Data: Difference between revisions
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{{Quotation|'''Data''' refer to a collection of facts usually collected by observations, measures or experiments. Data consist of numbers, words, or images. It is generally called abstract data in [[InfoVis|infovis]], since it refers to data that has no inherent spatial structure enabling further mapping to any geometry.|[Bertini and Lalanne, 2009]}} | {{Quotation|'''Data''' refer to a collection of facts usually collected by observations, measures or experiments. Data consist of numbers, words, or images. It is generally called abstract data in [[InfoVis|infovis]], since it refers to data that has no inherent spatial structure enabling further mapping to any geometry.|[Bertini and Lalanne, 2009]}} | ||
see [[Knowledge Discovery]] for an explanation of the relationship between [[Data]], [[Information]], [[Insight]], [[Model]], [[Pattern]], [[Hypothesis]], [[Knowledge]] and [[Knowledge Crystallization]]. | |||
see also: [[Data]], [[Information]], [[Knowledge]], [[Wisdom]] | see also: [[Data]], [[Information]], [[Knowledge]], [[Wisdom]] | ||
Revision as of 11:46, 21 August 2009
Data is simple facts, lacking any context. If data doesn't inform us, it's not information. Or equally valid, without context data it is simply the raw material from which we depart to understanding. 07012007 is data, that can hold many meanings, a date, a batch number, an anniversary...
[Dürsteler, 2007]
Data are observational measurements that have been recorded in some way, whereas information is data that is generalized, ordered and contextualized in ways that give them
meaning. Information thus is selective toward data, separating the important from the relatively unimportant.
[Mennis et al., 2000]
Data refer to a collection of facts usually collected by observations, measures or experiments. Data consist of numbers, words, or images. It is generally called abstract data in infovis, since it refers to data that has no inherent spatial structure enabling further mapping to any geometry.
[Bertini and Lalanne, 2009]
see Knowledge Discovery for an explanation of the relationship between Data, Information, Insight, Model, Pattern, Hypothesis, Knowledge and Knowledge Crystallization.
see also: Data, Information, Knowledge, Wisdom
References
- [Bertini and Lalanne, 2009] Bertini, E. and Lalanne, D. 2009. Surveying the complementary role of automatic data analysis and visualization in knowledge discovery. In Proceedings of the ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery: integrating Automated Analysis with interactive Exploration (Paris, France, July 28 - 28, 2009). VAKD '09. ACM, New York, NY, 12-20.
- [Dürsteler, 2007] Juan C. Dürsteler, Diagrams for Visualisation, Inf@Vis! (The digital magazine of InfoVis.net), Created at: Jan. 7, 2007, Retrieved at: Jan. 11, 2007, http://www.infovis.net/printMag.php?num=186&lang=2
- [Mennis et al., 2000] Jeremy L. Mennis, Donna J. Peuquet, and Liujian Qian, A conceptual framework for incorporating cognitive principles into geographical database representation, International Journal of Geographical Information Science, 14(6):510-520, 2000.