#Universal database models manual#
#Universal database models full#
#Universal database models professional#

#Universal database models professional#

Users can save even more time with the industry-specific extensions for Healthcare Telecommunications Manufacturing Banking, Investments, & Financial Services Insurance Professional Services and Travel. By re-using proven constructs, data modelers and designers can reduce data model development and maintenance time and save hundreds or even thousands of hours of time, as well as improve the quality of existing data models. They include proven models for common business functions such as ordering and managing products, handling shipments, invoicing, accounting and budgeting, managing human resources, contact management, and project management. The templates arm users with a powerful set of data models and data warehouse designs that jump-start database development projects. The UDMs provide common and industry models, reducing development time and ensuring quality. The models are available for nine different subject areas as a complete and integrated corporate model, and eight comprehensive industry-specific modeling areas. They make sophisticated data models more accessible to small and medium-sized businesses that previously had to forego them due to budget or IT constraints.

#Universal database models full#

The templates have been refined based on hundreds of implementations and are laid out for easy navigation, including full attributes and detailed definitions for every attribute. The Universal Data Models (UDMs) were developed by a team of experienced data modelers, led by Len Silverston. These time-proven templates have been continuously improved during the last 25 years and implemented by hundreds of companies, providing them with a complete set of data models that ensure quality, reduce development time, and facilitate standardization of existing data models and definitions. This research was supported through collaborations with Elekta, Philips, and Toshiba.Universal Data Models provide re-usable data model templates covering both common and industry models, developed by renowned author and modeling expert Len Silverston. Multivariate models may be necessary to further evaluate the complex relationship between neighboring OARs and observed outcomes. The current data-mining framework revealed both known and novel dosimetric and clinical relationships, underscoring the potential utility of this analytic approach. Conclusion: Our database platform has enabled large-scale analysis of dose-outcome relationships. Other notable relationships included dysphagia/pharyngeal constrictors, nausea/brainstem, nausea/spinal cord, weight-loss/mandible, and weight-loss/combined parotids. The data mining algorithm confirmed the following well-known toxicity/outcome relationships: dysphagia/larynx, voice changes/larynx, esophagitis/esophagus, xerostomia/combined parotids, and mucositis/oral mucosa.

#Universal database models manual#

Of these, 528 combinations met the 5%/Gy requirement, with further manual inspection revealing a number of reasonable models based on either reported literature or proximity between neighboring OAR. Results: A total of 57 individual and combined structures and 115 outcomes were queried, resulting in over 6,500 combinations for analysis. = )] increase by at least 5% per Gy (p<0.05), were further evaluated for clinical relevance using a custom graphical interface. A second result of our study is that universal quantification should be expressed directly in a database query language because most more » query optimizers do not recognize the rather indirect formulations available in SQL as relational division, and therefore produce very poor evaluation plans for many universal quantification queries. Thus, existential and universal quantification can be supported with equal efficiency by adding the recently proposed algorithm to a query evaluation system. Moreover, comparisons demonstrate that the recently proposed division algorithm evaluates a universal quantification predicate over two relations as fast as hash (semi-) join evaluates an existential quantification predicate over the same relations. Analytical and experimental performance comparisons illustrate the substantial differences among the algorithms. For each algorithm, we investigate the performance effects of explicit duplicate removal and referential integrity enforcement, variants for inputs larger than memory, and parallel execution strategies. In this article, we describe and compare three known algorithms and one recently proposed algorithm for relational division, the algebra operator that embodies universal quantification. One of the main reasons for this omission has been that universal quantification algorithms and their performance have not been explored for large databases. Universal quantification is not supported directly in most database systems despite the fact that it adds significant power to a system`s query processing and inference capabilities, in particular for the analysis of many-to-many relationships and of set-valued attributes.