Towards a parallel computationally efficient approach to scaling up data stream classification

Advances in hardware technologies allow to capture and process data in real-time and the resulting high throughput data streams require novel data mining approaches. The research area of Data Stream Mining (DSM) is developing data mining algorithms that allow us to analyse these continuous streams of data in real-time. The creation and real-time adaption of classification models from data streams is one of the most challenging DSM tasks. Current classifiers for streaming data address this problem by using incremental learning algorithms. However, even so these algorithms are fast, they are challenged by high velocity data streams, where data instances are incoming at a fast rate. This is problematic if the applications desire that there is no or only a very little delay between changes in the patterns of the stream and absorption of these patterns by the classifier. Problems of scalability to Big Data of traditional data mining algorithms for static (non streaming) datasets have been addressed through the development of parallel classifiers. However, there is very little work on the parallelisation of data stream classification techniques. In this paper we investigate K-Nearest Neighbours (KNN) as the basis for a real-time adaptive and parallel methodology for scalable data stream classification tasks.

Jersey milk suitability for Cheddar cheese production: process, yield, quality and financial impacts

The aim of this study was to first evaluate the benefits of including Jersey milk into Holstein-Friesian milk on the Cheddar cheese making process and secondly, using the data gathered, identify the effects and relative importance of a wide range of milk components on milk coagulation properties and the cheese making process. Blending Jersey and Holstein-Friesian milk led to quadratic trends on the size of casein micelle and fat globule and on coagulation properties. However this was not found to affect the cheese making process. Including Jersey milk was found, on a pilot scale, to increase cheese yield (up to + 35 %) but it did not affect cheese quality, which was defined as compliance with the legal requirements of cheese composition, cheese texture, colour and grading scores. Profitability increased linearly with the inclusion of Jersey milk (up to 11.18 p£ L-1 of milk). The commercial trials supported the pilot plant findings, demonstrating that including Jersey milk increased cheese yield without having a negative impact on cheese quality, despite the inherent challenges of scaling up such a process commercially. The successful use of a large array of milk components to model the cheese making process challenged the commonly accepted view that fat, protein and casein content and protein to fat ratio are the main contributors to the cheese making process as other components such as the size of casein micelle and fat globule were found to also play a key role with small casein micelle and large fat globule reducing coagulation time, improving curd firmness, fat recovery and influencing cheese moisture and fat content. The findings of this thesis indicated that milk suitability for Cheddar making could be improved by the inclusion of Jersey milk and that more compositional factors need to be taken into account when judging milk suitability.