Abstract
Many recent technologies increase the generation of data and its usages. There are concerns to store, retrieved large data, processing multiple queries and services simultaneously. The cube format of the data and dimensional databases can ease the process of retrieval and modelling the data efficiently and effectively. This study suggests few efficient ways to address the concerns using the concept of a data warehouse and analytical operations. It also offers the design aspect of a Hybrid analytical system by linking different functionalities under a Layered Architecture style. The preferred data are collected from those warehouses, later combined to form incremental successive upper-level data. This style supports a Hybrid system to give confidence by connecting various data suppliers of the distributed warehouse methods. It allows the ELT operations other than the normal ETL operations to handle large data to support the data lake. The suggestive functionalities engine is used to produce data patterns. The merit of the PDC tree is incorporated to provide some possible parallel operations. The findings are applied to a case study of data modelling to predict a potential future epidemic. Such a system generates several reports to help the users or the authority for handling such an epidemic in better efficient ways.
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Singh, Y.S., Das, P., Kirani, Y., Singh, Y.J. (2023). Design Aspects of a Multi-dimensional Hybrid Analytical Processing System. In: Goswami, S., Barara, I.S., Goje, A., Mohan, C., Bruckstein, A.M. (eds) Data Management, Analytics and Innovation. ICDMAI 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 137. Springer, Singapore. https://doi.org/10.1007/978-981-19-2600-6_48
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DOI: https://doi.org/10.1007/978-981-19-2600-6_48
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