arimbr
commented
2 years ago @fabulousDesigns Thanks for your submission! Closing this as I don't think that the topic is related to Airbyte, data integration, or more generally data engineering.
Closed fabulousDesigns closed 2 years ago
arimbr
commented
2 years ago @fabulousDesigns Thanks for your submission! Closing this as I don't think that the topic is related to Airbyte, data integration, or more generally data engineering.
Submission Details
Proposed article introduction
Sleep disorders have reached epidemic proportions worldwide, affecting the youth as well as the elderly, crossing the entire lifespan in both developed and developing countries. "Real-life" behavioral (sensor-based), molecular, digital, and epidemiological big data represent a source of an impressive wealth of information that can be exploited in order to advance the field of sleep research.
Big Data has many advantages because of its simplicity and cost reduction nature. Modern-day sleep data can be collected with much ease on a large scale using the benefits of Big Data. Today, since much technologically improvised, new technologically well-equipped tools are invented to collect data.
Key takeaways
Why big data? Sleep and digital and computational big data Sleep and imaging-based big data Sleep and portable devices (wearable sensors)
Article quality
The available articles related to the topic discuss sleep in relation to data collected from wearable sensors. This article digs deeper into this topic to discuss the use of computational big data and imaging-based big data in fighting sleep disorders.
References.
N/A.
The Format