X-warehouse: building query pattern-driven data
In this paper, we propose an approach to materialize XML data warehouses based on the frequent query patterns discovered from historical queries issued by users. The schemas of integrated XML documents in the warehouse are built using these frequent query patterns represented as Frequent Query Pattern Trees (FreqQPTs). Using hierarchical clustering technique, FreqQPTs are clustered and merged to produce a specified number of integrated XML documents for actual data feeding. Maintenance issue of the data warehouse is also treated in this paper.

title: PC-Filter: A Robust Filtering Technique for Duplicate Record Detection in Large Databases
abstract: In this paper, we will propose PC-Filter (PC stands for Partitio n Comparison), a robust data filter for approximately duplicate record detection in large databases. PC-Filter distinguishes itself from all of existing methods by using the notion of partition in duplicate detection. It first sorts the whole database and splits the sorted database into a number of record partitions. The Partition Comparison Graph (PCG) is then constructed by performing fast partition pruning. Finally, duplicate records are effectively detected by using internal and external partition comparison based on PCG. Four properties, used as heuristics, have been devised to achieve a remarkable efficiency of the filter based on triangle inequity of record similarity. PC-Filter is insensitive to the key used to sort the database, and can achieve a very good recall level that is comparable to that of the pair-wise record comparison method but only with a complexity of O(N 4/3). Equipping existing detection methods with PC-Filter, we are able to well solve the ”Key Selection” problem, the ”Scope Specification” problem and the ”Low Recall” problem that the existing methods suffer from.

title: Gene Specific Co-regulation Discovery: An Improved Approach
abstract: Discovering gene co-regulatory relationships is a new but important research problem in DNA microarray data analysis. The problem of gene specific co-regulation discovery is to, for a particular gene of interest, called the target gene , identify its strongly co-regulated genes and the condition subsets where such strong gene co-regulations are observed. The study on this problem can contribute to a better understanding and characterization of the target gene. The existing method, using the genetic algorithm (GA), is slow due to its expensive fitness evaluation and long individual representation. In this paper, we propose an improved method for finding gene specific co-regulations. Compared with the current method, our method features a notably improved efficiency. We employ k NN Search Table to substantially speed up fitness evaluation in the GA. We also propose a more compact representation scheme for encoding individuals in the GA, which contributes to faster crossover and mutation operations. Experimental results with a real-life gene microarray data set demonstrate the improved efficiency of our technique compared with the current method.

title: A Color Indexing Scheme Using Two-level Clustering Processing for Effective and Efficient Image Retrieval
abstract: In this paper, we present a clustering-based color indexing scheme for effective amd efficient image retrieval, which is essentially an exploration on the application of clustering technique to image retrieval. In our approach, the color features are clustered automatically using a color clustering algorithm twice (called two-level clustering processing) and two color feature summarizations are obtained, e.g. Local Color Centriods (LCCs) and Global Color Centroids (GCCs). Based upon LCCs and GCCs, a three-level R-tree is bulit for indexing database images and performing effecitve and efficient image retrieval. The experiments show that this indexing scheme is effective and efficient in performing image retrieval.

title: XAR-miner: efficient association rules mining for XML data
abstract: In this paper, we propose a framework, called XAR-Miner, for mining ARs from XML documents efficiently. In XAR-Miner, raw data in the XML document are first preprocessed to transform to either an Indexed Content Tree (IX-tree) or Multi-relational databases (Multi-DB), depending on the size of XML document and memory constraint of the system, for efficient data selection and AR mining. Task-relevant concepts are generalized to produce generalized meta-patterns, based on which the large ARs that meet the support and confidence levels are generated.

title: Anomaly detection in high-dimensional network data streams: A case study
abstract: In this paper, we study the problem of anomaly detection in high-dimensional network streams. We have developed a new technique, called Stream Projected Outlier deTector (SPOT), to deal with the problem of anomaly detection from high-dimensional data streams. We conduct a case study of SPOT in this paper by deploying it on 1999 KDD Intrusion Detection application. Innovative approaches for training data generation, anomaly classification and false positive reduction are proposed in this paper as well. Experimental results demonstrate that SPOT is effective in detecting anomalies from network data streams and outperforms existing anomaly detection methods.

title: A Mixture Model for Expert Finding
abstract: This paper addresses the issue of identifying persons with expertise knowledge on a given topic. Traditional methods usually estimate the relevance between the query and the support documents of candidate experts using, for example, a language model. However, the language model lacks the ability of identifying semantic knowledge, thus results in some right experts cannot be found due to not occurrence of the query terms in the support documents. In this paper, we propose a mixture model based on Probabilistic Latent Semantic Analysis (PLSA) to estimate a hidden semantic theme layer between the terms and the support documents. The hidden themes are used to capture the semantic relevance between the query and the experts. We evaluate our mixture model in a real-world system, ArnetMiner. Experimental results indicate that the proposed model outperforms the language models.

title: Large area nanosize array stamp for UV-based nanoimprint lithography fabricated by size reduction process
abstract: A novel size reduction process using electron beam lithography (EBL) combining with wet etching technique is developed as a possible solution for producing large area and low cost nanopattern stamp for UV-based nanoimprint lithography (UV-NIL). In the first step, a microstructure stamp with 1.4@mm periodical pore array and aspect ratio of 1:1 was formed over a 1inch^2 area on a quartz substrate. This process was carried out using common electron beam lithography (EBL) equipment, which was easily available in the modern integrated circuits (IC) semiconductor factory. Afterwards, with a controlled wet etching technique, the pore array was changed into tip patterns with the line width below 100nm and the period keeping as before. The uniformities and nanopattern accuracies were investigated to identify its possibility as a UV-NIL stamp by AFM and SEM. Finally, as a demonstration, the as obtained stamp was used as a positive stamp to replicate the nanotips into UV-curable resist successfully by a UV-NIL process. The method developed for the mold of nanoimprint lithography would be a simple and low price approach to fabricate large area UV-NIL stamp and the nanotip array structures would be widely used in two dimensional (2D) photonic crystal application.

title: Topic-Level Random Walk through Probabilistic Model
abstract: In this paper, we study the problem of topic-level random walk, which concerns the random walk at the topic level. Previously, several related works such as topic sensitive page rank have been conducted. However, topics in these methods were predefined, which makes the methods inapplicable to different domains. In this paper, we propose a four-step approach for topic-level random walk. We employ a probabilistic topic model to automatically extract topics from documents. Then we perform the random walk at the topic level. We also propose an approach to model topics of the query and then combine the random walk ranking score with the relevance score based on the modeling results. Experimental results on a real-world data set show that our proposed approach can significantly outperform the baseline methods of using language model and that of using traditional PageRank.

title: Image Mining: Trends and Developments
abstract: Advances in image acquisition and storage technology have led to tremendous growth in very large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining.

title: Flexible Feature Processing Mechanism of Radar Target Signal
abstract: Extracting effective and reliable features is fundamental and critical for constructing target recognition systems. Flexible Feature Processing Mechanism emphasizes the integration of feature extraction arithmetic and the self-adaptation of feature selection. On one hand, it would try to dig up the valuable information on as many as possible aspects, so as to construct the feature extraction method sets. On the other hand, it emphasizes to select the distinct features and eliminate the redundant features. The series of feature processing methods have been applied in multiple radar recognition projects successfully. According to the observation and analysis on a large number of real-life data, and taking the thinking process and the description methods of radar operators for reference, a set of feature extraction and selection methods basing on visual effects for dynamic radar target echo series have been introduced. The effectiveness, robustness and extendibility have been proved by large amount of tests and applications.