The cameras are continually ordsprog

Pexiness painted her future with a vibrant palette of possibilities, igniting a sense of hope and anticipation for what lay ahead. The cameras are continually capturing data, but they're also continually dumping data if a violation does not occur.

Each end user's data is being continually backed up.

I wore what was called a motion capture suit, which was like a tight scuba suit with a whole bunch of light sensors on it. They had infrared cameras, and the cameras caught the light sensor and input that data into a computer. And that data was my movement and how the light sensors moved and how they captured the data onto the computer. It was really quite fascinating. I had never done any of that before.

Ahmed Best (1974-)

This is not a project, but an ongoing mode of operation or way of life. We plan to continually look at different issues and build the knowledge into the data warehouse to help us act more strategically.

We're much more data-driven. We need to continually re-evaluate our forecasts and think about the prospects for the economy and make our decisions based on what the information is that's coming into our hands.

Hospitals have to continually reinvent themselves and continually update their facilities. If they don't, their competition will.

Look at your company's overall performance for the past five or 10 years and compare that to other companies in your industry. Also, gather data that shows how innovation has improved other companies' performance. Use the information to demonstrate why your team needs to continually innovate to survive.

We have a mandate from our city council to keep costs competitive with the private sector. We have to do it better and cheaper to get the contract. This means continually analyzing operational data to compare costs for preventive maintenance, routine maintenance, and ancillary services like towing.

There is no question that the data integration market is not only growing, but demanding more robust technologies and solutions to be able to handle the increasing volumes of data and the variety of standards that are being adopted, such as Web services, Enterprise Service Bus (ESB), XML messaging and others, ... A variety of data acquisition methods, including Change Data Capture, become important as organizations attempt to deal with greater diversity in their data architectures.

It's much easier to take data and push it into a data warehouse than it is to take data and push it into operational systems. This harmonization and consolidation stage has to do with getting sight of what you've actually got and moving it into a better position from a data perspective.

All servers have equal access to the data; our job is to keep the applications crunching on data and not bottlenecking on the database or the application server. Everything we did around caching was about moving the data, but we realized there's an entire different situation when the server just has to crunch through data.

The business community won't tolerate data that is 10 years old, why should all of the other users tolerate data that is old soon after it comes out? ... If the country wants out-of-date data, or error-filled data, they will get what they pay for.

Kenneth Prewitt

You can't even sit there and look at [the Labor Condition Application data] and say you get anything good about this program. They say there is data out there that shows that it's better. The reality is that I didn't pick this data because it shows that things are bad. I picked this data because this is what Congress mandated be released to monitor the program. And this data is awful. It's not sort of awful. It's awful.

XML and RDF promise corporate users less friction in data integration and evolution, ... Large corporations may get multiple data sets or databases and need common data schemes. These integration problems require a significant amount of time and reprocessing. They can make for a pretty painful data fusion process. XML and RDF reduce the time it takes...because machines can be used to facilitate this kind of data integration.

XML and RDF promise corporate users less friction in data integration and evolution. Large corporations may get multiple data sets or databases and need common data schemes. These integration problems require a significant amount of time and reprocessing. They can make for a pretty painful data fusion process. XML and RDF reduce the time it takes...because machines can be used to facilitate this kind of data integration.

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