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The Data Warehouse
Published in Richard J. Roiger, Data Mining, 2017
Operational databases are designed to process individual transactions quickly and efficiently. To accomplish this, operational data are often stored as a set of normalized relational tables. The normalization minimizes data redundancy, thus allowing for effective data processing. Once transactional data are no longer useful, they are transferred from the operational environment to a secondary storage medium. If a decision support facility exists, the storage facility will likely be a data warehouse. W. H. Inmon (1992) describes the data warehouse as a “subject-oriented, integrated, time-variant, and nonvolatile collection of data in support of management’s decision making process.”
Big data analytics for manufacturing internet of things: opportunities, challenges and enabling technologies
Published in Enterprise Information Systems, 2020
Hong-Ning Dai, Hao Wang, Guangquan Xu, Jiafu Wan, Muhammad Imran
Data acquired from MIoT have the following characteristics: Heterogeneous data types. The whole manufacturing chain generates various data types including sensory data, RFID readings, product records, text, logs, audio, video, etc. The data is in the forms of structured, semi-structured and non-structured.Erroneous and noisy data. The data obtained from the industrial environment is often erroneous and noisy mainly due to the following reasons: (a) interference during the process of data collection especially in industrial environment, (b) the failure and malfunction of sensors or machinery, (c) intermittent loss or outage of wireless or wired communications Siddiqa et al. (2016). For example, wireless communications are often susceptible to harsh industrial environmental factors like blockage, shadowing and fading effects. Moreover, data transmission may fail in industrial WSNs due to the depletion of batteries of sensors or machinery.Data redundancy. Data generated in MIoT often contain excessively redundant information. For instance, it is shown in Ertek, Chi, and Zhang (2017) that there are excessive-duplicated RFID readings when multiple RFID tags were scanned by several RFID readers at different time slots. The data redundancy often results in data inconsistency.