Schema Evolution publication categorizer

Many problems encountered when building applications of database systems involve the manipulation
of models. By “model,” we mean a complex structure that represents a design artifact,
such as a relational schema, object-oriented interface, UML model, XML DTD, web-site schema,
semantic network, complex document, or software configuration. Many uses of models involve
managing changes in models and transformations of data from one model into another. These uses
require an explicit representation of “mappings” between models. We propose to make database

Schema matching is a critical step in many applications, such as XML message mapping, data warehouse loading, and schema integration. In this paper, we investigate algorithms for generic schema matching, outside of any particular data model or application. We first present a taxonomy for past solutions, showing that a rich range of techniques is available. We then propose a new algorithm, Cupid, that discovers mappings between schema elements based on their names, data types, constraints, and schema structure, using a broader set of techniques than past approaches.

A model is a formal description of a complex application artifact, such as a database schema, an application interface, a UML model, an ontology, or a message format. The problem of merging such models lies at the core of many meta data applications, such as view integration, mediated schema creation for data integration, and ontology merging. This paper examines the problem of merging two models given correspondences between them. It presents requirements for conducting a merge and a specific algorithm that subsumes previous work.

Mappings between disparate models are fundamental to any
application that requires interoperability between heterogeneous
data and applications. Generating mappings is a laborintensive
and error prone task. To build a system that helps
users generate mappings, we need an explicit representation
of mappings. This representation needs to have well-defined
semantics to enable reasoning and comparison between mappings.
This paper first presents a powerful framework for
defining languages for specifying mappings and their associated
semantics. We examine the use of mappings and identify

Semantic mappings between data sources play a key
role in several data sharing architectures. Mappings
provide the relationships between data stored in different sources, and therefore enable answering queries
that require data from other nodes in a data sharing network. Composing mappings is one of the core
problems that lies at the heart of several optimization
methods in data sharing networks, such as caching frequently traversed paths and redundancy analysis.
This paper investigates the theoretical underpinnings of mapping composition. We study the problem

Model management is an approach to simplify the programming
of metadata-intensive applications. It offers developers powerful
operators, such as Compose, Diff, and Merge, that are applied to
models, such as database schemas or interface specifications, and
to mappings between models. Prior model management solutions
focused on a simple class of mappings that do not have executable
semantics. Yet many metadata applications require that mappings
be executable, expressed in SQL, XSLT, or other data transformation
languages.
In this paper, we develop a semantics for model-management

The difficulty inherent in schema matching has led
to the development of several generic match algorithms.
This paper describes how we adapted general
approaches to the specific task of aligning two ontologies
of human anatomy, the Foundational Model of
Anatomy and the GALEN Common Reference Model.
Our approach consists of three phases: lexical, structural
and hierarchical, which leverage different aspects
of the ontologies as they are represented in a
generic meta-model. Lexical matching identifies concepts
with similar names. Structural matching identifies

Semantic integration has been a long-standing challenge
for the database community. It has received
steady attention over the past two decades, and has
now become a prominent area of database research.
In this article, we first review database applications
that require semantic integration, and discuss the difficulties underlying the integration process. We then
describe recent progress and identify open research issues.
We will focus in particular on schema matching, a
topic that has received much attention in the database
community, but will also discuss data matching (e.g.,