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.

CS cache engine: data access accelerator for location-based service in mobile environments

Abstract: Location-based services (LBS) have emerged as one of the killer applications for mobile and pervasive computing environments. Due to limited bandwidth and scarce client resources, client-side data caching plays an important role of enhancing the data availability and improving the response time. In this demonstration, we present CS Cache Engine suitable for LBS. The underlying caching model is Complementary Space Caching (CS caching) scheme that we have recently presented in [citation]. Different from conventional data caching schemes, CS caching preserves a global view of the database by maintaining physical objects and capturing those objects in the server but not in the cache as Complementary Regions (CRs) in the cache. As a result, with the CS Cache Engine implementing CS caching, client assertiveness on their own answered queries is enhanced so that unnecessary requests over the wireless channel can be avoided; various kinds of location-based queries are naturally supported; and the client's ability to prefetch objects is introduced such that the response time can be further improved. In this demonstration paper, we discuss the architecture and the functionality of the CS Caching Engine that adopts CS caching. Specifically, for this demonstration, a tourist information named TravelGuide is prototyped with the support of this cache engine.

Recommending missing citations for newly granted patents

Abstract: The U.S. recently adopted a post-grant opposition procedure to encourage third parties to challenge the validity of newly granted patents by providing relevant prior patents that are missed during patent examination (i.e., missing citations). In this paper, we propose a recommendation system for missing citations for newly granted patents. The recommendation system, based on the patent citation network of a newly granted query patent, focuses on paths that start with the references of the query patent in the network. Our approach is to identify the relevancy of a candidate patent to the query patent by its citation relationship (paths) that are distinguished based on the direction, topology and semantics of the paths in the network. We consider six different types of paths between a candidate patent and a query patent based on their citation relationship and define a relevancy score for each path type. Accordingly, we rank candidate patents via a RankSVM model learned by using those relevancy scores as features. The experimental results show our approach significantly improves the average precision and recall performance compared to two baseline methods, i.e., Katz distance and text similarity.

An analytical study of GWAP-based geospatial tagging systems

Abstract: Geospatial tagging (geotagging) is an emerging and very promising application that can help users find a wide variety of location-specific information, and facilitate the development of future location-based services. Conventional geotagging systems share some limitations, such as the use of a two-phase operating model and the tendency to tag popular objects with simple contexts. To address these problems, geotagging systems based on the concept of ‘Games with a Purpose’ (GWAP) have been developed recently. In this study, we use analysis to investigate these new systems. Based on our analysis results, we design three metrics to evaluate the system performance, and develop five task assignment algorithms for a GWAP-based system. Using a comprehensive set of simulations under both synthetic and realistic mobility scenarios, we find that the Least-Throughput-First Assignment algorithm (LTFA) is the most effective approach because it can achieve competitive system utility, while its computational complexity remains moderate. We also find that, to improve the system utility, it is better to assign as many tasks as possible in each round. However, because players may feel annoyed if too many tasks are assigned at the same time, it is recommended that multiple tasks be assigned one by one in each round in order to achieve higher system utility.

Scheduling web requests in broadcast environments

Abstract: On-demand broadcast has been supported in the Internet to enhance system scalability. Unfortunately, most of existing on-demand scheduling algorithms did not consider the time constraints associated with web requests. This paper proposes a novel scheduling algorithm, called Slack Inverse Number of requests (SIN), that takes into account the urgency and productivity of serving pending requests. Trace-driven experiments demonstrate that SIN significantly out performs existing algorithms over a wide range of workloads.

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