Multiple Web Databases Research Articles

Automating Duplicate Detection for Lexical Heterogeneous Web Databases

Introduction: The need for efficient search engines has been identified with the everincreasing technological advancement and huge growing demand for data on the web. Method: Automating duplicate detection over a query results in identifying the records from multiple web databases that point to a similar real-world entity and return non-matching records to the end-users. The proposed algorithm in this paper is based on an unsupervised approach with classifiers over heterogeneous web databases that return more accurate results with high precision, Fmeasure, and recall. Different assessments have also been executed to analyze the efficacy of the proposed algorithm for the identification of duplicates. Result: Results show that the proposed algorithm has greater precision, F-score measure, and the same recall values as compared to standard UDD. Conclusion: This paper aims to introduce an algorithm that automates the process of duplicate detection for lexical heterogeneous web databases. Discussion: This paper concludes that the proposed algorithm outperforms the standard UDD.

Automatic construction of vertical search tools for the Deep Web

With the constant increase in the volume of information available on the Web, it is more dificult to find the specific information related to a given domain. Users are facing the problem of information overload, in which a query about a specialized subject (local information, e-commerce: hotels, airlines, car rental; science: biology, mathematics, medicine, etc.) on a web search engine, it returns a lot of web pages or results that in most of the cases are outside the domain of interest. This is one reason why the vertical search tools have become a necessity for users that seek specific-domain information from diferent databases available in the Web through input sources called Web Query Interfaces (ICWs). This paper describes an approach for automatic integration of ICWs, a crucial task to construct vertical search tools. The proposed methodology is validated by realizing a vertical search prototype called VSearch that allows users to transparently query multiple web databases in a specific-domain through a unified ICW. The proposed approach for automatic ICWs integration is based on: i) a hierarchical model called AEV for modeling the visual content of ICW; ii) semantic clustering for the identification of relationships between fields in ICWs; and iii) a field homogenization and unification process of AEV schemes for the construction of a unified ICW. The VSearch prototype was implemented and evaluated using a study case. The experimental results demonstrate the high precision in the integration phase and an efective methodology to create a functional vertical search tool for a given domain

RECORD MATCHING FOR WEB DATABASES BY DOMAIN-SPECIFIC QUERY PROBING

Record matching refers to the task of finding entries that refer to the same entity in two or more files, is a vital process in data integration. Most of the record matching methods are supervised, which requires the user to provide training data. These methods are not applicable for web database scenario, where query results dynamically generated on-the- fly. To address the problem of record matching in the Web database scenario, we present an unsupervised, online record matching method, UDD, which effectively identifies the duplicates from query result records of multiple web databases. First, same source duplicates are eliminated by using exact matching method the ―presumed‖ non duplicate records from the same source can be used as training examples . Starting from the non duplicate set, we use two cooperating classifiers a weight component similarity summing classifier and an SVM classifier, to iteratively identify duplicates in the query results from multiple Web databases.

Discovering the skyline of web databases

Many web databases are "hidden" behind proprietary search interfaces that enforce the top- k output constraint, i.e., each query returns at most k of all matching tuples, preferentially selected and returned according to a proprietary ranking function. In this paper, we initiate research into the novel problem of skyline discovery over top- k hidden web databases. Since skyline tuples provide critical insights into the database and include the top-ranked tuple for every possible ranking function following the monotonic order of attribute values, skyline discovery from a hidden web database can enable a wide variety of innovative third-party applications over one or multiple web databases. Our research in the paper shows that the critical factor affecting the cost of skyline discovery is the type of search interface controls provided by the website. As such, we develop efficient algorithms for three most popular types, i.e., one-ended range, free range and point predicates, and then combine them to support web databases that feature a mixture of these types. Rigorous theoretical analysis and extensive real-world online and offline experiments demonstrate the effectiveness of our proposed techniques and their superiority over baseline solutions.

Web Data Extraction and Alignment Tools: A survey

Data extraction from the web pages is the process of analyzing and retrieving relevant data out of the data sources (usually unstructured or poorly structure) in a specific pattern for further processing, involves addition of metadata and data integration details for further process in the data workflow. This survey describes overview of the different web data extraction and data alignment techniques. Extraction techniques are DeLa, DEPTA, ViPER, and ViNT. Data alignment techniques are Pairwise QRR alignment, Holistic alignment, Nested structure processing. Query Result pages are generated by using Web database based on Users Query. The data from these query result pages should be automatically extracted which is very important for many applications, such as data integration, which are needed to cooperate with multiple web databases. New method is proposed for data extraction t that combines both tag and value similarity. It automatically extracts data from query result pages by first identifying and segmenting the query result records (QRRs) in the query result pages and then aligning the segmented QRRs into a table. In which the data values from the same attribute are put into the same column. Data region identification method identify the noncontiguous QRRs that have the same parents according to their tag similarities. Specifically, we propose new techniques to handle the case when the QRRs are not contiguous, which may be due to presence of auxiliary information, such as a comment, recommendation or advertisement, and for handling any nested structure that may exist in the QRRs.

Combining Tag and Value Similarity for Data Extraction and Alignment

Web databases generate query result pages based on a user's query. Automatically extracting the data from these query result pages is very important for many applications, such as data integration, which need to cooperate with multiple web databases. We present a novel data extraction and alignment method called CTVS that combines both tag and value similarity. CTVS automatically extracts data from query result pages by first identifying and segmenting the query result records (QRRs) in the query result pages and then aligning the segmented QRRs into a table, in which the data values from the same attribute are put into the same column. Specifically, we propose new techniques to handle the case when the QRRs are not contiguous, which may be due to the presence of auxiliary information, such as a comment, recommendation or advertisement, and for handling any nested structure that may exist in the QRRs. We also design a new record alignment algorithm that aligns the attributes in a record, first pairwise and then holistically, by combining the tag and data value similarity information. Experimental results show that CTVS achieves high precision and outperforms existing state-of-the-art data extraction methods.

A Query Interface Matching Approach Based on Extended Evidence Theory for Deep Web

Matching query interfaces is a crucial step in data integration across multiple Web databases. Different types of information about query interface schemas have been used to match attributes between schemas. Relying on a single aspect of information is not sufficient and the matching results of individual matchers are often inaccurate and uncertain. The evidence theory is the state-of-the-art approach for combining multiple sources of uncertain information. However, traditional evidence theory has the limitations of treating individual matchers in different matching tasks equally for query interface matching, which reduces matching performance. This paper proposes a novel query interface matching approach based on extended evidence theory for Deep Web. Our approach firstly introduces the dynamic prediction procedure of different matchers' credibilities. Then, it extends traditional evidence theory with the credibilities and uses exponentially weighted evidence theory to combine the results of multiple matchers. Finally, it performs matching decision in terms of some heuristics to obtain the final matches. Our approach overcomes the shortage of traditional method and can adapt to different matching tasks. Experimental results demonstrate the feasibility and effectiveness of our proposed approach.

Record Matching over Query Results from Multiple Web Databases

Record matching, which identifies the records that represent the same real-world entity, is an important step for data integration. Most state-of-the-art record matching methods are supervised, which requires the user to provide training data. These methods are not applicable for the Web database scenario, where the records to match are query results dynamically generated on-the-fly. Such records are query-dependent and a prelearned method using training examples from previous query results may fail on the results of a new query. To address the problem of record matching in the Web database scenario, we present an unsupervised, online record matching method, UDD, which, for a given query, can effectively identify duplicates from the query result records of multiple Web databases. After removal of the same-source duplicates, the ?presumed? nonduplicate records from the same source can be used as training examples alleviating the burden of users having to manually label training examples. Starting from the nonduplicate set, we use two cooperating classifiers, a weighted component similarity summing classifier and an SVM classifier, to iteratively identify duplicates in the query results from multiple Web databases. Experimental results show that UDD works well for the Web database scenario where existing supervised methods do not apply.

An evidential approach to query interface matching on the deep Web

Matching query interfaces is a crucial step in data integration across multiple Web databases. The problem is closely related to schema matching that typically exploits different features of schemas. Relying on a particular feature of schemas is not sufficient. We propose an evidential approach to combining multiple matchers using Dempster–Shafer theory of evidence. First, our approach views the match results of an individual matcher as a source of evidence that provides a level of confidence on the validity of each candidate attribute correspondence. Second, it combines multiple sources of evidence to get a combined mass function that represents the overall level of confidence, taking into account the match results of different matchers. Our combination mechanism does not require the use of weighing parameters, hence no setting and tuning of them is needed. Third, it selects the top k attribute correspondences of each source attribute from the target schema based on the combined mass function. Finally it uses some heuristics to resolve any conflicts between the attribute correspondences of different source attributes. Our experimental results show that our approach is highly accurate and effective.

Web 3.0 Emerging

While Web 3.0 technologies are difficult to define precisely, the outline of emerging applications has become clear over the past year. We can thus essentially view Web 3.0 as semantic Web technologies integrated into, or powering, large-scale Web applications. The base of Web 3.0 applications resides in the resource description framework (RDF) for providing a means to link data from multiple Web sites or databases. With the SPARQL query language, a SQL-like standard for querying RDF data, applications can use native graph-based RDF stores and extract RDF data from traditional databases.

BACIIS

Life science Web databases are becoming increasingly essential in conducting everyday biological research. With more than 300 life science Web databases and the growing size of the life science data, searching and managing these complex data requires technology beyond that of traditional database systems. The open research issues include the interoperability of geographically distributed autonomous databases, which are generally only Web-accessible, and the seamless semantic-based integration of these databases with total transparency to the user. In this paper, the implementation of a Biological and Chemical Information Integration System (BACIIS) is presented. BACIIS supports the integration of multiple heterogeneous life science Web databases and allows the execution of global applications that extend beyond the boundaries of individual databases. This paper discusses the architecture of BACIIS. It also discusses the techniques used to extract and integrate data from the various life science Web databases.