Xiaoqin Xie

A search result ranking algorithm based on web pages and tags clustering

With the rapid development of network techniques, huge information resources glut the whole web world. Locating useful information effectively from the World Wide Web (WWW) is of wide interest. The huge volume of the return results makes the user only focus on the top results. So the ranking problem becomes the important task for the search systems. Now, so many ranking algorithms were proposed. But the existing search systems just use the relationships or links between user query and web pages in the traditional Web1.0 environment. Now, the success and popularity of social network systems, such as del.icio.us, Facebook, etc., have generated many interesting problems to the research community. This gives us a new viewpoint on how to improve the quality of information retrieval. This paper firstly summarizes the present ranking algorithms, and analyses their merits and demerits. Secondly, we present a new search ranking algorithm bases on web pages and tags clustering, and use several evaluating methods to assess and contrast with Google.

Extracting data records from web using suffix tree

There are many automatic methods that can extract lists of objects from the Web, but they often fail to handle multi-type pages automatically. This paper introduces a new method for record extraction using suffix tree which can find the repeated sub-string. Our method transfers a distinct group of tag paths appearing repeatedly in the DOM tree of the Web document to a sequence of integers firstly, and then builds a suffix tree by using this sequence. Four refining filter rules are defined. After the refining processes we can capture the useful data region patterns which can be used to extract data records. Experiments on real data show that this method is applicable for various web pages and can achieve higher accuracy and better robustness than previous methods.

An AHP-based evaluation model for service composition

In this paper we present the Analytic Hierarchy Process (AHP)-based composition evaluation model (ASCEM) for service composition, which provides quantitative and global evaluation for selecting composition plans. Quantitative factors and global computing formula are defined also. Thus ASCEM enables dynamic service composition. ASCEM features in following. Firstly, AHP enable a more objective weight-allocating for evaluation factors and the hierarchical model provides more scalability. Secondly, the evaluation parameters include not only the quality properties but also the properties of reasonability and granularity for business processes. Thirdly, this model gives a quantitative and global result. A file-workflow process example is taken to illustrate how to use ASCEM and the results prove that the model is feasible and correct.

A New Keywords Method to Improve Web Search

In order to let people be able to get information from the Web easily, search engine comes into being and continues to grow and develop. People begin to explore all kinds of ranking algorithms and try to give user a good result list. However, the expression format of the web information and user queries are very simple, which results in the difficulty of determining the relevance between user queries and web information. The success and popularity of social network systems, such as del.icio.us, Face book, etc., have generated many interesting problems to the research community. This gives us a new viewpoint on how to improve the quality of information retrieval. The contributions of our research are twofold. First, the existing ranking algorithms of search engine are classified. And we extend expression of queries by “keyword + tags&#8221, instead of keywords only. Second, a new ranking algorithm based on user feedback and semantic tags is proposed, and it is also compared with Google by several evaluation methods.

A Joint Link Prediction Method for Social Network

The popularity of social network services has caused the rapid growth of the users. To predict the links between users has been recognized as one of the key tasks in social network analysis. Most of the present link prediction methods either analyze the topology structure of social network graph or just concern the user’s interests. These will lead to the low accuracy of prediction. Furthermore, the large amount of user interest information increases the difficulties for common interest extraction. In order to solve the above problems, this paper proposes a joint social network link prediction method-JLPM. Firstly, we give the problem formulation. Secondly, we define a joint prediction feature model(JPFM) to describe user interest topic feature and network topology structure feature synthetically, and present corresponding feature extracting algorithm. JPFM uses the LDA topic model to extract user interest topics and uses a random walk algorithm to extract the network topology features. Thirdly, by transforming the link prediction problem to a classification problem, we use the typical SVM classifier to predict the possible links. Finally, experimental results on citation data set show the feasibility of our method.

A medical image retrieval method based on texture block coding tree

Abstract Content-based medical image retrieval (CBMIR) has been widely studied for computer aided diagnosis. Accurate and comprehensive retrieval results are effective to facilitate diagnosis and treatment. Texture is one of the most important features used in CBMIR. Most of existing methods utilize the distances between matching point pairs for texture similarity measurement. However, the distance based similarity measurements are of low tolerance to slight texture shifts, which result in an excessive sensitivity. Furthermore, with the increase of the number of texture points, their time complexity is in explosive growth. In this paper, a new medical image retrieval model is presented based on an iterative texture block coding tree. The corresponding methods for coarse-grained and fine-grained similarity matching are also proposed. Moreover, a multi-level index structure is designed to enhance the retrieval efficiency. Experimental results show that, our methods are of high efficiency and appropriate tolerance on slight shifts, and achieve a relative better retrieval performance in comparison of other existing methods.

Medical Image Clustering Algorithm Based on Graph Model

The algorithm of medical image is an important part of special field image clustering. There are many problems of technical aspects and the problem of specific area, so that the study of this direction is very challenging. The existing algorithm of clustering has requirement about shape and density of data object, and it cannot get a good result to the application of medical image clustering. In view of the above problem and under the guidance of knowledge of medical image, at first, detects texture from image, and T-LBP method is put forward. Then divides the preprocessed image into many spaces, and calculates LBP value of spaces. At last build spatial sequence LBP histogram. Based on the LBP histogram, the clustering method of MCST is proposed. The result of experiment shows that there are good result at time complexity and clustering result in the algorithm of this paper.

A Similarity Retrieval Method in Brain Image Sequence Database

Image mining is more than just an extension of data mining to image domain but an interdisciplinary endeavor. Very few people have systematically investigated this field. Similarity Retrieval in medical image sequence database is an important part in domain-specific application because there are several technical aspects which make this problem challenging. In this paper, we introduce a notion of image sequence similarity patterns (ISSP) for medical image database. These patterns are significant in medical images because the similarity for two medical images is not important, but rather, it is the similarity of objects each of which has an image sequence that is meaningful. We design the new algorithms with the guidance of the domain knowledge to discover the possible Space-Occupying Lesion (PSO) in brain images and ISSP for similarity retrieval. Our experiments demonstrate that the results of similarity retrieval are meaningful and interesting to medical doctors.

STEM: a suffix tree-based method for web data records extraction

To automatically extract data records from Web pages, the data record extraction algorithm is required to be robust and efficient. However, most of existing algorithms are not robust enough to cope with rich information or noisy data. In this paper, we propose a novel suffix tree-based extraction method (STEM) for this challenging task. First, we extract a sequence of identifiers from the tag paths of Web pages. Then, a suffix tree is built on top of this sequence and four refining filters are proposed to screen out data regions that might not contain data records. To evaluate model performance, we define an evaluation metric called pattern similarity and perform rigorous experiments on five real data sets. The promising experimental results have demonstrated that the proposed STEM is superior to the state-of-the-art algorithms like MDR, TPC and CTVS with respect to precision, recall and pattern similarity. Moreover, the time complexity of STEM is linear to the total number of HTML tags contained in Web pages, which indicates the potential applicability of STEM in a wide range of Web-scale data record extraction applications.

An Efficient Optimization Approach for Top-k Queries on Uncertain Data

Uncertain data is inherent in various important applications and Top-k query on uncertain data is an important query type for many applications. To tackle the performance issue of evaluating Top-k query on uncertain data, an efficient optimization approach was proposed in this paper. This method can anticipate the tuples most likely to become Top-k result based on dominant relationship analysis, greatly reducing the amount of data in query processing. When the database is updated, this method could determine whether the change affects the current query result, and help us to avoid unnecessary re-query. The experimental results prove the feasibility and effectiveness of this method.