Schema Evolution publication categorizer

Schema versioning provides a mechanism for handling change in the structure of database systems and has been investigated widely, both in the context of static and temporal databases. With the growing interest in spatial and spatio-temporal data as well as the mechanisms for holding such data, the spatial context within which data items are formatted also becomes an issue. This paper presents a generalized model that accommodates temporal, spatial and spatio-temporal schema versioning within databases.

The aim of temporal data models is to accommodate the nature of time naturally and directly and to avoid the ad hoc, one-off extensions commonly employed in information systems design. Schema versioning is one of a number of related areas dealing with the general problem of using multiple schemata for various database related tasks. In particular, schema versioning, and its weaker companion, schema evolution, deal with the need to retain current data and software system functionality in the face of changing database structure.

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.

Context
UML and XML are two of the most commonly used languages in software engineering processes. One of the most critical of these processes is that of model evolution and maintenance. More specifically, when an XML schema is modified, the changes should be propagated to the corresponding XML documents, which must conform with the new, modified schema.
Objective

Nowadays, one of the most important problems of software engineering continues to be the maintenance of both databases and applications. It is clear that any method that can reduce the impact that database modifications produce on application programs is valuable for software engineering processes. We have proposed such a method, by means of a database evolution architecture (MeDEA) that makes use of database views. By using views, changes in the structure of the database schema can be delayed until absolutely necessary. However, some conditions oblige modifications to be made.

Supporting legacy applications when the database schema evolves represents a long-standing challenge of practical and theoretical importance. Recent work has produced algorithms and systems that automate the process of data migration and query adaptation; how- ever, the problems of evolving integrity constraints and supporting legacy updates under schema and integrity constraints evolution are significantly more difficult and have thus far remained unsolved.

In this paper we present a formal model for the support of temporal schema versions in object-oriented databases. Its definition is partially based on a generic (ODMG compatible) object model and partially introduces new concepts. The proposed model supports all the schema changes which are usually considered in the OODB literature, for which an operational semantics and a formal analysis of their correct behaviour is provided.

HECATAEUS is an open-source software tool for enabling impact prediction, what-if analysis, and regulation of relational database schema evolution. We follow a graph theoretic approach and represent database schemas and database constructs, like queries and views, as graphs. Our tool enables the user to create hypothetical evolution events and examine their impact over the overall graph before these are actually enforced on it.

The problem of archiving and querying the history of a database is made more complex by the fact that, along with the database content, the database schema also evolves with time.

Data Stream Management Systems (DSMSs) do not statically respond to issued queries — rather, they continuously produce result streams to standing queries, and often operate in a context where any interruption can lead to data loss. Support for schema evolution in continuous query processing is currently unaddressed. In this work we address evolution in DSMSs by proposing semantics for three evolution primitives: Add Attribute and Drop Attribute (schema evolution), and Alter Data (data evolution).

The complexity, cost, and down-time currently created by the database schema evolution process is the source of incessant problems in the life of information systems and a major stumbling block that prevent graceful upgrades. Furthermore, our studies shows that the serious problems encountered by traditional information systems are now further exacerbated in web information systems and cooperative scientific databases where the frequency of schema changes has increased while tolerance for downtimes has nearly disappeared.

Grid environments, providing distributed infrastructures, computing resources and data storage, usually show a high degree of heterogeneity and change in their metadata. We propose a platform for collaborative management and maintenance of common metadata for grids. As the conceptual foundation of this platform, a meta model is presented which distinguishes structured descriptions and classification structures that both are modifiable.

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.

In this paper, we visit the problem of the management of inconsistencies emerging on ETL processes as results of evolution operations occurring at their 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 rules for the management of evolution events. Given a change at an element of the graph, our framework detects the parts of the graph that are affected by this change and highlights the way they are tuned to respond to it.

The deep Web has many challenges to be solved. Among them is schema matching. In this paper, we build a conceptual connection between the schema matching problem SMP and the fuzzy constraint optimization problem FCOP. In particular, we propose the use of the fuzzy constraint optimization problem as a framework to model and formalize the schema matching problem. By formalizing the SMP as a FCOP, we gain many benefits. First, we could express it as a combinatorial optimization problem with a set of soft constraints which are able to cope with uncertainty in schema matching.

During data warehouse design, the designer frequently encounters the problem of choosing among different alternatives for the same design construct. The behavior of the chosen design in the presence of evolution events is an important parameter for this choice. This paper proposes metrics to assess the quality of the warehouse design from the viewpoint of evolution. We employ a graph-based model to uniformly abstract relations and software modules, like queries, views, reports, and ETL activities. We annotate the warehouse graph with policies for the management of evolution events.

The administrators and designers of modern Information Systems face the problem of maintaining their systems in the presence of frequently occurring changes in any counterpart of it. In other words, when a change occurs in any point of the system –e.g., source, schema, view, software construct– they should propagate the change in all the involved parts of the system.

Databases are continuously evolving environments, where design constructs are added, removed or updated rather often. Small changes in the database configurations might impact a large number of applications and data stores around the system: queries and data entry forms can be invalidated, application programs might crash. HECATAEUS is a tool, which represents the database schema along with its dependent workload, mainly queries and views, as a uniform directed graph.

Databases are continuously evolving environments, where design constructs are added, removed or updated rather often. Research has extensively dealt with the problem of database evolution. Nevertheless, problems arise with existing queries and applications, mainly due to the fact that, in most cases, their role as integral parts of the environment is not given the proper attention. Furthermore, the queries are not designed to handle database evolution. In this paper, we introduce a graph-based model that uniformly captures relations, views, constraints and queries.

Traditional modeling techniques typically focus on the static part of databases and ignore their dynamic part (e.g., queries or data-centric workflows). In this paper, we first introduce and sketch a graph-based model that that uniformly captures relations, views, constraints and queries. We then present Hecataeus, a tool for implementing and visualizing the above framework

Schema evolution poses serious challenges in historical data management. Traditionally the archival data has been (i) either migrated under the current schema version, to ease querying, but compromising archival quality, or (ii) maintained under the original schema version in which they firstly appeared, leading to a perfect archival quality, but to a taxing query interface.

With the emergence of enterprise-wide information systems, ontologies have become by definition a valuable aid for efficient database schema modeling and integration, in addition to their use in other disciplines such as the semantic web and natural language processing. This paper presents another important utilization of ontologies in database schemas: schema evolution. Specifically, our research concentrates on a new three-layered approach for schema evolution.

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