Data gravity occurs when the volume of data in a warehouse increases and the number of uses also increases. In some cases, copying or moving data can be troublesome and expensive. Therefore, data tends to pull services, applications and other data into its warehouse. Primary examples of data gravity are data warehouses and data lakes. Data in these systems is inert. Scalable volumes of data often break existing infrastructure and processes, requiring risky and expensive fixes. Therefore, the best practice is to move design processing to data, not the other way around.
Machine-to-Machine (M2M) Communications is a technology that allows machines (devices) to communicate with each other through networks without the need for human intervention.
Data intensive refers to data structures in which most cells or fields in the data matrix or data set are filled, with minimal empty or zero values.
Attention mechanism is a technique that revolutionizes the world of artificial intelligence and deep learning in areas such as language processing, image recognition and even sound analysis.
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