Schema Evolution publication categorizer

More and more integration systems use ontologies to solve the problem of semantic heterogeneities between autonomous databases. To automate the integration process, a number of these systems suppose the existence of a shared domain ontology a priori referenced by the local ontologies embedded in the various sources. When the shared ontology evolves over the time, the evolution may concern (i) the ontology level, (2) the local schema level, and/or (3) the contents of sources.

A Data Warehouse (DW) can be used to integrate data from multiple distributed data sources. A DW can be seen as a set of materialized views that determine its schema and its content in terms of the schema and the content of the data sources. DW applications require high query performance. For this reason, the design of a typical DW consists of selecting views to materialize that are able to answer a set of input user queries. However, the cost of answering the queries has to be balanced against the cost of maintaining the materialized views.

Integrated engineering environments, based on federated database technology, are, among others, a means to control the integrity of and dependencies between product data created in many different engineering applications. Most of these applications support the management of versions of a product and its parts, continuing the engineers' tradition of keeping different versions of drawings and documents. Consequently, federations in engineering environments have to provide version management on their global layer as well.

We describe examples of problems of semantic heterogeneity in databases due to “domain evolution”, as it occurs in both single- and multidatabase systems. These problems occur when the semantics of values of a particular domain change over time in ways that are not amenable to applying simple mappings between “old” and “new” values. The paper also proposes facilities and strategies for solving such problems.

In heterogeneous data warehousing environments, autonomous data sources are integrated into a materialised integrated database. The schemas of the data sources and the integrated database may be expressed in different modelling languages. It is possible for either the data source schemas or the warehouse schema to evolve. This evolution may include evolution of the schema, or evolution of the modelling language in which the schema is expressed, or both.

Web Data Warehouses have been introduced to enable the analysis of integrated Web data. One of the main challenges in these systems is to deal with the volatile and dynamic nature of Web sources. In this work we address the effects of adding/removing/changing Web sources and data items to the Data Warehouse (DW) schema. By managing source evolution we mean the automatic propagation of these changes to the DW. The proposed approach is based on a wrapper/mediator architecture, which reduces the impact of Web source changes on the DW schema.

One of the fundamental difficulties associated with large- scale database interoperation s the cost and effort required for maintaining the federation in a consistent state after its launch. If only human intervention is to be assumed the task of maintaining logical federation consistency becomes insurmountable as the scale increases. The need for research towards a federation evolution model which takes into account evolving semantic aspects is becoming severe.

“Nothing endures but change. ” –By Heractilus Change is inevitable even for persistent information. Effectively managing change of persistent information, which includes the specification, execution and the maintenance of any derived information, is critical and must be addressed by all database systems. Today, for every data model there exists a well-defined set of change primitives that can alter both the structure (the schema) and the data. Several proposals also exist for incrementally propagating a primitive change to any derived information (or view).

The construction and maintenance of data warehouses (views) in large-scale environments composed of numerous distributed and evolving information sources (ISs) such as the WWW has received great attention recently. Such environments are plagued with changing information because ISs tend to continuously evolve by modifying not only their content but also their query capabilities and interface and by joining or leaving the environment at any time.

As a decision support information system, a data warehouse must provide high level quality of data and quality of service. In the DWQ project we have proposed an architectural framework and a repository of metadata which describes all the data warehouse components in a set of metamodels to which is added a quality metamodel, defining for each data warehouse metaobject the corresponding relevant quality dimensions and quality factors.

New application areas for databases like data warehousing and OLAP (Online Analytical Processing)deploy the multidimensional data model in order to describe the application domain. Consequently, OLAP systems are represented by a multidimensional database schema to adequately reflect the application semantics.
FIESTA presents a methodology for the evolution of such multidimensional schemas. Core of
the thesis is a schema evolution algebra which comprehends a formal multidimensional data
model together with corresponding schema evolution operations. Since OLAP systems are

Database systems offering a multidimensional schema on a logical level (e.g. OLAP systems) are often used in data warehouse environments. The user requirements in these dynamic application areas are subject to frequent changes. This implies frequent structural changes of the database schema. In this paper, we present a formal framework to describe evolutions of multidimensional schemas and their effects on the schema and on the instances. The framework is based on a formal conceptual description of a multidimensional schema and a corresponding schema evolution algebra.

This paper presents a new approach to schema evolution,
which combines the activities of schema integration and schema evolution into one framework. In previous work we have developed a general
framework to support schema transformation and integration in heterogeneous database architectures. Here we show how this framework also
readily supports evolution of source schemas, allowing the global schema
and the query translation pathways to be easily repaired, as opposed to
having to be regenerated, after changes to source schemas.

In this paper, we address the issues related to the evolution and maintenance of data warehousing systems, when underlying data sources change their schema capabilities. These changes can invalidate views at the data warehousing system. We present an approach for dynamically adapting views according to schema changes arising on source relations. This type of maintenance concerns both the schema and the data of the data warehouse. The main issue is to avoid the view recomputation from scratch especially when views are defined from multiple sources.

One core feature of EVE is Evolvable-SQL (ESQL),
an extension of SQL that allows the view
definer to express preferences for view evolution. Using
E-SQL, a user defining a view can specify what information
is indispensable, what information is replaceable
by similar information from other ISs, and
whether a changing view extent is acceptable. This is
done by attaching preference parameters to elements
of the view query. This then is the key to evolving a
view by rewriting its view definition into a possibly
non-equivalent one that still preserves the user’s
intended semantics.