Modelling data intensive web sites with OntoWeaver

This paper illustrates the OntoWeaver modelling approach, which relies on a set of comprehensive site ontologies to model all aspects of data intensive web sites and thus offers high level support for the design and development of data-intensive web sites. In particular, the OntoWeaver site ontologies comprise two components: a site view ontology and a presentation ontology. The site view ontology provides meta-models to allow for the composition of sophisticated site views, which allow end users to navigate and manipulate the underlying domain databases. The presentation ontology abstracts the look and feel for site views and makes it possible for the visual appearance and layout to be specified at a high level of abstractio

On the value of popular crystallographic databases for machine learning prediction of space groups

Predicting crystal structure information is a challenging problem in materials science that clearly benefits from artificial intelligence approaches. The leading strategies in machine learning are notoriously data-hungry and although a handful of large crystallographic databases are currently available, their predictive quality has never been assessed. In this article, we have employed composition-driven machine learning models, as well as deep learning, to predict space groups from well known experimental and theoretical databases. The results generated by comprehensive testing indicate that data-abundant repositories such as COD (Crystallography Open Database) and OQMD (Open Quantum Materials Database) do not provide the best models even for heavily populated space groups. Classification models trained on databases such as the Pearson Crystal Database and ICSD (Inorganic Crystal Structure Database), and to a lesser extent the Materials Project, generally outperform their data-richer counterparts due to more balanced distributions of the representative classes. Experimental validation with novel high entropy compounds was used to confirm the predictive value of the different databases and showcase the scope of the machine learning approaches employed.publishedVersio

The Pisa Stellar Evolution Data Base for low-mass stars

The last decade showed an impressive observational effort from the photometric and spectroscopic point of view for ancient stellar clusters in our Galaxy and beyond. The theoretical interpretation of these new observational results requires updated evolutionary models and isochrones spanning a wide range of chemical composition. With this aim we built the new "Pisa Stellar Evolution Database" of stellar models and isochrones by adopting a well-tested evolutionary code (FRANEC) implemented with updated physical and chemical inputs. In particular, our code adopts realistic atmosphere models and an updated equation of state, nuclear reaction rates and opacities calculated with recent solar elements mixture. A total of 32646 models have been computed in the range of initial masses 0.30 - 1.10 Msun for a grid of 216 chemical compositions with the fractional metal abundance in mass, Z, ranging from 0.0001 to 0.01, and the original helium content, Y, from 0.25 to 0.42. Models were computed for both solar-scaled and alpha-enhanced abundances with different external convection efficiencies. Correspondingly, 9720 isochrones were computed in the age range 8 - 15 Gyr, in time steps of 0.5 Gyr. The whole database is available to the scientific community on the web. Models and isochrones were compared with recent calculations available in the literature and with the color-magnitude diagram of selected Galactic globular clusters. The dependence of relevant evolutionary quantities on the chemical composition and convection efficiency were analyzed in a quantitative statistical way and analytical formulations were made available for reader's convenience.Comment: Accepted for publication in A&

Nutrient Estimation from 24-Hour Food Recalls Using Machine Learning and Database Mapping: A Case Study with Lactose.

The Automated Self-Administered 24-Hour Dietary Assessment Tool (ASA24) is a free dietary recall system that outputs fewer nutrients than the Nutrition Data System for Research (NDSR). NDSR uses the Nutrition Coordinating Center (NCC) Food and Nutrient Database, both of which require a license. Manual lookup of ASA24 foods into NDSR is time-consuming but currently the only way to acquire NCC-exclusive nutrients. Using lactose as an example, we evaluated machine learning and database matching methods to estimate this NCC-exclusive nutrient from ASA24 reports. ASA24-reported foods were manually looked up into NDSR to obtain lactose estimates and split into training (n = 378) and test (n = 189) datasets. Nine machine learning models were developed to predict lactose from the nutrients common between ASA24 and the NCC database. Database matching algorithms were developed to match NCC foods to an ASA24 food using only nutrients ("Nutrient-Only") or the nutrient and food descriptions ("Nutrient + Text"). For both methods, the lactose values were compared to the manual curation. Among machine learning models, the XGB-Regressor model performed best on held-out test data (R2 = 0.33). For the database matching method, Nutrient + Text matching yielded the best lactose estimates (R2 = 0.76), a vast improvement over the status quo of no estimate. These results suggest that computational methods can successfully estimate an NCC-exclusive nutrient for foods reported in ASA24

Model-driven performance evaluation for service engineering

Service engineering and service-oriented architecture as an integration and platform technology is a recent approach to software systems integration. Software quality aspects such as performance are of central importance for the integration of heterogeneous, distributed service-based systems. Empirical performance evaluation is a process of measuring and calculating performance metrics of the implemented software. We present an approach for the empirical, model-based performance evaluation of services and service compositions in the context of model-driven service engineering. Temporal databases theory is utilised for the empirical performance evaluation of model-driven developed service systems