16403469
PSALM: A Data Model for Pervasive Visualization
In this poster, we investigate how to achieve visualization in a pervasive visualization environment, especially on new platforms such as PDAs, cell phones, and other mobile devices that have very limited resources and usually are not considered appropriate platforms for visualization. We will use a clinic application as an example and our target application. In this system, patient data, such as CT and MRI, are stored in a central server in the hospital and physicians can access these data using a cell phone for a quick preview, a pocket PC or laptop for some initial diagnosis, and a powerful workstation for any further diagnosis.
5096474
Fusing Features in Direct Volume Rendered Images
In this paper, we propose a novel framework which can fuse multiple user selected features in different direct volume rendered images into a comprehensive image according to users’ preference. The framework relies on three techniques, i.e., user voting, genetic algorithm, and image similarity. In this framework, we transform the fusing problem to an optimization problem with a novel energy function which is based on user voting and image similarity. The optimization problem can then be solved by the genetic algorithm. Experimental results on some real volume data demonstrate the effectiveness of our framework.
5502752
Natural Textures for Weather Data Visualization
In this paper we present a novel method to visualize weather data with multi-layer controllable texture synthesis. Texture possesses multiple principal perceptual channels, which makes it good at encoding multiple data attributes contained in weather data. The natural textures existed in the real world especially provide plenty of choices to encode the data with visually pleasing images. A controllable texture synthesis method is developed to generate a large amount of textures which change the appearances of their individual perceptual dimensions according to the underlying distribution of data attributes. In order to encode more data attributes we further propose multi-layer texture synthesis. The background and foreground textures are separately synthesized and then combined together for display. In the end, we apply our method to some real-world weather data and demonstrate its effectiveness with a user study
3091056
Controllable and Progressive Edge Clustering for Large Networks
Node-link diagrams are widely used in information visualization to show relationships among data. However, when the size of data becomes very large, node-link diagrams will become cluttered and visually confusing for users. In this paper, we propose a novel controllable edge clustering method based on Delaunay triangulation to reduce visual clutter for node-link diagrams. Our method uses curves instead of straight lines to represent links and these curves can be grouped together according to their relative positions and directions. We further introduce progressive edge clustering to achieve continuous level-of-details for large networks.
13420664
Music Classification Using Significant Repeating Patterns
With the popularity of multimedia applications, a large amount of music data has been accumulated on the Internet. Automatic classification of music data becomes a critical technique for providing an efficient and effective retrieval of music data. In this paper, we propose a new approach for classifying music data based on their contents. In this approach, we focus on monophonic music features represented as rhythmic and melodic sequences. Moreover, we use repeating patterns of music data to do music classification. For each pattern discovered from a group of music data, we employ a series of measurements to estimate its usefulness for classifying this group of music data. According to the patterns contained in a music piece, we determine which class it should be assigned to. We perform a series of experiments and the results show that our approach performs on average better than the approach based on the probability distribution of contextual information in music.
15778343
Smart rebinning for compression of concentric mosaics
Concentric mosaics offer a quick solution to construct a virtual copy of a real environment, and navigate in the virtual environment. However, the huge amount of data associated with concentric mosaics is a heavy burden for its application. A 3D wavelet transform-based compressor has been proposed in previous work to compress the concentric mosaics. In this paper, we greatly improve the performance of the 3D wavelet coder with a data rearrangement mechanism called “smart rebinning”. The proposed scheme first aligns the concentric mosaic image shots along the horizontal direction and then rebins the shots into multi-perspective panoramas. Smart rebinning greatly improves the cross shot correlation and enables the coder to better explore the redundancy among shots. Experimental results show that the performance of the 3D wavelet coder improves an average of 4.3dB with the use of smart rebinning. The proposed coder outperforms MPEG-2 coding of concentric mosaics by an average of 3.7dB.
2511721
Fractal Modeling of IP Network Traffic at Streaming Speeds
This paper describes how to fit fractal models, online, on IP traffic data streams. Our approach relies on maintaining a sketch of the data stream and fitting straight lines: it yields algorithms that are fast, space-efficient, and accurate. We implemented our methods in AT&T’s Gigascope data stream management system, to demonstrate their practicality at streaming line speeds.
6442183
Routing with a Markovian Metric to Promote Local Mixing
Routing protocols have traditionally been based on finding shortest paths under certain cost metrics. A conventional routing metric models the cost of a path as the sum of the costs on the constituting links. This paper introduces the concept of a Markovian metric, which models the cost of a path as the cost of the first hop plus the cost of the second hop conditioned on the first hop, and so on. The notion of the Markovian metric is fairly general. It is potentially applicable to scenarios where the cost of sending a packet (or a stream of packets) over a link may depend on the previous hop of the packet (or the stream). Such scenario arises, for instance, in a wireless mesh network equipped with local mixing, a recent link layer advance. This scenario is examined as a case study for the Markovian metric. The local mixing engine sits between the routing and MAC layers. It maintains information about the packets each neighbor has, and identifies opportunities to mix the outgoing packets via network coding to reduce the transmissions in the air. We use a Markovian metric to model the reduction of channel resource consumption due to local mixing. This leads to routing decisions that can better take advantage of local mixing. We have implemented a system that incorporates local mixing and source routing using a Markovian metric in Qualnet. The experimental results demonstrate significant throughput gain and resource saving.
8596758
TIMBER: a native system for querying XML
XML has become ubiquitous, and XML data has to be managed in databases. The current industry standard is to map XML data into relational tables and store this information in a relational database. Such mappings create both expressive power problems and performance problems.In the TIMBER [7] project we are exploring the issues involved in storing XML in native format. We believe that the key intellectual contribution of this system is a comprehensive set-at-a-time query processing ability in a native XML store, with all the standard components of relational query processing, including algebraic rewriting and a cost-based optimizer.