Schema Evolution publication categorizer

We introduce a protocol for schema evolution in a globally distributed database management system with shared data, stateless servers, and no global membership. Our protocol is asynchronous--it allows different servers in the database system to transition to a new schema at different times--and online--all servers can access and update all data during a schema change. We provide a formal model for determining the correctness of schema changes under these conditions, and we demonstrate that many common schema changes can cause anomalies and database corruption.

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.

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.

A fundamental problem in information integration is to precisely specify the relationships, called mappings, between schemas. Designing mappings is a time-consuming process. To alleviate this problem, many mapping systems have been developed to assist the design of mappings. However, a benchmark for comparing and evaluating these systems has not yet been developed.

An increasing number of data sources now become available on the Web, but often their contents are only accessible through query interfaces. For a domain of interest, there often exist many such sources with varied coverage or querying capabilities. As an important step to the integration of these sources, we consider the integration of their query interfaces. More specifically, we focus on the crucial step of the integration: accurately matching the interfaces.

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.

We introduce the +Spicy mapping system. The system is based on a number of novel algorithms that contribute to increase the quality and expressiveness of mappings. +Spicy integrates the computation of core solutions in the mapping generation process in a highly efficient way, based on a natural rewriting of the given mappings. This allows for an efficient implementation of core computations using common runtime languages like SQL or XQuery and guarantees very good performances, orders of magnitude better than those of previous algorithms.

Ontology matching, aiming to obtain semantic correspondences between two ontologies, has played a key role in
data exchange, data integration and metadata management. Among numerous matching scenarios, especially the applications cross multiple domains, we observe an important problem, denoted as unbalanced ontology matching which requires to find the matches between an ontology describing a local domain knowledge and another ontology covering the information over multiple domains, is not well studied in the community.

We present the AgreementMaker system for matching real-world schemas and ontologies, which may consist of hundreds or even thousands of concepts. The end users of the system are sophisticated domain experts whose needs have driven the design and implementation of the system: they require a responsive, powerful, and extensible framework to perform, evaluate, and compare matching methods. The system comprises a wide range of matching methods addressing dierent levels of granularity of the components being matched (conceptual vs.

We address the problem of unsupervised matching of schema information from a large number of data sources into the

Schema mappings are high-level specifications that describe the relationship between two database schemas. They are an important tool in several areas of database research, notably in data integration and data exchange. However, a concrete theory of schema mapping optimization including the formulation of optimality criteria

The inversion of schema mappings has been identified as one of the fundamental operators for the development of a
general framework for metadata management. In fact, during the last years three alternative notions of inversion for

A schema mapping is a declarative specification of the relationship between instances of a source schema and a target schema. The data exchange (or data translation) problem asks: given an instance over the source schema, materialize an instance (or solution) over the target schema that satisfies the schema mapping. In general, a given source instance may have numerous different solutions. Among all the solutions, universal solutions and core universal solutions have been singled out and extensively studied.

Research has investigated mappings among data sources under two perspectives. On one side, there are studies of practical tools for schema mapping generation; these focus on algorithms to generate mappings based on visual specifications provided by users. On the other side, we have theoretical researches about data exchange. These study how to generate a solution -- i.e., a target instance -- given a set of mappings usually specified as tuple generating dependencies.

Data exchange is the process of converting an instance of one schema into an instance of a different schema according to a given specification. Recent data exchange systems have largely dealt with the case where the schemas are given a priori and transformations can only migrate data from the first schema to an instance of the second schema. In particular, the ability to perform data-metadata translations, transformation in which data is converted into metadata or metadata is converted into data, is largely ignored.

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.

The old problem of managing the history of database information is now made more urgent and complex by fast-spreading web information systems. Indeed, systems such as Wikipedia are faced with the challenge of managing the history of their databases in the face of intense database schema evolution. Our PRIMA system addresses this difficult problem by introducing two key pieces of new technology.

Translating data and data access operations between applications and databases is a longstanding data management problem. We present a novel approach to this problem, in which the relationship between the application data and the persistent storage is specified using a declarative mapping, which is compiled into bidirectional views that drive the data transformation engine. Expressing the application model as a view on the database is used to answer queries, while viewing the database in terms of the application model allows us to leverage view maintenance algorithms for update translation.

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.

Given a multirelational database scheme and a relational mapping f transforming it, an important question is whether the resulting scheme is equivalent to the original one. This question was addressed in the literature with respect to those relational schemes that satisfy the so called universal relation assumption; however, no study was ever concerned with multirelational (data base) schemes that do not necessarily satisfy this assumption.We present two general definitions of lossless transformation of the database scheme based on the so-called closed world and open world assumptions.

Detecting changes by comparing data snapshots is an important requirement for difference queries, active databases, and version and configuration management. In this paper we focus on detecting meaningful changes in hierarchically structured data, such as nested-object data. This problem is much more challenging than the corresponding one for relational or flat-file data.

Detecting and representing changes to data is important for active databases, data warehousing, view maintenance, and version and configuration management. Most previous work in change management has dealt with flat-file and relational data; we focus on hierarchically structured data. Since in many cases changes must be computed from old and new versions of the data, we define the hierarchical change detection problem as the problem of finding a \"minimum-cost edit script\" that transforms one data tree to another, and we present efficient algorithms for computing such an edit script.

We define the notion of independent views to indicate whether the range values of the two views may be achieved independently The concept of complementary views indicates when the domain element can be uniquely determined by the range values of the two complementary views We consider the relationship between independent and complementary views In unrestricted domains, a view (but not the identity or empty view) can have more than one complementary, independent view Databases, however, are more restricted domains They are finite power sets A view is monotonic if it preserves inclusion However,

The problem of integrating data from multiple data sources—either on the Internet or within enterprises—has received much attention in the database and AI communities. The focus has been on building data integration systems that provide a uniform query interface to the sources. A key bottleneck in building such systems has been the laborious manual construction of semantic mappings between the query interface and the source schemas. Examples of mappings are ldquoelement location maps to addressrdquo and ldquoprice maps to listed-pricerdquo.