Sentence examples for data processing that had from inspiring English sources

Some backstory on this: LinkedIn, Facebook and others in the lead-up to GDPR coming into effect moved data processing that had been going through Ireland to the US.

They used a revised and updated set of ocean observations, this time avoiding a quirk in the data processing that had skewed temperatures in the data-poor Southern Hemisphere.

There's a lot of local data processing that has to occur in real time, including the computations necessary to keep the car in its lane.

The conference also underscored that for all the technological advances in behavior-tracking, data-mining and payment processing that have made it easier and quicker than ever to buy online, the key to successful fashion e-commerce lies in solving a singular problem: how to re-create the feeling of shopping a bricks-and-mortar store to a digital storefront.

Without computerized data processing, that would have been impossible.

It has been debated as to whether participants of indicator-rating panels, who are usually expert clinicians, are qualified to rate the feasibility of indicators and to address operational issues of indicator implementation, such as the time and effort of data processing that is required after data has been collected [ 30].

Two of the big Indian data processing companies that had so far ignored this market, Wipro and TCS, have also come on board as service providers.

One of those assets was 1,000 shares of Computer Associates, a big software and data processing company that had experienced a huge run-up in its stock price.

The biomimetic approach used in numerous methods presented in this paper, especially for the contrasting phase, exploits the efficiency of visual data processing procedures that have been selected by natural evolution.

That has started to change in some areas of computing, such as analog signal processing, which requires the kind of ultrafast data processing that only photonic chips can provide.

Christopher Ré, computer scientist democratizing big data analytics through open source data-processing products that have the power of machine learning algorithms but can be integrated into existing and applied database systems.