6238577
Labeling Scheme and Structural Joins for Graph-Structured XML Data
When XML documents are modeled as graphs, many challenging research issues arise. In particular, query processing for graph-structured XML data brings new challenges because traditional structural join methods cannot be directly applied. In this paper, we propose a labeling scheme for graph-structured XML data. With this labeling scheme, the reachability relationship of two nodes can be judged efficiently without accessing other nodes. Based on this labeling scheme, we design efficient structural join algorithms to evaluate reachability queries. Experiments show that our algorithms have high efficiency and good scalability.
1993887
Locating Motifs in Time-Series Data
Finding motifs in time-series is proposed to make clustering of time-series subsequences meaningful, because most existing algorithms of clustering time-series subsequences are reported meaningless in recent studies. The existing motif finding algorithms emphasize the efficiency at the expense of quality, in terms of the number of time-series subsequences in a motif and the total number of motifs found. In this paper, we formalize the problem as a continuous top-k motif balls problem in an m-dimensional space, and propose heuristic approaches that can significantly improve the quality of motifs with reasonable overhead, as shown in our experimental studies.
17501980
Term Graph Model for Text Classification
Most existing text classification methods (and text mining methods at large) are based on representing the documents using the traditional vector space model. We argue that important information, such as the relationship among words, is lost. We propose a term graph model to represent not only the content of a document but also the relationship among the keywords. We demonstrate that the new model enables us to define new similarity functions, such as considering rank correlation based on PageRank-style algorithms, for the classification purpose. Our preliminary results show promising results of our new model.
4018890
Clustering Moving Objects for Spatio-temporal Selectivity Estimation
Many spatio-temporal applications involve managing and querying moving objects. In such an environment, predictive spatio-temporal queries become an important query class to be processed to capture the nature of moving objects. In this paper, we investigated the problem of selectivity estimation for predictive spatio-temporal queries. We propose a novel histogram technique based on a clustering paradigm. To avoid expensive computation costs, we developed linear time heuristics to construct such a histogram. Our performance study indicated that the new techniques improve the accuracy of the existing techniques by one order of magnitude.
14038708
Efficiently computing frequent tree-like topology patterns in a Web environment
Data mining has recently emerged in a number of real life applications, such as banking, supermarketing, and interneting. The authors investigate a novel data mining problem derived from the applications of WWW. They present an efficient algorithm for solving the problem. The initial implementation suggests that the algorithm is very efficient and scalable in practice.
6221918
Efficiently Matching Proximity Relationships in Spatial Databases
Spatial data mining recently emerges from a number of real applications, such as real-estate marketing, urban planning, weather forecasting, medical image analysis, road traffic accident analysis, etc. It demands for efficient solutions for many new, expensive, and complicated problems. In this paper, we investigate a proximity matching problem among clusters and features. The investigation involves proximity relationship measurement between clusters and features. We measure proximity in an average fashion to address possible nonuniform data distribution in a cluster. An efficient algorithm, for solving the problem, is proposed and evaluated. The algorithm applies a standard multistep paradigm in combining with novel lower and upper proximity bounds. The algorithm is implemented in several different modes. Our experiment results do not only give a comparison among them but also illustrate the efficiency of the algorithm.
18279251
Detection and analysis of table of contents based on content association
AbstractAs a special type of table understanding, the detection and analysis of tables of contents (TOCs) play an important role in the digitization of multi-page documents. Most previous TOC analysis methods only concentrate on the TOC itself without taking into account the other pages in the same document. Besides, they often require manual coding or at least machine learning of document-specific models. This paper introduces a new method to detect and analyze TOCs based on content association. It fully leverages the text information throughout the whole multi-page document and can be directly applied to a wide range of documents without the need to build or learn the models for individual documents. In addition, the associations of general text and page numbers are combined to make the TOC analysis more accurate. Natural language processing and layout analysis are integrated to improve the TOC functional tagging. The applications of the proposed method in a large-scale digital library project are also 
discussed.
2535403
Visualization for the document space
An information retrieval frame work that promotes graphical displays, and that will make documents in the computer visualizable to the searcher, is described. As examples of such graphical displays, two simulation results of using a Kohonen feature map to generate map displays for information retrieval are presented and discussed. The map displays are a mapping from a high-dimensional document space to a two-dimensional space. They show document relationships by various visual cues, such as dots, links, clusters, and areas, as well as their measurement and spatial arrangement. Using the map displays as an interface for document retrieval systems, the user is provided with richer visual information to support browsing and searching.<<ETX>>
41156376
Knowledge Extraction Based Multilayer Feedforward Neural Networks
8724659
Estimating device availability in pervasive peer-to-peer environment
In pervasive computing environments, devices often communicate in a peer-to-peer manner, either for passing messages or collaboratively running applications. For any particular device, information about the availability of other devices can lead to more efficient communication and better execution of distributed tasks. For example, a device can distribute computation tasks to reliable devices and temporarily avoid unreliable ones. Since there usually is not a central server to coordinate or monitor the communication in such environment, a key technical challenge is for each individual device to predict the availability of other devices. In this paper, we describe some methods for predicting the availability of other devices using historic availability data of those devices obtained in routine usage. In this scheme, each device separately maintains data about the past communication with the other devices, and predicts current and future availability using the statistics of those data. These methods do not require any centralized monitoring or extra probing, and thus have very low computation cost. These characteristics make them suitable for small devices in a peer-to-peer environment.
503545
Privacy-preserving clustering with distributed EM mixture modeling
Privacy and security considerations can prevent sharing of data, derailing data mining projects. Distributed knowledge discovery can alleviate this problem. We present a technique that uses EM mixture modeling to perform clustering on distributed data. This method controls data sharing, preventing disclosure of individual data items or any results that can be traced to an individual site.