Manohar (Manu) Kaul Aarhus University Data Intensive Systems Group Dept. of Computer Science Nygaard 346 Abogade 34, Aarhus, Denmark mkaul@cs.au.dk

News Available now a webpage to check for Co-Author Conflicts of Interest during conference submissions. About me I am a PhD student at the data-intensive systems group in the Department of Computer Science, which is part of the Faculty of Science, one of Aarhus University's faculties. I am working under the supervision of Prof. Christian S. Jensen on an EU project called Reduction . I recently completed my Masters program at Uppsala University, Sweden. My thesis titled Frequent Route Based Continuous Moving Object Location and Density Prediction on Road Networks was done under the co-supervision of Prof. Tore Risch at Uppsala Database Laboratory (UDBL) and Gyozo Gidofalvi from GeoInformatics Division at Royal Institute of Technology (KTH), Sweden. In 2000, I graduated with a B.E. Engineering (Hons) in Computer Systems Engineering from the Department of Computer Science and Computer Engineering, La Trobe University, Melbourne, Australia. Prior to my Masters I have worked for nearly 9 years in the I.T industry, primarily at ORACLE Utilities as a senior systems architect. My CV (in pdf) contains more details. Teaching Assistant for courses 2012 Contract Based Programming in Q2. Web Technology in Q3 **(double TA). Publications Manohar Kaul, Bin Yang, Christian S. Jensen Building Accurate 3D Spatial Networks to Enable Next Generation Intelligent Transportation Systems (Accepted) Proceedings of International Conference on Mobile Data Management (IEEE MDM), June 3-6 2013, Milan, Italy [pdf] [abstract] 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 laser scan data and thus yields an accurate 3D model. We present a filtering technique that is capable of pruning irrelevant laser scan points in a single pass, but assumes that the 2D network fits in internal memory and that the points are appropriately sorted. We also provide an external-memory filtering technique that makes no such assumptions. During lifting, a triangulated irregular network (TIN) surface is constructed from the remaining points. The 2D network is projected onto the TIN, and a 3D network is constructed by means of interpolation. We report on a large-scale empirical study that offers insight into the accuracy, efficiency, and scalability properties of the framework. Chenjuan Guo, Yu Ma, Bin Yang, Christian S. Jensen, Manohar Kaul EcoMark: Evaluating Models of Vehicular Environmental Impact Proceedings of ACM SIGSPATIAL GIS, Nov 7-9, 2012, Redondo Beach, CA, USA [pdf] [abstract] 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 reliably quantify the emissions of vehicles as they travel in a spatial network. Thus, a number of models have been proposed that aim to quantify the emissions of a vehicle based on GPS data from the vehicle and a 3D model of the spatial network the vehicle travels in. We develop an evaluation framework, called EcoMark, for such environmental impact models. In addition, we survey all eleven state-of-the-art impact models known to us. To gain insight into the capabilities of the models and to understand the effectiveness of the EcoMark, we apply the framework to all models. Gyozo Gidofalvi, Manohar Kaul, Christian Borgelt, Torben Bach Pedersen Frequent Route Based Continuous Moving Object Location and Density Prediction on Road Networks Proceedings of ACM SIGSPATIAL GIS, Nov 1-4, 2011, Chicago, IL, USA [pdf] [abstract] Emerging trends in urban mobility have accelerated the need for effective traffic prediction and management systems. The present paper proposes a novel approach to using continuously streaming moving object trajectories for traffic prediction and management. The approach continuously performs three functions for streams of moving object positions in road networks: 1) management of current evolving trajec tories, 2) incremental mining of closed frequent routes, and 3) prediction of near-future locations and densities based on 1) and 2). The approach is empirically evaluated on a large real-world data set of moving object trajectories, originating from a fleet of taxis, illustrating that detailed closed frequent routes can be efficiently discovered and used for prediction. Manohar Kaul, R. Khosla, Y. Mitsukura Intelligent Packet Shaper to avoid Network Congestion for Improved Streaming Video Quality at Clients Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, July 16-20, 2003, Kobe, Japan [pdf] [abstract] This paper proposes a traffic shaping algorithm based on neural networks, which adapts to a network over which streaming video is being transmitted. The purpose of this intelligent shaper is to eradicate traffic congestion and improve end-user video quality. It posseses the capability to predict, to a very high level of accuracy, a state of congestion based upon the training data collected about the network's behaviour.