OpenRaiser/Intern-Atlas huggingface dataset:
estWin: adaptively monitoring the recent change of frequent itemsets over online data streams
Authors: Joong Hyuk Chang, Won Suk Lee
Venue: CIKM
Year: 2003

Knowledge embedded in a data stream is likely to be changed as time goes by. Consequently, identifying the recent change of the knowledge quickly can provide valuable information for the analysis of the data stream. However, most of mining algorithms or frequency approximation algorithms for a data stream do not able to extract the recent change of information in a data stream adaptively. This paper proposes a sliding window-based method that finds recently frequent itemsets over an online data stream adaptively. The size of a window defines a desired life-time of the information in a newly generated transaction. Consequently, only recently generated transactions in the range of the window are considered to find the frequent itemsets of a data stream.
Replication and retrieval strategies of multidimensional data on parallel disks
Authors: Chung-Min Chen, Christine T. Cheng
Venue: CIKM
Year: 2003

Aside from enhancing data availability during disk failures, replication of data is also used to speed up I/O performance of read-intensive applications. There are two issues that need to be addressed: (a) data placement (Which disks should store the copies of each data block?) and (b) scheduling (Given a query Q, and a placement scheme P of the data, from which disk should each block in Q be retrieved so that retrieval time is minimized?) In this paper, we consider range queries and assume that the dataset is a multidimensional grid and r copies of each unit block of the grid must be stored among M disks. To accurately measure performance of a scheduling algorithm, we consider a metric that takes into account the scheduling overhead as well as the time it takes to retrieve the data blocks from the disks. We describe several combinations of data placement schemes and scheduling algorithms and analyze their performance for range queries with respect to the above metric. We then present simulation results for the most interesting case r=2, showing that the strategies do perform better than the previously known method, especially for large queries.
Auto-generation of topic hierarchies for web images from users' perspectives
Authors: Pu-Jen Cheng, Lee-Feng Chien
Venue: CIKM
Year: 2003

In this paper, we propose an approach to automatically generating a Yahoo!-like topic hierarchy for organizing Web images from users' perspectives. Relatively little effort has been devoted towards providing such a taxonomy simultaneously considering users' image requests for semantic and visual information. Based on the characteristic that a Web-image query may be refined by various attributes, the proposed approach hierarchically groups similar queries from search engine logs into topic classes at different semantic levels. The generated topic hierarchy has the advantages of organizing image data from users' perspectives for browsing, searching, annotation and users' needs analysis.A series of experiments have been conducted on real-world image search engine logs. Experimental results show that the proposed approach is feasible to generate topic hierarchies for Web images. Moreover, the generated hierarchy has been successfully applied to analysis of users' search interests, which have more focuses on some specific domains when compared with document requests.
Operational requirements for scalable search systems
Authors: Abdur Chowdhury, Greg Pass
Venue: CIKM
Year: 2003

Prior research into search system scalability has primarily addressed query processing efficiency [1, 2, 3] or indexing efficiency [3], or has presented some arbitrary system architecture [4]. Little work has introduced any formal theoretical framework for evaluating architectures with regard to specific operational requirements, or for comparing architectures beyond simple timings [5] or basic simulations [6, 7]. In this paper, we present a framework based upon queuing network theory for analyzing search systems in terms of operational requirements. We use response time, throughput, and utilization as the key operational characteristics for evaluating performance. Within this framework, we present a scalability strategy that combines index partitioning and index replication to satisfy a given set of requirement.
A reliable storage management layer for distributed information retrieval systems
Authors: Charles L. A. Clarke, Philip L. Tilker, Allen Quoc-Luan Tran, Kevin Harris, Antonio S. Cheng
Venue: CIKM
Year: 2003

We present a storage management layer that facilitates the implementation of parallel information retrieval systems, and related applications, on networks of workstations. The storage management layer automates the process of adding and removing nodes, and implements a dispersed mirroring strategy to improve reliability. When nodes are added and removed, the document collection managed by the system is redistributed for load balancing purposes. The use of dispersed mirroring minimizes the impact of node failures and system modifications on query performance.
Online duplicate document detection: signature reliability in a dynamic retrieval environment
Authors: Jack G. Conrad, Xi S. Guo, Cindy P. Schriber
Venue: CIKM
Year: 2003

As online document collections continue to expand, both on the Web and in proprietary environments, the need for duplicate detection becomes more critical. Few users wish to retrieve search results consisting of sets of duplicate documents, whether identical duplicates or close matches. Our goal in this work is to investigate the phenomenon and determine one or more approaches that minimize its impact on search results. Recent work has focused on using some form of signature to characterize a document in order to reduce the complexity of document comparisons. A representative technique constructs a 'fingerprint' of the rarest or richest features in a document using collection statistics as criteria for feature selection. One of the challenges of this approach, however, arises from the fact that in production environments, collections of documents are always changing, with new documents, or new versions of documents, arriving frequently, and other documents periodically removed. When an enterprise proceeds to freeze a training collection in order to stabilize the underlying repository of such features and its associated collection statistics, issues of coverage and completeness arise. We show that even with very large training collections possessing extremely high feature correlations before and after updates, underlying fingerprints remain sensitive to subtle changes. We explore alternative solutions that benefit from the development of massive meta-collections made up of sizable components from multiple domains. This technique appears to offer a practical foundation for fingerprint stability. We also consider mechanisms for updating training collections while mitigating signature instability. Our research is divided into three parts. We begin with a study of the distribution of duplicate types in two broad-ranging news collections consisting of approximately 50 million documents. We then examine the utility of document signatures in addressing identical or nearly identical duplicate documents and their sensitivity to collection updates. Finally, we investigate a flexible method of characterizing and comparing documents in order to permit the identification of non-identical duplicates. This method has produced promising results following an extensive evaluation using a production-based test collection created by domain experts.
Digital annotation of printed documents
Authors: Corsin Decurtins, Moira C. Norrie, Beat Signer
Venue: CIKM
Year: 2003

We present a general model and information server for the digital annotation of printed documents. The resulting annotation framework supports both informal and structured annotations as well as context-dependent services. A demonstrator application for mammography that features both enhanced writing and reading activities is described.
MASS: a multi-axis storage structure for large XML documents
Authors: Kurt W. Deschler, Elke A. Rundensteiner
Venue: CIKM
Year: 2003

Effective indexing for XML must consider both the query requirements of the XPath language and the dynamic nature of XML. We introduce MASS, a Multiple Axis Storage Structure, to provide scalable indexing for XPath expressions with guaranteed update performance. We describe the building blocks of MASS and provide results that demonstrate MASS's scalability. We show that MASS can outperform other state-of-the-art XML indexing solutions, even with constrained system resources.
Representing interests as a hyperlinked document collection
Authors: Michelle Jayne Fisher, Richard M. Everson
Venue: CIKM
Year: 2003

We describe a latent variable model for representing a user&apos;s interests as a hyperlinked document collection. By collecting hyper-text documents that a user views, creates or updates whilst at their computer, we are able to use not only the content of these documents but also the inter-connectivity of the collection to model the user&apos;s interests. The model uses Probabilistic Latent Semantic Analysis and Probabilistic Hypertext Induced Topic Selection and decomposes the user&apos;s document collection into a set of factors each of which represents a user&apos;s interest. This model can be used to personalise information access tasks such as a personalised search engine or a personalised news service. Our latent variable model&apos;s performance is compared with that of a more conventional vector space clustering algorithm.
Efficient ordering for XML data
Authors: Damien K. Fisher, Franky Lam, William M. Shui, Raymond K. Wong
Venue: CIKM
Year: 2003

With the increasing popularity of XML, there arises the need for managing and querying information in this form. Several query languages, such as XQuery, have been proposed which return their results in document order. However, most recent efforts focused on query optimization have disregarded order. This paper presents a simple yet elegant method to maintain document ordering for XML data. Analysis of our method shows that it is indeed efficient and scalable, even for changing data.
Speech user interfaces for information retrieval
Authors: Juan E. Gilbert, Yapin Zhong
Venue: CIKM
Year: 2003

The research proposed here concentrates on the problem of designing and developing a spoken query retrieval (SQR) system to access large document databases via voice. The main challenge is to identify and address issues related to designing an effective and efficient speech user interface (SUI), especially if the aim is to facilitate spoken queries of large document databases. Furthermore, the task of presenting large query result sets aurally should be performed such that the user's short term memory is not overloaded. In this paper, a framework allowing information retrieval to large document databases via voice is presented and findings from a research study using the framework will be discussed as well.
Intelligent metasearch engine for knowledge management
Authors: Eui-Hong Han, George Karypis, Doug Mewhort, Keith Hatchard
Venue: CIKM
Year: 2003

The explosive growth of available information sources and the resulting information overload pose several problems for users in many business organizations and educational institutions. First, searching through several information sources, one at a time, is a source of enormous frustration for users. Second, top-ranked documents in search results are frequently irrelevant to what users are interested in. To address these problems, we have developed ixmeta™, a powerful metasearch engine that gathers, evaluates, ranks, and reports the most relevant results from multiple information sources, including library catalogs, proprietary databases, intranets, and Web search engines. In addition to basic metasearch capabilities, ixmetafind uses personalization and clustering techniques to find the most relevant results for users. In this paper, we briefly describe technologies used in ixmetafind and present pinpoint™ from Sagebrush Corporation, the smart research tool™ in the kindergarten through twelfth grade (K-12) school environment. Pinpoint showcases ixmetafind in the knowledge management domain of the K-12 school environment.
Event analyzer: a tool for sequential data processing
Authors: Lilian Harada, Yuuji Hotta, Naoki Akaboshi, Kazumi Kubota, Tadashi Ohmori, Riichiro Take
Venue: CIKM
Year: 2003

In this paper we present a tool called Event Analyzer that processes events that compose a sequence. We present the data model in which Event Analyzer is based, as well as its query language that allows the expression of complex patterns to be searched over the sequence of events. The Event Analyzer has been developed and it now integrates the Fujitsu Symfoware e-Business Intelligence Suite Premium.
A study of parameter tuning for term frequency normalization
Authors: Ben He, Iadh Ounis
Venue: CIKM
Year: 2003

Most current term frequency normalization approaches for information retrieval involve the use of parameters. The tuning of these parameters has an important impact on the overall performance of the information retrieval system. Indeed, a small variation in the involved parameter(s) could lead to an important variation in the precision/recall values. Most current tuning approaches are dependent on the document collections. As a consequence, the effective parameter value cannot be obtained for a given new collection without extensive training data. In this paper, we propose a novel and robust method for the tuning of term frequency normalization parameter(s), by measuring the normalization effect on the within document frequency of the query terms. As an illustration, we apply our method on Amati \& Van Rijsbergen's so-called normalization 2. The experiments for the ad-hoc TREC-6,7,8 tasks and TREC-8,9,10 Web tracks show that the new method is independent of the collections and able to provide reliable and good performance.
SQL text parsing for information retrieval
Authors: David Holmes
Venue: CIKM
Year: 2003

The concept of using a relational database to perform information retrieval (IR) search functions is well established. Prior work demonstrates the capability to perform common functions and advanced ranking algorithms using standard, unchanged SQL. The previous work does not address the preprocessing of unstructured text within the relational model. In fact, the parsing of the unstructured data into a structured data set was done outside of the database, usually using sequential programming languages such as C. This work proves that IR preprocessing does not require proprietary application code to build the framework necessary for searching document databases. Furthermore, the resulting environment is relational and integrates with other data sources within an organization.
Exploring group mobility for replica data allocation in a mobile environment
Authors: Jiun-Long Huang, Ming-Syan Chen, Wen-Chih Peng
Venue: CIKM
Year: 2003

The growth in wireless communication technologies attracts a considerable amount of attention in mobile ad-hoc networks. Since mobile hosts in an ad-hoc network usually move freely, the topology of the network changes dynamically and disconnection occurs frequently. These characteristics make a mobile ad-hoc network be likely to be separated into several disconnected partitions, and the data accessibility is hence reduced. Several schemes are proposed to alleviate the reduction of data accessibility by replicating data items. However, little research effort was elaborated upon exploiting the group mobility where the group mobility refers to the phenomenon that several mobile nodes tend to move together. In this paper, we address the problem of replica allocation in a mobile ad-hoc network by exploring group mobility. We first analyze the group mobility model and derive several theoretical results. In light of these results, we propose a replica allocation scheme to improve the data accessibility. Several experiments are conducted to evaluate the performance of the proposed scheme. The experimental results show that the proposed scheme is able to not only obtain higher data accessibility but also produce lower network traffic than prior schemes.
Industrial evaluation of a highly-accurate academic IR system
Authors: Tahia Infantes-Morris, Philip J. Bernhard, Kevin L. Fox, Gary J. Faulkner, Kristina Stripling
Venue: CIKM
Year: 2003

In this paper we report the results of an independent experimental evaluation of an information retrieval (IR) system developed at the Illinois Institute of Technology (IIT). The system, which is called the Advanced Information Retrieval Engine (AIRE), consists of a set of tools and utilities providing indexing, extraction, searching and visualization. We evaluated AIRE on three data sets from the Text REtrieval Conference (TREC) - TREC 8, 9 and 10. Overall, our results indicate that AIRE is a highly accurate IR system. Compared with results published by IIT, in our experiments AIRE consistently scored higher in recall. AIRE also scored higher in precision, but only for automatic tasks. In manual tasks, AIRE scored lower in precision in our experiments, but we attributed that to factors external to AIRE. Our final conclusion is that AIRE is a highly accurate IR system.
Queueing analysis of relational operators for continuous data streams
Authors: Qingchun Jiang, Sharma Chakravarthy
Venue: CIKM
Year: 2003

Currently, stream data processing is an active area of research, which includes everything from algorithms and architectures for stream processing to modelling, and analysis of various components of a stream processing system. In this paper, we present an analysis of relational operators used for stream processing using queueing theory and study behaviors of streaming data in a query processing system. Our approach enables us to compute the fundamental performance metrics of relational operators ---select, project, and join over data streams. Furthermore, this approach establishes a way to find the probability distribution functions of both the number of tuples and the waiting time of tuples in the system. Finally, we designed and implemented a number of experiments to validate the accuracy and effectiveness of our analysis.
Dynamically maintaining frequent items over a data stream
Authors: Cheqing Jin, Weining Qian, Chaofeng Sha, Jeffrey Xu Yu, Aoying Zhou
Venue: CIKM
Year: 2003

It is challenge to maintain frequent items over a data stream, with a small bounded memory, in a dynamic environment where both insertion/deletion of items are allowed. In this paper, we propose a new novel algorithm, called hCount, which can handle both insertion and deletion of items with a much less memory space than the best reported algorithm. Our algorithm is also superior in terms of precision, recall and processing time. In addition, our approach does not request the preknowledge on the size of range for a data stream, and can handle range extension dynamically. Given a little modification, algorithm hCount can be improved to hCount*, which even owns significantly better performance than before.
Collaborative filtering with decoupled models for preferences and ratings
Authors: Rong Jin, Luo Si, ChengXiang Zhai, James P. Callan
Venue: CIKM
Year: 2003

In this paper, we describe a new model for collaborative filtering. The motivation of this work comes from the fact that two users with very similar preferences on items may have very different rating schemes. For example, one user may tend to assign a higher rating to all items than another user. Unlike previous models of collaborative filtering, which determine the similarity between two users only based on their rating performance, our model treats the user's preferences on items separately from the user's rating scheme. More specifically, for each user, we build two separate models: a preference model capturing which items are favored by the user and a rating model capturing how the user would rate an item given the preference information. The similarity of two users is computed based on the underlying preference model, instead of the surface ratings. We compare the new model with several representative previous approaches on two data sets. Experiment results show that the new model outperforms all the previous approaches that are tested consistently on both data sets.
User assisted text classification and knowledge management
Authors: Anne Kao, Lesley Quach, Steve Poteet, Steve Woods
Venue: CIKM
Year: 2003

While there are many aspects to managing corporate knowledge, one key issue is how to organize corporate documents into categories of interest. In this paper, we focus on using user assisted text classification in conjunction with a web portal, multiple document management systems and an ontology, to provide a powerful solution for organizing information about a company's technology. We propose a system that interacts with an author using an automatic text classifier to suggest controlled keywords to be used as metadata. The proposed approach does not require professional librarians or that the end users have extensive training. The use of a controlled vocabulary allows for a more consistent description of corporate documents, and promotes easier access by people across the company. It is easier to find similar documents which use different nomenclature. Finally, the interactive nature of the system results in a more correct and precise description of each document than a fully automatic system would.
Automated index management for distributed web search
Authors: Rinat Khoussainov, Nicholas Kushmerick
Venue: CIKM
Year: 2003

Distributed heterogeneous search systems are an emerging phenomenon in Web search, in which independent topic-specific search engines provide search services, and metasearchers distribute user&apos;s queries to only the most suitable search engines. Previous research has investigated methods for engine selection and merging of search results (i.e. performance improvements from the user&apos;s perspective) . We focus instead on performance from the service provider&apos;s point of view (e.g, income from queries processed vs. resources used to answer them). We consider a scenario in which individual search engines compete for user queries by choosing which documents (topics) to index. The difficulty here stems from the fact that the utilities of local engine actions should depend on the uncertain actions of competitors. Thus, naive strategies (e.g, blindly indexing lots of popular documents) are ineffective. We model the competition between search engines as a stochastic game, and propose a reinforcement learning approach to managing search index contents. We evaluate our approach using a large log of user queries to 47 real search engines.
Automatic identification of best entry points for focused structured document retrieval
Authors: Mounia Lalmas, Jane Reid
Venue: CIKM
Year: 2003

Focussed structured document retrieval employs the concept of best entry points (BEPs), which are intended to provide optimal starting-points from which users can browse to relevant document components. This paper describes two small-scale studies, using experimental data from the Shakespeare user study, which developed and evaluated different approaches to the problem of automatic identification of BEPs.