The basis for real-time analytics is to have all resources at disposal in the
moment they are called for . So far, special materialized data structures, called
cubes, have been created to efficiently serve analytical reports. Such cubes are
based on a fixed number of dimensions along which analytical reports can define
their result sets. Consequently, only a particular set of reports can be served
by one cube. If other dimensions are needed, a new cube has to be created or
existing ones have to be extended. In the worst case, a linear increase in the
number of dimensions of a cube can result in an exponential growth of its
storage requirements. Extending a cube can result in a deteriorating performance
of those reports already using it. The decision to extend a cube or build a new
one has to be considered carefully.
In any case, a wide variety of cubes may be
built during the lifetime of a system to serve reporting, thus increasing
storage requirements and also maintenance efforts.
Instead of working with a predefined set of reports, business
users should be able to formulate ad-hoc reports. Their playground should be the
entire set of data the company owns, possibly including further data from
external sources. Assuming a fast in-memory database, no more pre-computed
materialized data structures are needed. As soon as changes to data are
committed to the database, they will be visible for reporting.
The preparation
and conversion steps of data if still needed for reports are done during query
execution and computations take place on the fly. Computation on the fly during
reporting on the basis of cubes that do not store data, but only provide the
interface for reporting, solves a problem that has existed up to now and allows
for performance optimization of all analytical reports likewise
Tags
Analytics