OpenRaiser/Intern-Atlas huggingface dataset:
Merging Techniques for Performing Data Fusion on the Web
Authors: Theodora Tsikrika, Mounia Lalmas
Venue: CIKM
Year: 2001

Data fusion on the Web refers to the merging, into a unified single list, of the ranked document lists, which are retrieved in response to a user query by more than one Web search engine. It is performed by metasearch engines and their merging algorithms utilise the information present in the ranked lists of retrieved documents provided to them by the underlying search engines, such as the rank positions of the retrieved documents and their retrieval scores. In this paper, merging techniques are introduced that take into account not only the rank positions, but also the title and the summary accompanying the retrieved documents. Furthermore, the data fusion process is viewed as being similar to the combination of belief in uncertain reasoning and is modelled using Dempster-Shafer's theory of evidence. Our evaluation experiments indicate that the above merging techniques yield improvements in the effectiveness and that their effectiveness is comparable to that of the approach that merges the ranked lists by downloading and analysing the Web documents.
Question Answering in TREC
Authors: Ellen M. Voorhees
Venue: CIKM
Year: 2001

Traditional text retrieval systems return a ranked list of documents in response to a user's request. While a ranked list of documents can be an appropriate response for the user, frequently it is not. Usually it would be better for the system to provide the answer itself instead of requiring the user to search for the answer in a set of documents. The Text REtrieval Conference (TREC) is sponsoring a question answering "track" to foster research on the problem of retrieving answers rather than document lists.TREC is a workshop series sponsored by the National Institute of Standards and Technology and the U.S. Department of Defense [7]. The purpose of the conference series is to encourage research on text retrieval for realistic applications by providing large test collections, uniform scoring procedures, and a forum for organizations interested in comparing results. The conference has focused primarily on the traditional IR problem of retrieving a ranked list of documents in response to a statement of information need, but has also included other tasks, called tracks, that focus on new areas or particularly difficult aspects of information retrieval. A question answering track was introduced in TREC-8 1999. The track has generated wide-spread interest in the QA problem [2, 3, 4], and has documented significant improvements in question answering system effectiveness in its two-year history.This paper provides a brief summary of the findings of the TREC question answering track to date and discusses the future directions of the track. The paper is extracted from a fuller description of the track given in "The TREC Question Answering Track" [8]. Complete details about the TREC question answering track can be found in the TREC proceedings.
A Performance Comparison of bitmap indexes
Authors: Kesheng Wu, Ekow J. Otoo, Arie Shoshani
Venue: CIKM
Year: 2001

We present a comparison of two new word-aligned schemes with some schemes for compressing bitmap indexes, including the well-known byte-aligned bitmap code (BBC). On both synthetic data and real application data, the new word-aligned schemes use only 50% more space, but perform logical operations on compressed data 12 times faster than BBC. The new schemes achieve this performance advantage by guaranteeing that during logical operations every machine instruction performs useful work on words rather than on bytes or bits as in BBC.
A Relational Algebra for Data/Metadata Integration in a Federated Database System
Authors: Catharine M. Wyss, Dirk Van Gucht
Venue: CIKM
Year: 2001

The need for interoperability among databases has increased dramatically with the proliferation of readily available DBMS and application software. Even within a single organization, data from disparate relational databases must be integrated. A framework for interoperability in a federated system of relational databases should be inherently relational, so that it can use existing techniques for query evaluation and optimization where possible and retain the key features of SQL, such as a modest complexity and ease of query formulation. Our contribution is a logspace relational algebra, the Meta-Algebra (MA), for data/metadata integration among relational databases containing semantically similar information in schematically disparate formats. The MA is a simple yet powerful extension of the classical relational algebra (RA). The MA has a natural declarative counterpart, the Meta-Query Language (MQL), which we briefly describe. We state a result showing MQL and the MA are computationally equivalent, which enables us to algebratize MQL queries in fundamentally the same way as ordinary SQL queries. This algebratization in turn enables us to use MA equivalences to facilitate the application of known query optimization techniques to MQL query evaluation.
Dynamic Versioning Concurrency Control for Index-Based Data Access in Main Memory Database Systems
Authors: Ying Xia, Sung-Hee Kim, Sook-Kyoung Cho, Kee-Wook Rim, Hae-Young Bae
Venue: CIKM
Year: 2001

We present a concurrency control scheme using dynamic versioning for index-based data access in main memory database systems. This scheme enables read-only transactions read correct version without holding any locks or latches, while update transactions only obtain a few locks or latches without deadlocks. Efficient version management is designed to support high concurrency level and low space overhead. The interaction between dynamic versioning and indexing is considered so that all available versions can be accessed through indexing. Experiment results show that dynamic versioning can improve the performance in concurrent environment significantly.
Algorithm for Discovering Multivalued Dependencies
Authors: Men Hin Yan, Ada Wai-Chee Fu
Venue: CIKM
Year: 2001

INTRODUCTION While extracting functional dependencies has received considerable attention [6, 3, 4, 5], relatively less research effort has been put in finding multivalued dependencies. Based on the previously proposed techniques of discovering functional dependencies [3], we propose a new algorithm for finding multivalued dependencies from a large database. We assume the usual interpretation of a relation (or table) in the relational database model where no duplicate tuples are allowed. The definition of multivalued dependency is given as below: Let R be a relation schema and let X = X1 ; X2 ; :::; Xn be a subset of R , let Y = Y1 ; Y2 ; :::; Yn be a subset of R and let Z = R \\Gamma Y \\Gamma X. The multivalued dependency&lt;
Scaling Replica Maintenance In Intermittently Synchronized Mobile Databases
Authors: Wai Gen Yee, Edward Omiecinski, Michael J. Donahoo, Shamkant B. Navathe
Venue: CIKM
Year: 2001

To avoid the high cost of continuous connectivity, a class of mobile applications employs replicas of shared data that are periodically updated. Updates to these replicas are typically performed on a client-by-client basis--that is, the server individually computes and transmits updates to each client--limiting scalability. By basing updates on replica groups (instead of clients), however, update generation complexity is no longer bound by client population size. Clients then download updates of pertinent groups. Proper group design reduces redundancies in server processing, disk usage and bandwidth usage, and dimininishes the tie between the complexity of updating replicas and the size of the client population. In this paper, we expand on previous work done on group design, include a detailed I/O cost model for update generation, and propose a heuristic-based greedy algorithm for group computation. Experimental results with an adapted commercial replication system demonstrate a significant increase in overall scalability over the client-centric approach.
Selecting Relevant Instances for Efficient and Accurate Collaborative Filtering
Authors: Kai Yu, Xiaowei Xu, Martin Ester, Hans-Peter Kriegel
Venue: CIKM
Year: 2001

Collaborative filtering uses a database about consumers' preferences to make personal product recommendations and is achieving widespread success in both E-Commerce and Information Filtering Applications nowadays. However, the traditional collaborative filtering algorithms do not scale well to the ever-growing number of consumers. The quality of the recommendation also needs to be improved in order to gain more trust from the consumers. In this paper, we present a novel method to improve the scalability and the accuracy of the collaborative filtering algorithm. We introduce an information theoretic approach to measure the relevance of a consumer (instance) for predicting the preference for the given product (target concept). The proposed method reduces the training data set by selecting only highly relevant instances. Our experimental evaluation on the well-known EachMovie data set shows that our method doesn't only significantly speed up the prediction, but also results in a better accuracy.
Using LSI for Text Classification in the Presence of Background Text
Authors: Sarah Zelikovitz, Haym Hirsh
Venue: CIKM
Year: 2001

This paper presents work that uses Latent Semantic Indexing (LSI) for text classification. However, in addition to relying on labeled training data, we improve classification accuracy by also using unlabeled data and other forms of available "background" text in the classification process. Rather than performing LSI's singular value decomposition (SVD) process solely on the training data, we instead use an expanded term-by-document matrix that includes both the labeled data as well as any available and relevant background text. We report the performance of this approach on data sets both with and without the inclusion of the background text, and compare our work to other efforts that can incorporate unlabeled data and other background text in the classification process.
Model-based Feedback in the Language Modeling Approach to Information Retrieval
Authors: ChengXiang Zhai, John D. Lafferty
Venue: CIKM
Year: 2001

The language modeling approach to retrieval has been shown to perform well empirically. One advantage of this new approach is its statistical foundations. However, feedback, as one important component in a retrieval system, has only been dealt with heuristically in this new retrieval approach: the original query is usually literally expanded by adding additional terms to it. Such expansion-based feedback creates an inconsistent interpretation of the original and the expanded query. In this paper, we present a more principled approach to feedback in the language modeling approach. Specifically, we treat feedback as updating the query language model based on the extra evidence carried by the feedback documents. Such a model-based feedback strategy easily fits into an extension of the language modeling approach. We propose and evaluate two different approaches to updating a query language model based on feedback documents, one based on a generative probabilistic model of feedback documents and one based on minimization of the KL-divergence over feedback documents. Experiment results show that both approaches are effective and outperform the Rocchio feedback approach.
Self-Managing Technology in IBM DB2 Universal Database
Authors: Daniel C. Zilio, Sam Lightstone, Kelly A. Lyons, Guy M. Lohman
Venue: CIKM
Year: 2001

As the cost of both hardware and software falls due to technological advancements and economies of scale, the cost of ownership for database applications is increasingly dominated by the cost of people to manage them. Databases are growing rapidly in scale and complexity, while skilled database administrators (DBAs) are becoming rarer and more expensive. The scope of responsibility of DBAs is indeed daunting. This paper describes the self-managing technology in IBM DB2 Universal Database to illustrate how self-managing technology can enhance the usability of enterprise middleware and reduce the total cost of ownership (TCO).
A system for knowledge management in bioinformatics
Authors: Sudeshna Adak, Vishal S. Batra, Deo N. Bhardwaj, Pasumarti V. Kamesam, Pankaj Kankar, Manish P. Kurhekar, Biplav Srivastava
Venue: CIKM
Year: 2002

The emerging biochip technology has made it possible to simultaneously study expression (activity level) of thousands of genes or proteins in a single experiment in the laboratory. However, in order to extract relevant biological knowledge from the biochip experimental data, it is critical not only to analyze the experimental data, but also to cross-reference and correlate these large volumes of data with information available in external biological databases accessible online. We address this problem in a comprehensive system for knowledge management in bioinformatics called e2e. To the biologist or biological applications, e2e exposes a common semantic view of inter-relationship among biological concepts in the form of an XML representation called eXpressML, while internally, it can use any data integration solution to retrieve data and return results corresponding to the semantic view. We have implemented an e2e prototype that enables a biologist to analyze her gene expression data in GEML or from a public site like Stanford, and discover knowledge through operations like querying on relevant annotated data represented in eXpressML using pathways data from KEGG, publication data from Medline and protein data from SWISS-PROT.
Multi-level operator combination in XML query processing
Authors: Shurug Al-Khalifa, H. V. Jagadish
Venue: CIKM
Year: 2002

A core set of efficient access methods is central to the development of any database system. In the context of an XML database, there has been considerable effort devoted to defining a good set of primitive operators and inventing efficient access methods for each individual operator. These primitive operators have been defined either at the macro-level (using a "pattern tree" to specify a selection, for example) or at the micro-level (using multiple explicit containment joins to instantiate a single XPath expression).In this paper we argue that it is valuable to consider operations at each level. We do this through a study of operator merging: the development of a new access method to implement a combination of two or more primitive operators. It is frequently the case that access methods for merged operators are superior to a pipelined execution of separate access methods for each operator. We show operator merging to be valuable at both the micro-level and the macro-level. Furthermore, we show that the corresponding merged operators are hard to reason with at the other level.Specifically, we consider the influence of projections and set operations on pattern-based selections and containment joins. We show, through both analysis and extensive experimentation, the benefits of considering these operations all together. Even though our experimental verification is only with a native XML database, we have reason to believe that our results apply equally to RDBMS-based XML query engines.
Logical and physical support for heterogeneous data
Authors: Sihem Amer-Yahia, Mary F. Fernández, Rick Greer, Divesh Srivastava
Venue: CIKM
Year: 2002

Heterogeneity arises naturally in virtually all real-world data. This paper presents evolutionary extensions to a relational database system for supporting three classes of data heterogeneity: variational, structural and annotational heterogeneities. We define these classes and show the impact of these new features on data storage, data-access mechanisms, and the data-description language. Since XML is an important source of heterogeneity, we describe how the system automatically utilizes these new features when storing XML documents.
A self-managing data cache for edge-of-network web applications
Authors: Khalil Amiri, Sanghyun Park, Renu Tewari
Venue: CIKM
Year: 2002

Database caching at proxy servers enables dynamic content to be generated at the edge of the network, thereby improving the scalability and response time of web applications. The scale of deployment of edge servers coupled with the rising costs of their administration demand that such caching middleware be adaptive and self-managing. To achieve this, a cache must be dynamically populated and pruned based on the application query stream and access pattern. In this paper, we describe such a cache which maintains a large number of materialized views of previous query results. Cached "views" share physical storage to avoid redundancy, and are usually added and evicted dynamically to adapt to the current workload and to available resources. These two properties of large scale (large number of cached views) and overlapping storage introduce several challenges to query matching and storage management which are not addressed by traditional approaches. In this paper, we describe an edge data cache architecture with a flexible query matching algorithm and a novel storage management policy which work well in such an environment. We perform an evaluation of a prototype of such an architecture using the TPC-W benchmark and find that it reduces query response times by up to 75%, while reducing network and server load.
Semantic technology applications for homeland security
Authors: David Avant, M. Baum, Clemens Bertram, M. Fisher, Amit P. Sheth, Yashodhan S. Warke
Venue: CIKM
Year: 2002

Semantic Content Organization and Retrieval Engine (SCORE) is among the earliest commercialized Semantic Web technologies. Based on supporting and exploiting domain specific ontologies, it offers advanced capability in heterogeneous content processing analysis, and integration at a higher semantic level-- rather than merely syntactical and structural level approaches based on XML and RDF. These capabilities are now being demonstrated in addressing requirements of very demanding Homeland Security and National Security applications. This paper briefly describes two of them.
COOLCAT: an entropy-based algorithm for categorical clustering
Authors: Daniel Barbará, Yi Li, Julia Couto
Venue: CIKM
Year: 2002

In this paper we explore the connection between clustering categorical data and entropy: clusters of similar poi lower entropy than those of dissimilar ones. We use this connection to design an incremental heuristic algorithm, COOLCAT, which is capable of efficiently clustering large data sets of records with categorical attributes, and data streams. In contrast with other categorical clustering algorithms published in the past, COOLCAT's clustering results are very stable for different sample sizes and parameter settings. Also, the criteria for clustering is a very intuitive one, since it is deeply rooted on the well-known notion of entropy. Most importantly, COOLCAT is well equipped to deal with clustering of data streams(continuously arriving streams of data point) since it is an incremental algorithm capable of clustering new points without having to look at every point that has been clustered so far. We demonstrate the efficiency and scalability of COOLCAT by a series of experiments on real and synthetic data sets.
Using specification-driven concepts for distributed data management and dissemination
Authors: M. Brian Blake
Venue: CIKM
Year: 2002

At the MITRE Corporation-Center for Advanced Aviation System Development (CAASD), software engineers work closely with both analyst and domain experts to develop software simulations in the air traffic management domain. In this environment, software simulations are applications that take large amounts of real-world operational information, and through calculations, derivations, and display extends the original information to produce some new insight into the domain. This new insight or knowledge typically comes in the form of a pertinent set of data. Based on this set of information other research groups can further extend this knowledge. The challenge in this environment is a distributed data management system that will allow a distributed set of researchers to share their extended knowledge. This paper presents the motivation and design of such an architecture to support this collaborative knowledge/data sharing environment. This run-time configurable architecture is implemented using web-based technologies such as the Extensible Markup Language (XML), Java Servlets, Extensible Stylesheets (XSL), and a relational database management system (RDBMS).
Topic-based document segmentation with probabilistic latent semantic analysis
Authors: Thorsten Brants, Francine Chen, Ioannis Tsochantaridis
Venue: CIKM
Year: 2002

This paper presents a new method for topic-based document segmentation, i.e., the identification of boundaries between parts of a document that bear on different topics. The method combines the use of the Probabilistic Latent Semantic Analysis (PLSA) model with the method of selecting segmentation points based on the similarity values between pairs of adjacent blocks. The use of PLSA allows for a better representation of sparse information in a text block, such as a sentence or a sequence of sentences. Furthermore, segmentation performance is improved by combining different instantiations of the same model, either using different random initializations or different numbers of latent classes. Results on commonly available data sets are significantly better than those of other state-of-the-art systems.
Inferring query models by computing information flow
Authors: Peter Bruza, Dawei Song
Venue: CIKM
Year: 2002

The language modelling approach to information retrieval can also be used to compute query models. A query model can be envisaged as an expansion of an initial query. The more prominent query models in the literature have a probabilistic basis. This paper introduces an alternative, non-probabilistic approach to query modelling whereby the strength of information flow is computed between a query Q and a term w. Information flow is a reflection of how strongly w is informationally contained within the query Q. The information flow model is based on Hyperspace Analogue to Language (HAL) vector representations, which reflects the lexical co-occurrence information of terms. Research from cognitive science has demonstrated the cognitive compatibility of HAL representations with human processing. Query models computed from TREC queries by HAL-based information flow are compared experimentally with two probabilistic query language models. Experimental results are provided showing the HAL-based information flow model be superior to query models computed via Markov chains, and seems to be as effective as a probabilistically motivated relevance model.
Web-DL: an experience in building digital libraries from the web
Authors: Pável Calado, Altigran Soares da Silva, Berthier A. Ribeiro-Neto, Alberto H. F. Laender, Juliano Palmieri Lage, Davi de Castro Reis, Pablo A. Roberto, Monique V. Vieira, Marcos André Gonçalves, Edward A. Fox
Venue: CIKM
Year: 2002

The Web contains a huge volume of information, almost all unstructured and, therefore, difficult to manage. In Digital Libraries, however, information is explicitly organized, described, and managed. In this paper, we propose an architecture that allows the construction of digital libraries from the Web, using standard protocols and archival technologies, and incorporating powerful digital library and data extraction tools, thus benefiting from the breadth of the Web contents, but supporting services and organization available in digital libraries. The proposed architecture was applied to the Networked Digital Library of Theses and Dissertations, providing an important first step toward rapid construction of large DLs from the Web, as well as a large-scale solution for interoperability between independent digital libraries.