Schema Evolution publication categorizer

The book edited by Bellahsene, Bonifati and Rahm provides an overview of the ways in which the schema and ontology matching and mapping tools have addressed the above requirements and points to the open technical challenges. The contributions from leading experts are structured into three parts: large-scale and knowledge-driven schema matching, quality-driven schema mapping and evolution, and evaluation and tuning of matching tasks.

Data integration systems offer a uniform interface to a set of data sources. Despite recent progress, setting up and maintaining a data integration application still requires significant upfront effort of creating a mediated schema and semantic mappings from the data sources to the mediated schema. Many application contexts involving multiple data sources (e.g., the web, personal information management, enterprise intranets) do not require full integration in order to provide useful services, motivating a pay-as-you-go approach to integration.

The Clio project provides tools that vastly simplify information integration. Information integration requires data conversions to bring data in different representations into a common form. Key contributions of Clio are the definition of non-procedural schema mappings to describe the relationship between data in heterogeneous schemas, a new paradigm in which we view the mapping creation process as one of query discovery, and algorithms for automatically generating queries for data transformation from the mappings.

Clio is an existing schema-mapping tool that provides user-friendly means to manage and facilitate the complex task of transformation and integration of heterogeneous data such as XML over the Web or in XML databases. By means of mappings from source to target schemas, Clio can help users conveniently establish the precise semantics of data transformation and integration. In this paper we study the problem of how to efficiently implement such data transformation (i.e., generating target data from the source data based on schema mappings).

Whenever transformation of data is used to bridge the gap of different data formats, and a query is given in the destination format, query reformulation can speed up the transformation of data. We achieve this speed-up in transformation when only the required data segment, described by the computed reformulated query, is transformed. Whenever the required section of data is not too large, query reformulation allows transformation on demand, even when the input data is large.

Integration, transformation, and translation of data is increasingly important for modern
information systems and e-commerce applications. Views, and more generally, transformation
specifications, or mappings, provide the foundation for many data transformation
applications.
Mappings are usually specified manually by data administrators that are familiar with
the semantics of the data and have a good knowledge of the transformation language. The
task of generating and managing mappings is laborious, time consuming and error-prone

Extensible Markup Language (XML) has grown rapidly over the last decade to become
the de facto standard for heterogeneous data exchange. Its popularity is due in large
part to the ease with which diverse kinds of information can be represented as a result
of the self-describing nature and extensibility of XML itself. The ease and speed with
which information can be represented does not extend, however, to exchanging such
information between autonomous sources. In the absence of controlling standards,
such sources will typically choose differing XML representations for the same concept,

Constructing and maintaining semantic mappings are necessary but troublesome in data sharing systems. While most current work focuses on seeking automated techniques to solve this problem, this paper proposes a combination model for constructing exten-sible mappings between XML schemas. In our model, complex global mappings are constructed by first defining simple atomic mappings for each target schema element, and then combining them using a few basic operators. At the same time, we provide automated support for constructing such combined mappings.

Mapping and translating data stored in dierent formats continues to be an important problem in modern information systems. We present a novel framework for mapping among XML and relational schemas in which a high-level mapping is translated into semantically meaningful queries that transform source data into the target representation. Our approach works in two phases.

Merging and coalescing data from multiple and diverse
sources into different data formats continues to be an important
problem in modern information systems. Schema
Matching, the process of matching elements of a source
schema with elements of a target schema, and Schema Mapping,
the process of creating a query that maps between two
disparate schemas, are at the heart of data integration systems.
We demonstrate Clio, a semi-automatic schema mapping
tool developed at the IBM Almaden Research Center.
In this demonstration we showcase Clio’s mapping engine

At the heart of many data-intensive applications is the problem of quickly and accurately transforming data into a new form. Database researchers have long advocated the use of declarative queries for this process. Yet tools for creating, managing and understanding the complex queries necessary for data transformation are still too primitive to permit widespread adoption of this approach. We present a new framework that uses data examples as the basis for understanding and refining declarative schema mappings. We identify a small set of intuitive operators for manipulating examples.

To enable modern data intensive applications we must solve the data mapping problem in which a source (legacy) database is mapped into a different, but fixed, target schema. Schema mapping involves the discovery of a query or a set of queries that transform the source data into the new structure. We introduce an interactive mapping creation paradigm based on value correspondences that show how a value of a target attribute can be created from a set of values of source attributes.

Database middleware systems integrate data from multiple sources.
To be effective, such systemsmust provide a unified, queryable schema, and must be able to transform data from different sources to conform to this schema when queries against the schema are issued. The power of their query engines and their ability to connect to several information sources makes them a natural base for doing