An aggregate is a group made up of a collection of items. Aggregated data is data that has been summed or grouped - hopefully according to a logical pattern. We need aggregated data because a heap of unsorted data is useless. Thus if you had a database which collected names and addresses you would aggregate street names into one group, and family names into another (otherwise your scrambled data might lead you to believe Mr. Walnut lives on Smith Street).
You can manipulate aggregates to form other aggregates, for instance grouping all of your products made in Rome, Copenhagen and Paris into an aggregate of products manufactured in Europe. You can also enumerate the pieces within your aggregate so that you can make comparisons between them. Are the collective sales of the North American division larger or smaller than the European one (for instance)?
Usually, when observations and measurements are aggregated, these are called DATA.
Usually, when observations and measurements are aggregated, these are called DATA.
Usually, when observations and measurements are aggregated, these are called DATA.
In addition the raw data is aggregated into the current monthly log file in an anonymised fashion.
Aggregated financial data refers to the compilation and summary of financial information from multiple sources or individual entities into a single dataset. This data typically includes metrics such as revenues, expenses, profits, and other financial indicators, allowing for analysis and comparison across different periods or sectors. By aggregating data, organizations can identify trends, assess overall performance, and make informed financial decisions.
Aggregated knowledge would be knowledge collected from many sources.
Aggregated diamond nanorods are composed of pure carbon atoms, so the percentage of carbon in aggregated diamond nanorods is 100%.
An aggregated sentence is one that is a cluster of words. It does not make sense and it is not formed properly.
Aggregated data refers to information that has been compiled from multiple sources and summarized to provide a broader view. Examples include national census data, which combines individual responses to present demographic trends, and sales data that aggregates transactions across multiple stores to show overall performance. Other examples include average temperatures calculated from daily weather records over a month, and social media analytics that summarize user engagement metrics across various posts or campaigns.
Granularity refers to the level of detail of the data stored fact tables in a data warehouse. High granularity refers to data that is at or near the transaction level. Data that is at the transaction level is usually referred to as atomic level data. Low granularity refers to data that is summarized or aggregated, usually from the atomic level data. Summarized data can be lightly summarized as in daily or weekly summaries or highly summarized data such as yearly averages and totals.
No
Operational data is used to run day-to-day business operations and is typically structured, detailed, and transactional. Decision support data, on the other hand, is used to analyze trends, patterns, and make strategic decisions. Decision support data is often aggregated, summarized, and historical.