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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.


Papers
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Proceedings ArticleDOI
29 Oct 2012
TL;DR: Experimental results show that ABC significantly outperforms its counterpart and two baseline approaches in terms of both computational overhead and bundle quality.
Abstract: Prior research on viral marketing mostly focuses on promoting one single product item. In this work, we explore the idea of bundling multiple items for viral marketing and formulate a new research problem, called Bundle Configuration for SpreAd Maximization (BCSAM). Efficiently obtaining an optimal product bundle under the setting of BCSAM is very challenging. Aiming to strike a balance between the quality of solution and the computational overhead, we systematically explore various heuristics to develop a suite of algorithms, including κ-Bundle Configuration and Aggregated Bundle Configuration. Moreover, we integrate all the proposed ideas into one efficient algorithm, called Aggregated Bundle Configuration (ABC). Finally, we conduct an extensive performance evaluation on our proposals. Experimental results show that ABC significantly outperforms its counterpart and two baseline approaches in terms of both computational overhead and bundle quality.

10 citations

Proceedings ArticleDOI
01 Jul 2017
TL;DR: Extensive evaluations on 11.8 million bus trajectory data show that BTCI can effectively identify congestion cascades, the proposed congestion score is effective in extracting congested segments, and the proposed unified approach significantly outperforms alternative approaches in terms of extended precision.
Abstract: The knowledge of traffic health status is essential to the general public and urban traffic management. To identify congestion cascades, an important phenomenon of traffic health, we propose a Bus Trajectory based Congestion Identification (BTCI) framework that explores the anomalous traffic health status and structure properties of congestion cascades using bus trajectory data. BTCI consists of two main steps, congested segment extraction and congestion cascades identification. The former constructs path speed models from historical vehicle transitions and design a non-parametric Kernel Density Estimation (KDE) function to derive a measure of congestion score. The latter aggregates congested segments (i.e., those with high congestion scores) into traffic congestion cascades by unifying both attribute coherence and spatio-temporal closeness of congested segments within a cascade. Extensive evaluations on 11.8 million bus trajectory data show that (1) BTCI can effectively identify congestion cascades, (2) the proposed congestion score is effective in extracting congested segments, (3) the proposed unified approach significantly outperforms alternative approaches in terms of extended precision, and (4) the identified congestion cascades are realistic, matching well with the traffic news and highly correlated with vehicle speed bands.

10 citations

Proceedings Article
13 Sep 2010
TL;DR: The annual DMSN workshop is a leading international forum that covers all important aspects of sensor data management, including data acquisition, processing, and storage in remote wireless networks; the handling of uncertain sensor data; and the management of heterogeneous and sometimes sensitive sensor data in databases.
Abstract: It is our great pleasure to welcome you all to the 7th International Workshop on Data Management for Sensor Networks (DMSN'10), which takes place in Singapore on September 13, 2010. The annual DMSN workshop is a leading international forum that covers all important aspects of sensor data management, including data acquisition, processing, and storage in remote wireless networks; the handling of uncertain sensor data; and the management of heterogeneous and sometimes sensitive sensor data in databases. It brings together a wide range of researchers, practitioners, and users to explore and share scientific and industrial challenges that arise in the aforementioned contexts. We hope you find the workshop academically stimulating and the location interesting and enjoyable. One of our main objectives was to bring forward an exciting research program, spanning both predominant and emerging fields in data management for sensor networks. DMSN'10 received 10 submissions for research papers, out of those we accepted only 6 papers. The accepted papers were thematically organized in the following categories: Data Provenance, Query Processing, Mobile Sensor Networks and Outlier Detection in Sensor Networks. In addition to research contributions, DMSN'10 features an exciting keynote talk by Prof. Kian-Lee Tan (National University of Singapore, Singapore), with title: "What's NExT? Sensor + Cloud!?" Finally, the program also features a panel discussion with title "Future Directions in Sensor Data Management: A Panel Discussion", with panelists Dr. Yanlei Diao (University of Massachusetts Amherst, USA), Prof. Le Gruenwald (National Science Foundation, USA), Prof. Christian S. Jensen (Aarhus University) and Prof. Kian-Lee Tan (National University of Singapore, Singapore) Besides authors that provided the content of the program, several other people have contributed to the successful organization of DMSN'10. In particular, we would like to thank our technically and geographically diverse Technical Program Committee (TPC), which enabled us to make high quality decisions. Our TPC comprised of 32 members that spanned the following continents: North America (50%), Europe (31%) and Asia (19%). Our TPC board came from both Academia (87%) and Industrial Research Labs (13%). We owe our sincere gratitude to all of these members for their excellent work in reviewing the papers and providing valuable feedback under a tight schedule. Every paper was reviewed at least by 3 TPC members. We would like to thank Microsoft for granting us permission to use the Microsoft Conference Management System (CMT) and the entire CMT support team, for their help in setting up and managing the online review process. The latest features in CMT made it extremely easy to cope with virtually all aspects of the paper evaluation process.

10 citations

Proceedings ArticleDOI
21 Jun 2010
TL;DR: A notion of expected reverse ranks and evaluation of RR queries over imprecise data based on expected reverse ranks is proposed and the efficiency of the approach is demonstrated through experiments.
Abstract: The reverse rank of a (data) object o with respect to a given query object q (that measures the relative nearness of q to o) is said to be k when q is the k-th nearest neighbor of o in a geographical space. Based on the notion of reverse ranks, a Reverse Ranking (RR) query determines t objects with the smallest k's with respect to a given query object q. In many situations that locations of objects and a query object can be imprecise, objects would receive multiple possible k's. In this paper, we propose a notion of expected reverse ranks and evaluation of RR queries over imprecise data based on expected reverse ranks. For any object o, an expected reverse rank kk is a weighted average of possible reverse ranks for individual instances of o with respect to different instances of a given query object q by taking their probabilities into account. We devise and present incremental kk computation and two kk-Estimating algorithms to efficiently evaluate RR queries over imprecise data. The efficiency of our approach is demonstrated through experiments.

10 citations

Book ChapterDOI
TL;DR: A dynamic discrete-event based models for information dissemination systems in both single cell and multiple cell mobile environments is proposed and it is demonstrated that this novel approach can flexibly model channel borrowing and hand-off scenarios.
Abstract: In this paper, we propose dynamic discrete-event based models for information dissemination systems in both single cell and multiple cell mobile environments. We demonstrate that this novel approach can flexibly model channel borrowing and hand-off scenarios. Finally, we implement two simulation systems based on the single cell model. Our simulations show that broadcast channels can effectively alleviate traffic overload in a cell.

9 citations


Cited by
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01 Jan 2002

9,314 citations

Journal ArticleDOI

6,278 citations

Proceedings ArticleDOI
21 Aug 2011
TL;DR: A model of human mobility that combines periodic short range movements with travel due to the social network structure is developed and it is shown that this model reliably predicts the locations and dynamics of future human movement and gives an order of magnitude better performance.
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.

2,922 citations

01 Nov 2008

2,686 citations

Journal ArticleDOI
TL;DR: This review presents the emergent field of temporal networks, and discusses methods for analyzing topological and temporal structure and models for elucidating their relation to the behavior of dynamical systems.
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

2,452 citations