OpenRaiser/Intern-Atlas huggingface dataset:
Symbolic photograph content-based retrieval
Authors: Philippe Mulhem, Joo-Hwee Lim
Venue: CIKM
Year: 2002

Photograph retrieval systems face the difficulty to deal with the different ways to apprehend the content of images. We consider and demonstrate here the use of multiple index representations of photographs to achieve effective retrieval. The use of multiple indexes allows integration of the complementary strengths of different indexing and retrieval models. The proposed representation supports multiple labels for regions and attributes, and handles inferences and relationships. We define links between indexing levels and the related query modes. The experiment conducted on 2400 home photographs shows the behavior of the multiple indexing levels during retrieval.
Mining soft-matching association rules
Authors: Un Yong Nahm, Raymond J. Mooney
Venue: CIKM
Year: 2002

Variation and noise in database entries can prevent data mining algorithms, such as association rule mining, from discovering important regularities. In particular, textual fields can exhibit variation due to typographical errors, mispellings, abbreviations, etc.. By allowing partial or "soft matching" of items based on a similarity metric such as edit-distance or cosine similarity, additional important patterns can be detected. This paper introduces an algorithm, SoftApriori that discovers soft-matching association rules given a user-supplied similarity metric for each field. Experimental results on several "noisy" datasets extracted from text demonstrate that SoftApriori discovers additional relationships that more accurately reflect regularities in the data.
Capturing term dependencies using a language model based on sentence trees
Authors: Ramesh Nallapati, James Allan
Venue: CIKM
Year: 2002

We describe a new probabilistic Sentence Tree Language Modeling approach that captures term dependency patterns in Topic Detection and Tracking's (TDT) Story Link Detection task. New features of the approach include modeling the syntactic structure of sentences in documents by a sentence-bin approach and a computationally efficient algorithm for capturing the most significant sentence-level term dependencies using a Maximum Spanning Tree approach, similar to Van Rijsbergen's modeling of document-level term dependencies.The new model is a good discriminator of on-topic and off-topic story pairs providing evidence that sentence-level term dependencies contain significant information about relevance. Although runs on a subset of the TDT2 corpus show that the model is outperformed by the unigram language model, a mixture of the unigram and the Sentence Tree models is shown to improve on the best performance especially in the regions of low false alarms.
FREM: fast and robust EM clustering for large data sets
Authors: Carlos Ordonez, Edward Omiecinski
Venue: CIKM
Year: 2002

Clustering is a fundamental Data Mining technique. This article presents an improved EM algorithm to cluster large data sets having high dimensionality, noise and zero variance problems. The algorithm incorporates improvements to increase the quality of solutions and speed. In general the algorithm can find a good clustering solution in 3 scans over the data set. Alternatively, it can be run until it converges. The algorithm has a few parameters that are easy to set and have defaults for most cases. The proposed algorithm is compared against the standard EM algorithm and the On-Line EM algorithm.
"GeoPlot": spatial data mining on video libraries
Authors: Jia-Yu Pan, Christos Faloutsos
Venue: CIKM
Year: 2002

Are "tornado" touchdowns related to "earthquakes"? How about to "floods", or to "hurricanes"? In Informedia [14], using a gazetteer on news video clips, we map news onto points on the globe and find correlations between sets of points. In this paper we show how to find answers to such questions, and how to look for patterns on the geo-spatial relationships of news events. The proposed tool is "GeoPlot", which is fast to compute and gives a lot of useful information which traditional text retrieval can not find.We describe our experiments on 2-year worth of video data (~ 20 Gbytes). There we found that GeoPlot can find unexpected correlations that text retrieval would never find, such as those between "earthquake" and "volcano", and "tourism" and "wine".In addition, GeoPlot provides a good visualization of a data set's characteristics. Characteristics at all scales are shown in one plot and a wealth of information is given, for example, geo-spatial clusters, characteristic scales, and intrinsic (fractal) dimensions of the events' locations.
Ginga: a self-adaptive query processing system
Authors: Henrique Paques, Ling Liu, Calton Pu
Venue: CIKM
Year: 2002

Article Share on Ginga: a self-adaptive query processing system Authors: Henrique Paques Georgia Institute of Technology Georgia Institute of TechnologyView Profile , Ling Liu Georgia Institute of Technology Georgia Institute of TechnologyView Profile , Calton Pu Georgia Institute of Technology Georgia Institute of TechnologyView Profile Authors Info & Claims CIKM '02: Proceedings of the eleventh international conference on Information and knowledge managementNovember 2002 Pages 655–658https://doi.org/10.1145/584792.584910Online:04 November 2002Publication History 3citation398DownloadsMetricsTotal Citations3Total Downloads398Last 12 Months3Last 6 weeks0 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 Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Mining sequential patterns with constraints in large databases
Authors: Jian Pei, Jiawei Han, Wei Wang
Venue: CIKM
Year: 2002

Constraints are essential for many sequential pattern mining applications. However, there is no systematic study on constraint-based sequential pattern mining. In this paper, we investigate this issue and point out that the framework developed for constrained frequent-pattern mining does not fit our missions well. An extended framework is developed based on a sequential pattern growth methodology. Our study shows that constraints can be effectively and efficiently pushed deep into sequential pattern mining under this new framework. Moreover, this framework can be extended to constraint-based structured pattern mining as well.
Harmonic models for polyphonic music retrieval
Authors: Jeremy Pickens, Tim Crawford
Venue: CIKM
Year: 2002

Most work in the ad hoc music retrieval field has focused on the retrieval of monophonic documents using monophonic queries. Polyphony adds considerably more complexity. We present a method by which polyphonic music documents may be retrieved by polyphonic music queries. A new harmonic description technique is given, wherein the information from all chords, rather than the most significant chord, is used. This description is then combined in a new and unique way with Markov statistical methods to create models of both documents and queries. Document models are compared to query models and then ranked by score. Though test collections for music are currently scarce, we give the first known recall-precision graphs for polyphonic music retrieval, and results are favorable.
RHist: adaptive summarization over continuous data streams
Authors: Lin Qiao, Divyakant Agrawal, Amr El Abbadi
Venue: CIKM
Year: 2002

Maintaining approximate aggregates and summaries over data streams is crucial to handle the OLAP query workload that arises in applications, such as network monitoring and telecommunications. Furthermore, since the entire data is not available at all times the maintenance task must be done incrementally. We show that R(elaxed)Hist(ogram) is an appropriate summarization under data stream scenario. In order to reduce query estimation errors, we propose adaptive approaches which not only capture the data distribution, but also integrate independent query patterns. We introduce a workload decay model to efficiently capture global workload information and ensure that the query patterns from the recent past are weighted more than queries that are further in the past. We verify experimentally that our approach successfully adapts to continuously changing workload as well as data streams.
High-performing feature selection for text classification
Authors: Monica Rogati, Yiming Yang
Venue: CIKM
Year: 2002

This paper reports a controlled study on a large number of filter feature selection methods for text classification. Over 100 variants of five major feature selection criteria were examined using four well-known classification algorithms: a Naive Bayesian (NB) approach, a Rocchio-style classifier, a k-nearest neighbor (kNN) method and a Support Vector Machine (SVM) system. Two benchmark collections were chosen as the testbeds: Reuters-21578 and small portion of Reuters Corpus Version 1 (RCV1), making the new results comparable to published results. We found that feature selection methods based on chi2 statistics consistently outperformed those based on other criteria (including information gain) for all four classifiers and both data collections, and that a further increase in performance was obtained by combining uncorrelated and high-performing feature selection methods.The results we obtained using only 3% of the available features are among the best reported, including results obtained with the full feature set.
Structural extraction from visual layout of documents
Authors: Binyamin Rosenfeld, Ronen Feldman, Yonatan Aumann
Venue: CIKM
Year: 2002

Most information extraction systems focus on the textual content of the documents. They treat documents as sequences or of words, disregarding the physical and typographical layout of the information.. While this strategy helps in focusing the extraction process on the key semantic content of the document, much valuable information can also be derived form the document physical appearance. Often, fonts, physical positioning and other graphical characteristics are used to provide additional context to the information. This information is lost with pure-text analysis. In this paper we describe a general procedure for structural extraction, which allows for automatic extraction of entities from the document based on their visual characteristics and relative position in the document layout. Our structural extraction procedure is a learning algorithm, which knows how to automatically generalizes from examples. The procedure is a general one, applicable to any document format with visual and typographical information. We also then describe a specific implementation of the procedure to PDF documents, called PES (PDF Extraction System). PES works with PDF documents and is able to extract such fields such as Author(s), Title, Date, etc. with very high accuracy.
Meta-recommendation systems: user-controlled integration of diverse recommendations
Authors: J. Ben Schafer, Joseph A. Konstan, John Riedl
Venue: CIKM
Year: 2002

In a world where the number of choices can be overwhelming, recommender systems help users find and evaluate items of interest. They do so by connecting users with information regarding the content of recommended items or the opinions of other individuals. Such systems have become powerful tools in domains such as electronic commerce, digital libraries, and knowledge management. In this paper, we address such systems and introduce a new class of recommender system called meta-recommenders. Meta-recommenders provide users with personalized control over the generation of a single recommendation list formed from a combination of rich data using multiple information sources and recommendation techniques. We discuss experiments conducted to aid in the design of interfaces for a meta-recommender in the domain of movies. We demonstrate that meta-recommendations fill a gap in the current design of recommender systems. Finally, we consider the challenges of building real-world, usable meta-recommenders across a variety of domains.
Query association for effective retrieval
Authors: Falk Scholer, Hugh E. Williams
Venue: CIKM
Year: 2002

We introduce a novel technique for document summarisation which we call query association. Query association is based on the notion that a query that is highly similar to a document is a good descriptor of that document. For example, the user query "richmond football club" is likely to be a good summary of the content of a document that is ranked highly in response to the query. We describe this process of defining, maintaining, and presenting the relationship between a user query and the documents that are retrieved in response to that query. We show that associated queries are an excellent technique for describing a document: for relevance judgement, associated queries are as effective as a simple online query-biased summarisation technique. As future work, we suggest additional uses for query association including relevance feedback and query expansion.
Information retrieval on the semantic web
Authors: Urvi Shah, Timothy W. Finin, Anupam Joshi
Venue: CIKM
Year: 2002

We describe an approach to retrieval of documents that contain of both free text and semantically enriched markup. In particular, we present the design and implementation prototype of a framework in which both documents and queries can be marked up with statements in the DAML+OIL semantic web language. These statements provide both structured and semi-structured information about the documents and their content. We claim that indexing text and semantic markup together will significantly improve retrieval performance. Our approach allows inferencing to be done over this information at several points: when a document is indexed, when a query is processed and when query results are evaluated.
Semantic-based delivery of OLAP summary tables in wireless environments
Authors: Mohamed A. Sharaf, Panos K. Chrysanthis
Venue: CIKM
Year: 2002

With the rapid growth in mobile and wireless technologies and the availability, pervasiveness and cost effectiveness of wireless networks, mobile computers are quickly becoming the normal front-end devices for accessing enterprise data. In this paper, we are addressing the issue of efficient delivery of business decision support data in the form of summary tables to mobile clients equipped with OLAP front-end tools. Towards this, we propose a new on-demand scheduling algorithm, called SBS, that exploits both the derivation semantics among OLAP summary tables and the mobile clients' capabilities of executing simple SQL queries. It maximizes the aggregated data sharing between clients and reduces the broadcast length compared to the already existing techniques. The degree of aggregation can be tuned to control the tradeoff between access time and energy consumption. Further, the proposed scheme adapts well to different request rates, access patterns and data distributions. The algorithm effectiveness with respect to access time and power consumption is evaluated using simulation.
Similarity based retrieval from sequence databases using automata as queries
Authors: A. Prasad Sistla, Tao Hu, Vikas Chowdhry
Venue: CIKM
Year: 2002

Similarity based retrieval from sequence databases is of importance in many applications such as time-series, video and textual databases. In this paper, automata based formalisms are introduced for specifying queries over such databases. Various measures defining the distance of a database sequence from an automaton are defined. Efficient methods for similarity based retrieval are presented for each of the distance measures. These methods answer nearest neighbor queries (i.e. retrieval of k closest subsequences), or range queries (i.e., retrieval of all sequences with in a given distance).
Interactive methods for taxonomy editing and validation
Authors: W. Scott Spangler, Jeffrey T. Kreulen
Venue: CIKM
Year: 2002

Taxonomies are meaningful hierarchical categorizations of documents into topics reflecting the natural relationships between the documents and their business objectives. Improving the quality of these taxonomies and reducing the overall cost required to create them is an important area of research. Supervised and unsupervised text clustering are important technologies that comprise only a part of a complete solution. However, there exists a great need for the ability for a human to efficiently interact with a taxonomy during the editing and validation phase. We have developed a comprehensive approach to solving this problem, and implemented this approach in a software tool called eClassifier. eClassifier provides features to help the taxonomy editor understand and evaluate each category of a taxonomy and visualize the relationships between the categories. Multiple techniques allow the user to make changes at both the category and document level. Metrics then establish how well the resultant taxonomy can be modeled for future document classification. In this paper, we present a comprehensive set of viewing, editing and validation techniques we have implemented in the Lotus Discovery Server resulting in a significant reduction in the time required to create a quality taxonomy.
A compact and efficient image retrieval approach based on border/interior pixel classification
Authors: Renato O. Stehling, Mario A. Nascimento, Alexandre X. Falcão
Venue: CIKM
Year: 2002

This paper presents \bic (Border/Interior pixel Classification), a compact and efficient CBIR approach suitable for broad image domains. It has three main components: (1) a simple and powerful image analysis algorithm that classifies image pixels as either border or interior, (2) a new logarithmic distance (dLog) for comparing histograms, and (3) a compact representation for the visual features extracted from images. Experimental results show that the BIC approach is consistently more compact, more efficient and more effective than state-of-the-art CBIR approaches based on sophisticated image analysis algorithms and complex distance functions. It was also observed that the dLog distance function has two main advantages over vectorial distances (e.g., L1): (1) it is able to increase substantially the effectiveness of (several) histogram-based CBIR approaches and, at the same time, (2) it reduces by 50% the space requirement to represent a histogram.
A fast filtering scheme for large database cleansing
Authors: Sam Yuan Sung, Zhao Li, Sun Peng
Venue: CIKM
Year: 2002

Existing data cleansing methods are costly and will take very long time to cleanse large databases. Since large databases are common nowadays, it is necessary to reduce the cleansing time. Data cleansing consists of two main components, detection method and comparison method. In this paper, we first propose a simple and fast comparison method, TI-Similarity, which reduces the time for each comparison. Based on TI-Similarity, we propose a new detection method, RAR, to further reduce the number of comparisons. With RAR and TI-Similarity, our new approach for cleansing large databases is composed of two processes: Filtering process and Pruning process. In filtering process, a fast scan on the database is carried out with RAR and TI-Similarity. This process guarantees the detection of potential duplicate records but may introduce false positives. In pruning process, the duplicate result from the filtering process is pruned to eliminate the false positives using more trustworthy comparison methods. The performance study shows that our approach is efficient and scalable for cleansing large databases, and is about an order of magnitude faster than existing cleansing methods.
An iterative strategy for pattern discovery in high-dimensional data sets
Authors: Chun Tang, Aidong Zhang
Venue: CIKM
Year: 2002

High-dimensional data representation in which each data item (termed target object) is described by many features, is a necessary component of many applications. For example, in DNA microarrays, each sample (target object) is represented by thousands of genes as features. Pattern discovery of target objects presents interesting but also very challenging problems. The data sets are typically not task-specific, many features are irrelevant or redundant and should be pruned out or filtered for the purpose of classifying target objects to find empirical pattern. Uncertainty about which features are relevant makes it difficult to construct an informative feature space. This paper proposes an iterative strategy for pattern discovery in high-dimensional data sets. In this approach, the iterative process consists of two interactive components: discovering patterns within target objects and pruning irrelevant features. The performance of the proposed method with various real data sets is also illustrated.
Vulnerabilities in similarity search based systems
Authors: Ali Saman Tosun, Hakan Ferhatosmanoglu
Venue: CIKM
Year: 2002

Similarity based queries are common in several modern database applications, such as multimedia, scientific, and biomedical databases. In most of these systems, database responds with the tuple with the closest match according to some metric. In this paper we investigate some important security issues related to similarity search in databases. We investigate the vulnerability of such systems against users who try to copy the database by sending automated queries. We analyze two models for similarity search, namely reply model and score model. Reply model responds with the tuple with best match and score model responds with only the score of similarity search. For these models we analyze possible ways of attacks and strategies that can be used to detect attacks. Our analysis shows that in score model it is much easier to plug the vulnerabilities than in reply model. Sophisticated attacks can easily be used in reply model and the database is limited in capability to prevent such attacks.
How to improve the pruning ability of dynamic metric access methods
Authors: Caetano Traina Jr., Agma J. M. Traina, Roberto F. Santos Filho, Christos Faloutsos
Venue: CIKM
Year: 2002

Complex data retrieval is accelerated using index structures, which organize the data in order to prune comparisons between data during queries. In metric spaces, comparison operations can be specially expensive, so the pruning ability of indexing methods turns out to be specially meaningful. This paper shows how to measure the pruning power of metric access methods, and defines a new measurement, called "prunability," which indicates how well a pruning technique carries out the task of cutting down distance calculations at each tree level. It also presents a new dynamic access method, aiming to minimize the number of distance calculations required to answer similarity queries. We show that this novel structure is up to 3 times faster and requires less than 25% distance calculations to answer similarity queries, as compared to existing methods. This gain in performance is achieved by taking advantage of a set of global representatives. Although our technique uses multiple representatives, the index structure still remains dynamic and balanced.