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According to the analysis of existing standard ontologies built for context representation, one could add that it is currently not an option to link to those ontologies, if decidability and performance of reasoning matters One either inherits a high DL complexity or introduces undecidability For example, SOUPA [21] and time-entry, an OWL DL subset of OWL Time [47] used in OWL-S [73], are in SHOIF(D), and the OWL DL variant of the Descriptive Ontology for Linguistic and Cognitive Engineering, DOLCE-lite [41], is listed among the Top10 of the most challenging ontologies [42] The Suggested Upper Merged Ontology proposed by IEEE SUMO [79] is undecidable and was recently found to be inconsistent [48] Existing performance results of DL reasoners are often limited to the classi cation of static ontologies [112].

However, TCP does not have any mechanisms to differentiate between congestion-induced losses and other random losses As a result, when TCP observes random wireless losses, it wrongly interprets such losses as congestion losses, and cuts down its window, thus reducing the throughput of the connection This effect is more pronounced in low bandwidth wireless networks as window sizes are typically small and, hence, packet losses typically result in a retransmission timeout (resulting in the window size being cut down to one) due to the lack of enough duplicate acknowledgments for TCP to go into the fast retransmit phase Even in high-bandwidth wireless networks, if bursty random losses (due to cochannel interference or fading) are more frequent, this phenomenon of TCP experiencing a timeout is more likely, because of the multiple losses within a window resulting in the lack of sufficient number of acknowledgments to trigger a fast retransmit.

However, in the case of frequent updates (a KB submission, discarding, and re-submission cycle), the communication overhead introduced on loading the ontology can easily dominate the overall performance In this respect, the delay caused by ontology-based inferencing can become a major obstacle for its use in context-aware applications The whole process of determining the situation of a user, including the gathering and transformation of the relevant context data, is limited to about 2 seconds per classi cation Retraction can improve the performance for this type of application drastically, since only a small fraction of the ontology changes between two requests However, the standard DL interface DIG 11 [12] does not support the removal of speci c axioms, making it necessary to resubmit the complete ontology for each request For the DIG 2.

0 standard [9], it is therefore proposed to have a modular extension to the interface that supports incremental reasoning and retraction [11] Unfortunately, current reasoners provide only some kind of batch-oriented reasoning procedure A notable exception is RacerPro, which offers low-level retraction support for most of its statements..

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set of predictors and of subsets of predictors is a hallmark of multiple regression analysis Three Uses of Multiple Regression Analysis: Description, Prediction, and Theory Testing Multiple regression analysis can be used for description, simply to summarize the relationships of a set of predictors to a criterion at a single point in time, (eg, to summarize the relationship of a set of employee characteristics to job performance) Further, multiple regression analysis can be used for prediction, (eg, to predict the job performance of a set of job applicants based on their characteristics measured during the job application process) The use of MR in theory testing is perhaps the most important application for the development of psychology as a science: Ideas derived from theory and from previous research can be translated into hypotheses that are tested using multiple regression analysis, (eg.

, testing whether each of a set of predictors of job performance identi ed by theory or previous empirical research actually predict job performance across a variety of employment settings) Two Foci of This The testing of theoretical predictions through multiple regression is the key focus of this chapter Theory and prior research lead to identi cation of predictors of interest, and, in addition, characterization of the form of their relationships (eg linear, curvilinear, interactive) to the criterion Together, identi cation of predictors and characterization of forms of relationship are referred to as model speci cation After a model has been speci ed, multiple regression can be employed to test each aspect of the speci cation The second focus of the chapter is the identi cation of problems with the implementation of regression analyses, both from the perspective of the model and the perspective of the data themselves.