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Indoor spaces accommodate large populations of individuals. With appropriate indoor positioning, e.g., Bluetooth and RFID, in place, large amounts of trajectory data result that may serve as a foundation for a wide variety of applications, e.g., space planning, way finding, and security. This scenario calls for the indexing of indoor trajectories. Based on(More)
Embryonic stem cells (ESCs) repress the expression of exogenous proviruses and endogenous retroviruses (ERVs). Here, we systematically dissected the cellular factors involved in provirus repression in embryonic carcinomas (ECs) and ESCs by a genome-wide siRNA screen. Histone chaperones (Chaf1a/b), sumoylation factors (Sumo2/Ube2i/Sae1/Uba2/Senp6), and(More)
With the development of positioning technologies and the boosting deployment of inexpensive location-aware sensors, large volumes of trajectory data have emerged. However, efficient and scalable query processing over trajectory data remains a big challenge. We explore a new approach to this target in this paper, presenting a new framework for query(More)
The monitoring of a system can yield a set of measurements that can be modeled as a collection of time series. These time series are often sparse, due to missing measurements, and spatio-temporally correlated, meaning that spatially close time series exhibit temporal correlation. The analysis of such time series offers insight into the underlying system and(More)
The tracking of the locations of moving objects in large indoor spaces is important, as it enables a range of applications related to, e.g., security and indoor navigation and guidance. This paper presents a graph model based approach to indoor tracking that offers a uniform data management infrastructure for different symbolic positioning technologies,(More)
There have been major advances on automatically constructing large knowledge bases by extracting relational facts from Web and text sources. However, the world is dynamic: periodic events like sports competitions need to be interpreted with their respective timepoints, and facts such as coaching a sports team, holding political or business positions, and(More)
The reduction of greenhouse gas (GHG) emissions from transportation is essential for achieving politically agreed upon emissions reduction targets that aim to combat global climate change. So-called eco-routing and eco-driving are able to substantially reduce GHG emissions caused by vehicular transportation. To enable these, it is necessary to be able to(More)
The use of accurate 3D spatial network models can enable substantial improvements in vehicle routing. Notably, such models enable eco-routing, which reduces the environmental impact of transportation. We propose a novel filtering and lifting framework that augments a standard 2D spatial network model with elevation information extracted from massive aerial(More)
A driver’s choice of a route to a destination may depend on the route’s length and travel time, but a multitude of other, possibly hard-to-formalize aspects, may also factor into the driver’s decision. There is evidence that a driver’s choice of route is context dependent, e.g., varies across time, and that route choice also varies from driver to driver. In(More)
We are witnessing increasing interests in the effective use of road networks. For example, to enable effective vehicle routing, weighted-graph models of transportation networks are used, where the weight of an edge captures some cost associated with traversing the edge, e.g., greenhouse gas (GHG) emissions or travel time. It is a precondition to using a(More)