Wang-Chien Lee

Bio: Wang-Chien Lee is an academic researcher from Pennsylvania State University. The author has contributed to research in topics: Wireless sensor network & Nearest neighbor search. The author has an hindex of 60, co-authored 366 publications receiving 14123 citations. Previous affiliations of Wang-Chien Lee include Ohio State University & Verizon Communications.

In-broker Access Control for Information Brokerage Systems

Abstract: An XML brokerage system is a distributed XML database system that comprises data sources and brokers which, respectively, hold XML documents and document distribution information. Databases can be queried through brokers with no schema-relevant or geographical difference being noticed. However, all existing information brokerage systems view or handle query brokering and access control as two orthogonal issues: query brokering is a system issue that concerns costs and performance, while access control is a security issue that concerns information confidentiality. As a result, access control deployment strategies (in terms of where and when to do access control) and the impact of such strategies on end-to-end system performance are neglected by existing information brokerage systems. In addition, data source side access control deployment is taken-for-granted as the “right” thing to do. In this paper, we challenge this traditional, taken-for-granted access control deployment methodology, and we show that query brokering and access control are not two orthogonal issues because access control deployment strategies can have significant impact on the “whole” system’s end-to-end performance. We propose the first in-broker access control deployment strategy where access control is “pushed” from the boundary into the “heart” of the information brokerage system. We design and evaluate the inbroker access control scheme for information brokerage systems. Our experimental results indicate that information brokerage system builders should treat access control as a system issue as well.

Continuous Geo-Social Group Monitoring over Moving Users

Abstract: Recently a lot of research works have focused on geo-social group queries for group-based activity planning and scheduling in location-based social networks (LBSNs), which return a social cohesive user group with a spatial constraint. However, existing studies on geo-social group queries assume the users are stationary whereas in real LBSN applications all users may continuously move over time. Thus, in this paper we in-vestigate the problem of continuous geo-social groups monitoring (CGSGM) over moving users. A challenge in answering CGSGM queries over moving users is how to efficiently update geo-social groups when users are continuously moving. To address the CGSGM problem, we first propose a baseline algorithm, namely Baseline-BB, which recomputes the new geo-social groups from scratch at each time instance by utilizing a branch and bound (BB) strategy. To improve the inefficiency of BB, we propose a new strategy, called common neighbor or neighbor expanding (CNNE), which expands the common neighbors of edges or the neighbors of users in intermediate groups to quickly produce the valid group combinations. Based on CNNE, we propose another baseline algorithm, namely Baseline-CNNE. As these baseline algorithms do not maintain any intermediate results to facilitate further query processing, we develop an incremental algorithm, called incremental monitoring algorithm (IMA), which maintains the support, common neighbors and the neighbors of current users when exploring possible user groups for further updates and query processing. Finally, we conduct extensive experiments using three real datasets to validate our ideas and evaluate the proposed algorithms,

In-service chromatic dispersion and pass-band shape measurements for light path with modulated ASE source

Abstract: A tunable modulated broadband source and narrow-bandwidth filter are used to measure chromatic dispersion, group-delay ripple, and pass-band shape of DWDM-network light paths. This new method is simple and suitable for in-service measurements.

A Caching Mechanism for Improving Internet based Mobile Ad Hoc Networks Performance.

Abstract: Internet based mobile ad hoc networks (IMANETs) have several limitations to fulfill users’ demands to access various kinds of information such as limited accessibility to the wired Internet, insufficient wireless bandwidth, and longer message latency. In this paper, we address the issues involved in information search and access in IMANET. A broadcast based Simple Search (SS) algorithm and an aggregate caching mechanism are proposed for improving the information accessibility and reducing average communication latency in IMANET. We evaluate the impact of caching, cache management, and access points, which are connected to the Internet, through extensive simulation. The simulation results indicate that the proposed aggregate cache can significantly improve an IMANET performance in terms of throughput and average number of hops to access data.

Inferring Trip Occupancies in the Rise of Ride-Hailing Services

Abstract: The knowledge of all occupied and unoccupied trips made by self-employed drivers are essential for optimized vehicle dispatch by ride-hailing services (e.g., Didi Dache, Uber, Lyft, Grab, etc.). However, the occupancy status of vehicles is not always known to the service operators due to adoption of multiple ride-hailing apps. In this paper, we propose a novel framework, Learning to INfer Trips (LINT), to infer occupancy of car trips by exploring characteristics of observed occupied trips. Two main research steps, stop point classification and structural segmentation, are included in LINT. In the stop point classification step, we represent a vehicle trajectory as a sequence of stop points, and assign stop points with pick-up, drop-off, and intermediate labels. The classification of vehicle trajectory stop points produces a stop point label sequence. For structural segmentation, we further propose several segmentation algorithms, including greedy segmentation (GS), efficient greedy segmentation (EGS), and dynamic programming-based segmentation (DP) to infer occupied trip from stop point label sequences. Our comprehensive experiments on real vehicle trajectories from self-employed drivers show that (1) the proposed stop point classifier predicts stop point labels with high accuracy, and (2) the proposed segmentation algorithm GS delivers the best accuracy performance with efficient running time.

Friendship and mobility: user movement in location-based social networks

Abstract: Even though human movement and mobility patterns have a high degree of freedom and variation, they also exhibit structural patterns due to geographic and social constraints. Using cell phone location data, as well as data from two online location-based social networks, we aim to understand what basic laws govern human motion and dynamics. We find that humans experience a combination of periodic movement that is geographically limited and seemingly random jumps correlated with their social networks. Short-ranged travel is periodic both spatially and temporally and not effected by the social network structure, while long-distance travel is more influenced by social network ties. We show that social relationships can explain about 10% to 30% of all human movement, while periodic behavior explains 50% to 70%. Based on our findings, we develop a model of human mobility that combines periodic short range movements with travel due to the social network structure. We show that our model reliably predicts the locations and dynamics of future human movement and gives an order of magnitude better performance than present models of human mobility.

Temporal Networks

Abstract: A great variety of systems in nature, society and technology -- from the web of sexual contacts to the Internet, from the nervous system to power grids -- can be modeled as graphs of vertices coupled by edges The network structure, describing how the graph is wired, helps us understand, predict and optimize the behavior of dynamical systems In many cases, however, the edges are not continuously active As an example, in networks of communication via email, text messages, or phone calls, edges represent sequences of instantaneous or practically instantaneous contacts In some cases, edges are active for non-negligible periods of time: eg, the proximity patterns of inpatients at hospitals can be represented by a graph where an edge between two individuals is on throughout the time they are at the same ward Like network topology, the temporal structure of edge activations can affect dynamics of systems interacting through the network, from disease contagion on the network of patients to information diffusion over an e-mail network In this review, we present the emergent field of temporal networks, and discuss methods for analyzing topological and temporal structure and models for elucidating their relation to the behavior of dynamical systems In the light of traditional network theory, one can see this framework as moving the information of when things happen from the dynamical system on the network, to the network itself Since fundamental properties, such as the transitivity of edges, do not necessarily hold in temporal networks, many of these methods need to be quite different from those for static networks