By Luciano Floridi
This paintings fulfills the necessity for a conceptual and technical framework to enhance figuring out of data caliber (IQ) and knowledge caliber criteria. The which means and sensible implementation of IQ are addressed, because it is suitable to any box the place there's a have to deal with info and matters akin to accessibility, accuracy, completeness, foreign money, integrity, reliability, timeliness, usability, the position of metrics and so on are all part of details Quality.
In order to aid the cross-fertilization of idea and perform, the newest study is gifted during this booklet. The views of specialists from past the origins of IQ in computing device technological know-how are integrated: library and knowledge technology practitioners and lecturers, philosophers of knowledge, of engineering and know-how, and of technology are all individuals to this volume.
The chapters during this quantity are in keeping with the paintings of a collaborative learn undertaking concerning the humanities and arts examine Council and Google and led via Professor Luciano Floridi, college of Oxford.
This paintings can be of curiosity to an individual dealing with info, together with these from advertisement, public, governmental and educational corporations. The specialist editors’ contributions introduce problems with curiosity to scientists, database curators and philosophers, although the problems could be disguised within the language and examples universal to another discipline.
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Extra resources for The Philosophy of Information Quality
Data quality: Concepts, methodologies and techniques. Berlin/New York: Springer. , & Williamson, J. (2014). Mechanisms and the evidence hierarchy. Topoi. 1007/s11245-013-9220-9. , & Goble, C. (2011, June 14–17). Quality trust and utility of scientific data on the web: Towards a joint model. Paper presented at the WebSci’11, Koblenz. , Lee, Y. , & Pipino, L. L. (1997). Total data quality management: The case of IRI. Paper presented at the conference on information quality, Cambridge, MA. Lee, Y.
In D. McLeod, D. R. Sacks, & H. ) The 16th international conference on very large data bases (pp. 519–538). Las Altos: Morgan Kaufman. Wang, R. , Kon, H. , & Madnick, S. E. (1993a). Data quality requirements analysis and modelling. Paper presented at the ninth international conference of data engineering, Vienna. Wang, R. , Reddy, M. , & Gupta, A. (1993b). An object-oriented implementation of quality data products. Paper presented at the WITS-’93, Orlando. Wang, R. , Reddy, M. , & Kon, H. B. (1995).
W. (2013). The role of reference data sets in data deduplication. In preparation. , & Oinn, T. (2006). Taverna: A tool for building and running workflows of services. Nucleic Acids Research, 34(Web Server issue), W729–W732. , & Floridi, L. (2012). IQ: Purpose and dimensions. In 17th International Conference on Information quality (ICIQ 2012), Paris. , & Wang, R. (2002). AIMQ: A methodology for information quality assessment. Information & Management, 40(2), 133–146. Loshin, D. (2004). Enterprise knowledge management – The data quality approach (Series in Data management).
The Philosophy of Information Quality by Luciano Floridi