Function materialization in object bases: design, realization, andevaluation

View materialization is a well-known optimization technique of relational database systems. We present a similar, yet more powerful, optimization concept for object-oriented data models: function materialization. Exploiting the object-oriented paradigm-namely, classification, object identity, and encapsulation-facilitates a rather easy incorporation of function materialization into (existing) object-oriented systems. Only those types (classes) whose instances are involved in some materialization are appropriately modified and recompiled, thus leaving the remainder of the object system invariant. Furthermore, the exploitation of encapsulation (information hiding) and object identity provides for additional performance tuning measures that drastically decrease the invalidation and rematerialization overhead incurred by updates in the object base. First, it allows us to cleanly separate the object instances that are irrelevant for the materialized functions from those that are involved in the materialization of some function result, and this to penalize only those involved objects upon update. Second, the principle of information hiding facilitates fine-grained control over the invalidation of precomputed results. Based on specifications given by the data type implementor, the system can exploit operational semantics to better distinguish between update operations that invalidate a materialized result and those that require no rematerialization. The paper concludes with a quantitative analysis of function materialization based on two sample performance benchmarks obtained from our experimental object base system GOM     

Editorial

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Eye movements of young and older adults during reading.

The eye movements of young and older adults were tracked as they read sentences varying in syntactic complexity. In Experiment 1, cleft object and object relative clause sentences were more difficult to process than cleft subject and subject relative clause sentences; however, older adults made many more regressions, resulting in increased regression path fixation times and total fixation times, than young adults while processing cleft object and object relative clause sentences. In Experiment 2, older adults experienced more difficulty than young adults while reading cleft and relative clause sentences with temporary syntactic ambiguities created by deleting the that complementizers. Regression analyses indicated that readers with smaller working memories need more regressions and longer fixation times to process cleft object and object relative clause sentences. These results suggest that age-associated declines in working memory do affect syntactic processing. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Issues in nonlinear stochastic grey box identification

Grey box identification refers to the practice of identifying dynamical systems in model structures exploiting partial prior information. This contribution reviews a method for stochastic grey box identification and surveys experiences and lessons of applying it to a number of industrial processes. Issues to be addressed include advantages and costs of introducing stochastics into the model, the question of what contribution must be expected from the model designer as opposed to what can be formalized in computer algorithms, and an outlook on future plans to resolve present shortcomings.