@article{RichterDoellner2014,
  author    = {Richter, Rico and D{\"o}llner, J{\"u}rgen Roland Friedrich},
  title     = {Concepts and techniques for integration, analysis and visualization of massive 3D point clouds},
  series = {Computers, environment and urban systems},
  volume    = {45},
  journal   = {Computers, environment and urban systems},
  publisher = {Elsevier},
  address   = {Oxford},
  issn      = {0198-9715},
  doi       = {10.1016/j.compenvurbsys.2013.07.004},
  pages     = {114 -- 124},
  year      = {2014},
  abstract  = {Remote sensing methods, such as LiDAR and image-based photogrammetry, are established approaches for capturing the physical world. Professional and low-cost scanning devices are capable of generating dense 3D point clouds. Typically, these 3D point clouds are preprocessed by GIS and are then used as input data in a variety of applications such as urban planning, environmental monitoring, disaster management, and simulation. The availability of area-wide 3D point clouds will drastically increase in the future due to the availability of novel capturing methods (e.g., driver assistance systems) and low-cost scanning devices. Applications, systems, and workflows will therefore face large collections of redundant, up-to-date 3D point clouds and have to cope with massive amounts of data. Hence, approaches are required that will efficiently integrate, update, manage, analyze, and visualize 3D point clouds. In this paper, we define requirements for a system infrastructure that enables the integration of 3D point clouds from heterogeneous capturing devices and different timestamps. Change detection and update strategies for 3D point clouds are presented that reduce storage requirements and offer new insights for analysis purposes. We also present an approach that attributes 3D point clouds with semantic information (e.g., object class category information), which enables more effective data processing, analysis, and visualization. Out-of-core real-time rendering techniques then allow for an interactive exploration of the entire 3D point cloud and the corresponding analysis results. Web-based visualization services are utilized to make 3D point clouds available to a large community. The proposed concepts and techniques are designed to establish 3D point clouds as base datasets, as well as rendering primitives for analysis and visualization tasks, which allow operations to be performed directly on the point data. Finally, we evaluate the presented system, report on its applications, and discuss further research challenges.},
  language  = {en}
}
@article{WestphalAxelssonNeuhausetal.2014,
  author    = {Westphal, Florian and Axelsson, Stefan and Neuhaus, Christian and Polze, Andreas},
  title     = {VMI-PL: A monitoring language for virtual platforms using virtual machine introspection},
  series = {Digital Investigation : the international journal of digital forensics \& incident response},
  volume    = {11},
  journal   = {Digital Investigation : the international journal of digital forensics \& incident response},
  publisher = {Elsevier},
  address   = {Oxford},
  issn      = {1742-2876},
  doi       = {10.1016/j.diin.2014.05.016},
  pages     = {S85 -- S94},
  year      = {2014},
  abstract  = {With the growth of virtualization and cloud computing, more and more forensic investigations rely on being able to perform live forensics on a virtual machine using virtual machine introspection (VMI). Inspecting a virtual machine through its hypervisor enables investigation without risking contamination of the evidence, crashing the computer, etc. To further access to these techniques for the investigator/researcher we have developed a new VMI monitoring language. This language is based on a review of the most commonly used VMI-techniques to date, and it enables the user to monitor the virtual machine's memory, events and data streams. A prototype implementation of our monitoring system was implemented in KVM, though implementation on any hypervisor that uses the common x86 virtualization hardware assistance support should be straightforward. Our prototype outperforms the proprietary VMWare VProbes in many cases, with a maximum performance loss of 18\% for a realistic test case, which we consider acceptable. Our implementation is freely available under a liberal software distribution license. (C) 2014 Digital Forensics Research Workshop. Published by Elsevier Ltd. All rights reserved.},
  language  = {en}
}