Information Integration - the process of integration, evolution and versioning

At present, many information sources are available wherever you are. Most of the time, the information needed is spread across several of those information sources. Gathering this information is a tedious and time consuming job. Automating this process would assist the user in its task. Integration of the information sources provides a global information source with all information needed present. All of these information sources also change over time. With each change of the information source, the schema of this source can be changed as well. The data contained in the information source, however, cannot be changed every time, due to the huge amount of data that would have to be converted in order to conform to the most recent schema.\ud In this report we describe the current methods to information integration, evolution and versioning. We distinguish between integration of schemas and integration of the actual data. We also show some key issues when integrating XML data sources

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

Automated schema matching techniques: an exploratory study

Manual schema matching is a problem for many database applications that use multiple data sources including data warehousing and e-commerce applications. Current research attempts to address this problem by developing algorithms to automate aspects of the schema-matching task. In this paper, an approach using an external dictionary facilitates automated discovery of the semantic meaning of database schema terms. An experimental study was conducted to evaluate the performance and accuracy of five schema-matching techniques with the proposed approach, called SemMA. The proposed approach and results are compared with two existing semi-automated schema-matching approaches and suggestions for future research are made

Matching schemas of heterogeneous relational databases

Schema matching is a basic problem in many database application domains, such as data integration. The problem of schema matching can be formulated as follows, "given two schemas, Si and Sj, find the most plausible correspondences between the elements of Si and S j, exploiting all available information, such as the schemas, instance data, and auxiliary sources" [24]. Given the rapidly increasing number of data sources to integrate and due to database heterogeneities, manually identifying schema matches is a tedious, time consuming, error-prone, and therefore expensive process. As systems become able to handle more complex databases and applications, their schemas become large, further increasing the number of matches to be performed. Thus, automating this process, which attempts to achieve faster and less labor-intensive, has been one of the main tasks in data integration. However, it is not possible to determine fully automatically the different correspondences between schemas, primarily because of the differing and often not explicated or documented semantics of the schemas. Several solutions in solving the issues of schema matching have been proposed. Nevertheless, these solutions are still limited, as they do not explore most of the available information related to schemas and thus affect the result of integration. This paper presents an approach for matching schemas of heterogeneous relational databases that utilizes most of the information related to schemas, which indirectly explores the implicit semantics of the schemas, that further improves the results of the integration

An approach for matching schemes of heterogeneous relational databases.

Schema matching is a basic problem in many database application domains, such as data integration. The problem of schema matching can be formulated as follows, ldquogiven two schemas, Si and Sj, find the most plausible correspondences between the elements of Si and Sj, exploiting all available information, such as the schemas, instance data, and auxiliary sourcesrdquo. Given the rapidly increasing number of data sources to integrate and due to database heterogeneities, manually identifying schema matches is a tedious, time consuming, error-prone, and therefore expensive process. As systems become able to handle more complex databases and applications, their schemas become large, further increasing the number of matches to be performed. Thus, automating this process, which attempts to achieve faster and less labor-intensive, has been one of the main tasks in data integration. However, it is not possible to determine fully automatically the different correspondences between schemas, primarily because of the differing and often not explicated or documented semantics of the schemas. Several solutions in solving the issues of schema matching have been proposed. Nevertheless, these solutions are still limited, as they do not explore most of the available information related to schemas and thus affect the result of integration. This paper presents an approach for matching schemas of heterogeneous relational databases that utilizes most of the information related to schemas, which indirectly explores the implicit semantics of the schemas, that further improves the results of the integration

Intersection schemas as a dataspace integration technique

This paper introduces the concept of Intersection Schemas in the field of heterogeneous data integration and dataspaces. We introduce a technique for incrementally integrating heterogeneous data sources by specifying semantic overlaps between sets of extensional schemas using bidirectional schema transformations, and automatically combining them into a global schema at each iteration of the integration process. We propose an incremental data integration methodology that uses this technique and that aims to reduce the amount of up-front effort required. Such approaches to data integration are often described as pay-as-you-go. A demonstrator of our technique is described, which utilizes a new graphical user tool implemented using the AutoMed heterogeneous data integration system. A case study is also described, and our technique and integration methodology are compared with a classical data integration strategy