196155386
Compound brushing
This paper proposes a conceptual model called compound brushing for modeling the brushing techniques used in dynamic data visualization. In this approach brushing techniques are modeled as higraphs with five types of basic entities: data, selection, device, renderer, and transformation. Using this model, a flexible visual programming tool is designed not only to configure/control various common types of brushing techniques currently used in dynamic data visualization, but also to investigate new brushing techniques.
16294852
Interactive volume visualization on a heterogeneous message-passing multicomputer
This paper describes VOL2, an interactive general-purpose volume renderer based on ray casting and implemented on Pixel-Planes 5, a distributed-memory, message-passing multicomputer. VOL2 is a pipelined renderer using image-space task parallelism and object-space data partitioning. We describe the parallelization and load balancing techniques used in order to achieve interactive response and near-real-time frame rates. We also present a number of applications for our system and derive some general conclusions about operation of image-order rendering algorithms on message-passing multicomputers.
6332225
Observing a volume rendered fetus within a pregnant patient
Augmented reality systems with see-through headmounted displays have been used primarily for applications that are possible with today's computational capabilities. We explore possibilities for a particular application-in-place, real-time 3D ultrasound visualization-without concern for such limitations. The question is not "How well could we currently visualize the fetus in real time," but "How well could we see the fetus if we had sufficient compute power?" Our video sequence shows a 3D fetus within a pregnant woman's abdomen-the way this would look to a HMD user. Technical problems in making the sequence are discussed. This experience exposed limitations of current augmented reality systems; it may help define the capabilities of future systems needed for applications as demanding as real-time medical visualization.<<ETX>>
30643736
Compound Brushing Explained†
This paper proposes a conceptual model called compound brushing for modeling the brushing techniques used in dynamic data visualization. In this approach, brushing techniques are modeled as higraphs with five types of basic entities: data, selection, device, renderer, and transformation. Using this model, a flexible visual programming tool is designed not only to configure and control various common types of brushing techniques currently used in dynamic data visualization but also to investigate new brushing techniques.
9986453
Content-rich biological network constructed by mining PubMed abstracts
BackgroundThe integration of the rapidly expanding corpus of information about the genome, transcriptome, and proteome, engendered by powerful technological advances, such as microarrays, and the availability of genomic sequence from multiple species, challenges the grasp and comprehension of the scientific community. Despite the existence of text-mining methods that identify biological relationships based on the textual co-occurrence of gene/protein terms or similarities in abstract texts, knowledge of the underlying molecular connections on a large scale, which is prerequisite to understanding novel biological processes, lags far behind the accumulation of data. While computationally efficient, the co-occurrence-based approaches fail to characterize (e.g., inhibition or stimulation, directionality) biological interactions. Programs with natural language processing (NLP) capability have been created to address these limitations, however, they are in general not readily accessible to the public.ResultsWe present a NLP-based text-mining approach, Chilibot, which constructs content-rich relationship networks among biological concepts, genes, proteins, or drugs. Amongst its features, suggestions for new hypotheses can be generated. Lastly, we provide evidence that the connectivity of molecular networks extracted from the biological literature follows the power-law distribution, indicating scale-free topologies consistent with the results of previous experimental analyses.ConclusionsChilibot distills scientific relationships from knowledge available throughout a wide range of biological domains and presents these in a content-rich graphical format, thus integrating general biomedical knowledge with the specialized knowledge and interests of the user. Chilibot http://www.chilibot.net can be accessed free of charge to academic users.
14165227
Special issue: "Web retrieval and mining"
7740275
FIREMAN: a toolkit for firewall modeling and analysis
Security concerns are becoming increasingly critical in networked systems. Firewalls provide important defense for network security. However, misconfigurations in firewalls are very common and significantly weaken the desired security. This paper introduces FIREMAN, a static analysis toolkit for firewall modeling and analysis. By treating firewall configurations as specialized programs, FIREMAN applies static analysis techniques to check misconfigurations, such as policy violations, inconsistencies, and inefficiencies, in individual firewalls as well as among distributed firewalls. FIREMAN performs symbolic model checking of the firewall configurations for all possible IP packets and along all possible data paths. It is both sound and complete because of the finite state nature of firewall configurations. FIREMAN is implemented by modeling firewall rules using binary decision diagrams (BDDs), which have been used successfully in hardware verification and model checking. We have experimented with FIREMAN and used it to uncover several real misconfigurations in enterprise networks, some of which have been subsequently confirmed and corrected by the administrators of these networks
8882088
Design of Nearest Neighbor Classifiers Using an Intelligent Multi-objective Evolutionary Algorithm
The goal of designing optimal nearest neighbor classifiers is to maximize classification accuracy while minimizing the sizes of both reference and feature sets. A usual way is to adaptively weight the three objectives as an objective function and then use a single-objective optimization method for achieving this goal. This paper proposes a multi-objective approach to cope with the weight tuning problem for practitioners. A novel intelligent multi-objective evolutionary algorithm IMOEA is utilized to simultaneously edit compact reference and feature sets for nearest neighbor classification. Two comparison studies are designed to evaluate performance of the proposed approach. It is shown empirically that the IMOEA-designed classifiers have high classification accuracy and small sizes of reference and feature sets. Moreover, IMOEA can provide a set of good solutions for practitioners to choose from in a single run. The simulation results indicate that the IMOEA-based approach is an expedient method to design nearest neighbor classifiers, compared with an existing single- objective approach.
16942431
A graph model for E-commerce recommender systems
Information overload on the Web has created enormous challenges to customers selecting products for online purchases and to online businesses attempting to identify customers' preferences efficiently. Various recommender systems employing different data representations and recommendation methods are currently used to address these challenges. In this research, we developed a graph model that provides a generic data representation and can support different recommendation methods. To demonstrate its usefulness and flexibility, we developed three recommendation methods: direct retrieval, association mining, and high-degree association retrieval. We used a data set from an online bookstore as our research test-bed. Evaluation results showed that combining product content information and historical customer transaction information achieved more accurate predictions and relevant recommendations than using only collaborative information. However, comparisons among different methods showed that high-degree association retrieval did not perform significantly better than the association mining method or the direct retrieval method in our test-bed.