider (LSP) cannot ensure a user's privacy for an MkNN query: continuous disclosure of regions enable LSPs to refine more precise location of the user. We formulate this type of attack to a user's location privacy that arises from overlapping consecutive regions, and provide the first solution to counter this attack. Specifically, we develop algorithms which can process an MkNN query while protecting the user's privacy from the above attack. Extensive experiments validate the effectiveness of our privacy protection technique and the efficiency of our algorithm.     

Trip planning queries with location privacy in spatial databases

Privacy has become a major concern for the users of location-based services (LBSs) and researchers have focused on protecting user privacy for different location-based queries. In this paper, we propose techniques to protect location privacy of users for trip planning (TP) queries, a novel type of query in spatial databases. A TP query enables a user to plan a trip with the minimum travel distance, where the trip starts from a source location, goes through a sequence of points of interest (POIs) (e.g., restaurant, shopping center), and ends at a destination location. Due to privacy concerns, users may not wish to disclose their exact locations to the location-based service provider (LSP). In this paper, we present the first comprehensive solution for processing TP queries without disclosing a user’s actual source and destination locations to the LSP. Our system protects the user’s privacy by sending either a false location or a cloaked location of the user to the LSP but provides exact results of the TP queries. We develop a novel technique to refine the search space as an elliptical region using geometric properties, which is the key idea behind the efficiency of our algorithms. To further reduce the processing overhead while computing a trip from a large POI database, we present an approximation algorithm for privacy preserving TP queries. Extensive experiments show that the proposed algorithms evaluate TP queries in real time with the desired level of location privacy.     

The reflective mobile agent paradigm implemented in a smart office environment

Ubiquitous systems will integrate computers invisibly and unobtrusively in everyday objects. Data will be catched from single or multi-sensor devices and will be used for context extraction. New location-based services will be adapted to user preferences. For this the ubiquitous system needs to know user profiles, likings, and habits. As the user moves, these information must be made available at the new location of the user. Either the user carries the data on wearable or portable computers or the smart environment takes responsibility for transporting them. The amount of new devices and services makes an efficient use by centralized systems very difficult. The idea presented in this paper is that a virtual reflection of the user represented by a mobile agent accompanying in the smart environment. Mobile agents offer a possibility to encapsulate information of a person and the person’s preferences and perform location-based services of the ubiquitous system in the name of the user. Security and privacy are major concerns of such an agent system. This paper describes a ubiquitous mobile agent system named UbiMAS which has security extensions to provide high protection of agents and significant personal data. UbiMAS is applied in the smart doorplate project as part of a smart office environment.     

XBridge-Mobile: efficient XML keyword search on mobile web data

Mobile computing over intelligent mobile is affecting human’s habits of obtaining information over Internet, especially keyword search. Most of previous keyword search works are mainly focused on traditional web data sources, in which the performance can be improved by adding more computing power and/or building more offline-computed index. However, it is very challenging to apply the traditional keyword search methods to mobile web-based keyword search because mobile computing has many different features, e.g., frequent disconnections, variety of bandwidths, limited power of mobile devices, limited data size to be downloaded, etc.. To address this challenge, in this paper we design an adaptive mobile-based XML keyword search approach, called XBridge-Mobile, that can derive the semantics of a keyword query and generate a set of effective structured patterns by analyzing the given keyword query and the schemas of XML data sources. Each structured pattern represents one of user’s possible search intentions. The patterns will be firstly sent to the mobile client from web server. And then, the mobile client can select some interested patterns to load the results. By doing this, we can reduce the communication cost a lot between web server and mobile client because only the derived patterns and a few results need to be transferred, not all the keyword search results, by which we can save lots of expenses when the downloaded data is priced. In addition, we can economically maintain the frequent structured pattern queries in the mobile device, which can further reduce the expense of downloading data. At last, we analyze and propose a ranking function to measure the quality of keyword search results, design a set of algorithms to optimize mobile keyword search based on the maintained structured patterns, and present the experimental study of XBridge-Mobile with real XML datasets.     

Spatial cloaking for anonymous location-based services in mobile peer-to-peer environments

This paper tackles a privacy breach in current location-based services (LBS) where mobile users have to report their exact location information to an LBS provider in order to obtain their desired services. For example, a user who wants to issue a query asking about her nearest gas station has to report her exact location to an LBS provider. However, many recent research efforts have indicated that revealing private location information to potentially untrusted LBS providers may lead to major privacy breaches. To preserve user location privacy, spatial cloaking is the most commonly used privacy-enhancing technique in LBS. The basic idea of the spatial cloaking technique is to blur a user’s exact location into a cloaked area that satisfies the user specified privacy requirements. Unfortunately, existing spatial cloaking algorithms designed for LBS rely on fixed communication infrastructure, e.g., base stations, and centralized/distributed servers. Thus, these algorithms cannot be applied to a mobile peer-to-peer (P2P) environment where mobile users can only communicate with other peers through P2P multi-hop routing without any support of fixed communication infrastructure or servers. In this paper, we propose a spatial cloaking algorithm for mobile P2P environments. As mobile P2P environments have many unique limitations, e.g., user mobility, limited transmission range, multi-hop communication, scarce communication resources, and network partitions, we propose three key features to enhance our algorithm: (1) An information sharing scheme enables mobile users to share their gathered peer location information to reduce communication overhead; (2) A historical location scheme allows mobile users to utilize stale peer location information to overcome the network partition problem; and (3) A cloaked area adjustment scheme guarantees that our spatial cloaking algorithm is free from a “center-of-cloaked-area” privacy attack. Experimental results show that our P2P spatial cloaking algorithm is scalable while guaranteeing the user’s location privacy protection.     

用户情境感知的流式移动应用推荐

在目前流式应用分发系统中,客户端的移动应用分发都是依靠系统后台管理员人工操作或者简单地依靠位置信息为用户分发应用,没有考虑到用户在不同的情境活动下对应用的需求差异问题。针对上述问题,提出一种基于用户情境感知的流式应用推荐机制。该机制通过采集流式应用场景下用户的情境信息数据,利用机器学习Xgboost算法识别用户情境活动,并根据识别的用户情境来为用户推荐应用。同时,利用用户的反馈信息进一步提高用户个性化应用推荐的准确度。实验结果表明,Xgboost算法在准确率和时间开销上性能优于传统算法,在流式应用分发系统中有很高的实际应用价值。     

Protecting query privacy in location-based services

The popularity of location-based services (LBSs) leads to severe concerns on users’ privacy. With the fast growth of Internet applications such as online social networks, more user information becomes available to the attackers, which allows them to construct new contextual information. This gives rise to new challenges for user privacy protection and often requires improvements on the existing privacy-preserving methods. In this paper, we classify contextual information related to LBS query privacy and focus on two types of contexts—user profiles and query dependency: user profiles have not been deeply studied in LBS query privacy protection, while we are the first to show the impact of query dependency on users’ query privacy. More specifically, we present a general framework to enable the attackers to compute a distribution on users with respect to issuing an observed request. The framework can model attackers with different contextual information. We take user profiles and query dependency as examples to illustrate the implementation of the framework and their impact on users’ query privacy. Our framework subsequently allows us to show the insufficiency of existing query privacy metrics, e.g., k-anonymity, and propose several new metrics. In the end, we develop new generalisation algorithms to compute regions satisfying users’ privacy requirements expressed in these metrics. By experiments, our metrics and algorithms are shown to be effective and efficient for practical usage.     

Smooth Gaze: a framework for recovering tasks across devices using eye tracking

A user task is often distributed across devices, e.g., a student listening to a lecture in a classroom while watching slides on a projected screen and making notes on her laptop, and sometimes checking Twitter for comments on her smartphone. In scenarios like this, users move between heterogeneous devices and have to deal with task resumption overhead from both physical and mental perspectives. To address this problem, we created Smooth Gaze, a framework for recording the user’s work state and resuming it seamlessly across devices by leveraging implicit gaze input. In particular, we propose two novel and intuitive techniques, smart watching and smart posting, for detecting which display and target region the user is looking at, and transferring and integrating content across devices respectively. In addition, we designed and implemented a cross-device reading system SmoothReading that captures content from secondary devices and generates annotations based on eye tracking, to be displayed on the primary device. We conducted a study that showed that the system supported information seeking and task resumption, and improved users’ overall reading experience.     

移动位置服务的应用发展研究

移动位置服务是移动运营商和服务提供商联合推出的基于地理位置信息服务,用户通过移动终端获取位置信息及产品服务。中国的移动位置服务开展较早,但相比于美国等发达国家,并没有获得用户的广泛接受。本文通过研究移动位置服务的发展现状和用户行为,提出发展对策。     

Analysis of trajectories in mobile networks based on data about the network proximity

This paper is devoted to location-based mobile services. The movement (trajectory) data extraction from logs related to network proximity is considered. Usually, this type of pattern extraction (search) relates to trajectory databases containing geoposition information. We consider a model of context-aware computing (a context-aware browser) based on network proximity. A mobile phone is considered as a proximity sensor. The geoposition information is replaced with the network proximity. Any existing or specially created network node can be regarded as a sensor of presence that provides access to dynamically determined network content. The disclosure of the content depends on the set of rules describing the conditions of network’s proximity. An algorithm is given for calculating the trajectories in mobile networks based on information about the network’s proximity.     

Mosco: a privacy-aware middleware for mobile social computing

The proliferation of mobile devices coupled with Internet access is generating a tremendous amount of highly personal and sensitive data. Applications such as location-based services and quantified self harness such data to bring meaningful context to users’ behavior. As social applications are becoming prevalent, there is a trend for users to share their mobile data. The nature of online social networking poses new challenges for controlling access to private data, as compared to traditional enterprise systems. First, the user may have a large number of friends, each associated with a unique access policy. Second, the access control policies must be dynamic and fine-grained, i.e. they are content-based, as opposed to all-or-nothing. In this paper, we investigate the challenges in sharing of mobile data in social applications. We design and evaluate a middleware running on Google App Engine, named Mosco, that manages and facilitates sharing of mobile data in a privacy-preserving manner. We use Mosco to develop a location sharing and a health monitoring application. Mosco helps shorten the development process. Finally, we perform benchmarking experiments with Mosco, the results of which indicate small overhead and high scalability.