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Collection of Data
Published in Shyama Prasad Mukherjee, A Guide to Research Methodology, 2019
Probably the most well-known implementation of data integration is building an enterprise data warehouse. The benefit of a data warehouse enables a business to perform analyses based on the data stored in the data warehouse. This would not be possible to do on the data available only in the source system., which may not contain corresponding data, even though the data are identically named while they really refer to some different entities. This approach is also not that suitable where data sets are frequently updated, requiring the ETL process to be continuously re-executed for synchronization. Data hub or data lake approaches have been recently floated to combine unstructured or varied data into one location.
An Analysis of Machine Learning for Smart Transportation System (STS)
Published in Mohamed Lahby, Utku Kose, Akash Kumar Bhoi, Explainable Artificial Intelligence for Smart Cities, 2021
Prebuilt Connectors for Operational and Verifiable Data Stores: Complicity IoT ML gives simple admittance to information living in operational and authentic information stores for model preparing. It can recover this information on an occasional premise and course it through a computerized pipeline to change the information and train an AI model. Information can be facilitated on Amazon S3 or Microsoft Azure Data Lake Storage, just as nearby information stockpiling, and recovered utilizing prebuilt IoT Data Hub connectors.
Innovation capabilities as a mediator between big data and business model
Published in Journal of Enterprise Transformation, 2018
Ossi Ylijoki, Jukka Sirkiä, Jari Porras, Vesa Harmaakorpi
The other firm, Flashnode, is a Finnish startup offering services for interconnecting and synchronizing data. The firm’s business model relies on highly standardized integrations, which challenges the traditional tailored interface development approach. A key objective of automated integrations is to reduce the manual work done by client companies. They have an aggressive growth strategy. Their position as a data hub offers various opportunities to utilize big data. The company accumulates huge amounts of data in terms of transaction volumes.