The Experts below are selected from a list of 17649 Experts worldwide ranked by ideXlab platform
Tom Johnston - One of the best experts on this subject based on the ideXlab platform.
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future Assertion Time
Bitemporal Data#R##N#Theory and Practice, 2014Co-Authors: Tom JohnstonAbstract:This chapter introduces the concept of future Assertion Time. It explains the semantics of this concept, and responds to the objection that there is not and cannot be any such thing as future Assertion Time. It shows how the implementation of standard Assertion Time can be extended to include the management of future Assertion Time. It introduces the concept of temporal locking, and shows that without it, a situation parallel to the paradoxes of Time travel can occur in databases.
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Deferred Assertions and Other Pipeline Datasets
Managing Time in Relational Databases, 2010Co-Authors: Tom Johnston, Randall WeisAbstract:This chapter discusses the topic of pipeline datasets, in general, and of one kind of pipeline dataset—deferred Assertions—in particular. It begins by noting that deferred Assertions represent past, present, and future versions in future Assertion Time, but that past, present, and future versions also exist in past and present Assertion Time. This gives nine categories of temporal data, one of which is currently asserted current versions of things, which known as conventional data, physically located in production tables. The other eight categories correspond to pipeline datasets, being data that has those production tables as either their destinations or their origins. Deferred Assertions are the result of applying deferred transactions to the database. Instead of holding on to maintenance transactions until it is the right Time to apply them, Asserted Versioning applies them right away, but does not immediately assert them. These deferred Assertions may be updated or deleted by themselves. Just as deferred Assertions replace collections of transactions that have not yet been applied to the database, bitemporal data in any of the other seven categories replaces other physically external datasets. Asserted version tables contain data in all these temporal categories and, in doing so, internalize what would otherwise be physically distinct datasets.
Randall Weis - One of the best experts on this subject based on the ideXlab platform.
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Deferred Assertions and Other Pipeline Datasets
Managing Time in Relational Databases, 2010Co-Authors: Tom Johnston, Randall WeisAbstract:This chapter discusses the topic of pipeline datasets, in general, and of one kind of pipeline dataset—deferred Assertions—in particular. It begins by noting that deferred Assertions represent past, present, and future versions in future Assertion Time, but that past, present, and future versions also exist in past and present Assertion Time. This gives nine categories of temporal data, one of which is currently asserted current versions of things, which known as conventional data, physically located in production tables. The other eight categories correspond to pipeline datasets, being data that has those production tables as either their destinations or their origins. Deferred Assertions are the result of applying deferred transactions to the database. Instead of holding on to maintenance transactions until it is the right Time to apply them, Asserted Versioning applies them right away, but does not immediately assert them. These deferred Assertions may be updated or deleted by themselves. Just as deferred Assertions replace collections of transactions that have not yet been applied to the database, bitemporal data in any of the other seven categories replaces other physically external datasets. Asserted version tables contain data in all these temporal categories and, in doing so, internalize what would otherwise be physically distinct datasets.
Van Wyk, Barend Jacobus - One of the best experts on this subject based on the ideXlab platform.
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The use of non-cognitive factors to identify engineering students at risk
2020Co-Authors: Van Wyk, Barend JacobusAbstract:Introduction: Underprepared students lead to drop out, which in turn leads to economic loss and a shortage of high-level skills. Identifying and supporting students at risk is, therefore, important. As shown by the meta-analysis conducted by Credé and Kuncel (2008), non-cognitive factors (also known as non-intellective factors) such as study habits, emotional intelligence skills, and attitude rival standardized tests and previous grades as predictors of academic performance. The need to also consider non-cognitive factors to protect the investment in engineering education made by the South African society, is therefore essential. It is hoped that if study habits, emotional intelligence skills, and attitudes can be developed, students will be better equipped to navigate the educational landscape. This work provides a machine-learning-based methodology of how to identify students at risk and proposes a series of interventions to enhance study habits, emotional intelligence skills, and attitude. Methods: A sample of (n = 1439) undergraduate engineering students at TUT completed the risk profiling, administered by the SDS (Student Development and Support) division at TUT, from 2011 to 2015. Students in the sample all wrote a National Examination at the end of Grade 12 to obtain the National Senior Certificate (NSC). The students in the sample were admitted based only on their NSC results. All students in the sample enrolled for a three-year National Diploma in Engineering. The National Diploma comprises two years (four semesters) of theoretical study and one year (two semesters) of industry placement. The students in the sample completed the Emotional Skills Assessment Process (ESAP) and the Learning and Study Strategies Inventory (LASSI) during the first-year orientation period. Student success was then predicted based on the non-cognitive factors measured during the first-year orientation. The LASSI and ESAP results were used as independent variables. The Time students spend at TUT before dropping out from the qualification or graduating with the qualification, and the Credit Accumulation Rates (CAR) of students, were used as dependent variables. Results: The results obtained showed that some non-cognitive factors, identified during the beginning of the first year, are strongly linked to academic success. Although many factors have an impact on student academic success, the non-cognitive precursors to academic efficacy are often overlooked. The results obtained in this study show that interventions focusing on building emotional intelligence competencies, specifically on skills related to Assertion, Time Management, Self-Esteem, Stress Management, Deference, and Change Orientation, might have significant and lasting long-term effects on student success. Further work should focus on the design and delivery of suitable interventions
