Geospatial queries (GQ)

Abstract-Due to enormous complexity of the unstructured peer-to-peer networks as large-scale, self-configure, and dynamic systems, the models used to characterize these systems are either inaccurate, because of oversimplification, or analytically inapplicable, due to their high complexity. By recognizing unstructured peer-to-peer networks as "complex systems", we employ statistical models used before to characterize complex systems for formal analysis and efficient design of peer-to-peer networks. We provide two examples of application of this modeling approach that demonstrate its power. For instance, using this approach we have been able to formalize the main problem with normal flooding search, propose a remedial approach with our probabilistic flooding technique, and find the optimal operating point for probabilistic flooding rigorously, such that it improves scalability of the normal flooding by 99%.

Abstract. The problem of point-to-point fastest path computation in static spatial networks is extensively studied with many precomputation techniques proposed to speed-up the computation. Most of the existing approaches make the simplifying assumption that travel-times of the network edges are constant. However, with real-world spatial networks the edge travel-times are time-dependent, where the arrival-time to an edge determines the actual travel-time on the edge. In this paper, we study the online computation of fastest path in time-dependent spatial networks and present a technique which speeds-up the path computation. We show that our fastest path computation based on a bidirectional time-dependent A* search significantly improves the computation time and storage complexity. With extensive experiments using real data-sets (including a variety of large spatial networks with real traffic data) we demonstrate the efficacy of our proposed techniques for online fastest path computation.

Scientific Knowledge on the Subject: COPD progresses over decades so little is known about longitudinal changes in individual patients, and whether there are different patterns of disease progression in different patient subgroups.What this Study Adds to the Field: Computational modelling of CT biomarkers suggests there are two patterns of disease progression in COPD. These disease progression patterns or 'subtypes' can be used to stratify individuals into two groups with distinct clinical characteristics, and to stage individuals along their disease time-course. Early stages of both subtypes are identifiable in a proportion of 'healthy smokers' providing a biomarker of early COPD.

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The world-wide web (WWW) is the largest distributed information space and has grown to encompass diverse information resources. Although the web is growing exponentially, the individual's capacity to read and digest content is essentially fixed. The full economic potential of the web will not be realized unless enabling technologies are provided to facilitate access to web resources. Currently web personalization is the most promising approach to remedy this problem, and web mining, particularly web-usage mining, is considered a crucial component of any efficacious web-personalization system. In this paper, we describe a complete framework for web-usage mining to satisfy the challenging requirements of webpersonalization applications. For on-line and anonymous web personalization to be effective, web usage mining must be accomplished in real time as accurately as possible. On the other hand, web-usage mining should allow a compromise between scalability and accuracy to be applicable to real-life websites with numerous visitors. Within our web-usage-mining framework, we introduce a distributed user-tracking approach for accurate, scalable, and implicit collection of the usage data. We also propose a new model, the feature-matrices (FM) model, to discover and interpret users' access patterns. With FM, various spatial and temporal features of usage data can be captured with flexible precision so that we can trade off accuracy for scalability based on the specific application requirements. Moreover, tunable complexity of the FM model allows real-time and adaptive access pattern discovery from usage data. We define a novel similarity measure based on FM that is specifically designed for accurate classification of partial navigation patterns in real time. Our extensive experiments with both synthetic and real data verify correctness and efficacy of our webusage-mining framework for anonymous and efficient web personalization.

The problem of point-to-point shortest path computation in spatial networks is extensively studied with many approaches proposed to speed-up the computation. Most of the existing approaches make the simplifying assumption that weights (e.g., travel-time) of the network edges are constant. However, with real-world spatial networks the edge travel-times are time-dependent, where the arrivaltime to an edge determines the actual travel-time of the edge. With this paper, we study the applicability of existing shortest path algorithms to real-world large time-dependent spatial networks. In addition, we evaluate the importance of considering time-dependent edge travel-times for route planning in spatial networks. We show that time-dependent shortest path computation can reduce the traveltime by 36% on average as compared to the static shortest path computation that assumes constant edge travel-times.