Efficient Cube Construction for Smart City Data∗
Michael Scriney, Mark Roantree · Dublin City University Open Access Institutional Repository (Dublin City University) · 2016
To deliver powerful smart city environments, there is a re-quirement to analyse web produced data streams in close to real time so that city planners can employ up to date pre-dictive 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 ap-proaches and thus, provides a platform for predictive models in a smart city environment.