7584097
An Adaptive Web Cache Access Predictor Using Neural Network
This paper presents a novel approach to successfully predict Web pages that are most likely to be re-accessed in a given period of time. We present the design of an intelligent predictor that can be implemented on a Web server to guide caching strategies. Our approach is adaptive and learns the changing access patterns of pages in a Web site. The core of our predictor is a neural network that uses a backpropagation learning rule. We present results of the application of this predictor on static data using log files; it can be extended to learn the distribution of live Web page access patterns. Our simulations show fast learning, uniformly good prediction, and up to 82% correct prediction for the following six months based on a one-day training data. This long-range prediction accuracy is attributed to the static structure of the test Web site.
1825969
Agent space architecture for search engines
The future of computing is moving from individual processing units to communities of self organizing agents. In This work we propose an agent and network based architecture for parallel and distributed computing called agent space architecture. Our architecture builds upon the notions of agent and object space and utilizes multicast networks. The building blocks for our proposed architecture consist of an active processing unit called agent, a shared place for communication called space, and a communication medium called multicast network. One unique feature of our architecture is that we extend the concept of object space to become an active space. Our active space functions as a rendezvous, a repository, a cache, a responder, a notifier, and a manager of its own resources. The organization of our architecture is as general as network topology. Any number of agents, spaces, or networks can be added to achieve high performance. It is as scalable as Ethernet and adding agents or spaces is as easy as plug and play. High availability and fault tolerance is achieved through multiple agents, spaces, and networks. All these features are particularly beneficial for challenging applications such as search engine, which is used as a test case to implement and to test our proposed architecture.
17009479
Bidirectional hierarchical clustering for Web mining
We propose a new bidirectional hierarchical clustering system for addressing challenges of Web mining. The key feature of the approach is that it aims to maximize the intra-cluster similarity in the bottom-up cluster-merging phase and it ensures to minimize the inter-cluster similarity in the top-down refinement phase. This two-pass approach achieves better clustering than existing one-pass approaches. We also propose a new cluster-merging criterion for allowing more than two clusters to be merged in each step and a new measure of similarity for taking into consideration not only the inter-connectivity between clusters but also the internal connectivity within the clusters. These result in reducing the average complexity for creating the final hierarchical structure of clusters from O(n/sup 2/) to O(n). The hierarchical structure represents a semantic structure between concepts of clusters and is directly applicable to the future of semantic net.
10179927
Automatic web page classification in a dynamic and hierarchical way
Automatic classification of web pages is an effective way to deal with the difficulty of retrieving information from the Internet. Although there are many automatic classification algorithms and systems that have been proposed, most of them ignore the conflict between the fixed number of categories and the growing number of web pages going into the system. They also require searching through all existing categories to make any classification. We propose a dynamic and hierarchical classification system that is capable of adding new categories as required, organizing the web pages into a tree structure, and classifying web pages by searching through only one path of the tree structure. Our test results show that our proposed single-path search technique reduces the search complexity and increases the accuracy by 6% comparing to related algorithms. Our dynamic-category expansion technique also achieves satisfying results on adding new categories into our system as required.
8998832
Document Classifications based on Word Semantic Hierarchies
In this paper we proposed to automatically classify documents based on the meanings of words and the relationships between groups of meanings or concepts. Our proposed classification algorithm builds on the word structures provided by WordNet, which not only arranges words into groups of synonyms, called Synsets, but also arranges the Synsets into hierarchies representing the relationships between concepts. Most existing methods classify text documents based on the number of occurrences of words and some based on Synsets. Our approach goes one step further by using not only word occurrences and Synsets but also the relationships between Synsets. We also proposed a sense-based document representation based on the semantic hierarchies provided by WordNet. To classify a document, our approach extracts words occurred in the document and uses them to increase the weight of the Synsets corresponding to the words. Words with same meanings will increase the weight of their corresponding Synsets. As a result, we count the occurrences of senses. We also propagate the weight of a Synset upward to its related Synsets in the hierarchies and thus capture the relationships between concepts. In comparing to previous research, our approach increases the classification accuracy by 14%.
6302139
Speeding up keyword search for search engines
In the current information age, the dominant method for information search is by providing few keywords to a search engine. Keyword search is currently one of the most important operations in search engines and numerous other applications. In this paper we propose a new text indexing technique for improving the performance of keyword search. Our proposed technique not only speeds up searching operations but also the operations for inserting and for deleting keywords, which are particularly important for the ever increasing and dynamic changing databases such as that for search engines. We propose to partition all
6267168
A qualitative cross-national study of cultural influences on mobile data service design
As the use of mobile data services has spread across the globe, the effect of cultural differences on user requirements has become important issue. To date, however, little research has been conducted on the role cultural factors play in the design of mobile data services. This paper proposes a set of critical design attributes for mobile data services that takes cross-cultural differences into account. To determine these attributes, we devised a qualitative method and conducted in-depth long interviews in Korea, Japan, and Finland. We found 52 attributes considered important by mobile data service users, and 11 critical attributes that showed a clear correlation with characteristics of the user's culture. The paper concludes with a discussion of limitations and of implications for developers of mobile data services.
14865238
Adopting ontology to facilitate knowledge sharing
Ontology-enabled knowledge management experiences derived from a domain ontology development project at Intel Corporation are described and assessed.
6530554
Adaptive Index Utilization in Memory-Resident Structural Joins
We consider adaptive index utilization as a fine-grained problem in autonomic databases in which an existing index is dynamically determined to be used or not in query processing. As a special case, we study this problem for structural joins, the core operator in XML query processing, in the main memory. We find that index utilization is beneficial for structural joins only under certain join selectivity and distribution of matching elements. Therefore, we propose adaptive algorithms to decide whether to use an index probe or a data scan for each step of matching during the processing of a structural join operator. Our adaptive algorithms are based on the history, the look-ahead information, or both. We have developed a cost model to facilitate this adaptation and have conducted experiments with both synthetic and real-world data sets. Our results show that adaptively utilizing indexes in a structural join improves the performance by taking advantage of both sequential scans and index probes.
13929475
Effects of Web retail service quality and product categories on consumer behavior: a research model and empirical exploration
This paper explores how Web retail service quality at the point of purchase influences consumers' perceptions of value and willingness to buy. In particular, this study highlights how Web retail service quality has different effects on perceived product quality, value, and willingness to buy according to product categories. We provide a research model on the basis of service quality, product categories on the Web, and marketing theories for consumers' purchase behavior. Consumer perceptions of Web retail service quality are collected from a sample of shoppers who actively search for products over the Internet. The results imply that functional Web retail service quality has a direct effect on willingness to buy and technical Web retail service quality influences consumer perceptions of product quality and value. Furthermore, product categories moderate the relationships among Web retail service quality, perceived product quality, perceived value, and willingness to buy. Implications for Internet marketing and future research directions are discussed.
10441527
Efficient Support for Ordered XPath Processing in Tree-Unaware Commercial Relational Databases
In this paper, we present a novel ordered XPATH evaluation in treeunaware RDBMS. The novelties of our approach lies in the followings. (a) We propose a novel XML storage scheme which comprises only leaf nodes, their corresponding data values, order encodings and their root-to-leaf paths. (b) We propose an algorithm for mapping ordered XPATH queries into SQL queries over the storage scheme. (c) We propose an optimization technique that enforces all mapped SQL queries to be evaluated in a "left-to-right" join order. By employing these techniques, we show, through a comprehensive experiment, that our approach not only scales well but also performs better than some representative tree-unaware approaches on more than 65% of our benchmark queries with the highest observed gain factor being 1939. In addition, our approach reduces significantly the performance gap between tree-aware and tree-unaware approaches and even outperforms a state-of-the-art tree-aware approach for certain benchmark queries.