Schema Evolution publication categorizer

Biomedical ontologies are heavily used to annotate data, and different ontologies are often interlinked by ontology mappings. These ontology-based mappings and annotations are used in many applications and analysis tasks. Since biomedical ontologies are continuously updated dependent artifacts can become outdated and need to undergo evolution as well. Hence there is a need for largely automated approaches to keep ontology-based mappings up-to-date in the presence of evolving ontologies.

The continuous evolution of life science ontologies requires the adaptation of their associated mappings. We propose two approaches for tackling this problem in a largely automatic way: (1) a composition-based adaptation relying on the principle of mapping composition and (2) a diff-based adaptation algorithm individually handling change operations to update the mapping. Both techniques reuse unaffected correspondences, and adapt only the affected mapping part.

Mappings between related ontologies are increasingly used to support data integration and analysis tasks. Changes in the ontologies also require the adaptation of ontology mappings. So far the evolution of ontology mappings has received little attention albeit ontologies change continuously especially in the life sciences. We therefore analyze how mappings between popular life science ontologies evolve for different match algorithms. We also evaluate which semantic ontology changes primarily affect the mappings.

Semantic annotations are often used to enrich documents as clinical trials and
electronic health records. However, the usability of these annotations tends to decrease over
time due to the evolution of the domain ontologies. The maintenance of these annotations is
critical for tools that exploit them (eg, search engines and decision support systems) in order
to assure an acceptable level of performance. Despite the recent advances in ontology
evolution systems, the maintenance of semantic annotations remains an open problem. In

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.

Background
Ontologies are increasingly used to structure and semantically describe entities of domains, such as genes and proteins in life sciences. Their increasing size and the high frequency of updates resulting in a large set of ontology versions necessitates efficient management and analysis of this data.

Results

Mappings between related ontologies are increasingly used to support data integration and analysis tasks. Changes in the ontologies also require the adaptation of ontology mappings. So far the evolution of ontology mappings has received little attention albeit ontologies change continuously especially in the life sciences. We therefore analyze how mappings between popular life science ontologies evolve for different match algorithms. We also evaluate which semantic ontology changes primarily affect the mappings.

Schema evolution is an unavoidable consequence of the application development lifecycle. The two primary schemas in an application, the conceptual model and the persistent database model, must co-evolve or risk quality, stability, and maintainability issues. We study application-driven scenarios, where the conceptual model changes and the database and mapping must evolve in kind. We present a technique that, in most cases, allows those evolutions to progress automatically. We treat the mapping as data, and mine that data for patterns.

Schema evolution is an unavoidable consequence of the application development lifecycle. The two primary schemas in an application, the client conceptual object model and the persistent database model, must co-evolve or risk quality, stability, and maintainability issues. We present MoDEF, an extension to Visual Studio that supports automatic evolution of object-relational mapping artifacts in the Microsoft Entity Framework.

The development of new techniques and the emergence of new high- throughput tools have led to a new information revolution. The amount and the diversity of the information that need to be stored and processed have led to the adoption of data integration systems in order to deal with information extraction from disparate sources. The mediation between traditional databases and ontologies has been recognized as a cornerstone issue in bringing in legacy data with formal semantic meaning.

In this paper, we discuss the problem of performing impact prediction for changes that occur in the schema/structure of the data warehouse sources. We abstract Extract-Transform-Load (ETL) activities as queries and sequences of views. ETL activities and its sources are uniformly modeled as a graph that is annotated with policies for the management of evolution events. Given a change at an element of the graph, our method detects the parts of the graph that are affected by this change and highlights the way they are tuned to respond to it.

There is a growing need to define a semantic mapping from a database schema to an ontology. Such a mapping is an integral part of the data integration systems that use an ontology as a unified global view. However, both ontologies and database schemas evolve over time in order to accommodate updated information needs. Once the ontology and the database schema associated with a semantic mapping evolved, it is necessary and important to maintain the validity of the semantic mapping to reflect the new semantics in the ontology and the schema.

Many XML-relational systems, i.e., the systems that use an XML schema as an external schema and a relational schema as an internal schema of the data application representation level, require modifications of the data schemas in the course of time. Schema evolution is one of the ways to support schema modifications for the application at the DBMS level. A number of schema evolution support systems for different data models have been suggested.

Modern information systems, and web information systems in particular, are faced with frequent database schema changes, which generate the necessity to manage such evolution and preserve their history.

One of the most important challenges that software engineers (designers, developers) still have to face in their everyday work is the evolution of working database systems. As a step for the solution of this problem in this paper we propose MeDEA, which stands for Metamodel-based Database Evolution Architecture. MeDEA is a generic evolution architecture that allows us to maintain the traceability between the different artifacts involved in any database development process. MeDEA is generic in the sense that it is independent of the particular modeling techniques being used.

Abstract. One of the most severe problems related to database evolution
is how to reflect in the data level the changes that have occurred
in the conceptual schema of a database. This is specially relevant when
evolution operations affect ISA relationships. In this paper we present
our view of the evolution of ISA relationships, focusing on the artifacts
that generate the sentences for changing the data in a consistent way.

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

In relational databases a view definition is a query against the database, and a view materialization is the result of applying the view definition to the current database A view materialization over a database may change as relations in the database undergo modificationsIn this paper a mechanism is proposed in which the view is materialized at all times The problem which this mechanism addresses is how to quickly update the view in response to database changes A structure is maintained which provides information useful in minimizing the amount of work caused by updatesMethods are presented fo

One of the main challenges in data integration systems is the maintenance of the mappings between the mediation schema and the source schemas. In a dynamic environment, such mappings must be flexible enough in order to accommodate new data sources and new usersrsquo requirements. In this context, we address a novel and complex problem that consists in propagating a change event occurring at the source level or at the user level into the mediation level.

revious works in data integration can be classified according to the approach used to define objects at the mediation level. One of these approaches is called global-as-view (GAV) and requires that each object is expressed as a view (a mediation query) on the data sources. One important limit of this approach is the management of the evolutions in the system. Indeed, each time a change occurs at the source schema level, all the queries defining the mediation objects have to be reconsidered and possibly redefined.

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.

Current view technology supports only static views in the sense that views become undefined and hence obsolete as soon as the underlying information sources (ISs) undergo capability changes. We propose to address this new view evolution problem - which we call view synchronization - by a novel solution approach that allows affected view definitions to be dynamically evolved to keep them in synch with evolving ISs.

An important issue in data integration is the integration of semantically equivalent but schematically heterogeneous data sources. Declarative mechanisms supporting powerful source restructuring for such databases have been proposed in the literature, such as the SQL extension SchemaSQL. However, the issue of incremental maintenance of views defined in such languages remains an open problem.

A database view is a portion of the data structured in a way suitable to a specific
on views must be translated into updates on the underlying database. This
translation process in the relational model.
The procedure is as follows: first, a “complete” set of updates is defined such
(i) together with every update the set contains a “return” update, that is, one
back to the original state;
(ii) given two updates in the set, their composition is also in the set.
To translate a complete set, we define a mapping called a “translator,” that

A flexible database system needs to support changes to its
schema in order to facilitate the requirements of new applications
and to support interoperability within a multidatabase system. In this
paper, we present an approach to schema evolution through changes to the
EntityRelationship (ER) schema of a database. We enhance the graphical constructs