35283448
No Tag, a Little Nesting, and Great XML Keyword Search
Keyword search from Informational Retrieval (IR) can be seen as one most convenient processing mode catering for common users to obtain interesting information. As XML data becomes more and more widespread, the trend of adapting keyword search on XML data also becomes more and more active. In this paper, we first try nesting mechanism for XML keyword search, which just uses a little nesting skill. This attempt has several benefits. For example, it is convenient for common users, because they need not to know any organization knowledge of the target XML data. Secondly, the nesting pattern can be easily transformed into structural hints, which has same mechanism as what XML data model does. Finally, since there is no need of label information, we can retrieve XML fragments from different schemas. Besides, this paper also proposes a new similarity measuring method for retrieved XML fragments which can be from different schemas. Its kernel is KCAM (Keyword Common Ancestor Matrix) structure, which stores the level information of SLCA (Smallest Lowest Common Ancestor) node between two keywords. By mapping XML fragments into KCAMs, the structural similarity can be computed using matrix distance. KCAM distance can go well with the nesting keyword method.
7609568
Mining Maximal Correlated Member Clusters in High Dimensional Database
Mining high dimensional data is an urgent problem of great practical importance. Although some data mining models such as frequent patterns and clusters have been proven to be very successful for analyzing very large data sets, they have some limitations. Frequent patterns are inadequate to describe the quantitative correlations among nominal members. Traditional cluster models ignore distances of some pairs of members, so a pair of members in one big cluster may be far away. As a combination and complementary of both techniques, we propose the Maximal-Correlated-Member-Cluster (MCMC) model in this paper. The MCMC model is based on a statistical measure reflecting the relationship of nominal variables, and every pair of members in one cluster satisfy unified constraints. Moreover, in order to improve algorithm's efficiency, we introduce pruning techniques to reduce the search space. In the first phase, a Tri-correlation inequation is used to eliminate unrelated member pairs, and in the second phase, an Inverse-Order-Enumeration-Tree (IOET) method is designed to share common computations. Experiments over both synthetic datasets and real life datasets are performed to examine our algorithm's performance. The results show that our algorithm has much higher efficiency than the naive algorithm, and this model can discover meaningful correlated patterns in high dimensional database.
28786670
A Non-VSM kNN Algorithm for Text Classification
The text classification problem, which is the task of assigning natural language texts to predefined categories based on their content, has been widely studied. Traditional text classification use VSM (Vector Space Model), which views documents as vectors in high dimensional spaces, to represent documents. In this paper, we propose a non-VSM kNN algorithm for text classification. Based on correlations between categories and features, the algorithms first get k F-C tuples, which are the first k tuples in term of correlation value, from an unlabeled document. Then the algorithm predicts the category of the unlabeled documents via these tuples. We have evaluated the algorithm on two document collections and compared it against traditional kNN. Experimental results show that our algorithm outperforms traditional kNN in both efficiency and effectivity.
43500111
An Efficient Text Categorization Algorithm Based on Category Memberships
Text Categorization is the process of automatically assigning predefined categories to free text documents. Although there have existed a large number of text classification algorithms, most of them are either inefficient or too complex. In this paper, we propose the concept of category memberships, which stand for the degrees that words belonging to categories. Based on category memberships, a simple but efficient algorithm is presented. To evaluate our new algorithm, we have conducted experiments using Newsgroup_18828 text collection to compare it with Naive Bayes and k-NN. Experimental results show that our algorithm outperforms Naive Bayes and k-NN if a suitable category membership function is adopted.
236427
Efficient XML query processing in mediators
With the advent of XML as the standard for data representation and exchange over the Internet, there are ever increasing applications; E-commerce application in particular, using XML as a mediator model. It raises the issue of how to evaluate XML queries efficiently in mediator systems. The authors address the problem of multiple query optimization in an XML based mediator. Because the XML query language differs from SQL/OQL in its pattern matching and regular path expression constructs, the method of identifying common subexpressions of multiple XML queries is different accordingly. We present the steps to identify common subexpressions. To obtain the global query plan, we propose some heuristics to reduce the whole search space. By using a statistical method to estimate the usage frequency of the common subexpressions in XML queries, a selective caching and materializing of remote data can be accurately made, and this will greatly improve query response time.
36655206
Understanding User Operations on Web Page in WISE
Unlike Internet portals, a typical Web Information System contains lots of user operations. However, existing Web design frameworks focus exclusively on data presentation: the process of user operations is still achieved through low-level programming. That makes WIS hard to be constructed or maintained. In a Web Information System auto-construction Environment (WISE), the translation of user operations to concrete data processing code in persistent data sources is explored. This paper discusses the semantics of typical operations. The implementation code of those operations can be auto-generated with the aid of the mapping from template attributes to persistent objects.
6514016
ERGDB: Estrogen Responsive Genes Database
ERGDB is an integrated knowledge database dedicated to genes responsive to estrogen. Genes included in ERGDB are those whose expression levels are experimentally proven to be either up-regulated or down-regulated by estrogen. Genes included are identified based on publications from the PubMed database and each record has been manually examined, evaluated and selected for inclusion by biologists. ERGDB aims to be a unified gateway to store, search, retrieve and update information about estrogen responsive genes. Each record contains links to relevant databases, such as GenBank, LocusLink, Refseq, PubMed and ATCC. The unique feature of ERGDB is that it contains information on the dependence of gene reactions on experimental conditions. In addition to basic information about the genes, information for each record includes gene functional description, experimental methods used, tissue or cell type, gene reaction, estrogen exposure time and the summary of putative estrogen response elements if the gene's promoter sequence was available. Through a web interface at http://sdmc.i2r.a-star.edu.sg/ergdb/ cgi-bin/explore.pl users can either browse or query ERGDB. Access is free for academic and non-profit users.
2552931
KBERG: KnowledgeBase for Estrogen Responsive Genes
Estrogen has a profound impact on human physiology affecting transcription of numerous genes. To decipher functional characteristics of estrogen responsive genes, we developed KnowledgeBase for Estrogen Responsive Genes (KBERG). Genes in KBERG were derived from Estrogen Responsive Gene Database (ERGDB) and were analyzed from multiple aspects. We explored the possible transcription regulation mechanism by capturing highly conserved promoter motifs across orthologous genes, using promoter regions that cover the range of [−1200, +500] relative to the transcription start sites. The motif detection is based on ab initio discovery of common cis-elements from the orthologous gene cluster from human, mouse and rat, thus reflecting a degree of promoter sequence preservation during evolution. The identified motifs are linked to transcription factor binding sites based on the TRANSFAC database. In addition, KBERG uses two established ontology systems, GO and eVOC, to associate genes with their function. Users may assess gene functionality through the description terms in GO. Alternatively, they can gain gene co-expression information through evidence from human EST libraries via eVOC. KBERG is a user-friendly system that provides links to other relevant resources such as ERGDB, UniGene, Entrez Gene, HomoloGene, GO, eVOC and GenBank, and thus offers a platform for functional exploration and potential annotation of genes responsive to estrogen. KBERG database can be accessed at .