5703198
On the Structural Algorithm of Filtering Graphs for Layout
When the amount of information in visualization becomes large enough, users can not perceive all elements at the same time. This problem can be solved by removing parts of the information through the process of Filtering. In this paper, we present a novel method for filtering a graph by measuring the important role property of a node. The basic idea of this approach is to quantify the importance of a node as the degree to which it has direct and indirect relationships with the other nodes in a graph. All the nodes are ranked according to their Node Importance Scores, and those less important nodes and their associated edges are then removed or invisible. In comparison with the rule_based approach, our approach is a structure_based one that makes use of the linkage of nodes rather than their semantics. It can therefore be applied to filter any kinds of connected graphs. The examples and applications provided have demonstrated that our approach can effectively reduce the visual complexities.
17691685
WFMS-based Data Integration for e-Learning
As more and more organisations and institutions are moving towards the e-learning strategy, more and more disparate data are distributed by different e-learning systems. How to effectively use this vast amount of distributed data becomes a big challenge. This paper addresses this challenge and works out a new mechanism to implement data integration for e-learning. A workflow management system based (WFMS-based) data integration model is contributed to the e-learning
8009887
A Framework of Filtering, Clustering and Dynamic Layout Graphs for Visualization
Many classical graph visualization algorithms have already been developed over the past decades. However, these algorithms face difficulties in practice, such as the overlapping node problem, large graph layout and dynamic graph layout. In order to solve these problems, this paper aims to systematically address algorithmic issues related to a novel framework that describes the process of graph visualization applications. First of all, a framework for graph visualization is described. As the important parts of this framework, we then develop two effective algorithms for filtering and clustering large graphs for the layouts. As for the dynamic graph layout, a new approach to removing overlapping nodes called force-transfer algorithm is developed. The framework has been implemented in a prototype called PGA to demonstrate the performance of the proposed algorithms. Finally, a case study is provided.
18862916
Clustering graphs for visualization via node similarities
Graph visualization is commonly used to visually model relations in many areas. Examples include Web sites, CASE tools, and knowledge representation. When the amount of information in these graphs becomes too large, users, however, cannot perceive all elements at the same time. A clustered graph can greatly reduce visual complexity by temporarily replacing a set of nodes in clusters with abstract nodes. This paper proposes a new approach to clustering graphs. The approach constructs the node similarity matrix of a graph that is derived from a novel metric of node similarity. The linkage pattern of the graph is thus encoded into the similarity matrix, and then one obtains the hierarchical abstraction of densely linked subgraphs by applying the k-means algorithm to the matrix. A heuristic method is developed to overcome the inherent drawbacks of the k-means algorithm. For clustered graphs we present a multilevel multi-window approach to hierarchically drawing them in different abstract level views with the purpose of improving their readability. The proposed approaches demonstrate good results in our experiments. As application examples, visualization of part of Java class diagrams and Web graphs are provided. We also conducted usability experiments on our algorithm and approach. The results have shown that the hierarchically clustered graph used in our system can improve user performance for certain types of tasks.
5775998
Identification of clusters in the Web graph based on link topology
The Web graph has recently been used to model the link structure of the Web. The studies of such graphs can yield valuable insights into Web algorithms for crawling, searching and discovery of Web communities. This paper proposes a new approach to clustering the Web graph. The proposed algorithm identifies a small subset of the graph as "core" members of clusters, and then incrementally constructs the clusters by a selection criterion. Two qualitative criteria are proposed to measure the quality of graph clustering. We have implemented our algorithm and tested a set of arbitrary graphs with good results. Applications of our approach include graph drawing and Web visualization.
12511265
A Scalable Algorithm for Adjusting Node-Node Overlaps
The node-node overlaps, which occur in drawing of labeled graphs, and more importantly, in the dynamic drawing, have been a problem in graph visualization. Several approaches have recently been proposed to solve such a problem. These approaches, however, lack the scalabilities of dealing with large number of overlapping nodes. Providing a formal description of the problem, this paper presents a scalable version of our previous approach called the force-transfer algorithm. Compared to other methods, our new algorithm is able to achieve the compact adjusted layout of a large graph
6353695
Success at E-Governing: A Case Study of ESDLife in Hong Kong
Most e-government initiatives are fully owned and operated by government organizations. While this approach has many advantages, it can be costly and become government-centric. In this paper, a commercial-centric approach to establishing an electronic service delivery (ESD) is presented. The Hong Kong ESDLife uses a commercially owned and run ESD platform, which allows for commercial activities to be co-hosted with government services. It also adopts a customer-centric approach to the marketing of its services. An analysis of its success factors in relation to suggested best practices had been carried out. Although there are indications that ESDLife has achieved user critical mass, there are issues related to its commercial-centric approach that need sensitive handling.
43590220
Lower Bounds and Parameterized Approach for Longest Common Subsequence
In this paper, different parameterized versions of the longest common subsequence (LCS) problem are extensively investigated and computational lower bound results are derived based on current research progress in parameterized computation. For example, with the number of sequences as the parameter k, the problem is unlikely to be solvable in time f(k)no(k), where n is the length of each sequence and f is any recursive function. The lower bound result is asymptotically tight in consideration of the dynamic programming approach of time O(nk). Computational lower bounds for polynomial-time approximation schemes (PTAS) for the LCS problem are also derived. It is shown that the LCS problem has no PTAS of time f(1/ e)no(1/ e) for any recursive function f, unless all SNP problems are solvable in subexponential time. Compared with former results on this problem, this result has its significance. Finally a parameterized approach for the LCS problem is discussed, which is more efficient than the dynamic programming approach, especially when applied to large scale sequences.
42013296
On the computational hardness based on linear FPT-reductions
The notion of linear fpt-reductions has been recently introduced to derive strong computational lower bounds for well-known NP-hard problems. In this paper, we formally investigate the notion of W[t]-hardness under the linear fpt-reduction, and study the structural properties of the corresponding complexity classes. Additional complexity lower bounds on important computational problems are established. Some observations on structural properties of the standard parameterized hierarchy, the W -hierarchy, are also presented.
329318
The garbage collection advantage: improving program locality
As improvements in processor speed continue to outpace improvements in cache and memory speed, poor locality increasingly degrades performance. Because copying garbage collectors move objects, they have an opportunity to improve locality. However, no static copying order is guaranteed to match program traversal orders. This paper introduces <i>online object reordering</i> (OOR) which includes a new dynamic, online class analysis for Java that detects program traversal patterns and exploits them in a copying collector. OOR uses run-time method sampling that drives just-in-time (JIT) compilation. For each <i>hot</i> (frequently executed) method, OOR analysis identifies the hot field accesses. At garbage collection time, the OOR collector then copies referents of hot fields together with their parent. Enhancements include static analysis to exclude accesses in cold basic blocks, heuristics that decay heat to respond to phase changes, and a separate space for hot objects. The overhead of OOR is on average negligible and always less than 2% on Java benchmarks in Jikes RVM with MMTk. We compare program performance of OOR to static class-oblivious copying orders (e.g., breadth and depth first). Performance variation due to static orders is often low, but can be up to 25%. In contrast, OOR matches or improves upon the best static order since its history-based copying tunes memory layout to program traversal.
12321641
Locally Balanced Incremental Hierarchical Discriminant Regression
Incremental hierarchical discriminant regression faces several challenging issues: (a) a large input space with a small output space and (b) nonstationary statistics of data sequences. In the first case (a), there maybe few distinct labels in the output space while the input data distribute in a high dimensional space. In the second case (b), a tree has to be grown when only a limited data sequence has been observed. In this paper, we present the Locally Balanced Incremental Hierarchical Discriminant Regression (LBIHDR) algorithm. A novel node self-organization and spawning strategy is proposed to generate a more discriminant subspace by forming multiple clusters for one class. The algorithm was successfully applied to different kinds of data set.