Schema Evolution publication categorizer

We introduce schema covering, the problem of identifying
easily understandable common objects for describing large
and complex schemas. Defining transformations between schemas
is a key objective in information integration. However, this
process often becomes cumbersome when the schemas are large
and structurally complex. If such complex schemas can be broken
into smaller and simpler objects, then simple transformations
defined over these smaller objects can be reused to define suitable
transformations among the complex schemas. Schema covering

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.

Many data integration solutions in the market today include tools for schema mapping, to help users visually relate elements of different schemas. Schema elements are connected with lines, which are interpreted as mappings, i.e. high-level logical expressions capturing the relationship between source and target data-sets; these are compiled into queries and programs that convert source-side data instances into target-side instances.

Data exchange is the process of converting an instance of one schema into an instance of a different schema according to a given specification. Recent data exchange systems have largely dealt with the case where the schemas are given a priori and transformations can only migrate data from the first schema to an instance of the second schema. In particular, the ability to perform data-metadata translations, transformation in which data is converted into metadata or metadata is converted into data, is largely ignored.

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