A. Aamodt, &. M. Nygård, ]. J. Aczel, B. Bouchon-meunier, G. Coletti et al., Entropies, characterizations, applications and some history In Modern information processing : From theory to applicationsAdriaans 96] P. Adriaans & D. Zantinge. Data mining Situation assessment based on spatially ambigous multisensor measurements Human Factors Analysis of Different Types of Uncertainties in Complex Systems Design rules : The power of modularity, Shahbazian E. & Rogova G., editeurs, Human Systems Integration to Enhance Maritime Domain Awareness for Port/Harbour Security, pp.191-222, 1995.

]. D. Ballou-95, &. H. Ballou, D. Pazer, R. Ballou, H. Wang et al., Modeling completeness versus consistency tradeoffs in information decision contexts The management of probabilistic data Knowledge and Data Engineering Stratégie de fusion d'informations exploitant le réseau des sources Data quality : Concepts, methodologies and techniques Fuzzy systems design principles : Building fuzzy if-then rule basesBisdikian 07] C. Bisdikian. Quality of information trade-offs in the detection of transient phenomena. In E. Carapezza, editeur, Unattended Ground, Sea, and Air Sensor Technologies and Applications IX Evaluation of Information Fusion techniques Part 1 : System Level Assessment Advances and Challenges in Multisensor Data and Information Processing, volume Sub-Series D : Information and Communication Security 8 of NATO Security through Science Series Measures of effectiveness for high-level fusionBloch 03] I. Bloch, editeur. Fusion d'information en traitement du signal et des images Editorial : reasoning with uncertainty in expert systems A risk based model for quantifying the impact of information quality Systems Design : Principles of Hierarchical Decomposition , 2011. http://it.toolbox.com/blogs/enterprise-solutions/ systems-design-principles-of-hierarchical-decomposition-48204 An introduction to the fuzzy sets and possibility theory-based treatment of flexible queries and uncertain or imprecise databases An essay to characterise information fusion systems MYS- TIQ : a system for finding more answers by using probabilities An outline of a theory of semantic information, Designing Information Systems to Optimize the Accuracy-Timeliness Tradeoff. Information Systems Research Modeling Information Manufacturing Systems to Determine Information Product Quality. Management Science 8ème Atelier sur la Fouille de Données ComplexesBerti-Equille 06] L. Berti-Equille. Qualité des données. Techniques de l'ingénieur A. Motro & P. Smets, editeurs, Uncertainty Management in Information Systems 9th Conference on Information Fusion (FUSION) Proceedings of SIGMODBuckles 82] B. P. Buckles & F. E. Petry. A fuzzy representation of data for relational databases. Fuzzy Sets and SystemsCalvo 02] T. Calvo, A. Kolesárová, M. Komorníková & R. Mesiar. Aggregation Operators : Properties, Classes and Construction Methods Tomasa Calvo Studies in Fuzziness and Soft Computing, pp.51-72, 1982.

R. Y. Bibliographiques, D. Caseau, &. S. Krob, and . Peyronnet, Complexité des systèmes d'information : une famille de mesures de la complexité scalaire d'un schéma d'architecture, Charatan 99a] F. Charatan. Family Compensated for Death after Illegible Prescription, pp.23-30, 1999.

]. F. Charatan-99b and . Charatan, Medical errors kill almost 100000 Americans a year, BMJ, vol.319, issue.7224, p.1519, 1999.
DOI : 10.1136/bmj.319.7224.1519

G. Chen and &. David-kotz, A Survey of Context-Aware Mobile Computing Research, 2000.

R. Cheng, S. Singh, and &. S. Prabhakar, U-DBMS : A database system for managing constantly-evolving data, Proceedings of VLDB, pp.1271-1274, 2005.

H. Christiansen and &. D. Martinenghi, Simplification of Database Integrity Constraints Revisited : A Transformational Approach Logic Based Program Synthesis and Transformation, LNCS, vol.3018, pp.178-197, 2004.

E. F. Codd, A relational model of data for large shared data banks, Communications of the ACM, vol.13, issue.6, pp.377-387, 1970.
DOI : 10.1145/362384.362685

]. D. Collett-97 and . Collett, Modelling survival data in medical research, p.132, 1997.
DOI : 10.1007/978-1-4899-3115-3

]. P. Costa, K. Laskey, E. Blasch, and &. Jousselme, Towards unbiased evaluation of uncertainty reasoning : The URREF ontology, International conference on information fusion (FUSION12), p.64, 2012.

P. Cykana, A. Paul, and &. M. Stern, DoD guidelines on data quality management, Proceedings of the Conference on Information Quality (ICIQ), pp.154-171, 1996.

R. De-amicis, &. Batini, ]. H. Decker, and &. D. Martinenghi, A methodology for data quality assessment on financial data Classifying Integrity Checking Methods with Regard to Inconsistency Tolerance, Studies Commun. Sci. SCKM Proceedings of the 10th International ACM SIGPLAN Conference on Principles and Practice of Declarative Programming, PPDP '08, pp.44-195, 2004.

H. Decker and &. D. Martinenghi, Modeling, measuring and monitoring the quality of information Advances in Conceptual Modeling -Challenging Perspectives, LNCS, vol.5833, issue.26, pp.212-221, 2009.

]. B. Desai, An introduction to database systems, 29 RÉFÉRENCES BIBLIOGRAPHIQUES [Dey 01] A. Dey. Understanding and Using Context, pp.4-7, 1990.

N. Di-ruocco, J. &. Scheiwiller, and . Sotnykova, La qualité des données : concepts de base et technique d'amélioration, La qualité et la gouvernance des données au service de la performance des entreprises

X. L. Dong and &. F. Naumann, Data fusion, Proc. VLDB Endowment 16th International Conference on Information Fusion, pp.1654-1655, 2009.
DOI : 10.14778/1687553.1687620

URL : https://hal.archives-ouvertes.fr/hal-01348220

&. H. Dubois and . Prade, A review of fuzzy set aggregation connectives, Information Sciences, vol.36, issue.1-2, pp.85-121, 1985.
DOI : 10.1016/0020-0255(85)90027-1

&. H. Dubois and . Prade, Representation and combination of uncertainty with belief functions and possibility measures, Computational Intelligence, vol.5, issue.1, pp.244-264, 1988.
DOI : 10.1016/0165-0114(78)90029-5

S. A. Ehikioya, A characterization of information quality using fuzzy logic, 18th International Conference of the North American Fuzzy Information Processing Society, NAFIPS (Cat. No.99TH8397), pp.635-639, 1999.
DOI : 10.1109/NAFIPS.1999.781771

L. English, Improving data warehouse and business information quality : Methods for reducing costs and increasing profits, p.44, 1999.

S. Ethiraj and &. D. Levinthal, Modularity and Innovation in Complex Systems, Management Science, vol.50, issue.2, pp.159-173, 2004.
DOI : 10.1287/mnsc.1030.0145

]. A. Feigenbaum-91 and . Feigenbaum, Total quality control (3ème édition) McGraw-Hill, p.22, 1991.

B. and D. Finetti, Theory of probability, p.151, 1974.
DOI : 10.1002/9781119286387

&. B. Fisher and . Kingma, Criticality of data quality as exemplified in two disasters, Information & Management, vol.39, issue.2, pp.109-116, 2001.
DOI : 10.1016/S0378-7206(01)00083-0

E. Fisher, S. Lauria, &. R. Chengalur-smith, and . Wang, Introduction to information quality, p.49

P. Gader, A. Mendez-vasquez, K. Chamberlin, J. Bolton, and &. A. Zare, A graphical interpretation of the Choquet integral, IEEE International Geoscience and Remote Sensing Symposium, IGARSS '04, pp.1605-1608, 2004.

R. Bibliographiques-[-gardyn-97-]-e and . Gardyn, A Data Quality Handbook For A Data Warehouse, Proceedings of the Conference on Information Quality (ICIQ), pp.267-290, 1997.

J. Grant and &. A. Hunter, A graphical interpretation of the Choquet integral Measuring inconsistency in knowledgebases, Fuzzy Sets and Systems IEEE Transactions on Fuzzy Systems Journal of Intelligent Information Systems, vol.92, issue.27 2, pp.167-189, 1997.

]. P. Gustavsson-05, &. T. Gustavsson, . Planstedt-]-l, &. A. Hall, and . Kandel, The road towards multi-hypothesis intention simulation agents architecture -fractal information fusion modeling The evolution from expert systems to fuzzy expert systems, IEEE Proceedings of the Winter Simulation Conference A. Kandel, editeur, Fuzzy Expert Systems, pp.3-21, 1992.

M. Higashi and &. G. Klir, MEASURES OF UNCERTAINTY AND INFORMATION BASED ON POSSIBILITY DISTRIBUTIONS, International Journal of General Systems, vol.10, issue.4, pp.43-58, 1983.
DOI : 10.1080/03081078208960799

]. D. Hunt-92 and . Hunt, Quality in america : How to implement a competitive quality program, Business One Irwin, vol.22, p.23, 1992.

]. J. James, A New, Evidence-based Estimate of Patient Harms Associated with Hospital Care, Journal of Patient Safety, vol.9, issue.3, pp.122-128, 2013.
DOI : 10.1097/PTS.0b013e3182948a69

]. J. Juran, Juran on leadership for quality, p.22, 1989.

E. Kim, W. Kim, and &. Y. Lee, IT spending growth to be slower than expected in 2014 due to pricing pressure, Gartner says, Classifier fusion using local confidence. LNAI 2366, p.59, 2002.

]. J. Kim and &. J. Shao, Statistical methods for handling incomplete data, p.31, 2013.

]. W. King-78, &. J. King, L. Rodriguez, G. Klein, &. T. Klir et al., Evaluating Management Information Systems Can Humans Detect Errors in Data ? Impact of Base Rates, Incentives, and Goalss Multisensor data fusion for object detection, classification and identification (Tutorial) Fuzzy sets, uncertainty and information On measuring uncertainty and uncertainty-based information : Recent development Uncertainty and information : Foundations of generalized information theory Uncertainty modeling and analysis in engineering and the science User Perceptions of Information Quality in World Wide Web Information Retrieval Behaviour The New Dynamics of Strategy : sense making in a complex and complicated world, SPIE Defense, Security+Sensing, 6 Mai 2014. viiKovac 97] R. Kovac, Y.W. Lee & L. Pipino. Total Data Quality Management : the case of IRI Proceedings of the International Conference on Information Quality (ICIQ), pp.43-51, 1978.

]. S. Kwan-96, F. Kwan, &. D. Olken, and . Rotem, Uncertain, Incomplete, and Inconsistent Data in Scientific and Statistical Databases, A. Motro & P. Smets, editeurs, Uncertainty Management in Information Systems, pp.127-153, 1996.
DOI : 10.1007/978-1-4615-6245-0_5

]. K. Laudon-11, &. J. Laudon, . Laudon-]-l, C. L. Lecornu, P. J. Guillou et al., Pearson Custom Publishing Le Bras. Contribution à l'étude des meures de l'intérêt des règles d'association et à leurs propriétés algorithmiques REFEROCOD : A probabilistic method to medical coding support C2i : A tool to gather medical indexed information, Management information systems : Managing the digital firm Annual International Conference of the IEEE Engineering in Medicine and Biology 9th International Conference on Information Technology and Applications in Biomedicine (ITAB), pp.41-142, 2009.

. Cauvin and . Anterocod, Actuarial survival curves applied to medical coding support for chronic diseases, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp.1158-1161, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00609271

R. Bibliographiques-lee, D. M. Diane, R. Strong, &. R. Kahn, and . Wang, AIMQ : a methodology for information quality assessment, Information & Management, vol.40, issue.44, pp.133-146, 2002.

E. Lefebvre, M. Hadzagic, and &. É. Bossé, Advances and challenges in multisensor data and information processing, chapitre On quality of information in multi-source fusion environments Llinas. Handbook of multisensor data fusion : theory and practice, chapitre 25 : Assessing the performance of multisensor fusion processes, pp.60-655, 1996.

V. V. Mandke, &. M. Nayar, and ]. R. Mason, Information integrity?a structure for its definition Measuring information output : A communication systems approach, Proceedings of the International Conference on Information Quality (ICIQ) ix [Matlin 77] G. Matlin. How to Survive a Management Assessment. MIS Quarterly, pp.314-338, 1977.

V. Mayer-schonberger, &. K. Cukier, D. M. Meyen, and &. M. Willshire, Big data : A revolution that will transform how we live, work and think A data quality engineering framework, Proceedings of the Conference on Information Quality (ICIQ), pp.95-116, 1997.

]. C. Murphy-00 and . Murphy, Combining belief functions when evidence conflicts, Decision Support Systems, vol.29, issue.1, pp.1-9, 2000.
DOI : 10.1016/S0167-9236(99)00084-6

M. Mutsuzaki, M. Theobald, A. De-keijzer, J. Widom, P. Agrawal et al., Trio-One : Layering uncertainty and lineage on a conventional DBMS, Proceedings of CIDR, pp.269-274, 2007.

R. J. Bibliographiques, &. G. O-'brien, S. Marakas, R. Parsons, &. T. Pautke et al., Quality-driven query answering, chapitre Information quality criteria Management information systems, 10ème édition . McGraw-Hill/IrwinOMG 08] Object Management Group OMG. UML Profile for Modeling Quality of Service and Fault Tolerance Characteristics and Mechanisms Specification , Version 1.1 Ce qu'il faut savoir sur les dispositifs médicaux implantables [DMI] Qualitative methods for reasoning under uncertainty Information and database quality, chapitre The organisation's most important data issues Mining imperfect data : Dealing with contamination and incomplete records Peralta. Data Quality Evaluation in Data Integration Systems Exploring information superiority : A methodology for measuring the quality of information and its impact on shared awareness Information quality measurement of medical encoding support based on usability Database management systems, From Databases to Information Systems -Information Quality Makes the Difference Proceedings of the International Conference on Information Quality (ICIQ)Pipino 02] L. Pipino, Y.W. Lee & R. Wang. Data quality assessment. Communications of the ACM, pp.244-260, 2001.

]. T. Mcgraw-hill, T. Redman, T. Redman, and . Redman, A Model for Web Services Discovery with QoS Data quality : Management and technology. Bantam Books Data quality for the information age Data Quality Management Past, Present, and Future : Towards a Management System for Data, Handbook of Data Quality : Research and Practice, pp.28-29, 1992.

R. Bibliographiques, R. Rempt, G. Rogova, and &. V. Nimier, The Navy in the 21st Century, Part II : Theater Air and Missile Defense Reliability in information fusion : Literature survey A new concept of knowledge, Rogova 10] G. Rogova & É. Bossé. Information quality in information fusion. 13th Conference on Information Fusion (FUSION), pp.63-58, 2001.

M. Scannapieco, A. Virgillito, C. Marchetti, M. Mecella, &. Adams et al., The DaQuinCIS architecture: a platform for exchanging and improving data quality in cooperative information systems, Proceedings of the 1994 First Workshop on Mobile Computing Systems and Applications, WMCSA '94, pp.551-582, 1994.
DOI : 10.1016/j.is.2003.12.004

]. T. Schuck-10, &. Schuck, ]. L. Blaschsebastian-coleman-13, ]. G. Sebastian-coleman, G. Shafer et al., Measuring data quality for ongoing improvement : a data quality assessment framework A mathematical theory of evidence The role of process metadata and data quality perceptions in decision-making The collected papers of claude e. shannon Human-automation interaction, 13th Conference on Information Fusion Proceedings of the American Philosophical Society, pp.151-50, 1962.

&. K. Singh-13-]-rajeev-pratap-singh, N. Pattanaik, J. Singpurwalla, &. T. Booker, and . Bement, An Approach to Composite QoS Parameter based Web Service Selection, Fuzzy logic and probability applications : Bridging the gap, chapitre Probability theory, pp.470-477, 2013.

A. , S. D. Singpurwalla, &. J. Booker, F. Smarandache, and &. J. Dezert, Membership functions and probability measures of fuzzy sets (with comments) Advances and applications of dsmt for information fusion, Advances in the Dempster-Shafer theory of evidence, pp.150-867, 1994.

M. A. Solano and &. G. Jernigan, Enterprise data architecture principles for High-Level Multi-Int fusion : A pragmatic guide for implementing a heterogeneous data exploitation framework, Proceedings of the 15th International Conference on Information Fusion (FUSION'12), pp.867-874, 2012.

A. Sølvberg and &. D. Kung, Information systems engineering : An introduction, p.14, 1993.
DOI : 10.1007/978-3-642-78001-1

R. Srivastava and R. Srivastava, Reliability modeling of information systems with human elements : A new perspective A note on Internal Control Systems with Control Components in Series, The Accounting Review, vol.LX, issue.3, pp.88-504, 1983.

K. Stefanidis, E. Pitoura, and &. P. Vassiliadis, Managing contextual preferences, Information Systems, vol.36, issue.8, pp.1158-1180, 2011.
DOI : 10.1016/j.is.2011.06.004

]. D. Strong-97, Y. Strong, &. R. Lee, and . Wang, Data quality in context, Communications of the ACM, vol.40, issue.5, pp.103-110, 1940.
DOI : 10.1145/253769.253804

B. Stvilia and &. L. Gasser, An activity theoretical model for information quality change. First Monday Introduction to target recognition. The Institution of Electrical Engineers, pp.68-112, 2005.

I. G. Todoran, L. Lecornu, A. Khenchaf, and &. J. Le-caillec, Information quality evaluation in fusion systems, Proceedings of the 16th International Conference on Information Fusion (FUSION'13), pp.906-913, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00846769

]. I. Todoran-14a, L. Todoran, A. Lecornu, &. J. Khenchaf, and . Le-caillec, Assessing information quality in information fusion systems, NATO Symposium on Analysis Support to Decision Making in Cyber Defence & Security, p.104, 2014.

]. I. Todoran-14b, L. Todoran, A. Lecornu, &. J. Khenchaf, and . Le-caillec, A Methodology to Evaluate Important Dimensions of Information Quality in Systems, JDIQ) -(sous révision), 2014.
DOI : 10.1145/2744205

]. I. Todoran-14c, L. Todoran, A. Lecornu, &. J. Khenchaf, and . Le-caillec, Toward the quality evaluation in complex information systems, Signal Processing, Sensor/Information Fusion and Target Recognition XXIII. Proceedings of SPIE Defense, Security, and Sensing Symposium, pp.90910-2014

R. Bibliographiques-[-toumi-07, ]. A. Toumi, E. Turban, J. Aronson, and &. P. Liang, Intégration des bases de connaissances dans les systèmes d'aide à la décision : Application à l'aide à la reconnaissance de cibles radar noncoopératives Decision support systems and intelligent systems, p.113, 2005.

]. V. Verykios, &. Wang, R. Ziad, M. Lee, and Y. , The Purdue University Data Quality Project, pp.119-137, 2002.

E. Waltz and &. J. Llinas, Multisensor data fusion, chapitre 11 : System modeling and performance evaluation, pp.389-423, 1990.

]. Y. Wand-96, &. R. Wand, and . Wang, Anchoring data quality dimensions in ontological foundations, Communications of the ACM, vol.39, issue.11, pp.86-95, 1996.
DOI : 10.1145/240455.240479

]. R. Wang, M. P. Reddy, and &. H. Kon, Toward quality data: An attribute-based approach, Decision Support Systems, vol.13, issue.3-4, pp.349-372, 1995.
DOI : 10.1016/0167-9236(93)E0050-N

]. R. Wang and &. D. Strong, Beyond Accuracy: What Data Quality Means to Data Consumers, Journal of Management Information Systems, vol.1, issue.3, pp.5-33, 1996.
DOI : 10.1109/69.404034

R. Wang, M. Ziad, and &. Y. Lee, Data quailty, p.34, 2002.

]. L. Warren-99 and . Warren, Strategic information synthesis by globular knowledge fusion, 1999 Information, Decision and Control. Data and Information Fusion Symposium, Signal Processing and Communications Symposium and Decision and Control Symposium. Proceedings (Cat. No.99EX251), pp.407-412, 1999.
DOI : 10.1109/IDC.1999.754192

]. W. Weaver-49 and . Weaver, The Mathematics of Communication, Scientific American, vol.181, issue.1, pp.11-15, 1949.
DOI : 10.1038/scientificamerican0749-11

E. Weise, Study : Medication errors harm 1.5M a year. USA Today, 21 juillet http://usatoday30.usatoday.com/money/industries/ health/2006-07-20-drug-errors_x.htm, consulté le 31 mai 2014, Snape & M. Marchington. Managing with total quality management, p.67, 1998.

]. L. Xu, A. Krzyzak, and &. C. Suen, Methods of combining multiple classifiers and their applications to handwriting recognition, IEEE Transactions on Systems, Man, and Cybernetics, vol.22, issue.3, p.166, 1992.
DOI : 10.1109/21.155943

]. R. Yager-87a and . Yager, TOWARD A THEORY OF CONJUNCTIVE VARIABLES, International Journal of General Systems, vol.10, issue.3, pp.203-227, 1987.
DOI : 10.1016/0165-0114(80)90060-3

]. R. Yager-87b, &. R. Yager, R. Kennes, and . Yager, On the Dempster-Shafer framework and new combination rules Connectives and quantifiers in fuzzy sets Uncertainty Management for Intelligence Analysis, Data Fusion for Situation Monitoring, Incident Detection, Alert and Response Management, pp.93-138, 1987.

J. Ye, S. Mckeever, L. Coyle, S. Neely, and &. S. Dobson, Resolving uncertainty in context integration and abstraction, Proceedings of the 5th international conference on Pervasive services, ICPS '08, pp.131-140, 2008.
DOI : 10.1145/1387269.1387292

L. Zeng, B. Benatallah, A. Ngu, M. Dumas, J. Kalagnanam et al., QoS-aware middleware for Web services composition, Zhu 03] Y. Zhu. Multisensor decision and estimation fusion, pp.311-327, 2003.
DOI : 10.1109/TSE.2004.11

H. Zimmermann and &. P. Zysno, Latent connectives in human decision making. Fuzzy Sets and SystemsZins 07] C. Zins. Conceptual approaches for defining data, information, and knowledge, Journal of the American Society for Information Science and Technology, vol.4, issue.54 4 10, pp.37-51, 1980.