Pedro J. Abad

MAVE: Multilevel wrApper Verification systEm

Wrappers are pieces of software used to extract data from websites and structure them for further application processing. Unfortunately, websites are continuously evolving and structural changes happen with no forewarning, which usually results in wrappers working incorrectly. Thus, wrappers maintenance is necessary for detecting whether wrapper is extracting erroneous data. The solution consists of using verification models to detect whether wrapper output is statistically similar to the output produced by the wrapper itself when it was successfully invoked in the past. Current proposals present some weaknesses, as the data used to build these models are supposed to be homogeneous, independent, or representative enough, or following a single predefined mathematical model. In this paper, we present MAVE, a novel multilevel wrapper verification system that is based on one-class classification techniques to overcome previous weaknesses. The experimental results show that our proposal outperforms accuracy of current solutions.

Applying ant colony hybrid metaheuristics to wrapper verification

First application of Ant Colony Metaheuristic to verify information extracted by web wrappers.New multilevel verification system that improves the results achieved by current techniques.Enumeration of current techniques weakness.Reformulation of wrapper verification problem as a combinational optimization problem.Applying non-parametric testing techniques to ascertain the statistical significance among results. Wrappers are pieces of software used to extract data from websites and structure them for further application processing. Unfortunately, websites are continuously evolving and structural changes happen with no forewarning, which usually results in wrappers working incorrectly. Thus, wrappers maintenance is necessary for detecting whether wrapper is extracting erroneous data. The solution consists of using verification models to detect whether wrapper output is statistically similar to the output produced by the wrapper itself when it was successfully invoked in the past. Current proposals present some weaknesses, as the data used to build these models are supposed to be homogeneous or that the features of this data set can be mapped to an n-dimensional space of independent dimensions when there is a correlation among their features. In this paper, a new verification system based on the Best-Worst Ant System (BWAS) is presented to overcome previous weaknesses. The experimental results show an accuracy improvement of 7.5% over current solutions.

Data-Aware Conformance Checking for Declarative Business Process Models

Since the accurate management of business processes is receiving increasing attention, conformance checking, i.e., verifying whether the observed behaviour matches a modelled behaviour, is becoming more and more critical. Moreover, declarative languages are more frequently used to provide an increased flexibility. However, little work has been conducted to deal with conformance checking of declarative models. Furthermore, only control-flow perspective is usually considered although other perspectives (e.g., data) are crucial. In addition, most approaches exclusively check the conformance without providing any diagnostics. To enhance the accurate management of flexible business processes, this work presents a constraint-based approach for conformance checking over declarative models (including control-flow and data perspectives) and for providing related diagnosis.

Mining Web Pages Using Features of Rendering HTML Elements in the Web Browser

The Web is the largest repository of useful information available for human users, but it is usual that Web Pages do not provide an API to get access to its information automatically. In order to solve this problem, Information Extractors are developed. We present a new methodology to induce Information Extractors from the Web. It is based on rendering HTML elements in the Web browser. The methodology uses a KDD process to mining a dataset with features of the elements in the Web page. An experimentation over 10 web sites has been made and the results show the effectiveness of the methodology.

Feature Selection to Improve Information Verifier Models in EAI

Reduce maintenance costs of Enterprise Application Integration (EAI) solutions becomes a challenge when we are trying to integrate friendly web applications. This problem can be solved using automated systems that allow to navigate, extract, structure and verify relevant information. The extracted information is characterized using complex models that later are used to check whether the information is valid. In this paper we will demonstrate empirically that feature selection techniques simplify and improve the models obtained for verification of information.

JISBD03 - Feature Selection to Improve Information Verifier Models in EAI

Reduce maintenance costs of Enterprise Application Integration (EAI) solutions becomes a challenge when we are trying to integrate friendly web applications. This problem can be solved using automated systems that allow to navigate, extract, structure and verify relevant information. The extracted information is characterized using complex models that later are used to check whether the information is valid. In this paper we will demonstrate empirically that feature selection techniques simplify and improve the models obtained for verification of information.

Diagnosis of a Chopper Controlled DC Motor by Boosting

This paper proposes a methodology to diagnose a transient state of a dynamic system using boosting. The methodology is composed by two steps: one off-line process and another on-line process. The off-line phase begins gathering data from the system, both when it is running free of fault and when the system is running in each fault mode. A segmentation and normalization algorithm is used to reduce the large amount of gathered data. The final step is the generation of a decision tree by a classification tool. The boosting technique is used with the aim of improving the classification results. The on-line process of the methodology consists of evaluating a new reading of the system sensors with the generated decision trees. The diagnosis of the system is the result of this evaluation which has very low computational cost due to the simplicity of the decision trees. Also, the implementation cost is very low due to this simplicity