8871501
Improving backward recovery in workflow systems
The notion of compensation is widely used as means of backward recovery in long-lived transactions as well as in business processes supported by workflow management systems. In general, it is non-trivial to design compensating tasks for tasks in the context of a workflow. Actually, a task does not have to be compensatable. In this paper, we first look into the requirements that a compensating task has to satisfy. Then we introduce a new mechanism called confirmation. With the help of confirmation, we can modify some non-compensatable tasks so that they become compensatable. This greatly improves backward recovery for workflow applications in the case of failures. To effectively incorporate confirmation and compensation into the workflow management environment, a three-level bottom-up workflow design method is introduced. The implementation issues of this design are also discussed.
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.
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.
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.
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.
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.
8597703
Evaluation and Application of Recognition Confidence in OCR
Recognition confidence plays an important role in the selection of rejection threshold and the combination of multiple classifiers. In this paper, we first present a systematic theory on classifier's confidence, which includes the definition, the concept of generalized confidence, optimal rejection theorem and the relationship between confidence value and recognition rate. Then we propose a method for the evaluation of recognition confidence. The theory and method are strongly supported by the practice in handwritten numeral recognition and off-line handwritten Chinese character recognition.
1977537
Active layout engine: Algorithms and applications in variable data printing
Variable Data Printing (VDP) refers to the process of generating and printing dynamic or personalized contents. A core technology required by highly customized VDP applications is the automatic document layout design engine, whose task is to adjust the original design or generate a new layout to present variable contents. This paper presents a novel document layout design engine, called Active Layout Engine (ALE). 'Active' reflects several unique features of the engine: first, through linear text block modeling and two-pass constraint solving algorithm, it supports a rich set of layout operations, such as simultaneous optimization of text block width and height, integrated image cropping, and non-rectangular text wrapping. Second, it does not rely on a particular layout description language and thus can actively pursue emerging formats and standards. This paper describes the various technical aspects of ALE: linearization of the text block modeling, two-pass constraint solving algorithm, format-neutral Active Layout Template (ALT), system optimization, and typical VDP applications around the core engine.
41156376
Knowledge Extraction Based Multilayer Feedforward Neural Networks
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.
15723189
Impact of imperfect OCR on part-of-speech tagging
Part-of-speech (POS) tagging is the foundation of natural language processing (NLP) systems, and thus has been an active area of research for many years. However, one question remains unanswered: How will a POS tagger behave when the input text is not error-free? This issue can be of great importance when the text comes from imperfect sources like optical character recognition (OCR). This paper analyzes the performance of both individual POS taggers and combination systems on imperfect text. Experimental results show that a POS tagger's accuracy decreases linearly with the character error rate and the slope indicates a tagger's sensitivity to input text errors.