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.

Location-Dependent Skyline Query

Abstract: Given a set of data points with both spatial coordinates and non-spatial attributes, point a location-dependently dominates point b with respect to a query point q if a is closer to q than b and meanwhile a dominates b. A location- dependent skyline query (LDSQ) issued at point q is to retrieve all the points that are not location-dependently dominated by other points with regard to q. In this paper, we focus on the query processing and result validation of LDSQ over static objects. Two algorithms, namely brute-forth and delta-scanning, are proposed. The former serves as the baseline algorithm while the latter significantly improves the performance via space pruning. We further conduct a comprehensive simulation to demonstrate the performance of proposed algorithms.

A New Storage Scheme for Approximate Location Queries in Object-Tracking Sensor Networks

Abstract: Energy efficiency is one of the most critical issues in the design of wireless sensor networks. Observing that many sensor applications for object tracking can tolerate a certain degree of imprecision in the location data of tracked objects, this paper studies precision-constrained approximate queries that trade answer precision for energy efficiency. We develop an energy-conserving approximate storage (EASE) scheme to efficiently answer approximate location queries by keeping error-bounded imprecise location data at some designated storage node. The data impreciseness is captured by a system parameter called the approximation radius. We derive the optimal setting of the approximation radius for our storage scheme based on the mobility pattern and devise an adaptive algorithm to adjust the setting when the mobility pattern is not available a priori or is dynamically changing. Simulation experiments are conducted to validate our theoretical analysis of the optimal approximation setting. The simulation results show that the proposed EASE scheme reduces the network traffic from a conventional approach by up to 96 percent and, in most cases, prolongs the network lifetime by a factor of 2-5.

Maximizing Acceptance Probability for Active Friending in On-Line Social Networks

Abstract: Friending recommendation has successfully contributed to the explosive growth of on-line social networks. Most friending recommendation services today aim to support passive friending, where a user passively selects friending targets from the recommended candidates. In this paper, we advocate recommendation support for active friending, where a user actively specifies a friending target. To the best of our knowledge, a recommendation designed to provide guidance for a user to systematically approach his friending target, has not been explored in existing on-line social networking services. To maximize the probability that the friending target would accept an invitation from the user, we formulate a new optimization problem, namely, \emph{Acceptance Probability Maximization (APM)}, and develop a polynomial time algorithm, called \emph{Selective Invitation with Tree and In-Node Aggregation (SITINA)}, to find the optimal solution. We implement an active friending service with SITINA in Facebook to validate our idea. Our user study and experimental results manifest that SITINA outperforms manual selection and the baseline approach in solution quality efficiently.

SSW: A Small-World-Based Overlay for Peer-to-Peer Search

Abstract: Peer-to-peer (P2P) systems have become a popular platform for sharing and exchanging voluminous information among thousands or even millions of users. The massive amount of information shared in such systems mandates efficient semantic-based search instead of key-based search. The majority of existing proposals can only support simple key-based search rather than semantic-based search. This paper presents the design of an overlay network, namely, semantic small world (SSW), that facilitates efficient semantic-based search in P2P systems. SSW achieves the efficiency based on four ideas: 1) semantic clustering, where peers with similar semantics organize into peer clusters, 2) dimension reduction, where to address the high maintenance overhead associated with capturing high-dimensional data semantics in the overlay, peer clusters are adaptively mapped to a one-dimensional naming space, 3) small world network, where peer clusters form into a one-dimensional small world network, which is search efficient with low maintenance overhead, and 4) efficient search algorithms, where peers perform efficient semantic-based search, including approximate point query and range query in the proposed overlay. Extensive experiments using both synthetic data and real data demonstrate that SSW is superior to the state of the art on various aspects, including scalability, maintenance overhead, adaptivity to distribution of data and locality of interest, resilience to peer failures, load balancing, and efficiency in support of various types of queries on data objects with high dimensions.

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