Atakan Kurt

Classification of XSLT-Generated web documents with support vector machines

XSLT is a transformation language mainly used for converting XML documents to HTML or other formats. Due to its simplicity and flexibility XML has replaced traditional EDI file formats. Most e-business applications store data in XML, convert XML into HTML using XSTL, and publish the HTML documents to the web. In this paper we argue that the use of XSLT presents an opportunity rather than a challenge to web document classification. We show that it is possible to combine the advantages of both HTML and XML into classification of documents at the XSLT transformation stage, named XSLT classification, to attain higher classification rates using Support Vector Machines (SVM). The results are both expected and promising. We believe that XSLT classification can become a favorable classification method over HTML or XML classification where XSLT stylesheets are available.

A middleware approach to storing and querying XML documents in relational databases

In this paper we present a middleware for storing and retrieving XML documents in relational databases. To store XML documents in RDBMS, several mapping approaches can be used. We chose structure independent approach. This approach stores XML documents in fixed-schema tables and does not require a direct extension of SQL. So the middleware can be used with any RDBMS with minor changes in the interface. The middleware offers two alternative methods -namely XRel and Edge- for storing XML in the database. The Edge method is a straightforward method, while XRel utilizes path summary information for faster query processing. We present a comparative experimental study on the performance of insertion and retrieval of two types of XML documents along with a set of XPath queries executed though the XPath query processor which is a part of the middleware.

An Experimental Study on Query Processing Efficiency of Native-XML and XML-Enabled Database Systems

With XML becoming a standard for representing semi-structured documents on the web and a standard for data exchange between different systems, some database companies are adding XML support to their existing database systems, while some other companies coming out with pure or native database systems for XML. In this paper, we present an experimental study on the query processing efficiency of a native-XML database system and an XML-enabled database system on a selected set of queries including operations from text-processing, DML and relation algebra. The experiments are conducted on two well-known commercial database systems using the web interfaces based on HTTP. The cost metrics we used are CPU time, the numbers of physical and logical reads. The queries were run on identical machines for 3 different sizes of documents with and without indexing. A subset of experimental results is presented and overall results are discussed. Generally speaking the XML-enabled system performed better.

A web classification framework based on XSLT

Data on the web is gradually changing format from HTML to XML/XSLT driven by various software and hardware requirements such as interoperability and data-sharing problems between different applications/platforms, devices with vairous capabilities like cell phones, PDAs. This gradual change introduces new challenges in web page and web site classification. HTML is used for presentation of content. XML represents content in a hierarchical manner. XSLT is used to transform XML documents into different formats such as HTML, WML. There are certain drawbacks in HTML and XML classifications for classifying a web page. In this paper we propose a new classification method based on XSLT which is able to combine the advantages of HTML and XML classifications. We also introduce a web classification framework utilizing XSLT classification. Finally we show that using Naive Bayes classifier XSLT classification outperfoms both HTML and XML classifications.

On-Line Pruning of ZBDD for Path Delay Fault Coverage Calculation *

Zero-suppressed BDDs (ZBDDs) have been used in the nonenumerative path delay fault (PDF) grading of VLSI circuits. One basic and one cut-based grading algorithm are proposed to grade circuits with polynomial and exponential number of PDFs, respectively. In this article, we present a new ZBDD-based basic PDF grading algorithm to enable grading of some circuits with exponential number of PDFs without using the cut-based algorithm. The algorithm overcomes the memory overflow problems by dynamically pruning the ZBDD at run-time. This new algorithm may give exact or pessimistic coverage depending on the statuses of the pruned nodes. Furthermore, we re-assess the performance of the static variable ordering heuristics in ZBDDs for PDF coverage calculation [1]. The proposed algorithm combined with the efficient static variable ordering heuristics can avoid ZBDD size explosion in many circuits. Experimental results for ISCAS85 benchmarks show that the proposed algorithm efficiently grades circuits.

Artificial neural networks based modelling of carbon monoxide: Effects of spatial parameters

Web systems for air quality information and pollution modeling provide access to daily measured and predicted valuable information for the internet users. There are just a few examples around the world in this area. A recently developed web system at http://airpol.fatih.edu.tr is one such application. It provides information about air quality prediction for Istanbul, Turkey in a user friendly interface. New studies have been performed to increase the prediction efficiencies of the system and presented in this work.