Vassia Atanassova: 47-55

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	\| title = Temporality and intuitionistic fuzzy data warehouses		\| title = Temporality and intuitionistic fuzzy data warehouses
	\| shortcut = nifs/10/4/70-74		\| shortcut = nifs/10/4/47-55
	}}		}}
	{{issue/author		{{issue/author
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	{{issue/data		{{issue/data
	\| conference = Eighth [[International Conference on Intuitionistic Fuzzy Sets]], Varna, 20-21 June 2004		\| conference = Eighth [[International Conference on Intuitionistic Fuzzy Sets]], Varna, 20-21 June 2004
	\| issue = [[Notes on Intuitionistic Fuzzy Sets/10/4\|"Notes on Intuitionistic Fuzzy Sets", Volume 10 (2004) Number 4]], pages 70-74		\| issue = [[Notes on Intuitionistic Fuzzy Sets/10/4\|"Notes on Intuitionistic Fuzzy Sets", Volume 10 (2004) Number 4]], pages 47-55
	\| file = NIFS-10-4-70-74.pdf		\| file = NIFS-10-4-47-55.pdf
	\| format = PDF		\| format = PDF
	\| size = ~~4784~~		\| size = 9935
	\| abstract = Data warehouse is an amalgamated view on the data within an enterprise and a first step in integrating enterprise systems. Data warehouses are used for analysing enterprise data online, offering the possibility to aggregate and compare data along dimensions relevant in the application domain. In intuitionistic fuzzy data warehouses each object has its degree of compliance and non-compliance. Typically time is one of the dimensions we find in data warehouses allowing comparisons of different periods. The instances of dimensions, however, change over time, organisations unite and separate, organisational structures emerge and vanish, or evolve. In current data warehouse architectures these changes cannot be represented adequately since all dimensions are considered as orthogonal, putting restrictions on the validity of queries defined over several eras. In this paper we propose an architecture for temporal data warehouse systems, which allows the accommodation of the temporal dimension of data belonged to evolving hierarchical structures. We present how uncertainty in the time component influences the degrees of compliance of the information.		\| abstract = Data warehouse is an amalgamated view on the data within an enterprise and a first step in integrating enterprise systems. Data warehouses are used for analysing enterprise data online, offering the possibility to aggregate and compare data along dimensions relevant in the application domain. In intuitionistic fuzzy data warehouses each object has its degree of compliance and non-compliance. Typically time is one of the dimensions we find in data warehouses allowing comparisons of different periods. The instances of dimensions, however, change over time, organisations unite and separate, organisational structures emerge and vanish, or evolve. In current data warehouse architectures these changes cannot be represented adequately since all dimensions are considered as orthogonal, putting restrictions on the validity of queries defined over several eras. In this paper we propose an architecture for temporal data warehouse systems, which allows the accommodation of the temporal dimension of data belonged to evolving hierarchical structures. We present how uncertainty in the time component influences the degrees of compliance of the information.
	\| keywords = data warehouse, intuitionistic fuzzy sets, OLAP, temporality		\| keywords = data warehouse, intuitionistic fuzzy sets, OLAP, temporality

Vassia Atanassova: Created page with "{{PAGENAME}} {{PAGENAME}} {{PAGENAME}} {{PAGENAME}} {{issue/title | title = Temporality and intuitionistic fuzzy data warehouses | shortcut = nifs/10/4/70-74 }} {{issue/author | author = Panagiotis Chountas | institution = Health Care Computing Group, School of..."

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Created page with "{{PAGENAME}} {{PAGENAME}} {{PAGENAME}} {{PAGENAME}} {{issue/title | title = Temporality and intuitionistic fuzzy data warehouses | shortcut = nifs/10/4/70-74 }} {{issue/author | author = Panagiotis Chountas | institution = Health Care Computing Group, School of..."

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[[Category:Publications on intuitionistic fuzzy sets|{{PAGENAME}}]]
[[Category:Publications in Notes on IFS|{{PAGENAME}}]]
[[Category:Publications in 2004 year|{{PAGENAME}}]]
[[Category:ICIFS conference publications|{{PAGENAME}}]]
{{issue/title
| title = Temporality and intuitionistic fuzzy data warehouses
| shortcut = nifs/10/4/70-74
}}
{{issue/author
| author = Panagiotis Chountas
| institution = Health Care Computing Group, School of Computer Science, University of Westminster
| address = Watford Road, Northwick Park, London, HA1l 3TP, UK
| email-before-at = chountp
| email-after-at = wmin.ac.uk
}}
{{issue/author
| author = Ilias Petrounias
| institution = Department of Computation, UMIST
| address = PO Box 88, Manchester M60 1QD, UK
}}
{{issue/author
| author = Chris Vasilakis
| institution = Health Care Computing Group, School of Computer Science, University of Westminster
| address = Watford Road, Northwick Park, London, HA1l 3TP, UK
}}
{{issue/author
| author = Elia El-Darzi
| institution = Health Care Computing Group, School of Computer Science, University of Westminster
| address = Watford Road, Northwick Park, London, HA1l 3TP, UK
}}
{{issue/author
| author = Andy Tseng
| institution = Department of Computation, UMIST
| address = PO Box 88, Manchester M60 1QD, UK
}}
{{issue/author
| author = Boyan Kolev
| institution = CLBME — Bulgarian Academy of Sciences
| address = Bl. 105, Sofia-1113, BULGARIA
}}
{{issue/author
| author = Vassilis Kodogiannis
| institution = Health Care Computing Group, School of Computer Science, University of Westminster
| address = Watford Road, Northwick Park, London, HA1l 3TP, UK
}}
{{issue/author
| author = Peter Georgiev
| institution = CLBME — Bulgarian Academy of Sciences
| address = Bl. 105, Sofia-1113, BULGARIA
}}
{{issue/data
| conference = Eighth [[International Conference on Intuitionistic Fuzzy Sets]], Varna, 20-21 June 2004
| issue = [[Notes on Intuitionistic Fuzzy Sets/10/4|"Notes on Intuitionistic Fuzzy Sets", Volume 10 (2004) Number 4]], pages 70-74
| file = NIFS-10-4-70-74.pdf
| format = PDF
| size = 4784
| abstract = Data warehouse is an amalgamated view on the data within an enterprise and a first step in integrating enterprise systems. Data warehouses are used for analysing enterprise data online, offering the possibility to aggregate and compare data along dimensions relevant in the application domain. In intuitionistic fuzzy data warehouses each object has its degree of compliance and non-compliance. Typically time is one of the dimensions we find in data warehouses allowing comparisons of different periods. The instances of dimensions, however, change over time, organisations unite and separate, organisational structures emerge and vanish, or evolve. In current data warehouse architectures these changes cannot be represented adequately since all dimensions are considered as orthogonal, putting restrictions on the validity of queries defined over several eras. In this paper we propose an architecture for temporal data warehouse systems, which allows the accommodation of the temporal dimension of data belonged to evolving hierarchical structures. We present how uncertainty in the time component influences the degrees of compliance of the information.
| keywords = data warehouse, intuitionistic fuzzy sets, OLAP, temporality
| references =
# S. Anahory, Murray D. Data warehousing in the real world: A practical approach for building Decision Support Systems, Addison-Wesley 1997
# M.H. Bohlen et al. Point- versus Interval-Based Temporal Data Models. In Proc. of 14th I[CDE, IEEE Computer Society Press, pp. 192-201, Orlando, Florida, USA, 1998.
# Chountas, P., Petrounias I., Atanassov K., Kodogiannis, V. El-Darzi E., Representation & Querying of Temporal Conflict” FQAS 2002, DBLP, LNAI, No. 2522, pp.112-123, SpringerVerlag ;
# O. Etzion, S. Jajodia, and S. Sripada, (editors). Temporal Databases: Research and Practice, LNCS, 1998.
# W.H. Inmon. Building the Data Warehouse. Second Edition, John Wiley & Sons, New York, 1996,
# C.S. Jensen and R.T. Snodgrass. Temporal Data Management. In IEEE Transactions on Knowledge and Data Engineering, Vol. 11(1): pp. 36-44, 1999. ,
# R. Kimball. The Data Warehouse Toolkit Jon Wiley & Sons, New York, 1996.
# T.B.Pedersen and C.S. Jensen. Multidimensional Data Modeling for Complex Data. In Proc. of 15th ICDE, IEEE Computer Society, pp. 336-345, Sydney, Australia, 1999.
# A. Trevsky, I.Gati, “Studies of similarity”, Cognition and Categorization, Hillsdale, NJ: Erlbaum, 1978
# M. Jarke, M. Lenzerini, Y. Vassiliou, and P. Vassiliadis. Fundamentals of Data Warehouses. Springer-Verlag, 2000.
# Atanassov K., [[Generalized Nets in Artificial Intelligence. Volume 1: Generalized Nets and Expert Systems|Generalized Nets in Artificial Intelligence, Vol. 1: Generalized Nets and Expert Systems]], “Prof. M. Drinov” Publ. House, Sofia, 1998
# Kolev, B., P. Chountas, [[Issue:Intuitionistic fuzzy relational databases|Intuitionistic Fuzzy Relational Databases]], In: - Proceedings of the Seventh International Conference on Intuitionistic Fuzzy Sets, 23-24 August 2003, Sofia, Bulgaria, 109-113
# Kolev, B., Intuitionistic Fuzzy Relational Databases and Translation of the Intuitionistic Fuzzy SQL”, in press
# Atanassov K. [[Intuitionistic Fuzzy Sets: Theory and Applications|Intuitionistic Fuzzy Sets]], Springer-Verlag, Heidelberg, 1999
| citations =
| see-also =
}}

Issue:Temporality and intuitionistic fuzzy data warehouses - Revision history

Vassia Atanassova: 47-55