OpenRaiser/Intern-Atlas huggingface dataset:
Dynamic Generation of Data Broadcasting Programs for a Broadcast Disk Array
Authors: Ming-Syan Chen, Wen-Chih Peng
Venue: CIKM
Year: 2000

We explore in this paper the problem of generating hierarchical broadcast programs with the data access frequencies and the number of broadcast disks in a broadcast disk array given. Specifically, we first transform the problem of generating hierarchical broadcast programs into the one of constructing a channel allocation tree with variant-fanout. By exploiting the feature of tree generation with variantfanout, we develop a heuristic algorithm VF K to minimize the expected delay of data items in the broadcast program. Performance of these algorithms is analyzed. It is shown by our simulation results that by exploiting the feature of variant-fanout in constructing the channel allocation tree, the solution obtained by algorithm VF K is of very high quality and is in fact very close to the optimal one.
A Distributed Multi-Agent System for Collaborative Information Management and Sharing
Authors: James Chen, Shawn R. Wolfe, Stephen D. Wragg
Venue: CIKM
Year: 2000

In this paper, we present DIAMS, a system of distributed, collaborative agents to help users access, manage, share and exchange information. A DIAMS personal agent helps its owner find information most relevant to current needs. It provides tools and utilities for users to manage their information repositories with dynamic organization and virtual views. Flexible hierarchical display is integrated with indexed query search to support effective information access. Automatic indexing methods are employed to support user queries and communication between agents. Contents of a repository are kept in object-oriented storage to facilitate information sharing. Collaboration between users is aided by easy sharing utilities as well as automated information exchange. Matchmaker agents are designed to establish connections between users with similar interests and expertise. DIAMS agents provide needed services for users to share and learn information from one another on the World Wide Web. KEYWO...
Estimating Nested Selectivity in Object-Oriented Databases
Authors: Wan-Sup Cho, Wook-Shin Han, Kyu-Young Whang, Ki-Hyung Hong
Venue: CIKM
Year: 2000

A searc h condition in object-oriented queries consists of nested predicates, eac h of which is a predicate on a path expression. In this paper, w e present a new selectivit y estimation technique for nested predicates. Selectivity of a nested predicate, neste d selectivity, is de ned as the ratio of the number of quali ed objects of the starting class in the path expression to the total number of objects of the class. The new technique takes into account the e ects of direct representation of manyto-many relationships. Many-to-many relationships frequently occur in object-oriented databases, but have n o t been properly handled in con ven tional selectivit yestimation techniques. F or many-to-many relationships, we generalize the block-hit function originally proposed by B. Yao allowing the cases where one object belongs to more than one block. The most signi cant advantage of our technique is that the accuracy of the estimation is far enhanced with only a small additional overhead. We present an e cient method for obtaining the statistical information that is needed for our estimation technique. We analyze the accuracy of our estimation technique and compare the result with those of conven tional ones. The experimental result shows there is a signi cant d eviation in the estimation obtained by c o n ven tional ones, con rming the advan tage of our technique.
A Query based Approach for Integrating Heterogeneous Data Sources
Authors: Ruxandra Domenig, Klaus R. Dittrich
Venue: CIKM
Year: 2000

Article Free Access Share on A query based approach for integrating heterogeneous data sources Authors: Ruxandra Domenig Department of Information Technology, University of Zurich, Switzerland Department of Information Technology, University of Zurich, SwitzerlandView Profile , Klaus R. Dittrich Department of Information Technology, University of Zurich, Switzerland Department of Information Technology, University of Zurich, SwitzerlandView Profile Authors Info & Claims CIKM '00: Proceedings of the ninth international conference on Information and knowledge managementNovember 2000 Pages 453–460https://doi.org/10.1145/354756.354853Online:06 November 2000Publication History 11citation821DownloadsMetricsTotal Citations11Total Downloads821Last 12 Months13Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
A Meta Model and an Infrastructure for the Non-Transparent Replication of Object Databases
Authors: Werner Dreyer, Klaus R. Dittrich
Venue: CIKM
Year: 2000

The distribution of data within or between organizations is crucial for many applications. Such a distribution may be transparent, as it is embodied in distributed database systems to increase their performance, reliability and availability. Other systems either do not require transparent data distribution or even explicitly need their own copies of the data. Examples are data warehouses which collect base data from different transaction processing systems, or financial data distribution systems in banking where data are bought from external providers and distributed to different working groups. Such systems need non-transparent, partial and selective replication of databases. If object databases are used, appropriate replication systems have to cope with the additional complexity of object models. Todays replication systems do not meet these requirements. Consequently, we introduce a meta model that specifies the abstract functionality which is needed for the non-transparent, partial and selective replication of object databases. This meta model allows incorporation of its replication concepts in a variety of object models. In addition, we present the design of a replication infrastructure, based on the meta model, which serves as the foundation for concrete realizations of object replication systems. The development of a replication system for a time series database management system proved the viability of the proposed solution.
Dimensionality Reduction and Similarity Computation by Inner Product Approximations
Authors: Ömer Egecioglu, Hakan Ferhatosmanoglu
Venue: CIKM
Year: 2000

We d e v elop dynamic dimensionality reduction based on the appro ximationof the standard inner-product.The innerproduct, by itself, is used as a distance measure in a wide area of applications such as document databases, e.g.latent semantic indexing (LSI).A rst order approximation to the inner-product is usually obtained from the Cauchy-Schw arz inequality.The method proposed in this paper re nes such a n appro ximation b y using higher order pow er symmetric functions of the components of the v ectors, whic h a r e p o w ers of the p-norms of the vectors for p = 1 2 : : : m .We s h o w h o w to compute xed coe cients that work as universal weights based on the moments of the probability density function assumed for the distribution of the components of the input vectors in the data set.Our experiments on syn thetic and document d a t a s h o w that with this technique, the similarity between tw o objects in high dimensional space for certain applications can be accurately approximated by a signi cantly low er dimensional representation.
Retrieval from Captioned Image Databases Using Natural Language Processing
Authors: David Elworthy
Venue: CIKM
Year: 2000

It might appear that natural language processing should improve the accuracy of information retrieval systems, by making available a more detailed analysis of queries and documents. Although past results appear to show that this is not so, if the focus is shifted to short phrases rather than full documents, the situation becomes somewhat different. The ANVIL system uses a natural language technique to obtain high accuracy retrieval of images which have been annotated with a descriptive textual caption. The natural language techniques also allow additional contextual information to be derived from the relation between the query and the caption, which can help users to understand the overall collection of retrieval results. The techniques have been successfully used in a information retrieval system which forms both a testbed for research and the basis of a commercial system.
Vector Approximation based Indexing for Non-uniform High Dimensional Data Sets
Authors: Hakan Ferhatosmanoglu, Ertem Tuncel, Divyakant Agrawal, Amr El Abbadi
Venue: CIKM
Year: 2000

With the proliferation of multimedia data, there is increasing need to support the indexing and searching of high dimensional data. Recently, a vector approximation based technique called VA-file has been proposed for indexing high dimensional data. It has been shown that the VA-file is an effective technique compared to the current approaches based on space and data partitioning. The VA-file gives good performance especially when the data set is uniformly distributed. Real data sets are not uniformly distributed, are often clustered, and the dimensions of the feature vectors in real data sets are usually correlated. More careful analysis for nonuniform or correlated data is needed for effectively indexing high dimensional data. We propose a solution to these problems and propose the VA+-file, a new technique for indexing high dimensional data sets based on vector approximations. We conclude with an evaluation of nearest neighbor queries and show that the VA+-file technique res...
Learning a Monolingual Language Model from a Multilingual Text Database
Authors: Rayid Ghani, Rosie Jones
Venue: CIKM
Year: 2000

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Data Replication for External Searching in Static Tree Structures
Authors: Susanne E. Hambrusch, Chuan-Ming Liu
Venue: CIKM
Year: 2000

This paper explores the use of data replication to improve external searching in static tree structures. We present general and ecient mappings from the nodes of a tree T to blocks of size B when nodes of T can be replicated. The amount of replication is controlled and block utilization and blocknumber are optimized. We consider total node replication (measuring the total space used) and individual node replication (measuring the replication of indivdual nodes). For an arbitrary tree T of size N and height h, we show that by using at most 3 2 N space one can achieve a blocknumber proportional to the optimal blocknumber of dh=Be. We show that when every node can be replicated only a constant number of times, no signicant reduction in the blocknumber may be possible. Our work also shows that generating mappings in which all but one block contain exactly B nodes increases the blocknumber by at most 2. 1.
Fast Supervised Dimensionality Reduction Algorithm with Applications to Document Categorization & Retrieval
Authors: Eui-Hong Han, George Karypis
Venue: CIKM
Year: 2000

Retriev al techniques based on dimensionalit y reduction, such as Latent S e m a n tic Indexing (LSI), have been shown to improve the quality of the information being retrieved by c a pturing the latent meaning of the words present in the documents. Unfortunately, the high computational and memory requirements of LSI and its inabilit yto compute an e ective dimensionality reduction in a supervised setting limits its applicability. In this paper we p r e s e n t a fast supervised dimensionality reduction algorithm that is derived from the recen tly dev eloped cluster-based unsupervised dimensionality reduction algorithms. We experimentally evaluate the quality of the low er dimensional spaces both in the context of document categorization and improvements in retrieval performance on a variety of di erent document collections. Our experiments sho w that the lower dimensional spaces computed by our algorithm consistently improve the performance of traditional algorithms such as C4.5, k-nearestneigh bor, and Support V ector Machines (SVM), by a n a verage of 2% to 7%. F urthermore, the supervised lower dimensional space greatly improves the retriev al performance when compared to LSI.
A Semi-Supervised Document Clustering Technique for Information Organization
Authors: Han-joon Kim, Sang-goo Lee
Venue: CIKM
Year: 2000

This paper discusses a new type of semi-supervised docu-ment clustering that uses partial supervision to partition a large set of documents. Most clustering methods organizes documents into groups based only on similarity measures. Unfortunately, the traditional approaches to document clus-tering are often unable to correctly discern structural details hidden within the document corpus because their algorithms inherently strongly depend on the document themselves and their similarity to each other. In this paper, we attempt to isolate more semantically coherent clusters by employing the domain-specific knowledge provided by a document analyst. By using external human knowledge to guide the clustering mechanism with some flexibility when creating the clusters, clustering efficiency can be considerably enhanced. As a ba-sic clustering strategy, we use a variant of complete-linkage agglomerative hierarchical clustering, and develop the con-cepts (or seeds) of requested clusters by exploiting user-relevance feedback. Although the proposed method is slow when applied to large document collection, it yields higher quality clusters. Through experiments using the Reuters-21578 corpus, we show that the proposed method outper-forms unsupervised clustering method.
Space Efficient Bitmap Indexing
Authors: Nick Koudas
Venue: CIKM
Year: 2000

There exists a well known tradeo between the performance of queries on a collection of tables and the space devoted to the indices indexing the attributes in these tables. We introduce additional parameters in the analysis of this tradeo namely the query and data distribution on the attribute instance.
DEADLINER: Building a New Niche Search Engine
Authors: Andries Kruger, C. Lee Giles, Frans Coetzee, Eric J. Glover, Gary William Flake, Steve Lawrence, Christian W. Omlin
Venue: CIKM
Year: 2000

Article Free Access Share on DEADLINER: building a new niche search engine Authors: A. Kruger NEC Research Institute, 4 Independence Way, Princeton, NJ NEC Research Institute, 4 Independence Way, Princeton, NJView Profile , C. L. Giles NEC Research Institute, 4 Independence Way, Princeton, NJ NEC Research Institute, 4 Independence Way, Princeton, NJView Profile , F. M. Coetzee NEC Research Institute, 4 Independence Way, Princeton, NJ NEC Research Institute, 4 Independence Way, Princeton, NJView Profile , E. Glover NEC Research Institute, 4 Independence Way, Princeton, NJ NEC Research Institute, 4 Independence Way, Princeton, NJView Profile , G. W. Flake NEC Research Institute, 4 Independence Way, Princeton, NJ NEC Research Institute, 4 Independence Way, Princeton, NJView Profile , S. Lawrence NEC Research Institute, 4 Independence Way, Princeton, NJ NEC Research Institute, 4 Independence Way, Princeton, NJView Profile , C. Omlin NEC Research Institute, 4 Independence Way, Princeton, NJ NEC Research Institute, 4 Independence Way, Princeton, NJView Profile Authors Info & Claims CIKM '00: Proceedings of the ninth international conference on Information and knowledge managementNovember 2000 Pages 272–281https://doi.org/10.1145/354756.354829Online:06 November 2000Publication History 30citation656DownloadsMetricsTotal Citations30Total Downloads656Last 12 Months5Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
Learning to Extract Hierarchical Information from Semi-structured Documents
Authors: Wai Lam, Wai-Yip Lin
Venue: CIKM
Year: 2000

Article Free Access Share on Learning to extract hierarchical information from semi-structured documents Authors: Wai-Yip Lin Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, Hong Kong Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, Hong KongView Profile , Wai Lam Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, Hong Kong Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, Hong KongView Profile Authors Info & Claims CIKM '00: Proceedings of the ninth international conference on Information and knowledge managementNovember 2000 Pages 250–257https://doi.org/10.1145/354756.354826Published:06 November 2000Publication History 13citation848DownloadsMetricsTotal Citations13Total Downloads848Last 12 Months30Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
Rule-Assisted Prefetching in Web-Server Caching
Authors: Bin Lan, Stéphane Bressan, Beng Chin Ooi, Kian-Lee Tan
Venue: CIKM
Year: 2000

Web servers manage large numbe rof documents of widely variable sizes. Moreover, the access patterns on the documents may also c hange over time. While some documents are highly popular over a prolonged period of time, we expe c tnewly added documents to increase in popularity while demand for most older documents decreases. It is therefore important to design e ective caching strategy at the web server. In this paper, we present our approach to the problem. Our main contribution lies in the design of a novel prefetching strategy, called RAP. RAP identi es a set of association rules from the Web server's access log. Unlike existing mining strategy, RAP's miner values recently added log records more than earlier log records. Based on the rules, RAP predicts and prefetches documents from users initial requests. We conducted extensive study to evaluate RAP. The results show that RAP signi cantly outperforms existing schemes. We also show that the mining and caching cost is relatively low.
Collection Selection and Results Merging with Topically Organized U.S. Patents and TREC Data
Authors: Leah S. Larkey, Margaret E. Connell, James P. Callan
Venue: CIKM
Year: 2000

We investigate three issues in distributed information retrieval, considering both TREC data and U.S. Patents: (1) topical organization of large text collections, (2) collection ranking and selection with topically organized collections (3) results merging, particularly document score normalization, with topically organized collections. We find that it is better to organize collections topically, and that topical collections can be well ranked using either INQUERY&apos;s CORI algorithm, or the Kullback-Leibler divergence (KL), but KL is far worse than CORI for non-topically organized collections. For results merging, collections organized by topic require global idfs for the best performance. Contrary to results found elsewhere, normalized scores are not as good as global idfs for merging when the collections are topically organized. Keywords Information retrieval, Collection selection, Topical organization. 1. INTRODUCTION We have developed a distributed system for the search and class...
Language Models for Financial News Recommendation
Authors: Victor Lavrenko, Matthew D. Schmill, Dawn J. Lawrie, Paul Ogilvie, David D. Jensen, James Allan
Venue: CIKM
Year: 2000

ABSTRACT We present a unique approa h to identifying news stories that in uen e the behavior of nan ial markets. Spe i ally, we des ribe the design and implementation of nalyst, a system that an re ommend interesting news stories { stories that are likely to a e t market behavior. nalyst operates by orrelating the ontent of news stories with trends in nan ial time series. We identify trends in time series using pie ewise linear tting and then assign labels to the trends a ording to an automated binning pro edure. We use language models to represent patterns of language that are highly asso iated with parti ular labeled trends. nalyst an then identify and re ommend news stories that are highly indi ative of future trends. We evaluate the system in terms of its ability to re ommend the stories that will a e t the behavior of the sto k market. We demonstrate that stories re ommended by nalyst ould be used to pro tably predi t forth oming trends in sto k pri es.
Persistence of information on the web: Analyzing citations contained in research articles
Authors: Steve Lawrence, Frans Coetzee, Gary William Flake, David M. Pennock, Robert Krovetz, Finn Årup Nielsen, Andries Kruger, C. Lee Giles
Venue: CIKM
Year: 2000

We analyze the persistence of information on the web, looking at the percentage of invalid URLs contained in academic articles within the CiteSeer (ResearchIndex) database. The number of URLs contained in the papers has increased from an average of 0.06 in 1993 to 1.6 in 1999. We found that a significant percentage of URLs are now invalid, ranging from 23% for 1999 articles, to 53% for 1994. We also found that for almost all of the invalid URLs, it was possible to locate the information (or highly related information) in an alternate location, primarily with the use of search engines. However, the ability to relocate missing information varied according to search experience and effort expended. Citation practices suggest that more information may be lost in the future unless these practices are improved. We discuss persistent URL standards and their usage, and give recommendations for citing URLs in research articles as well as for finding the new location of invalid URLs.