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4.2.2 Fuzzy Case Observing FADs from ASDB, a CF of 0.31 is found (Table 2). Despite of this low CF, the dependence degree shown between HorizonType and OC content was more informative than in the crisp case. It reflected better the expert knowledge. Even though, initially, the soil scientist expected a higher degree, it can be explained due to the influence of soils placed at Southeast Mesoenvironment in ASDB. Indeed, due to the arid nature of this climate, it could be expected that one of the main soil forming factors, OC content incorporated to soil from vegetation, were low and homogenous. The latter conclusion can be checked regarding Table 4. Moreover, fuzzy data mining let us obtain a higher number of rules than in crisp case. This supposes, quantitatively, a higher volume of discovered knowledge. A good example of correspondence or ”fusion” between databases and expert knowledge could be obtained comparing ARs from Sierra of Gádor with Southeast ones. The former had rules with ”moderate” and ”high” OC content in consequent, whereas the latter had a ”low” value in consequent. Sierra of Gádor has a higher mean altitude and mean annual rainfall, and, consequently, more vegetation in soil and horizons (especially in Ah type). Looking at this, the fuzzy model reflects more accurately soil forming processes as melanization and accumulation. We can also examine others IFSDB in addition to Mesoenvironment. I.e., Protocol constitute an important source of variability in ASDB. Comparing ”Perez” and ”Alias” categories, the former has more ARs (Table 6) and relates more categories, reflecting a more detailed and precise knowledge than ”Alias”. ”Perez” protocols (including field description, analysis and other techniques) seem to be more reliable than ”Alias” ones. 5 CONCLUSIONS We have seen how large databases can be divided into homogeneous subsets defining one or more discriminant attributes. This division, followed by a knowledge discovery process, can allow us to discover previously unnoticed relations in data. We conclude that, for this particular case, knowledge extracted by means of fuzzy data mining was more suitable to ”fusion” or comparison with expert knowledge that crisp. Moreover, fuzzy data mining was sensitive to low support categories as [%OrganicCarbon = Low] or [HorizonType = Bk or Btk], discarded in crisp data mining. We could confirm that fuzzy data mining is highly sensitive to latent knowledge in ASDBs. That fact is AN EXPERIENCE IN MANAGEMENT OF IMPRECISE SOIL DATABASES very important for a soil scientist, since lets us apply it with the assurance that imprecision and uncertainty factors (IFASDB) will not distort or alter the knowledge discovery process. As a future task, we propose to solve this same problem in a general case. With a domain expert aid, we must define the set of criteria for database decomposition but also discern when fuzzy techniques get better results than crisp ones. REFERENCES 163 Agrawal, R., Imielinski, T., and Swami, A. (1993). Mining association rules between sets of items in large databases. In Proc. Of the 1993 ACM SIGMOD Conference, pages 207–216. Alias, J. (1986). Mapa de suelos de Mula. Mapa 1:100000 y memoria. LUCDEME; MAPA-ICONA-University of Murcia. Alias, J. (1987). Mapa de suelos de Cehegin. Mapa 1:100000 y memoria. LUCDEME; MAPA-ICONA- University of Murcia. Berzal, F., Blanco, I., Sánchez, D., Serrano, J., , and Vila, M. (2003). A definition for fuzzy approximate dependencies. Fuzzy Sets and Systems. Submitted. Berzal, F., Blanco, I., Sánchez, D., and Vila, M. (2001). A new framework to assess association rules. In Hoffmann, F., editor, Advances in Intelligent Data Analysis. Fourth International Symposium, IDA’01. Lecture Notes in Computer Science 2189, pages 95–104. Springer-Verlag. Berzal, F., Blanco, I., Sánchez, D., and Vila, M. (2002). Measuring the accuracy and interest of association rules: A new framework. Intelligent Data Analysis. An extension of (Berzal et al., 2001), submitted. Blanco, I., Martín-Bautista, M., Sánchez, D., and Vila, M. (2000). On the support of dependencies in relational databases: strong approximate dependencies. Data Mining and Knowledge Discovery. Submitted. Bosc, P. and Lietard, L. (1997). Functional dependencies revisited under graduality and imprecision. In Annual Meeting of NAFIPS, pages 57–62. Bui, E. and Moran, C. (2003). A strategy to fill gaps in soil over large spatial extents: An example from the murray-darlin basin of australia. Geoderma, 111, pages 21–44. Cazemier, D., Lagacherie, P., and R., M.-C. (2001). A possibility theory approach from estimating available water capacity from imprecise information contained in soil databases. Geoderma, 103, pages 113–132. Delgado, G., Delgado, R., Gamiz, E., Párraga, J., Sánchez Marañon, M., Medina, J., and Martín-García, J. (1991). Mapa de Suelos de Vera. LUCDEME, ICONA-Universidad de Granada.
164 ENTERPRISE INFORMATION SYSTEMS VI Delgado, M., Marín, N., Sánchez, D., and Vila, M. (2003). Fuzzy association rules: General model and applications. IEEE Transactions on Fuzzy Systems 11(2), pages 214–225. Delgado, M., Martín-Bautista, M., Sánchez, D., and Vila, M. (2000). Mining strong approximate dependencies from relational databases. In Proceedings of IPMU’2000. FAO (1998). The world reference base for soil resources. world soil resources. Technical Report 84, ISSS/AISS/IBG/ISRIC/FAO, Rome. Hussain, F., Liu, H., Tan, C., and Dash, M. (1999). Discretization: An enabling technique. Technical report, The National University of Singapore. Oyonarte, C. (1990). Estudio Edáfico de la Sierra de Gádor (Almera). Evaluación para usos forestales. PhD thesis, University of Granada. Pérez Pujalte, A. (1987). Mapa de suelos de Tabernas. Mapa 1:100000 y memoria. LUCDEME; MAPA- ICONA-CSIC. Pérez Pujalte, A. (1989). Mapa de suelos de Sorbas. Mapa 1:100000 y memoria. LUCDEME; MAPA-ICONA- CSIC. Qian, H., Klinka, K., and Lavkulich, L. (1993). Relationships between color value and nitrogen in forest mineral soils. Can. J. Soil Sci., 73, pages 61–72. Sánchez, D., Sánchez, J. R., Serrano, J. M., and Vila, M. A. (2004). Association rules over imprecise domains involving fuzzy similarity relations. To be submitted to Estylf 2004. Sánchez-Marañón, M. (1992). Los suelos del Macizo de Sierra Nevada. Evaluación y capacidad de uso (in Spanish). PhD thesis, University of Granada. Sánchez-Marañón, M., Delgado, R., Párraga, J., and Delgado, G. (1996). Multivariate analysis in the quantitative evaluation of soils for reforestation in the sierra nevada (southern spain). Geoderma, 69, pages 233– 248. Scheinost, A. and Schwertmann, U. (1999). Color identification of iron oxides and hydroxisulfates: Uses and limitations. Soil Sci. Soc. Am. J., 65, pages 1463– 1461. Serrano, J. (2003). Fusin de Conocimiento en Bases de Datos Relacionales: Medidas de Agregacin y Resumen (in Spanish). PhD thesis, University of Granada. Shortliffe, E. and Buchanan, B. (1975). A model of inexact reasoning in medicine. Mathematical Biosciences,23, pages 351–379. Soil-Survey-Staff (1975). Soil Taxonomy. U.S. Dept. Agri. Handbook No. 436. Ulery, A. and Graham, R. (1993). Forest-fire effects on soil color and texture. Soil Sci. Soc. Am. J., 57, pages 135– 140. Webster, R. (1977). Quantitative and Numerical Methods in Soil Classification and Survey. Claredon Press, Oxford. Zadeh, L. (1983). A computational approach to fuzzy quantifiers in natural languages. Computing and Mathematics with Applications, 9(1):149–184. Table 1: Discriminant attributes for the soil database Mesoenvironment Soil profile Horizon Sierra Nevada [SN] 35 103 Sierra Gádor[SB] 70 176 Southeast [SE] 89 262 Bibliographic source Soil profile Horizon Ph.D. Thesis [MARAÑON] 35 103 Ph.D. Thesis [OYONARTE] 70 176 LUCDEME sheet 1014, Vera.[VERA] 29 76 LUCDEME [TABERNA] sheet 1030, Tabernas. 14 37 LUCDEME sheet 1031, Sorbas. [SOR- BAS] 24 72 LUCDEME sheet 912, Cehegn. [CEHE- GIN] 10 32 LUCDEME sheet 911, Mula. [MULA] 12 45 Acting protocol Soil profile Horizon Sánchez-Marañon, M. [SANCHEZ] 35 103 Oyonarte, C. [CECILIO] 70 176 Pérez-Pujalte, A. [PEREZ] 67 185 Alías, L. [ALIAS] 22 77 Table 2: Obtained CF in ASDB (HorizonT ype → %OrganicCarbon) Approx. Dep. CF 0.09 Assoc. Rules [C] ⇒ [L],CF0.8 [Bw] ⇒ [L], CF0.7 [Ah] ⇒ [L],CF-0.39 [Ah] ⇒ [M],CF0.41 F. Approx. Dep. CF 0.31 F. Assoc. Rules [Ck] ⇒ [L], CF 0.53 [C] ⇒ [L],CF0.69 [Bwk] ⇒ [L], CF 0.23 [Bw] ⇒ [L], CF 0.41 [Bw] ⇒ [M], CF 0.25 [Btk] ⇒ [L], CF 0.81 [Bt] ⇒ [L], CF1 [Bk] ⇒ [L], CF 0.49 [Ap] ⇒ [L],CF0.50 [Ah] ⇒ [L],CF-0.01 [Ah] ⇒ [M],CF0.13 [Ah] ⇒ [H], CF 0.28
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vi TABLE OF CONTENTS PART 1 - DATAB
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viii TABLE OF CONTENTS A WIRELESS A
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CONFERENCE COMMITTEE Honorary Presi
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Hung, P. (AUSTRALIA) Jahankhani, H.
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Invited Papers
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2 ENTERPRISE INFORMATION SYSTEMS VI
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10 ENTERPRISE INFORMATION SYSTEMS V
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EVOLUTIONARY PROJECT MANAGEMENT: MU
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MANAGING COMPLEXITY OF ENTERPRISE I
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ASSESSING EFFORT PREDICTION MODELS
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ORGANIZATIONAL AND TECHNOLOGICAL CR
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ASAP Processes W Project Kickoff A
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since it means changing the relevan
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ACME-DB: AN ADAPTIVE CACHING MECHAN
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ACME-DB: AN ADAPTIVE CACHING MECHAN
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Hit Rate (%) ACME-DB: AN ADAPTIVE C
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5.3.6 Effect on all Policies by Int
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ACKNOWLEDGEMENTS We would like to t
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This paper describes the data sampl
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The major limit A of the operation
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RELATIONAL SAMPLING FOR DATA QUALIT
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TOWARDS DESIGN RATIONALES OF SOFTWA
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((Brown et al., 1998), pp. 159-190,
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sive data filtering, presentation (
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• implementation changes in servi
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MEMORY MANAGEMENT FOR LARGE SCALE D
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Data Rate [bytes/sec] 1600000 14000
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E.g., DV Camcorder Camera Packets (
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Procedure CheckOptimal (n rs 1 ...n
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MEMORY MANAGEMENT FOR LARGE SCALE D
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PART 2 Artificial Intelligence and
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110 ENTERPRISE INFORMATION SYSTEMS
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Page 127 and 128: DYNAMIC MULTI-AGENT BASED VARIETY F
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(k� 1) (k) (k�1) (k) p � w
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Table 5: Performance Comparison of
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A USER-CENTERED METHODOLOGY TO GENE
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carries out the second phase of the
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1 1 1 1 2 PROCESS STORE ENTITY EDGE
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constraints rules. KOGGE (Ebert et
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PART 4 Software Agents and Internet
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CABA 2 L A BLISS PREDICTIVE COMPOSI
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y disables evidencing severe mental
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Figure 4: Symbols emission in DAR-H
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they evidence a generalization erro
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A METHODOLOGY FOR INTERFACE DESIGN
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and mobility loss coupled with redu
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Tradeoff: The default input is poss
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Sessions on the VABS which are held
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A CONTACT RECOMMENDER SYSTEM FOR A
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A CONTACT RECOMMENDER SYSTEM FOR A
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- "Going to the sources". The user
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Here are the matrix W and P for our
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EMOTION SYNTHESIS IN VIRTUAL ENVIRO
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theme turns out to be surprisingly
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6 FROM FEATURES TO SYMBOLS 6.1 Face
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sions are described by different em
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Electronic Imaging 2000 Conference
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to the accessibility problem for th
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Figure 3: Hierarchical View of the
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4 CONCLUSIONS AND FUTURE WORK On th
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PERSONALISED RESOURCE DISCOVERY SEA
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profile, the user defines his curre
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Abdelkafi, N. .....................
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