Dataspaces are an abstraction in data management That aim to Overcome Reviews some of the problems Encountered in data integration system. This is a great way to get your money back into the system. It’s a great way to get your money back. Labor-intensive aspects of data integration are postponed until they are absolutely needed. [1] [2] [3] [4] [5] [6] [7] [8]

Traditionally, data integration and data exchange systems have a lot to offer. 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, the precise relations between the terms used in each schema and the fact that they are not identical. As a result, a significant up-front effort is required in order to set up a data integration system.

Dataspaces shift the emphasis to a data co-existence. For example, a DataSpace Support Platform (DSSP) can provide keyword search over all of its data sources. When more sophisticated operations are required, such as relational-style queries, data mining , or monitoring over certain sources, then additional effort may 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,

Data graphs play an important role in dataspaces systems. They work was fact based (triple gold “data entities” made up of subject-predicate-object) [9] data modeling approach qui supports the “pay-as-you-go” Technical Described Above. They support data co-existence and are therefore an ideal technique for semantic integration . Search and relational-style queries and analytics can work simultaneously on data graphs which is another important property of dataspaces.

Applications of dataspaces

Personal information management

The aim of this article is to provide an easy-to-use and easy-to-use information system. Recent desktop search tools are an important first step for PIM, but are limited to keyword queries. Our desktops typically contain some structured data (eg, spreadsheets ) and there are important associations between disparate items on the desktop. Hence, the next step for PIM is to allow the user to search the desktop in more meaningful ways. For example, “find the list of juniors that took my database last quarter,” or “compute the aggregate balance of my bank accounts.” SIGMOD this year. “This is the first time I have ever been to Hawaii. Finally, we would like to query about sources, eg, “find all the papers where I acknowledged a particular grant,” “find all the experiments run by a particular student,” or “find all spreadsheets that have a variance column.

The principles of dataspaces in this example are

  1. A PIM tool must enable access to all information on the desktop, and not just an explicitly or implicitly chosen subset, and
  2. While PIM often involves integrating data from multiple sources, we can not assume users will invest the time to integrate. Instead, most of the time the system will have to provide best-effort results, and tighter integrations will be created only in cases where the benefits will clearly outweigh the investment.

Scientific data management

Consider a scientific research group working on environmental observation and forecasting. They may be monitoring a coastal ecosystem through weather stations, shore- and buoy-mounted sensors and remote imagery. In addition they could be running atmospheric and fluid-dynamics models that simulate past, current and near future conditions. The computations may require importing data and model outputs from other groups, such as river flows and ocean circulation forecasts. The observations and simulations are the inputs to the programs, the results of which are given in the table below. estuary. Such a group can easily amass millions of data in just a few years. While it may be for each file, someone in the group knows where it is and what it means, no one person can know the entire holdings nor what every file means. This article is about the nature of the weather conditions and how it affects the temperature of the wind. Data product (graph, isoline plot, animation), forecast or hindcast, and so forth. What is the difference between a product and a product? Which finite element grid? How long was the simulation time step? Which atmospheric dataset was used as input? What code version was used? Which finite element grid? How long was the simulation time step? Which atmospheric dataset was used as input?

Groups will need to federate with other groups to create scientific dataspaces of regional or national scope. They will need to be able to export their data in standard scientific formats, and at granularities (sub-file or multiple file) that do not necessarily correspond to the partitions they use to store the data. Users of the federated dataspace, the data of which is used to determine the extent to which the data are collected. Such collections may require local copies or additional indices for fast search.

This scenario illustrates several dataspace requirements, including

  1. A dataspace-wide catalog,
  2. Support for data lineage and
  3. Creating collections and indexes over entities that span more than one participating source.

See also

  • Data mapping
  • Data integration
  • Semantic integration
  • Information integration
  • Semantic query


  1. Jump up^ Belhajjame, K .; Paton, NW ; Embury, SM; Fernandes, AAA; Hedeler, C. (2013). “Incrementally improving dataspaces based on user feedback”. Information Systems . 38 (5): 656. doi : 10.1016 / .
  2. Jump up^ Belhajjame, K .; Paton, NW ; Embury, SM; Fernandes, AAA; Hedeler, C. (2010). “Feedback-based annotation, selection and refinement of schema mappings for dataspaces”. Proceedings of the 13th International Conference on Extending Database Technology – EDBT ’10 . p. 573. ISBN  9781605589459 . Doi : 10.1145 / 1739041.1739110 .
  3. Jump up^ Talukdar, PP; Ives, ZG; Pereira, F. (2010). “Automatically incorporating new sources into keyword search-based data integration”. Proceedings of the 2010 International Conference on Management of Data – SIGMOD ’10 . p. 387. ISBN  9781450300322 . Doi : 10.1145 / 1807167.1807211 .
  4. Jump up^ Sarma, AD; Dong, X. (L .; Halevy, AY (2009) “Data Modeling in Support Dataspace Platforms”.. Conceptual Modeling: Foundations and Applications .. Lecture Notes in Computer Science 5600 . 122. p. ISBN  978-3-642- 02462-7 . doi :10.1007 / 978-3-642-02463-4_8 .
  5. Jump up^ Dong, XL; Halevy, A .; Yu, C. (2008). “Data integration with uncertainty”. The VLDB Journal . 18 (2): 469. doi : 10.1007 / s00778-008-0119-9 .
  6. Jump up^ Howe, B .; Maier, D .; Rayner, N .; Rucker, J. (2008). “Quarrying dataspaces: Schemaless profiling of unfamiliar information sources”. 2008 IEEE 24th International Conference on Data Engineering Workshop . p. 270. ISBN  978-1-4244-2161-9 . Doi : 10.1109 / ICDEW.2008.4498331 .
  7. Jump up^ Dong, X .; Halevy, A. (2007). “Indexing dataspaces”. Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data – SIGMOD ’07 . p. 43. ISBN  9781595936868 . Doi : 10.1145 / 1247480.1247487 .
  8. Jump up^ Franklin, M .; Halevy, A .; Maier, D. (2005). “From databases to dataspaces”. ACM SIGMOD Record . 34 (4): 27. doi : 10.1145 / 1107499.1107502 .
  9. Jump up^ [1]ZDNet, Actian adds SPARQL City’s graph analytics engine to its arsenal.

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