Abstract
Recent developments in technology leads to the development in real time applications, sensor technology and various online services which in turn is responsible for generating large amount of data which can be used for analysis. To accommodate and use this data in decision making systems is the big challenge. Decision making system demands the ad hoc querying and ad hoc reporting capabilities. Ad hoc data analysis requires current as well as historical data. Multidimensional data analysis supports such ad hoc data analysis on historical and current data. Hence the multidimensional structure of the data is pillar in the field of data analysis. Performing multidimensional data analysis requires aggregation at various levels of dimensions to perform the analysis of any type of decision-making system. Traditionally the aggregations are stored using data cubes. Generation of multidimensional data cube is a big challenge for the data generated from the advanced technological systems such as IoT (Internet of Thing) platforms. This makes difficult to implement the cube architecture. One more challenge in the traditional data analysis system is the technical expertise requires for using it effectively. This may de-motivate the non-technical data analysts or researchers. Hence, it is the requirement of this era that these analytical systems must be update itself with advance statistical techniques, data mining and machine learning algorithms to cope up with changes in technology and user requirements. This chapter focuses on the architecture of the analysis toolbox which uses the on – the – fly query generation technique. This chapter gives details of the numerous operations and customizations provided by the system to have efficient, accurate, and quick decision making. The functionality is demonstrated with the help of case studies using sensor data and business data.
Published Version
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