"trivial data" is a correct and usable phrase in written English.
It refers to data or information that is unimportant, insignificant, or of little significance. It can be used in various contexts, such as discussing research findings, data analysis, or data management. Example: In this study, the researchers collected both significant and trivial data to fully understand the participants' behaviors and attitudes towards the topic. Note: The context in which the phrase is used may affect the interpretation of "trivial data." It is important to provide enough context and clarify the intended meaning to avoid misunderstandings.
Today even the most trivial data can effortlessly be glorified by presenting it in a polychromatic bar chart with three-dimensional shading.
Identification of data value: High-value data actively used by the enterprise is differentiated from redundant, outdated or trivial data.
This is, as I mentioned earlier, the one place where its troves of seemingly trivial data add up.
So if you're one of the poor souls who drives up and down Highway 101 every day you'll probably start noticing billboards with various pieces of interesting, trivial data points it has lifted from Mixpanel Trends, a free data set that it recently released to anyone who wants to know what people are doing with those 15 billion actions and on which devices or whatnot.
In-memory UI rendering models are used to provide a rapid response UI during navigation and trivial data filtering or searching.
A look-up table (LUT) technique is adopted to link temporal noise data to the synaptic multipliers of a CRBM, trained to model simple, non-trivial data distributions.
In this article we focus on (diffusive) PDEs with a non-trivial data type requirement such as having vector or complex field variables.
An X-machine is a general computational machine that can model: (a) non-trivial data structures as a typed memory tuple and (b) the dynamic part of a system by employing transitions, which are not labelled with simple inputs but with functions that operate on inputs and memory values.
Its six cameras transform real-time views of the road into data (corresponding to "non-trivial data collection" in our data chain).
Over the past 5 years, with the avalanche of high-throughput sequencing data in particular, the pressure on research groups has risen drastically to establish routines in non-trivial data processing.
We called this framework the "data chain," illustrating the main characteristics of data startups and the fact that data companies can be involved both in a physical layer (storage and processing) and a digital layer (non-trivial data collection, interpretation and actionable data production).
