45097070
Spatial ability and visual navigation: an Empirical Study
Abstract In this paper, we describe an empirical study of individuals' spatial navigation strategies and a number of performance and preference measures with regard to the design of a novel 3D visualisation. The underlying semantic space of the user interface consists of a collection of papers from the three most recent ACM SIGCHI conference proceedings, visualised as a virtual reality network. This network was automatically constructed based on semantic similarities derived from latent semantic analysis. We studied die search strategies and general preferences of eleven subjects who used this system to find papers on various topics. The study has led to a number of interesting findings, which should be valuable for designers and evaluators of 3D user interfaces. The results highlight the importance of structural elements in the design of a semantically based user interface, because search strategies of users relied heavily on these mechanisms in the design. The results of this study also demonstrate that...
46129433
Behavioural Patterns of Collaborative Writing with Hypertext - A State Transition Approach
This study investigates behavioural patterns of collaborative writing with a hypertext system by using a state-transition approach. State-transition models are empirically developed for capturing the dynamic nature of collaborative writing. Users were frequently engaged in tasks such as exploration, organization, and editing, whereas the use of collaborative support functions was transient in nature. The study shows that state-transition analysis is an important approach to task analysis, requirements engineering, and human-computer interaction studies. This study has produced some valuable experiences and lessons for researchers and practitioners in collaborative writing.
468820
Tracing knowledge diffusion
Knowledge diffusion is the adaptation of knowledge in a broad range of scientific and engineering research and development. Tracing knowledge diffusion between science and technology is a challenging issue due to the complexity of identifying emerging patterns in a diverse range of possible processes. In this article, we describe an approach that combines complex network theory, network visualization, and patent citation analysis in order to improve the means for the study of knowledge diffusion. In particular, we analyze patent citations in the field of tissue engineering. We emphasize that this is the beginning of a longer-term endeavor that aims to develop and deploy effective, progressive, and explanatory visualization techniques for us to capture the dynamics of the evolution of patent citation networks. The work has practical implications on resource allocation, strategic planning, and science policy.
2965625
Visualizing the evolution of a subject domain: a case study
We explore the potential of information visualization techniques in enhancing existing methodologies for domain analysis and modeling. In this case study, we particularly focus on visualizing the evolution of the hypertext field based on author co-citation patterns, including the use of a sliding-window scheme to generate a series of annual snapshots of the domain structure, and a factor-referenced color-coding scheme to highlight predominant specialties in the field.
45076304
Spatial Semantic Virtual Worlds: The Structural Duality and Beyond
15424500
Semantically modified diffusion limited aggregation for visualizing large-scale networks
Diffusion-limited aggregation (DLA) is a model of fractal growth. Computer models can simulate the fast aggregation of millions of particles. We propose a modified version of DLA, called semantically modified DLA (SM-DLA), for visualizing large-scale networks. SM-DLA introduces similarity measures between particles so that instead of attaching to the nearest particle in the aggregation, a new particle is stochastically directed to attach to particles that are similar to it. The results of our initial experiment with a co-citation network using SM-DLA are encouraging, suggesting that the algorithm has the potential as an alternative paradigm for visualizing large-scale networks. Further studies in this direction are recommended.
42163647
Nearly optimal one-to-many parallel routing in star networks
Star networks were proposed recently as an attractive alternative to the well-known hypercube models for interconnection networks. Extensive research has been performed that shows that star networks are as versatile as hypercubes. This paper is an effort in the same direction. Based on the well-known paradigms, we study the one-to-many parallel routing problem on star networks and develop an improved routing algorithm that finds n-1 node-disjoint paths between one node and a set of other n-1 nodes in the n-star network. These parallel paths are proven of minimum length within a small additive constant, and our algorithm has an optimal time complexity. This result significantly improves the previous known algorithms for the problem. Moreover, the algorithm well illustrates an application of the orthogonal partition of star networks, which was observed by the original inventors of the star networks but seems generally overlooked in the subsequent study. We should also point out that similar problems are already studied for hypercubes and have proven useful in designing efficient and fault tolerant routing algorithms on hypercube networks.
12972622
DIKNN: An Itinerary-based KNN Query Processing Algorithm for Mobile Sensor Networks
Current approaches to k nearest neighbor (KNN) search in mobile sensor networks require certain kind of indexing support. This index could be either a centralized spatial index or an in-network data structure that is distributed over the sensor nodes. Creation and maintenance of these index structures, to reflect the network dynamics due to sensor node mobility, may result in long query response time and low battery efficiency, thus limiting their practical use. In this paper, we propose a maintenance-free, itinerary-based approach called density-aware itinerary KNN query processing (DIKNN). The DIKNN divides the search area into multiple cone-shape areas centered at the query point. It then performs a query dissemination and response collection itinerary in each of the cone-shape areas in parallel. The design of the DIKNN scheme also takes into account challenging issues such as the the dynamic adjustment of the search radius (in terms of number of hops) according to spatial irregularity or mobility of sensor nodes. The simulation results show that DIKNN yields substantially better performance and scalability over previous work, both as k increases and as the sensor node mobility increases. It outperforms the second runner with up to 50% saving in energy consumption and up to 40% reduction in query response time, while rendering the same level of query result accuracy.
40672752
The existence of hyper-L triple-loop networks
204988011
A class of additive multiplicative graph functions
For a fixed graph G, the capacity function for G, Po, is defined by Pc(H)= lim,__,®[yo(H')] TM, where y6(H) is the maximum number of disjoint G's in H. In [2], Hsu proved that Pr2 can be viewed as a lower bound for multiplicative increasing graph functions. But it was not known whether Pr2 is multiplicative or not. In this paper, we prove that Pc is multiplicative and additive for some graphs G which include K2. Some properties of Pc are also discussed in this paper.
704070
Model-Guided Empirical Optimization for Multimedia Extension Architectures: A Case Study
Compiler technology for multimedia extensions must effectively utilize not only the SIMD compute engines but also the various levels of the memory hierarchy: superword registers, multi-level caches and TLB. In this paper, we describe a compiler that combines optimization across all levels of the memory hierarchy with automatic generation of SIMD code for multimedia extensions. At the high-level, model-guided empirical optimization is used to transform code to optimize for all levels of the memory hierarchy. This compiler interacts with a backend compiler exploiting superword-level parallelism that takes sequential code as input and produces SIMD code. This paper discusses how we have combined these technologies into a single framework. Through a case study with matrix multiply, we observe performance results that outperform the hand-tuned Intel MKL library, and achieve performance that is within 4% of the ATLAS self-tuning library with architectural defaults and more than 4X faster than the native Intel compiler.