Efficient cube construction for smart city data

To deliver powerful smart city environments, there is a requirement to analyse web produced data streams in close to real time so that city planners can employ up to date predictive models in both short and long term planning. Data cubes, fused from multiple sources provide a popular input to predictive models. A key component in this infrastructure is an efficient mechanism for transforming web data (XML or JSON) into multi-dimensional cubes. In our research, we have developed a framework for efficient transformation of XML data from multiple smart city services into DWARF cubes using a NoSQL storage engine. Our evaluation shows a high level of performance when compared to other approaches and thus, provides a platform for predictive models in a smart city environment

Mapping longitudinal studies to risk factors in an ontology for dementia

A common activity carried out by healthcare professionals is to test various hypotheses on longitudinal study data in an effort to develop new and more reliable algorithms that might determine the possibility of developing certain illnesses. The In-MINDD project provides input from a number of European dementia experts to identify the most accurate model of inter-related risk factors which can yield a personalised dementia risk quotient and profile. This model is then validated against the large population-based prospective Maastricht Aging Study (MAAS) dataset. As part of this overall goal, the research presented in this paper demonstrates how we can automate the process of mapping modifiable risk factors against large sections of the aging study and thus, use information technology to provide more powerful query interfaces

A methodology for classification and validation of customer datasets

Purpose- The purpose of this research is to develop a method to classify customers according to their value to an organization. This process is complicated by the disconnected nature of a customer record in an industry such as insurance. With large numbers of customers, it is of significant benefit to managers and company analysts to create a broad classification for all customers. Design/Methodology/Approach- The initial step is to construct a full customer history and extract a feature set suited to Customer Lifetime Value calculations. This feature set must then be validated to determine its ability to classify customers in broad terms. Findings- Our method successfully classifies customer datasets with an accuracy of 90%. We also discovered that by examining the average value for key variables in each customer segment, an algorithm can label the group of clusters with an accuracy of 99.3%. Research limitations/implications- Working with a real-world dataset, it is always the case that some features are unavailable as they were never recorded. This can impair the algorithm’s ability to make good classifications in all cases. Originality/Value- We believe that this research makes a novel contribution as it automates the classification of customers but in addition, our approach provides a high level classification result (recall and precision identifies the best cluster configuration) and detailed insights into how each customer is classified by two validation metrics. This supports managers in terms of market spend on new and existing customers