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The support of such scenarios requires management, inquiry and delivery of contextdependent user pro les and preferences including the creation, modi cation and deletion of user data (i.e. general information about the user, user behaviour and user preferences). In the following sections, an approach to structuring a user pro le that considers the above requirements and enables the inclusion of situational constraints is described. Second, a proposal for a pro le management component and related interaction with clients is detailed. 5.2.4.1 Pro le Structure The overall high-level user pro le structure is depicted in Figure 5.16. Each user pro le can be separated into different sections that include application-dependent user information. An application could also be interpreted as a virtual application i.e. a group of actual applications that adhere to the same semantics and hence are capable of processing the related user data. Each application-related section includes at least one so-called pro le subset, the Default Pro le Subset (Default Subset); more details will be described while introducing the basic elements of the pro le structure: Pro les, Pro le Subsets, Quali ers and User Data. The dependencies of these basic elements are depicted in Figure 5.16. Pro le. A user pro le contains all persistent data about a user user data required by all applications and services the user is subscribed to. Pro le Subset. A Pro le Subset contains an application-related subset of all persistent user data. Besides the actual user data, it also contains Quali ers. A user can have several Pro le Subsets, which can be distinguished into Default and Conditional Pro le Subsets. However, there has to be exactly one Default Pro le Subset for each supported application including

Cohen, J (1990) Things I have learned (so far) American Psychologist, 45, 1304 1312 Cohen, J (1994) The earth is round (p < 05) American Psychologist, 49, 997 1003 Cohen, J, Cohen, P, West, S G, & Aiken, L S (2003) Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed) Hillsdale, NJ: Erlbaum Diaconis, P (1985) Theories of data analysis: From magical thinking through classical statistics In D C Hoaglin, F Mosteller, & J W Tukey (Eds), Exploring data tables, trends, and shapes (pp 1 36) New York: Wiley Draper, N R, & Smith, H (1998) Applied regression analysis (3rd ed) New York: Wiley Fox, J (1991) Regression diagnostics Sage University Papers Series on Quantitative Applications in the Social Sciences, 07 79 Newbury Park, CA: Sage Fox, J (2000) Nonparametric simple regression: Smoothing scatterplots.

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Sage University Papers Series on Quantitative Applications in the Social Sciences, 07 130 Thousand Oaks, CA: Sage Gail, M H, Brinton, L A, Byar, D P, Corle, D K, Green, SB, Schairer, C, & Mulvihill, J J (1989) Projecting individualized probabilities of developing breast cancer for white females who are being examined annually Journal of the National Cancer Institute, 81, 1879 1886 Janis, I L, & Feshbach, S (1953) Effects of fear-arousing communications Journal of Abnormal and Social Psychology, 48, 78 92 Kirk, R E (1995) Experimental design: Procedures for the behavioral sciences (3rd ed) Paci c Grove, CA: Brooks Cole Kruskal, W H (1960) Discussion of the papers of Messrs Anscombe and Daniel Technometrics, 2, 257 258 Little, R J A, & Rubin, D B (1987) Statistical analysis with missing data New York: Wiley McClelland, G H (2000).

Nasty data: Unruly, ill-mannered observations can ruin your analysis In H T Reis & C M Judd (Eds), Handbook of research methods in social and personality psychology (pp 393 411) New York: Cambridge..

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Figure 6.3. Model system. The cell scaffold bioreactor system for tissue engineering has been designed to modulate cell differentiation and functional assembly by providing environmental factors described in Fig. 6.2.