We study algorithms for matching user tracks, consisting of time-ordered location points, to paths in the road network. Previous work has focused on the scenario where the location data is linearly ordered and consists of fairly dense and regular samples. In this work, we consider the multi-track map matching, where the location data comes from different trips on the same route, each with very sparse samples. This captures the realistic scenario where users repeatedly travel on regular routes and samples are sparsely collected, either due to energy consumption constraints or because samples are only collected when the user actively uses a service. In the multi-track problem, the total set of combined locations is only partially ordered, rather than globally ordered as required by previous map-matching algorithms. We propose two methods, the iterative projection scheme and the graph Laplacian scheme, to solve the multi-track problem by using a single-track map-matching subroutine. We also propose a boosting technique which may be applied to either approach to improve the accuracy of the estimated paths. In addition, in order to deal with variable sampling rates in single-track map matching, we propose a method based on a particular regularized cost function that can be adapted for different sampling rates and measurement errors. We evaluate the effectiveness of our techniques for reconstructing tracks under several different configurations of sampling error and sampling rate.

Map matching is the procedure for determining the path of a user on a map from a sequence of location data (which we refer to as track). This process serves as a common preprocessing step for reasoning about traffic on the road network as well as for providing better location-based services [3, 5, 1]. Converting a track to a topological path on a map not only makes it easier to reason about paths, but also leads to reduced storage requirements and more efficient operations on the same. Some examples of such operations are comparison, querying and retrieval of tracks based on similarity to other tracks [1].

Mobile devices are increasingly equipped with hardware and software services allowing them to determine their locations, but support for building location-aware applications remains rudimentary. This paper proposes tracks of location coordinates as a high-level abstraction for a new class of mobile applications including ride sharing, location-based collaboration, and health monitoring. Each track is a sequence of entries recording a person's time, location, and application-specific data. StarTrack provides applications with a comprehensive set of operations for recording, comparing, clustering and querying tracks. StarTrack can efficiently operate on thousands of tracks.

Abstract This article surveys the use of eye tracking in investigations of online search. Three eye tracking experiments that we undertook are discussed and compared to additional work in this area, revealing recurring behaviors and trends. The first two studies are described in greater detail in Granka, Joachims, & Gay (2004), Lorigo et al. (2006), and Pan et al. (2007), and the third study is described for the first time in this article. These studies reveal how users view the ranked results on a search engine results page (SERP), the relationship between the search result abstracts viewed and those clicked on, and whether gender, search task, or search engine influence these behaviors. In addition, we discuss a key challenge that arose in all three studies that applies to the use of eye tracking in studying online behaviors which is due to the limited support for analyzing scanpaths, or sequences of eye fixations. To meet this challenge, we present a preliminary approach that involves a graphical visualization to compare a path with a group of paths. We conclude by summarizing our findings and discussing future work in further understanding online search behavior with the help of eye tracking.

Application-level multicast systems are vulnerable to attacks that impede nodes from receiving desired data. Live streaming protocols are especially susceptible to packet loss induced by malicious behavior. We describe SecureStream, an application-level live streaming system built using a pull-based architecture that results in improved tolerance of malicious behavior. SecureStream is implemented as a layer running over Fireflies, an intrusion-tolerant membership protocol. Our paper describes the SecureStream system and offers simulation and experimental results confirming its resilience to attack

The use of clusters of computers as an environment for high performance computing has been shown to be promising. However, the efficient use of such systems still requires advances that make the application development process be simpler and more productive. The development of cluster monitoring tools is essential to achieve this advances. We present PM/sup 2/P, a tool for use in clusters of personal computers that provides a graphic visualization of the temporal execution of distributed applications that use the MPI standard for message passing. The tool uses an approach involving the parallel port to read the time of events that occur in all different machines of a cluster. It also simulates the execution of task precedence graphs and allocates tasks of a graph to the machines of a cluster, among other functionalities.