Schema Evolution publication categorizer

Recent position papers demand more schema flexibility, such as the ability to handle variational data [3, 42]. Many agile software developers have long since turned towards NoSQL database systems such as MongoDB 1, Couchbase 2, or ArangoDB 3 which are schema-flexible, or even altogether schema-free. They allow to store datasets in different structural versions to co-exist. Yet even when the database management system does not maintain an explicit schema, there is commonly an implicit schema, as the application code makes assumptions about the structure of the stored data.

The purely manual specification of semantic correspondences between schemas is almost infeasible for very large schemas or when many different schemas have to be matched. Hence, solving such large-scale match tasks asks for automatic or semi-automatic schema matching approaches. Large-scale matching needs especially be supported for XML schemas and different kinds of ontologies due to their increasing use and size, e.g. in e-business, web and life science appli-cations.

Schema evolution is the increasingly important ability to adapt deployed schemas to changing requirements. Effective support for schema evolution is challenging since schema changes may have to be propagated, correctly and efficiently, to instance data and dependent schemas, mappings, or applications.

In practice, any database management system sometimes needs reorganization, that is, a change in some
aspect of the logical and/or physical arrangement of a database. In traditional practice, many types of reorganization
have required denying access to a database (taking the database offline) during reorganization.
Taking a database offline can be unacceptable for a highly available (24-hour) database, for example, a
database serving electronic commerce or armed forces, or for a very large database. A solution is to reorganize

The research area of ontology engineering seems to have reached a certain level of maturity, considering the vast amount of contemporary methods and tools for formalising and applying knowledge representation models. However, there is still little understanding of, and support for, the evolutionary aspects of ontologies. This is particularly crucial in distributed and collaborative settings such as the Semantic Web, where ontologies naturally co-evolve with their communities of use.

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.

Research on ontologies is becoming widespread in the biomedical informatics community. At the same time, it has become apparent that the challenges of properly constructing and maintaining ontologies have proven more difficult than many workers in the field initially expected. Discovering general, feasible methods has thus become a central activity for many of those hoping to reap the benefits of ontologies. This paper reviews current methods in the construction, maintenance, alignment, and evaluation of ontologies.

Ontologies, are explicit specifications of conceptualisations, and as such
serve as a backbone of many Information Systems (ISs) as knowledge bearing
artefacts representing mainly domain knowledge. As the use of ontologies in
several kinds of ISs increased in the recent years significantly, the question
of how to maintain these ontologies, gained more importance.
Although, there are no sophisticated methods available yet to support
all the aspects of change management for ontologies, it surely is an active
research field. Most of the work has been done under the titles of Ontology

Model management is a generic approach to solving problems of data programmability where precisely engineered mappings are required. Applications include data warehousing, e-commerce, object-to-relational wrappers, enterprise information integration, database portals, and report generators. The goal is to develop a model management engine that can support tools for all of these applications. The engine supports operations to match schemas, compose mappings, diff schemas, merge schemas, translate schemas into different data models, and generate data transformations from mappings.

After more than 20 years of research and practice in software configuration management (SCM), constructing consistent configurations of versioned software products still remains a challenge. This article focuses on the version models underlying both commercial systems and research prototypes. It provides an overview and classification of different versioning paradigms and defines and relates fundamental concepts such as revisions, variants, configurations, and changes. In particular, we focus on intensional versioning, that is, construction of versions based on configuration rules.

We briefly motivate and present a new online bibliography on schema evolution, an area which has recently gained much interest in both research and practice.

One of the fundamental aspects of information and database systems is that they change. Moreover, in so doing they evolve, although the manner and quality of this evolution is highly dependent on the mechanisms in place to handle it. While changes in data are handled well, changes in other aspects, such as structure, rules, constraints, the model, etc., are handled to varying levels of sophistication and completeness. In order to study this in more detail a workshop on Evolution and Change in Data Management was held in Paris in November 1999.

Whatever programming paradigm for data processing we choose, data has the tendency to live on the other side or to eventually end up there. The major paradigms for data processing are Cobol, object, relational and XML; each paradigm offers many facets and many versions; each paradigm provides specific forms of data models (object models, relational schemas, XML schemas, etc.). Each data-processing application depends on a horde of interrelated data models and artifacts that are derived from data models (such as data-access layers).

Database reorganization can be defined as changing some aspect of the way
database m arranged logically and/or physically. An example is changing from toone
to a one-to-many relationship. Reorganizatmn m a necessary function
system. This paper introduces the basic concepts of reorganization, including
performed. Many types of reorganization are described and classified into logical/
levels. Then pragmatic issues such as reorganization strategies, a survey of
commercial reorganization facilities, case studies, and database administration

Object-oriented database systems ususally exhibit specific advantages over traditional database management systems and programming languages. Among them stand the ease of writing, maintaining and debugging application programs, code modularity, inheritance, persistency and sharability. Of particular interest to software engineering and computer-aided design applications is also the ability to dynamically change the object definitions and the opportunity to define incrementally composite objects.

We provide an extensive overview of existing research in the field of software refactoring. This research is compared and discussed based on a number of different criteria: the refactoring activities that are supported, the specific techniques and formalisms that are used for supporting these activities, the types of software artifacts that are being refactored, the important issues that need to be taken into account when building refactoring tool support, and the effect of refactoring on the software process. A running example is used to explain and illustrate the main concepts.

Previous taxonomies of software evolution have focused on the purpose of the change (i.e., the why)
rather than the underlying mechanisms. This paper proposes a taxonomy of software evolution based
on the characterizing mechanisms of change and the factors that influence these mechanisms. The
taxonomy is organized into the following logical groupings: temporal properties, objects of change,
system properties, and change support.
The ultimate goal of this taxonomy is to provide a framework that positions concrete tools, formalisms

Time is a pervasive dimension of reality as everything evolves as time elapses. Information systems and applications at least mirror, and often have to capture, the time-varying and evolutionary nature of the phenomena they model and the activities they support. This aspect has been acknowledged and long studied in the field of temporal databases but it truly applies also to the World Wide Web, although it has not seemingly considered as a primary issue yet.

Object oriented database management systems (OODBMS) have enormous advantages in comparison to relational systems in modeling highly complex and dynamic application scenarios. Still they lack some flexibility which could help their promotion into widely spread industrial use. Especially engineering environments frequently require schema changes to handle evolving designs. The main contribution of the paper is twofold: firstly, we give an overview of schema evolution mechanisms, describing the state of the art in research.

We review the use on ontologies for the integration
of heterogeneous information sources. Based
on an in-depth evaluation of existing approaches to
this problem we discuss how ontologies are used to
support the integration task. We evaluate and compare
the languages used to represent the ontologies
and the use of mappings between ontologies as well
as to connect ontologies with information sources.
We also ask for ontology engineering methods and
tools used to develop ontologies for information integration.
Based on the results of our analysis we

With the grown availability of large and specialized online ontologies, the questions about the combined use of independently developed ontologies have become even more important. Although there is already a lot of research done in this area, there are still many open questions. In this paper we try to classify the problems that may arise into a common framework. We then use that framework to examine several projects that aim at some ontology combination task, thus sketching the state of the art.

With networked information access to heterogeneous data sources, the problem of terminology provision and interoperability of controlled vocabulary schemes such as thesauri becomes increasingly urgent. Solutions are needed to improve the performance of full-text retrieval systems and to guide the design of controlled terminology schemes for use in structured data, including metadata. Thesauri are created in different languages, with different scope and points of view and at different levels of abstraction and detail, to accommodate access to a specific group of collections.

In a multidatabase system, schematic conflicts between
two objects are usually of interest only when the
objects have some semantic similarity. We use the concept
of semantic proximity, which is essentially an abstraction/
mapping between the domains of the two objects associated
with the context of comparison. An explicit though
partial context representation is proposed and the specificity
relationship between contexts is defined. The contexts are
organized as a meet semi-lattice and associated operations
like the greatest lower bound are defined. The context of

One of the fundamental principles of the database approach is that a database allows a
nonredundant, unified representation of all data managed in an organization. This is
achieved only when methodologies are available to support integration across
organizational and application boundaries.
Methodologies for database design usually perform the design activity by separately
producing several schemas, representing parts of the application, which are subsequently
merged. Database schema integration is the activity of integrating the schemas of existing

Changes in the real world may require both the database population and the database schema
to evolve. Particularly, this is the case in CAD/CAM and CASE database systems, in which the
design objects constantly evolve in every aspect. On the other hand, the prototyping of a
database design may also involve changes to both the structure and behavior of a schema.
Unfortunately, most of the current systems offer little support for schema evolution. In this
paper, we survey the recent development of the research in the schema evolution of objectoriented