1452553
Understanding research trends in conferences using paperLens
PaperLens is a novel visualization that reveals trends, connections, and activity throughout a conference community. It tightly couples views across papers, authors, and references. PaperLens was developed to visualize 8 years (1995-2002) of InfoVis conference proceedings and was then extended to visualize 23 years (1982-2004) of the CHI conference proceedings. This paper describes how we analyzed the data and designed PaperLens. We also describe a user study to focus our redesign efforts along with the design changes we made to address usability issues. We summarize lessons learned in the process of design and scaling up to the larger set of CHI conference papers.
2712263
How users interact with biodiversity information using TaxonTree
Biodiversity databases have recently become widely available to the public and to other researchers. To retrieve information from these resources, users must understand the underlying data schemas even though they often are not content experts. Many other domains share this problem.We developed an interface, TaxonTree, to visualize the taxonomic hierarchy of animal names. We applied integrated searching and browsing so that users need not have complete knowledge either of appropriate keywords or the organization of the data.Our qualitative user study of TaxonTree in an undergraduate course is the first to describe usage patterns in the biodiversity domain. We found that tree-based interaction and visualization aided users' understanding of the data. Most users approached biodiversity data by browsing, using common, general knowledge rather than the scientific keyword expertise necessary to search using traditional interfaces. Users with different levels of interest in the domain had different interaction preferences.
6809024
TreePlus: Interactive Exploration of Networks with Enhanced Tree Layouts
Despite extensive research, it is still difficult to produce effective interactive layouts for large graphs. Dense layout and occlusion make food Webs, ontologies and social networks difficult to understand and interact with. We propose a new interactive visual analytics component called TreePlus that is based on a tree-style layout. TreePlus reveals the missing graph structure with visualization and interaction while maintaining good readability. To support exploration of the local structure of the graph and gathering of information from the extensive reading of labels, we use a guiding metaphor of "plant a seed and watch it grow." It allows users to start with a node and expand the graph as needed, which complements the classic overview techniques that can be effective at (but often limited to) revealing clusters. We describe our design goals, describe the interface and report on a controlled user study with 28 participants comparing TreePlus with a traditional graph interface for six tasks. In general, the advantage of TreePlus over the traditional interface increased as the density of the displayed data increased. Participants also reported higher levels of confidence in their answers with TreePlus and most of them preferred TreePlus
22808943
Exploring content-actor paired network data using iterative query refinement with NetLens
Networks have remained a challenge for information retrieval and visualization because of the rich set of tasks that users want to accomplish. This paper demonstrates a tool, NetLens, to explore a content-actor paired network data model. The NetLens interface was designed to allow users to pose a series of elementary queries and iteratively refine visual overviews and sorted lists. This enables the support of complex queries that are traditionally hard to specify in node-link visualizations. NetLens is general and scalable in that it applies to any dataset that can be represented with our abstract content-actor data model
33958226
Performance evaluation of a communication optimization model in network-based parallel computing
Network based distributed computing has been gaining popularity over the past decade. Many parallel programming languages and related parallel programming modes are becoming widely accepted. However, the execution of parallel applications on distributed systems has been hampered by the high communication overhead. To reduce the communication overhead and the completion time of a parallel application, we propose a key message model for parallel computing on network of workstations (NOWs). In the key message model, all messages generated in a key message path are prioritized. A key message path in a task graph is defined as the path that is optimized by the key message algorithm. All messages generated in a key message path are prioritized. Besides, the key message algorithm automatically finds the key message paths. In this paper, we first describe the algorithm that identifies the key messages to be prioritized in a parallel application, then analyze the cost of the algorithm, and finally evaluate the performance of the algorithm in a simulation. Our preliminary analysis of the algorithm shows improvement over the system which does not use prioritization scheme.
29035719
Extracting User Profiles from E-mails Using the Set-Oriented Classifier
More and more people rely on e-mails rather than postal letters to communicate to each other. Although e-mails are more convenient, letters still have many positive features. The ability to handle "anonymous recipient" is one of them. This paper proposes a software agent that performs the routing task as human beings for the anonymous recipient e-mails. The software agent named "TWIMC (To Whom It May Concern)" receives anonymous recipient e-mails, analyze it, and then routes the e-mail to the mostly qualified person (i.e., email account) inside the organization. The agent employs the Set-oriented Classifier System (SCS) that is a genetic algorithm classifier that uses set representation internally. The comparison of SCS with the Support Vector Machine (SVM) shows that the SCS outperforms SVM under noisy environment.
13264531
An efficient scattered data approximation using multilevel B-splines based on quasi-interpolants
In this paper, we propose an efficient approximation algorithm using multilevel B-splines based on quasi-interpolants. Multilevel technique uses a coarse to fine hierarchy to generate a sequence of bicubic B-spline functions whose sum approaches the desired interpolation function. To compute a set of control points, quasi-interpolants gives a procedure for deriving local spline approximation methods where a B-spline coefficient only depends on data points taken from the neighborhood of the support corresponding the B-spline. Experimental results show that the smooth surface reconstruction with high accuracy can be obtained from a selected set of scattered or dense irregular samples.
2176970
Implications of programmable general purpose processors for compression/encryption applications
With the growth of the Internet and mobile communication industry, multimedia applications form a dominant computer workload. Media workloads are typically executed on Application Specific Integrated Circuits (ASICs), application specific processors (ASPs) or general purpose processors (GPPs). GPPs are flexible and allow changes in the applications and algorithms better than ASICs and ASPs. However, executing these applications on GPPs is done at a high cost. In this paper, we analyze media compression/decompression algorithms from the perspective of the overhead of executing them on a programmable general purpose processor versus ASPs. We choose nine encode/decode programs from audio, image/video andencryption applications. The instruction mix, memory access and parallelism aspects during the execution of these programs are analyzed. Memory access latency is observed to be the main factor influencing the execution time on general purpose processors. Most of these compression/decompression algorithms involve processing the data through execution phases (e.g. quantization, encoding, etc) and temporary results are stored and retrieved between these phases. A metric called overhead memory-access bandwidth per input/output byte is defined to characterize the temporary memory activity of each application. We observe that more than 90% of the memory accesses made by these programs are temporary data stores and loads arising from the general purpose nature of the execution platform. We also study the data parallelism in these applications, indicating the ability of instruction level and data level parallel processors to exploit the parallelism in these applications. The parallelism ranges from 6 to 529 in encode processes and 18 to 558 in decode processes.
14687618
Using a Hybrid Genetic Algorithm and Fuzzy Logic for Metabolic Modeling
The identification of metabolic systems is a complex task due to the complexity of the system and limited knowledge about the model. Mathematical equations and ODE's have been used to capture the structure of the model, and the conventional optimization techniques have been used to identify the parameters of the model. In general, however, a pure mathematical formulation of the model is difficult due to parametric uncertainty and incomplete knowledge of mechanisms. In this paper, we propose a modeling approach that (1) uses fuzzy rule-based model to augment algebraic enzyme models that are incomplete, and (2) uses a hybrid genetic algorithm to identify uncertain parameters in the model. The hybrid genetic algorithm (GA) integrates a GA with the simplex method in functional optimization to improve the GA's convergence rate. We have applied this approach to modeling the rate of three enzyme reactions in E. coli central metabolism. The proposed modeling strategy allows (1) easy incorporation of qualitative insights into a pure mathematical model and (2) adaptive identification and optimization of key parameters to fit system behaviors observed in biochemical experiments.