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Aggregate data is high-level data which is acquired by combining individual-level data. For instance, the output of an industry is an aggregate of the firms’ individual outputs within that industry. [1] Aggregate data are applied in statistics, data warehouses, and in economics. There is a distinction between aggregate data and individual data.
An aggregate project plan (APP) is the process of creating development goals and objectives and using these goals and objectives to improve productivity as well as development capabilities. The purpose of this process is generally to ensure that each project will accomplish its development goals and objectives.
Aggregate functions present a bottleneck, because they potentially require having all input values at once. In distributed computing , it is desirable to divide such computations into smaller pieces, and distribute the work, usually computing in parallel , via a divide and conquer algorithm .
An aggregate is a type of summary used in dimensional models of data warehouses to shorten the time it takes to provide answers to typical queries on large sets of data. The reason why aggregates can make such a dramatic increase in the performance of a data warehouse is the reduction of the number of rows to be accessed when responding to a query.
An aggregate pattern is an important statistical concept in many fields that rely on statistics to predict the behavior of large groups, based on the tendencies of subgroups to consistently behave in a certain way.
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Aggregate planning is a marketing activity that does an aggregate plan for the production process, in advance of 3 to 18 months, to give an idea to management as to what quantity of materials and other resources are to be procured and when, so that the total cost of operations of the organization is kept to the minimum over that period.
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