An automated ETL for online datasets

While using online datasets for machine learning is commonplace today, the quality of these datasets impacts on the performance of prediction algorithms. One method for improving the semantics of new data sources is to map these sources to a common data model or ontology. While semantic and structural heterogeneities must still be resolved, this provides a well established approach to providing clean datasets, suitable for machine learning and analysis. However, when there is a requirement for a close to real time usage of online data, a method for dynamic Extract-Transform-Load of new sources data must be developed. In this work, we present a framework for integrating online and enterprise data sources, in close to real time, to provide datasets for machine learning and predictive algorithms. An exhaustive evaluation compares a human built data transformation process with our system’s machine generated ETL process, with very favourable results, illustrating the value and impact of an automated approach

An automated ETL for online datasets

While using online datasets for machine learning is commonplace today, the quality of these datasets impacts on the performance of prediction algorithms. One method for improving the semantics of new data sources is to map these sources to a common data model or ontology. While semantic and structural heterogeneities must still be resolved, this provides a well established approach to providing clean datasets, suitable for machine learning and analysis. However, when there is a requirement for a close to real time usage of online data, a method for dynamic Extract-Transform-Load of new sources data must be developed. In this work, we present a framework for integrating online and enterprise data sources, in close to real time, to provide datasets for machine learning and predictive algorithms. An exhaustive evaluation compares a human built data transformation process with our system’s machine generated ETL process, with very favourable results, illustrating the value and impact of an automated approach

An automated ETL for online datasets

While using online datasets for machine learning is commonplace today, the quality of these datasets impacts on the performance of prediction algorithms. One method for improving the semantics of new data sources is to map these sources to a common data model or ontology. While semantic and structural heterogeneities must still be resolved, this provides a well established approach to providing clean datasets, suitable for machine learning and analysis. However, when there is a requirement for a close to real time usage of online data, a method for dynamic Extract-Transform-Load of new sources data must be developed. In this work, we present a framework for integrating online and enterprise data sources, in close to real time, to provide datasets for machine learning and predictive algorithms. An exhaustive evaluation compares a human built data transformation process with our system’s machine generated ETL process, with very favourable results, illustrating the value and impact of an automated approach

Post-marketing evaluation of adhd drug treatment in children and young adults

Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder affecting 3-5% of children. In the UK, three drugs are licensed for its treatment; methylphenidate, dexamfetamine and atomoxetine. There is a lack of evidence on the prescribing of these to UK patients; however the common belief, particularly in the media, is that these drugs are over-prescribed. In addition, ADHD was once considered a condition of childhood alone; however increasing evidence suggests that the condition persists into adulthood in a significant number of patients. Again, there is little data on the use of these medications in older adolescents and young adults. Finally, in recent times, there has been much debate and concern over the safety of these drugs due to a number of spontaneous reports of sudden death in patients taking these medications. In light of these issues, this study had the following objectives; 1) to examine the utilisation of these drugs; 2) to examine prescribing of these medications to older patients; 3) to examine the safety of these medications, in particular the issue of sudden death. This was a pharmacoepidemiological study which mainly utilised data from the General Practice Research Database (GPRD), a computerised database of anonymised patient records from approximately 5% of the UK population. The study showed that 1) prevalence of prescribing of these drugs has increased significantly over the last decade, however the prevalence of prescribing is much lower than prescribing rates reported in other countries; 2) prevalence of prescribing of these drugs decreases dramatically in older patients; 3) no increase in the rate of death or sudden death in patients taking these drugs was detected when compared to mortality rates from the general population

A Collaborative Model of Service Delivery for Individuals with Specific Learning Difficulties

This paper will discuss a collaborative, multidisciplinary approach to service delivery for young people and adults with specific learning difficulties. Influenced by research findings and a gap in service provision, the BUA service has been set up to provide screening, assessment, training and support to young people and adults with specific learning difficulties. The paper will consider the etiology of specific learning difficulties and current practices with particular reference to service provision and best practice models in the delivery of services. The paper will also discuss the BUA Centre’s innovative approach with the Institute of Technology, Blanchardstown and the Dyscovery Centre, Wales in the areas of screening, assessment and training, and in developing best practice teaching methods

Lawrence v. Texas: Does This Mean Increased Privacy Rights for Gay and Lesbian Teacher?

This article addresses the Supreme Court\u27s 2003 decision in Lawrence v. Texas and its implications for the rights of gay and lesbian public school teachers. The authors provide a context by reviewing the teacher role-model theory, traditional standards used in dismissals for immoral conduct, and pre-Lawrence cases regarding public employees\u27 privacy rights. Then they analyze Lawrence v. Texas, which struck down a Texas law imposing criminal penalties for persons of the same sex engaging in certain sexual conduct. The final section explores implications of the expanded liberty right announced in Lawrence for public school teachers and their lifestyle choices

Lawrence v. Texas: Does This Mean Increased Privacy Rights for Gay and Lesbian Teacher?

This article addresses the Supreme Court\u27s 2003 decision in Lawrence v. Texas and its implications for the rights of gay and lesbian public school teachers. The authors provide a context by reviewing the teacher role-model theory, traditional standards used in dismissals for immoral conduct, and pre-Lawrence cases regarding public employees\u27 privacy rights. Then they analyze Lawrence v. Texas, which struck down a Texas law imposing criminal penalties for persons of the same sex engaging in certain sexual conduct. The final section explores implications of the expanded liberty right announced in Lawrence for public school teachers and their lifestyle choices

A method for automated transformation and validation of online datasets

While using online datasets for machine learning is commonplace today, the quality of these datasets impacts on the performance of prediction algorithms. One method for improving the semantics of new data sources is to map these sources to a common data model or ontology. While semantic and structural heterogeneities must still be resolved, this provides a well established approach to providing clean datasets, suitable for machine learning and analysis. However, when there is a requirement for a close to real time usage of online data, a method for dynamic Extract-Transform-Load of new sources data must be developed. In this work, we present a framework for integrating online and enterprise data sources, in close to real time, to provide datasets for machine learning and predictive algorithms. An exhaustive evaluation compares a human built data transformation process with our system’s machine generated ETL process, with very favourable results, illustrating the value and impact of an automated approach

An architecture and services for constructing data marts from online data sources

The Agri sector has shown an exponential growth in both the requirement for and the production and availability of data. In parallel with this growth, Agri organisations often have a need to integrate their in-house data with international, web-based datasets. Generally, data is freely available from official government sources but there is very little unity between sources, often leading to significant manual overhead in the development of data integration systems and the preparation of reports. While this has led to an increased use of data warehousing technology in the Agri sector, the issues of cost in terms of both time to access data and the financial costs of generating the Extract-Transform-Load layers remain high. In this work, we examine more lightweight data marts in an infrastructure which can support on-demand queries. We focus on the construction of data marts which combine both enterprise and web data, and present an evaluation which verifies the transformation process from source to data mart