Schema Evolution publication categorizer

We describe a tool, TReMer, for performing the merge
operation in a model-driven development setting. Merge is arguably one of the most
important model management operations and is useful as a way of consolidating a set
of models to gain a unified perspective, to understand their interactions, or to perform
various types of end-to-end analysis over them.
TReMer draws on the theory developed in our earlier work where we
describe how a set of models can be merged w.r.t. known or hypothesized relationships

If a modeling task is distributed, it will frequently be necessary
to merge models developed by different team members.
Existing approaches to model merging make assumptions
about the types of model to be merged, and the nature of the
relationship between them. This makes it hard to compare
approaches. In this paper, we present a manifesto for research
on model merging. We propose a framework for comparing
different approaches to merging, by treating merge as
an algebraic operator over models and model relationships.
We specify the algebraic properties of an idealized merge

Many problems in information integration rely on specifications, called schema mappings, that model the relationships between schemas. Schema mappings for both relational and nested data are well-known. In this work, we present a new formalism for schema mapping that extends these existing formalisms in two significant ways. First, our nested mappings allow for nesting and correlation of mappings. This results in a natural programming paradigm that often yields more accurate specifications.

Modern information systems often store data that has
been transformed and integrated from a variety of sources.
This integration may obscure the original source semantics
of data items. For many tasks, it is important to be
able to determine not only where data items originated,
but also why they appear in the integration as they do and
through what transformation they were derived. This problem
is known as data provenance. In this work, we consider
data provenance at the schema and mapping level. In particular,
we consider how to answer questions such as “what

Data exchange is the problem of finding an instance of a target schema, given an instance of a source schema and a specification of the relationship between the source and the target. Theoretical foundations of data exchange have recently been investigated for relational data.In this paper, we start looking into the basic properties of XML data exchange, that is, restructuring of XML documents that conform to a source DTD under a target DTD, and answering queries written over the target schema.

We consider the problem of mapping data in peer-topeer
data-sharing systems. Such systems often rely on
the use of mapping tables listing pairs of corresponding
values to search for data residing in different peers.
In this paper, we address semantic and algorithmic issues
related to the use of mapping tables. We begin by
arguing why mapping tables are appropriate for data
mapping in a peer-to-peer environment. We discuss alternative
semantics for these tables and we present a
language that allows the user to specify mapping tables

There are many problems requiring a semantic account of a database
schema. At its best, such an account consists of mapping formulas between the
schema and a formal conceptual model or ontology (CM) of the domain. This
paper describes the underlying principles, algorithms, and a prototype of a tool
which infers such semantic mappings when given simple correspondences from
table columns in a relational schema to datatype properties of classes in an on-
tology. Although the algorithm presented is necessarily heuristic, we offer formal

. Much data is published on the Web in XML format satisfying schemas, and to make the Semantic Web a reality, such data needs to be interpreted with respect to ontologies. Interpretation is achieved through a semantic mapping between the XML schema and the ontology. We present work on the heuristic construction of complex such semantic mappings, when given an initial set of simple correspondences from XML schema attributes to datatype properties in the ontology. To accomplish this, we first offer a mapping formalism to capture the semantics of XML schemas.

demo

To achieve interoperability, modern information systems
and e-commerce applications use mappings to translate
data from one representation to another. In dynamic environments
like the Web, data sources may change not
only their data but also their schemas, their semantics, and
their query capabilities. Such changes must be reflected
in the mappings. Mappings left inconsistent by a schema
change have to be detected and updated. As large, complicated
schemas become more prevalent, and as data is
reused in more applications, manually maintaining mappings