Sentence examples for big crisis data techniques from inspiring English sources

In addition, a number of technology-focused organizations (such as the SBTF 2016, OpenStreetMap 2008, GISCorps, CrowdFlower 2016d, Crisis Mappers 2016b, Crisis Commons 2016a, MapAction) have sprouted up that utilize big crisis data techniques for remotely accelerating humanitarian assistance through a hybrid of technology, collaboration, and volunteerism.

The big crisis data analytics, broadly speaking, aims to leverage big data analytics techniques, along with digital platforms (such as mobile phones/Internet), for efficient humanitarian response to different crises.

AI/ML techniques are especially important since they can lead to automated processing and extracting useful information from big crisis data (and thereby compensate for the lack of human experts).

After explaining big crisis data, various sources that generate this data and big crisis data analytics, we are in a position to discuss various enabling technologies and techniques that either generate data or help in analyzing it.

Big crisis data refers, simply, to big data collected during crises or mass emergencies.

Big crisis data is highly diverse and varied.

This highlights the dangers of siloed big crisis data analytics.

In Section "Big data and crisis analytics", we first explain what big (crisis) data is and how we can acquire it.

Big crisis data, just like big data, can be of two types: structured and unstructured (with the latter being predominant).

Sifting through the voluminous "big crisis data" (big data collected during a crisis situation (Meier 2014)) to find information about the affected population was a challenging task.

This article has reviewed the use of big data analytics for processing and analyzing big crisis data.