15987233
Two-phase perspective ray casting for interactive volume navigation
Volume navigation is the interactive exploration of volume data sets by "flying" the view point through the data, producing a volume rendered view at each frame. The authors present an inexpensive perspective volume navigation method designed to run on a PC platform with accelerated 3D graphics hardware. They compute perspective projections at each frame, allow trilinear interpolation of sample points, and render both gray scale and RGB volumes by volumetric compositing. The implementation handles arbitrarily large volumes, by dynamically swapping data within the local depth-limited frustum into main memory as the viewpoint moves through the volume. They describe a new ray casting algorithm that takes advantage of the coherence inherent in adjacent frames to generate a sequence of approximate animated frames much faster than they could be computed individually. They also take advantage of the 3D graphics acceleration hardware to offload much of the alpha blending and resampling from the CPU.
15918536
Protocols for distributed video streaming
With the explosive growth of video applications over the packet switched networks, many approaches have been proposed to stream video effectively over packet switched, best-effort networks. In our previous work, we proposed a framework with a receiver driven protocol to coordinate simultaneous video streaming from multiple senders to a single receiver in order to achieve higher throughput, and to increase tolerance to packet loss and delay due to network congestion. The receiver-driven protocol employs two algorithms: the rate allocation and packet partition. The rate allocation algorithm determines the sending rate for each sender while the packet partition algorithm ensures no sender sends the same packets, and at the same time, minimizes the probability of late packets. We extend the rate allocation scheme to be used with forward error correction (FEC) in order to minimize the probability of packet loss in a bursty loss environment such as one due to network congestion. Using both simulations and actual Internet experiments, we demonstrate the effectiveness of our rate allocation scheme in reducing packet loss, and hence, achieving higher visual quality for the streamed video.
17197783
Multiple sender distributed video streaming
With the explosive growth of video applications over the Internet, many approaches have been proposed to stream video effectively over packet switched, best-effort networks. We propose a receiver-driven protocol for simultaneous video streaming from multiple senders to a single receiver in order to achieve higher throughput, and to increase tolerance to packet loss and delay due to network congestion. Our receiver-driven protocol employs a novel rate allocation algorithm (RAA) and a packet partition algorithm (PPA). The RAA, run at the receiver, determines the sending rate for each sender by taking into account available network bandwidth, channel characteristics, and a prespecified, fixed level of forward error correction, in such a way as to minimize the probability of packet loss. The PPA, run at the senders based on a set of parameters estimated by the receiver, ensures that every packet is sent by one and only one sender, and at the same time, minimizes the startup delay. Using both simulations and Internet experiments, we demonstrate the effectiveness of our protocol in reducing packet loss.
7825329
Zero-Latency Data Warehousing for Heterogeneous Data Sources and Continuous Data Streams
In this paper, a framework for building an overall Zero-Latency Data Warehouse system (ZLDWH) is provided. Such a ZLDWH requires tasks such as data changes detection, continuous loading and updating of new data, autonomous analysis and the execution of actions to be completed in the Data Warehouse (DWH). For this purpose, we combine (1) an existing solution for the continuous data integration and (2) the known approach of Active Data Warehousing (ADWH) by introducing protocols that enable the correct collaboration between the two. The continuous data integration is realized on the one hand by asynchronous communication between multiple information sources and the DWH through a message queuing system, and on the other hand by the continuous loading and updating. To obtain a low-latency time for this cycle, an “active rule evaluation” is provided between the data feeding stage and its loading into the DWH. This is realized by applying ECA-Techniques (EventCondition-Action) to obtain a more “active” characteristic of the DWH. A second goal of this paper is to take into consideration a special class of information source namely continuous data streams. Continuous data streams are information sources in which data arrives in high-volume, rapidly, bursty, and needs to be processed continuously. Therefore novel protocols and techniques are needed for capturing, storing and evaluate analysis decision based on new coming data from such continuous data stream sources.
6438691
Event-Feeded Dimension Solution
From the point of view of a data warehouse system its part of collecting and receiving information from other systems is crucial for all subsequent business intelligence applications. The incoming information can be classified generally in two types, the state-snapshot data and the state-change or event data usually called transactional data, which contains information about the change processes applied on the instances of information objects. On the way towards active data warehouses it becomes more important to provide complete data with minimal latency. We focus in this paper on dimensional data provided by any data-master application. The information transfer is done via messages containing the change-information of the dimension instances. The receiving data warehouse system is able to validate the event-messages, reconstruct the complete history of the dimension and provide a well applicable “comprehensive slowly changing dimension” (cSCD) interface for well-performing queries on the historical and current state of the dimension. A prototype implementation of “active integration” of a data warehouse is proposed.
15193485
Towards the Stream Analysis Model in Grid-based Zero-Latency Data Stream Warehouse
Recent emerging applications increasingly generate continuous, larger amounts of valuable data. The demand of conducting advanced analysis over fast and huge data streams to capture trends, patterns, and exceptions become crucial. However, fully extracting the latent knowledge within the data stream is a challenging task because of insufficient technology. While Data Warehouse (DWH) technologies have resulted in considerable information processing efficiencies, there is still a significant delay in the time to deliver mission critical information to data consumers. Traditional data stream processing focuses on statistical approaches hence produces approximate results. In this paper, we introduce the Stream Analysis Model with a Grid-based ZeroLatency Data Stream Warehouse (GZLDSWH) framework which allows to perform analytical processing on continuous data streams and to trigger relevant actions depending on patterns discovered in event streams without using statistical approximation. Essential data stream elements are captured, analysed on the fly and finally evaluated to detect abnormalities while the entire data streams are stored within a Grid and integrated into a virtual DWH for further analysis in the case of ambiguity or uncertainty.
14226937
Self-Learning IP Traffic Classification Based on Statistical Flow Characteristics
A number of key areas in IP network engineering, management and surveillance greatly benefit from the ability to dynamically identify traffic flows according to the applications responsible for their creation. Currently such classifications rely on selected packet header fields (e.g. destination port) or application layer protocol decoding. These methods have a number of shortfalls e.g. many applications can use unpredictable port numbers and protocol decoding requires high resource usage or is simply infeasible in case protocols are unknown or encrypted. We propose a framework for application classification using an unsupervised machine learning (ML) technique. Flows are automatically classified based on their statistical characteristics. We also propose a systematic approach to identify an optimal set of flow attributes to use and evaluate the effectiveness of our approach using captured traffic traces.
14182338
A Linear Algorithm for Polygonal Approximations of Thick Curves
The concept of fuzzy segment was introduced in [2]. In this paper we suggest a notion of strict fuzzy segment and provide a linear algorithm for recognizing it thanks to a simple property of convex hull. A linear method to decompose a thick curve into strict fuzzy segments is also given. The quality of decomposition can be easily controlled by setting two threshold values: the maximum order and the minimum density of the fuzzy segments. The algorithm is fast, practical and multi-purpose.