https://minerva-access.unimelb.edu.au/bitstream/handle/11343/197439/1207.0203v1.pdf

Internet of Things (IoT): A vision, architectural elements, and future directions

Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268

IEEE Transactions on Pattern Analysis and Machine Intelligence

Recent technological advancements have led to a deluge of data from distinctive domains (e.g., health care and scientific sensors, user-generated data, Internet and financial companies, and supply chain systems) over the past two decades. The term big data was coined to capture the meaning of this emerging trend. In addition to its sheer volume, big data also exhibits other unique characteristics as compared with traditional data. For instance, big data is commonly unstructured and require more real-time analysis. This development calls for new system architectures for data acquisition, transmission, storage, and large-scale data processing mechanisms. In this paper, we present a literature survey and system tutorial for big data analytics platforms, aiming to provide an overall picture for nonexpert readers and instill a do-it-yourself spirit for advanced audiences to customize their own big-data solutions. First, we present the definition of big data and discuss big data challenges. Next, we present a systematic framework to decompose big data systems into four sequential modules, namely data generation, data acquisition, data storage, and data analytics. These four modules form a big data value chain. Following that, we present a detailed survey of numerous approaches and mechanisms from research and industry communities. In addition, we present the prevalent Hadoop framework for addressing big data challenges. Finally, we outline several evaluation benchmarks and potential research directions for big data systems.

Toward Scalable Systems for Big Data Analytics: A Technology Tutorial

Online 3D reconstruction is gaining newfound interest due to the availability of real-time consumer depth cameras. The basic problem takes live overlapping depth maps as input and incrementally fuses these into a single 3D model. This is challenging particularly when real-time performance is desired without trading quality or scale. We contribute an online system for large and fine scale volumetric reconstruction based on a memory and speed efficient data structure. Our system uses a simple spatial hashing scheme that compresses space, and allows for real-time access and updates of implicit surface data, without the need for a regular or hierarchical grid data structure. Surface data is only stored densely where measurements are observed. Additionally, data can be streamed efficiently in or out of the hash table, allowing for further scalability during sensor motion. We show interactive reconstructions of a variety of scenes, reconstructing both fine-grained details and large scale environments. We illustrate how all parts of our pipeline from depth map pre-processing, camera pose estimation, depth map fusion, and surface rendering are performed at real-time rates on commodity graphics hardware. We conclude with a comparison to current state-of-the-art online systems, illustrating improved performance and reconstruction quality.

/pdf/real-time-3d-reconstruction-at-scale-using-voxel-hashing-41a8l8088q.pdf

Real-time 3D reconstruction at scale using voxel hashing

Visualization and computer graphics

The goal of visualization is to effectively and accurately communicate data. Visualization research has often overlooked the errors and uncertainty which accompany the scientific process and describe key characteristics used to fully understand the data. The lack of these representations can be attributed, in part, to the inherent difficulty in defining, characterizing, and controlling this uncertainty, and in part, to the difficulty in including additional visual metaphors in a well designed, potent display. However, the exclusion of this information cripples the use of visualization as a decision making tool due to the fact that the display is no longer a true representation of the data. This systematic omission of uncertainty commands fundamental research within the visualization community to address, integrate, and expect uncertainty information. In this chapter, we outline sources and models of uncertainty, give an overview of the state-of-the-art, provide general guidelines, outline small exemplary applications, and finally, discuss open problems in uncertainty visualization.

Overview and State-of-the-Art of Uncertainty Visualization

Feature tracking algorithms for instationary vector fields are usually based on a correspondence analysis of the features at different time steps. This paper introduces a method for feature tracking which is based on the integration of stream lines of a certain vector field called feature flow field. We analyze for which features the method of feature flow fields can be applied, we show how events in the flow can be detected using feature flow fields, and we show how to construct the feature flow fields for particular classes of features. Finally, we apply the technique to track critical points in a 2D instationary vector field.

Feature Flow Fields

Preface. Part I: Visualization Algorithms and Techniques. Efficient occlusion culling for large model visualization D. Bartz, et al. Localizing vector field topology G. Scheuermann, et al. Feature tracking with skeleton graphs B. Vrolijk, et al. Correspondence analysis: visualizing property-profiles of time-dependent 3D datasets K. Fries, et al. Specializing visualization algorithms S. Diehl. Isosurface extraction for large-scale datasets Y. Livnat, et al. Part II: Volume Visualization. Topologically-accentuated volume rendering I. Fujishiro, et al. Reconstruction issues in volume visualization T. Theussl, et al. High quality splatting and volume synthesis R. Crawfis, J. Huang. Cellfast: interactive unstructured volume rendering and classification C.M. Wittenbrink, et al. Cell projection of meshes with non-planar faces M. Max, et al. Segmentation and texture-based hierarchical rendering techniques for large-scale real-color biomedical image data J. Meyer, et al. Part III: Information Visualization. eBusiness click stream analysis S.G. Eick. Hierarchical exploration of large multivariate data sets J. Yang, et al. Visualization of multidimensional data using structure preserving projection methods W. de Leeuw, R. van Liere. Visualizing process information and the health status of wastewater treatment plants: a case study of the ESPRIT-project WaterCIME P. Dannenmann, H. Hagen. Part IV: Multiresolution Methods. Data structures for 3D multi-tessellations: an overview E. Danovaro, et al. A data model for adaptive multi-resolution scientific data P.J. Rhodes, et al. Multiresolution representation of datasets with material interfaces B.F. Gregorski, et al.Generalizing lifted tensor product wavelets to irregular polygonal domains M. Bertram, et al. Ranked representation of vector fields B. Nakshatrala, et al. Part V: Modelling Techniques. Procedural volume modeling, rendering, and visualization D. Ebert, P. Rheingans. Surface approximation to point cloud data using volume modeling A. Huang, G.M. Nielson. Enriching volume modelling with scalar fields M. Chen, et al. Fast methods for computing isosurface topology with Betti numbers S.F. Konkle, et al. Surface interpolation by spatial environment graphs R. Mencl, H Muller. Part VI: Interaction Techniques and Architectures. Preset based interaction with high dimensional parameter spaces J.J. van Wijk, C.W.A.M. van Overveld. Visual interaction to solve complex optimization problems A. Hinnenburg, D.A. Keim. Visualizing cosmological time A.J. Hanson, et al. Component-based intelligent visualization H. Hagen, et al. Author Index.

Data visualization : the state of the art

Wavelet-based methods have proven their efficiency for visualization at different levels of detail, progressive transmission, and compression of large data sets. The required core of all wavelet-based methods is a hierarchy of meshes that satisfies subdivision-connectivity. This hierarchy has to be the result of a subdivision process starting from a base mesh. Examples include quadtree uniform 2D meshes, octree uniform 3D meshes, or 4-to-1 split triangular meshes. In particular, the necessity of subdivision-connectivity prevents the application of wavelet-based methods on irregular triangular meshes. In this paper, a "wavelet-like" decomposition is introduced that works on piecewise constant data sets over irregular triangular surface meshes. The decomposition/reconstruction algorithms are based on an extension of wavelet-theory allowing hierarchical meshes without property. Among others, this approach has the following features: it allows exact reconstruction of the data set, even for nonregular triangulations, and it extends previous results on Haar-wavelets over 4-to-1 split triangulations.

https://hal.inria.fr/hal-01708566/document

Georges-Pierre Bonneau

Papers

Visualization and computer graphics

Overview and State-of-the-Art of Uncertainty Visualization

Feature Flow Fields

Data visualization : the state of the art

Multiresolution analysis on irregular surface meshes