Matching Large XML Schemas

Current schema matching approaches still have to improve for very large and complex schemas. Such schemas are increasingly written in the standard language W3C XML schema, especially in E-business applications. The high expressive power and versatility of this schema language, in particular its type system and support for distributed schemas and namespaces, introduce new issues. In this paper, we study some of the important problems in matching such large XML schemas. We propose a fragment-oriented match approach to decompose a large match problem into several smaller ones and to reuse previous match results at the level of schema fragments

Using Element Clustering to Increase the Efficiency of XML Schema Matching

Schema matching attempts to discover semantic mappings between elements of two schemas. Elements are cross compared using various heuristics (e.g., name, data-type, and structure similarity). Seen from a broader perspective, the schema matching problem is a combinatorial problem with an exponential complexity. This makes the naive matching algorithms for large schemas prohibitively inefficient. In this paper we propose a clustering based technique for improving the efficiency of large scale schema matching. The technique inserts clustering as an intermediate step into existing schema matching algorithms. Clustering partitions schemas and reduces the overall matching load, and creates a possibility to trade between the efficiency and effectiveness. The technique can be used in addition to other optimization techniques. In the paper we describe the technique, validate the performance of one implementation of the technique, and open directions for future research

Semantic Integration Approach to Efficient Business Data Supply Chain: Integration Approach to Interoperable XBRL

As an open standard for electronic communication of business and financial data, XBRL has the potential of improving the efficiency of the business data supply chain. A number of jurisdictions have developed different XBRL taxonomies as their data standards. Semantic heterogeneity exists in these taxonomies, the corresponding instances, and the internal systems that store the original data. Consequently, there are still substantial difficulties in creating and using XBRL instances that involve multiple taxonomies. To fully realize the potential benefits of XBRL, we have to develop technologies to reconcile semantic heterogeneity and enable interoperability of various parts of the supply chain. In this paper, we analyze the XBRL standard and use examples of different taxonomies to illustrate the interoperability challenge. We also propose a technical solution that incorporates schema matching and context mediation techniques to improve the efficiency of the production and consumption of XBRL data

Quality of XBRL US GAAP Taxonomy: Empirical Evaluation using SEC Filings

The primary purpose of a data standard is to improve the comparability of data created by multiple standard users. Given the high cost of developing and implementing data standards, it is desirable to be able to assess the quality of data standards. We develop metrics for measuring completeness and relevancy of a data standard. These metrics are evaluated empirically using the US GAAP taxonomy in XBRL and SEC filings produced using the taxonomy by approximately 500 companies. The results show that the metrics are useful and effective. Our analysis also reveals quality issues of the GAAP taxonomy and provides useful feedback to the taxonomy users. The SEC has mandated that all publicly listed companies must submit their filings using XBRL beginning mid 2009 to late 2014 according to a phased-in schedule. Thus our findings are timely and have practical implications that will ultimately help improve the quality of financial data

Intelligent matching for public internet web services ? towards semi-automatic internet services mashup

In this paper, we propose an Internet public Web service matching approach that paves the way for(semi-)automatic service mashup. We will first provide the overview of the solution, which requires a detailed review of two fundamental models ? schema/graph matching and semantic space. Based on the conceptual model and the literature study, the complete service matching approach is then provided with four essential steps ? semantic space, parameter tree, similarity measures, and WSDL operation matching. The system demonstration that proves the concept proposed in this approach is finally presented. The solution has the potential to facilitate the Internet services mashup

Efficient processing of complex XSD using Hive and Spark

The eXtensible Markup Language (XML) files are widely used by the industry due to their flexibility in representing numerous kinds of data. Multiple applications such as financial records, social networks, and mobile networks use complex XML schemas with nested types, contents, and/or extension bases on existing complex elements or large real-world files. A great number of these files are generated each day and this has influenced the development of Big Data tools for their parsing and reporting, such as Apache Hive and Apache Spark. For these reasons, multiple studies have proposed new techniques and evaluated the processing of XML files with Big Data systems. However, a more usual approach in such works involves the simplest XML schemas, even though, real data sets are composed of complex schemas. Therefore, to shed light on complex XML schema processing for real-life applications with Big Data tools, we present an approach that combines three techniques. This comprises three main methods for parsing XML files: cataloging, deserialization, and positional explode. For cataloging, the elements of the XML schema are mapped into root, arrays, structures, values, and attributes. Based on these elements, the deserialization and positional explode are straightforwardly implemented. To demonstrate the validity of our proposal, we develop a case study by implementing a test environment to illustrate the methods using real data sets provided from performance management of two mobile network vendors. Our main results state the validity of the proposed method for different versions of Apache Hive and Apache Spark, obtain the query execution times for Apache Hive internal and external tables and Apache Spark data frames, and compare the query performance in Apache Hive with that of Apache Spark. Another contribution made is a case study in which a novel solution is proposed for data analysis in the performance management systems of mobile networks.Unidad de Gestión de Investigación y Proyección Social from the Escuela Politécnica Naciona

XML Matchers: approaches and challenges

Schema Matching, i.e. the process of discovering semantic correspondences between concepts adopted in different data source schemas, has been a key topic in Database and Artificial Intelligence research areas for many years. In the past, it was largely investigated especially for classical database models (e.g., E/R schemas, relational databases, etc.). However, in the latest years, the widespread adoption of XML in the most disparate application fields pushed a growing number of researchers to design XML-specific Schema Matching approaches, called XML Matchers, aiming at finding semantic matchings between concepts defined in DTDs and XSDs. XML Matchers do not just take well-known techniques originally designed for other data models and apply them on DTDs/XSDs, but they exploit specific XML features (e.g., the hierarchical structure of a DTD/XSD) to improve the performance of the Schema Matching process. The design of XML Matchers is currently a well-established research area. The main goal of this paper is to provide a detailed description and classification of XML Matchers. We first describe to what extent the specificities of DTDs/XSDs impact on the Schema Matching task. Then we introduce a template, called XML Matcher Template, that describes the main components of an XML Matcher, their role and behavior. We illustrate how each of these components has been implemented in some popular XML Matchers. We consider our XML Matcher Template as the baseline for objectively comparing approaches that, at first glance, might appear as unrelated. The introduction of this template can be useful in the design of future XML Matchers. Finally, we analyze commercial tools implementing XML Matchers and introduce two challenging issues strictly related to this topic, namely XML source clustering and uncertainty management in XML Matchers.Comment: 34 pages, 8 tables, 7 figure

Harvesting Application Information for Industry-Scale Relational Schema Matching

Consider the problem of migrating a company's CRM or ERP database from one application to another, or integrating two such databases as a result of a merger. This problem requires matching two large relational schemas with hundreds and sometimes thousands of fields. Further, the correct match is likely complex: rather than a simple one-to-one alignment, some fields in the source database may map to multiple fields in the target database, and others may have no equivalent fields in the target database. Despite major advances in schema matching, fully automated solutions to large relational schema matching problems are still elusive. This paper focuses on improving the accuracy of automated large relational schema matching. Our key insight is the observation that modern database applications have a rich user interface that typically exhibits more consistency across applications than the underlying schemas. We associate UI widgets in the application with the underlying database fields on which they operate and demonstrate that this association delivers new information useful for matching large and complex relational schemas. Additionally, we show how to formalize the schema matching problem as a quadratic program, and solve it efficiently using standard optimization and machine learning techniques. We evaluate our approach on real-world CRM applications with hundreds of fields and show that it improves the accuracy by a factor of 2-4x