This thesis presents the design and evaluation of the location of a cell phone user, to enable more effective performance monitoring. One of the end-uses I propose is in emergency management, by means of a framework that distributes its functionality between establishing data-set characteristics that are relevant to the problem and a visual tool to evaluate resource-scheduling proposals. Manikarnika is a modular framework, which finds translation in a prototype for Reverse 111. The first steps in the process were to establish whether the parameters I hypothesized as useful, indeed were. Using a statistically significant amount of traces, obtained from real calls placed on the network, the utility of the location metric was established. In order to investigate utilizing a second metric of reputation, a benchmark for evaluating ideas from Social Networks research was proposed, in order to move from arbitrary testing to a more systematic environment. This dissertation details the measurement, design and evaluation of an end-to-end and modular framework for Emergency Management, where the functionality is distributed in order to easily incorporate the changing parameters of sources of information, emergency events, resource requirements of these events and identifying callers that might be able to provide better insight into a situation that is essentially very dynamic. The chasm between research proposals for various end-uses and the application of the same to real life is one that I have tried to bridge in my work. By incorporating pieces from core Electrical Engineering measurements and simulation and extending the use of what was originally a tool built for training Emergency Responders to analyze various resource scheduling agents, which take into account a diversity of administrative domains, I lay the ground work for one possible solution, Reverse 111, which proposes the use of proactive crowd-sourcing for emergency response, with easy extensions to commercial location-based applications.

基于地理位置的社交网络近年得到了非常多的关注。为了提升用戶粘性和吸引用戶，社交問路提供商会提供給用戶基于地理信息的广告和优惠券等服务。方了让广告和优惠券的投递更有效， 预测用戶下个可能访问的地点变得尤为重要。但是，预测地点一个不可避免的挑战就是數一百万计的候选地点构成了庞大的預測空间，使得整个预测过程变成复杂且缓慢。在本论文中，我們利用用戶签到的类別信息对潜在的用戶运动模式進行了建模并提出了一个混合隐马尔可夫模型去预测用戶下个可能访问的地点类别。基于预测出的类别，我們继而对用戶可能访问的地点进行了預測。在类別层次进行建模的好处是能有效地減少候选地点的个數并且能准确地描述用戶行动的实际意义。一般来說，用戶的行为会受到令人偏好的影响，基于这个現象，我們还运用分类的方法对用戶根据其令人愛好的不同進行了划分并对每个组群制定各自的隐马尔可夫模型。实验結果表示如果先预测用可能访问的地点类别，能使得地点预测空间极大地减少预测精度也会变高。 / 在预测用可能访问的地点之后，另外一个很重要的问题是选择将优惠券投递给哪些用从而将产品或地点的影响最大化。在实际运用中，这种将影响最大化的算法会遇到速度上的壁垒。在本论文中，我们研究了在基于地理位置的社交网络中的影响最大化问题，并提出了一个分割方法能有效地提升算法的运行速度。实验结果显示我们的算法在于业界标准方法达到几乎一致的影响力的前提下，能更快地运行。 / Location-based social networks have been gaining increasing popularity in recent years. To increase users’ engagement with location-based services, it is important to provide attractive features, one of which is geo-targeted ads and coupons. To make ads and coupons delivery more effective, it is essential to predict the location that is most likely to be visited by a user at the next step. However, an inherent challenge in location prediction is a huge prediction space, with millions of distinct check-in locations as prediction target. In this thesis we exploit the check-in category information to model the underlying user movement pattern. We propose a framework which uses a mixed hidden Markov model to predict the category of user activity at the next step and then predicts the most likely location given the estimated category distribution. The advantages of modeling on the category level include a significantly reduced prediction space and a precise expression of the semantic meaning of user activities. In addition, as user check-in behaviors are heavily influenced by their preferences, we take a clustering approach to group users with similar preferences, and train a separate hidden Markov model for each group. Extensive experimental results show that, with the predicted category distribution, the number of location candidates for prediction is much smaller, while the location prediction accuracy becomes higher. / Choosing the right users to deliver the coupons and maximizing the influence spread is also an important problem in LBSN, which is called influence maximization problem. In practice speed is an important issue to solve the influence maximization problem. In this thesis, we study the influence maximization problem in location-based social networks and propose a scalable partition approach to solve the influence maximization problem efficiently. Experimental results show that our partition approach achieves quite similar influence spread performance with the original influence maximization approach, while running much faster. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Zhu, Zhe. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 93-101). / Abstracts also in Chinese. / Abstract --- p.i / Acknowledgement --- p.vi / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Background Study --- p.11 / Chapter 2.1 --- Location prediction --- p.11 / Chapter 2.2 --- Influence maximization --- p.16 / Chapter 3 --- User Activity and Location Prediction in Location-based Social Networks --- p.20 / Chapter 3.1 --- Data Analysis --- p.20 / Chapter 3.1.1 --- Data Collection --- p.21 / Chapter 3.1.2 --- Dataset Properties --- p.22 / Chapter 3.2 --- User Activity Prediction --- p.26 / Chapter 3.2.1 --- Definitions --- p.27 / Chapter 3.2.2 --- Category Prediction based on HMM --- p.28 / Chapter 3.2.3 --- Mixed HMM with Temporal and Spatial Covariates --- p.34 / Chapter 3.2.4 --- User Preference Modeling --- p.41 / Chapter 3.3 --- Location Prediction --- p.43 / Chapter 3.4 --- Experimental Evaluation --- p.45 / Chapter 3.4.1 --- Data Preparation --- p.46 / Chapter 3.4.2 --- Category Prediction --- p.47 / Chapter 3.4.3 --- Location Prediction --- p.51 / Chapter 3.5 --- Summary --- p.58 / Chapter 4 --- A Partition Approach to Scalable Influence Maximization in Location-based Social Networks --- p.60 / Chapter 4.1 --- Problem definition --- p.60 / Chapter 4.2 --- Influence probability --- p.62 / Chapter 4.2.1 --- Base model --- p.62 / Chapter 4.2.2 --- Distance and similarity model --- p.65 / Chapter 4.2.3 --- Location entropy model --- p.72 / Chapter 4.3 --- Partition approach --- p.74 / Chapter 4.4 --- Evaluation --- p.79 / Chapter 4.4.1 --- Data preparation --- p.79 / Chapter 4.4.2 --- Precision evaluation --- p.80 / Chapter 4.4.3 --- Influence spread evaluation --- p.83 / Chapter 4.4.4 --- Running time --- p.86 / Chapter 4.5 --- Summary --- p.88 / Chapter 5 --- Conclusion --- p.90 / Bibliography --- p.93

Abstract Title: Innovation and its influence of LBS company: Jiepang in China Level: Final assignment for Master of Business Administration   Author: Yang OU, Zijian LI   Supervisor: Maria. Fregidou-Malama   Date: 2012-June   Aim: Chinese firms desire to win the market, and they should consider innovation. The aim of the study is to explore the effects of innovation for LBS firm to gain success in China. Method: The main research method is case study. Jiepang as our case company is a LBS firm in China. And we interview two persons from different department, and one is its co-founder. Result & Conclusions: Innovative action and behavior must cover whole organization, all employees should be participant. In China, LBS is a rising industry, but LBS firms face to many threats from competitors and substitutes too. Thereby in order to survive in the market and gain success, innovation is a very important strategy for LBS firm, but not the unique method.       Suggestions for future research: Open innovation process is becoming a hot topic currently, so how to innovate with customer or user, how to cooperate with them? And if the data collection can take from multi-cases, it would be make research result more convincing.         Contribution of the thesis: In this study, we introduce and popularize a new industry, LBS in China. And we give a deeper understanding of the importance of innovation and how innovation can improve the competitiveness of the company in this industry.

In typical location-based services (LBS), moving objects (e.g., GPS-enabled mobile phones) report their locations through a wireless network. An LBS server can use the location information to answer various types of continuous queries, e.g., \Give me the ID of a battalion which is the closest to a military base within the next hour." Due to hardware limitations, location data reported by the moving objects are often uncertain. In this paper, we study efficient methods for the execution of Continuous Possible Nearest Neighbor Query (CPoNNQ) that accesses imprecise location data. A CPoNNQ is a standing query (which is active during a period of time) such that, at any time point, all moving objects that have non-zero probabilities of being the nearest neighbor of a given query point are reported. To handle the continuous nature of a CPoNNQ, a simple solution is to require moving objects to continuously report their locations to the LBS server, which evaluates the query at every time step. To save communication bandwidth and mobile devices' batteries, we develop two filter-based protocols for CPoNNQ evaluation. Our protocols install filter bounds" on moving objects, which suppress unnecessary location reporting and communication between the server and the moving objects. Through extensive experiments, we show that our protocols can effectively reduce communication and energy costs while maintaining a high query quality. / published_or_final_version / Computer Science / Master / Master of Philosophy

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Thesis (M. S.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2009. / Committee Chair: Owen, Henry; Committee Member: Copeland, John; Committee Member: Giffin, Jonathon; Committee Member: Howard, Ayanna; Committee Member: Riley, George.

Online social networking services entice millions of users to spend hours every day interacting with each other. At the same time, thanks to the widespread and growing popularity of mobile devices equipped with location-sensing technology, users are now increasingly sharing details about their geographic location and about the places they visit. This adds a crucial spatial and geographic dimension to online social services, bridging the gap between the online world and physical presence. These observations motivate the work in this dissertation: our thesis is that the spatial properties of online social networking services offer important insights about users' social behaviour. This thesis is supported by a set of re.sults related to the measurement and the analysis of such spatial properties. First, we present a comparative study of three online social services: we find that geographic distance constrains social connections, although users exhibit heterogeneous spatial properties. Furthermore, we demonstrate that by considering only social or only spatial factors it is not possible to reproduce the observed properties. Therefore, we investigate how these factors are jointly influencing the evolution of online social services. The resulting observations are then incorporated in a new model of network growth which is able to reproduce the properties of real systems. Then, we outline two case studies where we exploit our findings in real application scenarios. The first concerns building a link prediction system to find pairs of users likely to connect on online social services. Even though spatial proximity fosters the creation of social ties, the computational challenge is accurately and efficiently to discern when being close in space results in a new social connection. We address this problem with a system that uses, alongside other information, features based on the places that users visit. The second example presents a method to extract geographic information about users sharing online videos to understand whether such videos are going to become locally or globally popular. This information is then harnessed to build caching policies that consider which items should be prioritised in memory, thus improving performance of content delivery networks. We summarise our findings with a discussion about the implications of our results, debating potential future research trends and practical applications.

Location-based services (LBS) are available on a variety of mobile platforms like cell phones, PDA's, etc. and an increasing number of users subscribe to and use these services. Two of the popular models of information flow in LBS are the client-server model and the peer-to-peer model, in both of which, existing approaches do not always provide privacy for all parties concerned. In this work, I study the feasibility of applying cryptographic protocols to design privacy-preserving solutions for LBS from an experimental and theoretical standpoint. In the client-server model, I construct a two-phase framework for processing nearest neighbor queries using combinations of cryptographic protocols such as oblivious transfer and private information retrieval. In the peer-to-peer model, I present privacy preserving solutions for processing group nearest neighbor queries in the semi-honest and dishonest adversarial models. I apply concepts from secure multi-party computation to realize our constructions and also leverage the capabilities of trusted computing technology, specifically TPM chips. My solution for the dishonest adversarial model is also of independent cryptographic interest. I prove my constructions secure under standard cryptographic assumptions and design experiments for testing the feasibility or practicability of our constructions and benchmark key operations. My experiments show that the proposed constructions are practical to implement and have reasonable costs, while providing strong privacy assurances.

This study reviews factors that identify U.S. Hispanics as being an ideal target market for adopting Location Based Services (LBS). By using the diffusion of innovation theory, an observed pattern of Hispanics’ adoption of technology, advertisements, smartphones and various smartphone value-added services reveals U.S. Hispanics to be more likely to adopt LBS than non-Hispanics. The study also identifies the top U.S. cell phone wireless providers and analyzes their marketing position towards U.S. Hispanics. AT&T, Sprint, T-Mobile and Verizon are noted as marketing their services to U.S. Hispanics via in-culture messages and campaigns. The four wireless providers also utilize LBS as a profitable tool and market LBS to their customers, regardless of ethnicity.

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