Jan-Ming Ho

Travel time prediction with support vector regression

Travel time prediction is essential for the development of advanced traveler information systems. In this paper, we apply support vector regression (SVR) for travel-time predictions and compare its results to the other baseline travel-time prediction methods using real highway traffic data. Since support vector machines have greater generalization ability and guarantee global minima for given training data, it is believed that support vector regression performs well for time series analysis. Compared to other baseline predictors, our results show that the SVR predictor can reduce significantly both relative mean errors and root mean squared errors of predicted travel times. We demonstrate the feasibility of applying SVR in travel-time prediction and prove that SVR is applicable and perform well for traffic data analysis.

Travel-time prediction with support vector regression

Travel time is a fundamental measure in transportation. Accurate travel-time prediction also is crucial to the development of intelligent transportation systems and advanced traveler information systems. We apply support vector regression (SVR) for travel-time prediction and compare its results to other baseline travel-time prediction methods using real highway traffic data. Since support vector machines have greater generalization ability and guarantee global minima for given training data, it is believed that SVR will perform well for time series analysis. Compared to other baseline predictors, our results show that the SVR predictor can significantly reduce both relative mean errors and root-mean-squared errors of predicted travel times. We demonstrate the feasibility of applying SVR in travel-time prediction and prove that SVR is applicable and performs well for traffic data analysis.

Discovering informative content blocks from Web documents

In this paper, we propose a new approach to discover informative contents from a set of tabular documents (or Web pages) of a Web site. Our system, InfoDiscoverer, first partitions a page into several content blocks according to HTML tag in a Web page. Based on the occurrence of the features (terms) in the set of pages, it calculates entropy value of each feature. According to the entropy value of each feature in a content block, the entropy value of the block is defined. By analyzing the information measure, we propose a method to dynamically select the entropy-threshold that partitions blocks into either informative or redundant. Informative content blocks are distinguished parts of the page, whereas redundant content blocks are common parts. Based on the answer set generated from 13 manually tagged news Web sites with a total of 26,518 Web pages, experiments show that both recall and precision rates are greater than 0.956. That is, using the approach, informative blocks (news articles) of these sites can be automatically separated from semantically redundant contents such as advertisements, banners, navigation panels, news categories, etc. By adopting InfoDiscoverer as the preprocessor of information retrieval and extraction applications, the retrieval and extracting precision will be increased, and the indexing size and extracting complexity will also be reduced.

Zero skew clock routing with minimum wirelength

The deferred-merge embedding (DME) algorithm, which embeds any given connection topology to create a clock tree with zero skew while minimizing total wirelength, is presented. The algorithm always yields exact zero skew trees with respect to the appropriate delay model. Experimental results show an 8% to 15% wire length reduction over some previous constructions. The DME algorithm may be applied to either the Elmore or linear delay model, and yields optimal total wirelength for linear delay. DME is a very fast algorithm, running in time linear in the number of synchronizing elements. A unified BB+DME algorithm, which constructs a clock tree topology using a top-down balanced bipartition (BB) approach and then applies DME to that topology, is also presented. The experimental results indicate that both the topology generation and embedding components of the methodology are necessary for effective clock tree construction. >

Zero skew clock net routing

The authors present an algorithm, called the zero skew segment tree method (ZSTM), for the clock net routing problem. To eliminate the lock skew and minimize the total wire length, ZSTM recursively partitions the sink nodes into two subsets which have equal loadings and minimum sum of diameters, and then constructs a zero skew segment tree according to the partitioning result. The final layout of the clock net can be decided by the channel information of the routing region. Experiments showed that ZSTM improved the wire length by 15% and the maximum delay by 3% over the published results. It also completely eliminated the clock skew.<<ETX>>

Performance analysis for voice/data integration on a finite-buffer mobile system

Personal communication service (PCS) networks offer mobile users diverse telecommunication applications, such as voice, data, and image, with different bandwidth and quality-of-service (QoS) requirements. This paper proposes an analytical model to investigate the performance of an integrated voice/data mobile network with finite data buffer in terms of voice-call blocking probability, data loss probability, and mean data delay. The model is based on the movable-boundary scheme that dynamically adjusts the number of channels for voice and data traffic. With the movable-boundary scheme, the bandwidth can be utilized efficiently while satisfying the QoS requirements for voice and data traffic. Using our model, the impact of hot-spot traffic in the heterogeneous PCS networks, in which the parameters (e.g., number of channels, voice, and data arrival rates) of cells can be varied, can be effectively analyzed. In addition, an iterative algorithm based on our model is proposed to determine the handoff traffic, which computes the system performance in polynomial-bounded time. The analytical model is validated by simulation.

New algorithms for the rectilinear Steiner tree problem

An approach to constructing the rectilinear Steiner tree (RST) of a given set of points in the plane, starting from a minimum spanning tree (MST), is discussed. The main idea in this approach is to find layouts for the edges of the MST that maximize the overlaps between the layouts, thus minimizing the cost (i.e. wire length) of the resulting rectilinear Steiner tree. Two algorithms for constructing rectilinear Steiner trees from MSTs, which are optimal under the conditions that the layout of each edge of the MST is an L shape or any staircase, respectively, are described. The first algorithm has linear time complexity and the second algorithm has a higher polynomial time complexity. Steiner trees produced by the second algorithm have a property called stability, which allows the rerouting of any segment of the tree, while maintaining the cost of the tree, and without causing overlaps with the rest of the tree. Stability is a desirable property in VLSI global routing applications. >

Extracting classification knowledge of Internet documents with mining term associations: a semantic approach

In this paper, we present a system that extracts and generalizes terms from Internet documents to represent classification knowledge of a given class hierarchy. We propose a measurement to evaluate the importance of a term with respect to a class in the class hierarchy, and denote it as support. With a given threshold, terms with high supports are sifted as keywords of a class, and terms with low supports are filtered out. To further enhance the recall of this approach, Mining Association Rules technique is applied to mine the association between terms. An inference model is composed of these association relations and the previously computed supports of the terms in the class. To increase the recall rate of the keyword selection process. we then present a polynomialtime inference algorithm to promote a term, strongly associated to a known keyword, to a keyword. According to our experiment results on the collected Internet documents from Yam search engine, we show that the proposed methods In the paper contribute to refine the classification knowledge and increase the recall of keyword selection.

Mining Web informative structures and contents based on entropy analysis

We study the problem of mining the informative structure of a news Web site that consists of thousands of hyperlinked documents. We define the informative structure of a news Web site as a set of index pages (or referred to as TOC, i.e., table of contents, pages) and a set of article pages linked by these TOC pages. Based on the Hyperlink Induced Topics Search (HITS) algorithm, we propose an entropy-based analysis (LAMIS) mechanism for analyzing the entropy of anchor texts and links to eliminate the redundancy of the hyperlinked structure so that the complex structure of a Web site can be distilled. However, to increase the value and the accessibility of pages, most of the content sites tend to publish their pages with intrasite redundant information, such as navigation panels, advertisements, copy announcements, etc. To further eliminate such redundancy, we propose another mechanism, called InfoDiscoverer, which applies the distilled structure to identify sets of article pages. InfoDiscoverer also employs the entropy information to analyze the information measures of article sets and to extract informative content blocks from these sets. Our result is useful for search engines, information agents, and crawlers to index, extract, and navigate significant information from a Web site. Experiments on several real news Web sites show that the precision and the recall of our approaches are much superior to those obtained by conventional methods in mining the informative structures of news Web sites. On the average, the augmented LAMIS leads to prominent performance improvement and increases the precision by a factor ranging from 122 to 257 percent when the desired recall falls between 0.5 and 1. In comparison with manual heuristics, the precision and the recall of InfoDiscoverer are greater than 0.956.

Minimum diameter spanning trees and related problems

The problem of finding a minimum diameter spanning tree (MDST) of a set of n points in the Euclidean space is considered. The diameter of a spanning tree is the maximum distance between any two poi...