Wireless Internet technologies have been developing and users are now able to access more information anywhere through small screen mobile devices. However, due to the limits of cost, bandwidth and screen size in a wireless environment, it is important to minimize interactions between a mobile user and his handheld device, as well as the amount of data transmitted. In this paper we present an interactive evolutionary approach for user-oriented Web search by using mobile devices. To verify this approach, a series of experiments has been conducted. The results show that our approach can allocate the information a user needs within only a few user-system interactions. It substantially reduces the number of retrieved pages a user has to visit. This is especially an important benefit to mobile users.

The recognition limit of luminance difference in the human visual system (HVS) has not been studied systematically. In this paper, surround adapted Weber-Fechner fraction is calculated based on the crispening effect. It is found that surround adapted fractions have reduced to 1/3 of the traditional Weber-Fechner fractions. As compared with Breitmeyer's experiments, the presented result is a reasonable one. It can be used as some guide to design the digital display system when a designer needs to decide bit count of digital signal in considering of the limit of brightness level, and as the inspection tool of display manufacturing of brightness smear, defect, and so on.

We developed a Web-based education system called "Web-Com". It supports synchronous and asynchronous learning. It consists of an interactive web browser and voice server. Web-Com provides a multi-layer drawable canvas on which the user can draw annotations. Each layer can be shared with other users in real-time via the Internet to enable synchronous learning. In conjunction with the voice server, Web-Com can support voice communication. It can also replay the process of annotation in order, which enables asynchronous learning. Finally, a subject experiment is conducted to evaluate the scheme's workability and explore various issues that arise during the course of learning. The experimental results show that learners can learn fairly interactively with an instructor in a Web-Based class using Web-Com's synchronous style.

The Internet has become an important medium for effective marketing and efficient operations for many institutions. Visitors of a particular web site leave behind valuable information on their preferences, requirements, and demands regarding the offered products and/or services. Understanding these requirements online, i.e., during a particular visit, is both a difficult technical challenge and a tremendous business opportunity. Web sites that can provide effective online navigation suggestions to their visitors can exploit the potential inherent in the data such visits generate every day. However, identifying, collecting, and maintaining the necessary knowledge that navigation suggestions are based on is far from trivial. We propose a methodology for acquiring and maintaining this knowledge efficiently using data mart and web mining technology. Its effectiveness has been shown in an application for a bank's web site.

Web browsing task is based on depth-first searching scheme, so that searching relevant information from Web may be very tedious. In this paper, we propose personal browsing assistant system based on user intentions modeling. Before explicitly requested by a user, this system can analyze the prefetched resources from the hyperlinked Webpages and compare them with the estimated user intention, so that it can help him to make a better decision like which Webpage should be requested next. More important problem is the semantic heterogeneity between Web spaces. It makes the understandability of locally annotated resources more difficult. We apply semantic annotation, which is a transcoding procedure with the global ontology. Therefore, each local metadata can be semantically enriched, and efficiently comparable. As testing bed of our experiment, we organized three different online clothes stores whose images are annotated by semantically heterogeneous metadata. We simulated virtual customers navigating these cyberspaces. According to the predefined preferences of customer models, they conducted comparison-shopping. We have shown the reasonability of supporting the Web browsing, and its performance was evaluated as measuring the total size of browsed hyperspace.

The performance of en-route web caching mainly depends on where the caches are located and how the cache contents are managed. In this paper, we address the problem of proxy placement in en-route web caching for tree networks, i.e., computing the optimal locations for placing k web proxies in a network such that some specified objectives are achieved. Based on our proposed model, we formulate this problem as an optimization problem and compute the optimal locations using a computationally efficient dynamic programming-based algorithm. We also extend our solution for tree networks to solve the same problem for autonomous systems. Finally, we implement our algorithms and evaluate our model on several performance metrics through extensive simulation experiments. We also compare the performance of our model with the best available heuristic KMPC model, as well as the random proxy placement model. The implementation results show that our model outperforms all the other models with respect to all performance metrics considered. The average improvements of our model over the KMPC model and the random proxy placement model are about 31.9 percent and 58.6 percent in terms of all the performance metrics considered.

Peer-to-peer (P2P) web caching has been studied recently as it can exploit local caches of peers for web caching without additional infrastructure. However, dynamic join/leave behaviors or local caching strategies of the peers due to their autonomy in a P2P network may limit the performance of P2P web caching. To overcome these limitations, we propose an effective directory-based P2P web caching system under dynamic participation of peers. We introduce the object lifetime in a P2P network considering the lifetimes of both an object in the local cache of a peer and a peer who owns the object, and utilize this object lifetime information for neighbor selection and storage management in the P2P web caching framework. For the neighbor selection, the proposed system utilizes the object lifetimes in selecting accurately a neighbor who would still retain the requested object and still remain in the P2P network. To improve the storage management, the proposed system uses efficiently the entire cache storage of the P2P network in such a way that the object is stored selectively in the local cache of the peer who requested it, considering the object lifetime. The trace-driven simulation results show that the proposed system has higher accuracy and fewer redirection failures than the conventional directory-based P2P web caching system in the feasible P2P network.

The development of information/communication technology has made it possible to access substantial amounts of data and retrieve information. However, it is often difficult to locate the desired information, and it becomes necessary to spend considerable time determining how to access specific available data. This paper describes a method to quantitatively evaluate the usability of large-scale information-oriented websites and the effects of improvements made to the site design. This is achieved by utilizing the Cognitive Walkthrough for the Web and website modeling using Markov chains. We further demonstrate that we can greatly improve usability through simple modification of the link structure by applying our approach to an actual informational database website with over 40,000 records.

In this paper, a Web-based management system for the building network is described. We developed a multi-protocol converter based on SoC and embedded Linux. It requires an appropriate operating system for handling protocols and an advanced development environment very similar to embedded linux. The multi-protocol converter integrates control networks of RS-485 and LonWorks devices to BAS through TCP/IP protocol or a client with Java applet. The system consists of three-tier architecture, such as BAS or clients, a multi-protocol converter, and control devices. In order to compare the feasibility of system architecture, it was applied to a small BAS system. By using UML, we modeled a Web-based control system with a unified TCP/IP socket communication and the system architecture. The developed system includes the inverter motor control system with modbus protocol for the RS485 network. The experiment results show that the multi-protocol converter using embedded Linux is a flexible and effective way to build a Web-based monitoring and control system.

This paper presents speech-driven Web retrieval models which accept spoken search topics (queries) in the NTCIR-3 Web retrieval task. The major focus of this paper is on improving speech recognition accuracy of spoken queries and then improving retrieval accuracy in speech-driven Web retrieval. We experimentally evaluated the techniques of combining outputs of multiple LVCSR models in recognition of spoken queries. As model combination techniques, we compared the SVM learning technique with conventional voting schemes such as ROVER. In addition, for investigating the effects on the retrieval performance in vocabulary size of the language model, we prepared two kinds of language models: the one's vocabulary size was 20,000, the other's one was 60,000. Then, we evaluated the differences in the recognition rates of the spoken queries and the retrieval performance. We showed that the techniques of multiple LVCSR model combination could achieve improvement both in speech recognition and retrieval accuracies in speech-driven text retrieval. Comparing with the retrieval accuracies when an LM with a 20,000/60,000 vocabulary size is used in an LVCSR system, we found that the larger the vocabulary size is, the better the retrieval accuracy is.

Over the past few years, there has been significant interest in content-aware routing that use the information found in the payload of packets to provide intelligent request distribution. As these content-aware routing mechanisms have become an increasingly important building block for Internet service providers, the network behavior and effectiveness of such mechanisms are unclear. In this paper we analyze the network dynamic of a busy Web site with the content-aware routing mechanism. We find that some unique characteristics of Web traffic may limit the effectiveness of the content-aware switching. Based on these observations, we also propose solutions to remedy these deficiencies.

CDN (Content Delivery Networks) improves end-user performance by replicating web contents on a group of geographically distributed servers. However, repeatedly keeping the entire replica of the original objects into many content servers consumes too much server resource. This problem becomes more serious for the large-sized objects such as streaming media, e.g. high quality video. In this paper, we therefore propose an efficient replication method for layered video streams in CDN, which can reduce user response delays and storage costs simultaneously. Based on an analytical formulation of the cooperative replication of layers and segments of each video stream, we derive a replication algorithm which solves next three problems quantitatively. (1) How many servers should be selected to replicate a given video stream? (2) For a single video stream, how many layers and segments should be stored in a given server? (3) After selecting a group of servers for each video stream, how do we allocate the replication priority (i.e. order) to each server? Simulation results verify that the proposed algorithm efficiently resolves the above problems and provides much better performance than conventional methods.

In this paper, we consider a data mining problem for semi-structured data. Modeling semi-structured data as labeled ordered trees, we present an efficient algorithm for discovering frequent substructures from a large collection of semi-structured data. By extending the enumeration technique developed by Bayardo (SIGMOD'98) for discovering long itemsets, our algorithm scales almost linearly in the total size of maximal tree patterns contained in an input collection depending mildly on the size of the longest pattern. We also developed several pruning techniques that significantly speed-up the search. Experiments on Web data show that our algorithm runs efficiently on real-life datasets combined with proposed pruning techniques in the wide range of parameters.

An important issue in applying machine learning algorithms to Natural Language Processing areas such as Named Entity Recognition tasks is to overcome the lack of tagged corpora. Several bootstrapping methods such as co-training have been proposed as a solution. In this paper, we present a different approach using the Web resources. A Named Entity (NE) tagged corpus is generated from the Web using about 3,000 names as seeds. The generated corpus may have a lower quality than the manually tagged corpus but its size can be increased sufficiently. Several features are developed and the decision list is learned using the generated corpus. Our method is verified by comparing it to both the decision list learned on the manual corpus and the DL-CoTrain method. We also present a two-level classification by cascading highly precise lexical patterns and the decision list to improve the performance.

This paper proposes a Real-Time Compression Architecture (RTCA), which maximizes the efficiency of web services, while reducing the response time at the same time. The developed architecture not only guarantees the freshness of compressed contents but also minimizes the time needed to compress the message, especially when the traffic is heavy.

A huge amount of information is being accumulated on the Internet as the Internet usage spreads and numbers of Web pages increase. However, it is also becoming very difficult to find required information, even when the information exists. The actual value of the Web is thus much lower than its potential value. In order to solve this problem, technologies which allow machines to handle Web content in an efficient, accurate, and flexible way by using machine-readable metadata are being developed. This paper is a survey of knowledge representation on the Web, and the utilization of metadata and ontology for data integration and information sharing, with a focus on the Semantic Web concept.

In this paper, we present Neighbor Community Finder (NCF, for short), a tool for finding Web communities related to given URLs. While existing link-based methods of finding communities, such as HITS, trawling, and Companion, use algorithms running on a Web graph whose vertices are pages and edges are links on the Web, NCF uses an algorithm running on an inter-site graph whose vertices are sites and edges are global-links (links between sites). Since the phrase "Web site" is used ambiguously in our daily life and has no unique definition, NCF uses directory-based sites proposed by the authors as a model of Web sites. NCF receives URLs interested in by a user and constructs an inter-site graph containing neighbor sites of the given URLs by using a method of identifying directory-based sites from URL and link data obtained from the actual Web on demand. By computational experiments, we show that NCF achieves higher quality than Google's "Similar Pages" service for finding pages related to given URLs corresponding to various topics selected from among the directories of Yahoo! Japan.

We propose a feedback controller to efficiently control web requests especially on overloaded networks. The controller is designed based on a feedback closed loop that prevents overload of web server and enforces target CPU utilization via controlling the amount of input web requests. The main contribution of this letter is the use of feedback control theory to design the controller that delicately regulates web requests even under the dynamic changes in processing power of web server. In contrast with many previous heuristic methods, the proposed controller uses a systematic approach to adaptively control web requests taking account of the dynamic behavior of web server. Simulation results performed in overloaded cases show that the proposed controller lets web server effectively control input web requests and reach its CPU utilization to desired levels in relatively small settling times.

In this letter we suggest sets of features to classify genres of web documents. Web documents are different from textual documents in that they contain URL and HTML tags within the pages. We introduce the features specific to web documents, which are extracted from URL and HTML tags. Experimental results enable us to evaluate their characteristics and performances. On the basis of the experimental results, we implement a user interface of a web search engine that presents documents grouped by genres.

Providing workflow function is one of the most important research issues in the next generation Internet services such as Web Service and Grid Computing. Scalability for Internet scale services, reliability for unstable Internet resources, and management functions of workflow systems are the essential requirements in these environments. However, existing workflow enactment models for enterprises could not meet these requirements. This paper proposes the PeerFlow that is a P2P based workflow enactment model, to provide workflow functions for the next generation Internet services. To apply P2P model to the workflow enactment model, we introduce the concept of the instance buddy and the index data of workflow instances, then propose the principle architecture of the PeerFlow. The instance buddy enables the autonomous processing of peers, and it is used for recovery and monitoring functions. This paper also presents the recovery capabilities of PeerFlow with formal proofs for the reliability issues and a performance evaluation with SimPy, the Python simulation package.