35000642
WIEAS: Helping to Discover Web Information Sources and Extract Data from Them
In recent years, more and more information appeared on the web. Extracting information from the web and converting them into regular format become significantly important work. After observing a number of web sites, we found that most of useful information is contained in the web sources, which have a large number of similarly structured web documents. So in this paper we present an approach for discovering the useful information sources from the web and extracting information from them. A useful web information source discovering method and a novel information extraction method are proposed. We also develop a prototype system WIEAS (Web Information Extraction, Analysis And Services) to implement our idea, and use the information extracted by WIEAS to provide plentiful services.
27299411
Mining Models of Composite Web Services for Performance Analysis
Web service composition provides a way to build value-added services and web applications by integrating and composing existing web services. In this paper, a composite web service is modeled using queueing network for the purpose of performance analysis. Each component web service participating the composite web service corresponds to one service center. The control flow between component web services is represented by the Markov chain that describes the transition of customers between service centers. To perform performance analysis, the Markov chain should be known first. However, a web service is usually a black box and only its interfaces can be seen externally, so the internal control flow can be only estimated from history execution logs. This paper gives a method that mines the Markov chain of a composite web service from its execution logs. Then, bottlenecks identification and performance analysis are conducted for the queueing network model. Experimental results show that this model mining method is effective and efficient.
7452488
A Mixed Process Neural Network and its Application to Churn Prediction in Mobile Communications
Churn prediction is an increasingly pressing issue in today's ever-competitive commercial environments, especially in mobile communication arena. In this paper, a mixed process neural network (MPNN) based on Fourier orthogonal base function has been proposed to support churn management, which can deal with both static value and time-varied continuous value simultaneously. To further improve its performance, an optimized network, c-MPNN, has been presented, which adopts Fourier expansion based preprocessing and hidden layer combination techniques to optimize MPNN's structure. Most important of all, our method has been used in real applications in China Mobile. Experiments based on the real datasets also show that our proposed churn prediction method has good maneuverability and performance
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.
42968954
Association rules enhanced classification of underwater acoustic signal
The classification of underwater acoustic signals is one of the important fields of pattern recognition. Inspired by the experience of training human experts in sonar, we propose a two-phase training algorithm to exploit association rules to reveal understandable intrinsic rules which contribute to correct classification in known mis-classification data sets. Preliminary experimental results demonstrate the potential of these classification association rules to enhance the classification accuracy of underwater acoustic signals.
18946429
Identifying Indirect Attribute Correspondences in Multilingual Schemas
A fundamental problem in schema integration is attribute correspondences identification. This paper studies a problem of identifying indirect attribute correspondences in multilingual schemas. As far as we know, previous researches of schema integration mainly attempted to solve schema matching in monolingual schemas, neglecting schema matching in multilingual schemas. In this paper, through analyzing two schemas of a large financial corporate of China, we discover relationship of the attributes between the given multilingual schemas. And with domain ontology, indirect attribute correspondences are identified in multilingual schemas. The method extends the abilities of our previous work, and can deal with directional as well as indirect attribute correspondence identification in multilingual schemas. Moreover, a computer-aided system to deal with the problem with the method is provided, which shows its effectiveness in practice
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 .