Dataspaces are an abstraction in data management that aim to overcome some of the problems encountered in data integration system. The aim is to reduce the effort required to set up a data integration system by relying on existing matching and mapping generation techniques, and to improve the system in "pay-as-you-go" fashion as it is used. Labor-intensive aspects of data integration are postponed until they are absolutely needed.

PropertyValue
dbpedia-owl:abstract
  • Dataspaces are an abstraction in data management that aim to overcome some of the problems encountered in data integration system. The aim is to reduce the effort required to set up a data integration system by relying on existing matching and mapping generation techniques, and to improve the system in "pay-as-you-go" fashion as it is used. Labor-intensive aspects of data integration are postponed until they are absolutely needed. Traditionally, data integration and data exchange systems have aimed to offer many of the purported services of dataspace systems. Dataspaces can be viewed as a next step in the evolution of data integration architectures, but are distinct from current data integration systems in the following way. Data integration systems require semantic integration before any services can be provided. Hence, although there is not a single schema to which all the data conforms and the data resides in a multitude of host systems, the data integration system knows the precise relationships between the terms used in each schema. As a result, significant up-front effort is required in order to set up a data integration system. Dataspaces shift the emphasis to a data co-existence approach providing base functionality over all data sources, regardless of how integrated they are. For example, a DataSpace Support Platform (DSSP) can provide keyword search over all of its data sources, similar to that provided by existing desktop search systems. When more sophisticated operations are required, such as relational-style queries, data mining, or monitoring over certain sources, then additional effort can be applied to more closely integrate those sources in an incremental fashion. Similarly, in terms of traditional database guarantees, initially a dataspace system can only provide weaker guarantees of consistency and durability. As stronger guarantees are desired, more effort can be put into making agreements among the various owners of data sources, and opening up certain interfaces (e.g. , for commit protocols).
  • Der Begriff Dataspace bzw. Datenraum bezeichnet einen relativ jungen Ansatz zur Informationsintegration, mittels dessen Problematiken physischer als auch virtueller Integrationsansätze entgegnet werden soll. Um den Erstellungsaufwand einer Integrationsarchitektur gering zu halten, wird bei Dataspaces keine feste Zielstruktur vorab benötigt. Stattdessen kommen existierende Matching- und Mapping-Techniken zur Anwendung, um Daten unabhängig vom Grad der Integration in einem System zur Verfügung zu stellen. Dataspaces wachsen nach und nach mit ihrer Aufgabe bei Bedarf Pay-As-You-Go. Wenn z.  B. aufwändigere Anfragen über in Beziehung stehende Daten benötigt werden, können diese als Integritätsbedingung definiert werden. Dadurch wird der Integrationsaufwand auf den Zeitpunkt des Entstehens des Informationsbedarfs verschoben.
dbpedia-owl:wikiPageExternalLink
dbpedia-owl:wikiPageID
  • 26705941 (xsd:integer)
dbpedia-owl:wikiPageInLinkCount
  • 10 (xsd:integer)
dbpedia-owl:wikiPageOutLinkCount
  • 15 (xsd:integer)
dbpedia-owl:wikiPageRevisionID
  • 545868290 (xsd:integer)
dbpprop:hasPhotoCollection
dcterms:subject
rdf:type
rdfs:comment
  • Der Begriff Dataspace bzw. Datenraum bezeichnet einen relativ jungen Ansatz zur Informationsintegration, mittels dessen Problematiken physischer als auch virtueller Integrationsansätze entgegnet werden soll. Um den Erstellungsaufwand einer Integrationsarchitektur gering zu halten, wird bei Dataspaces keine feste Zielstruktur vorab benötigt.
  • Dataspaces are an abstraction in data management that aim to overcome some of the problems encountered in data integration system. The aim is to reduce the effort required to set up a data integration system by relying on existing matching and mapping generation techniques, and to improve the system in "pay-as-you-go" fashion as it is used. Labor-intensive aspects of data integration are postponed until they are absolutely needed.
rdfs:label
  • Dataspaces
  • Dataspaces
owl:sameAs
http://www.w3.org/ns/prov#wasDerivedFrom
foaf:isPrimaryTopicOf
is dbpedia-owl:wikiPageRedirects of
is owl:sameAs of
is foaf:primaryTopic of