Selecting and Allocating Cubes in Multi-Node OLAP Systems: An Evolutionary Approach

AbstrAct OLAP queries are characterized by short answering times. Materialized cube views, a pre-aggregation and storage of group-by values, are one of the possible answers to that condition. However, if all possible views were computed and stored, the amount of necessary materializing time and storage space would be huge. BLOCKINSelecting BLOCKINthe BLOCKINmost BLOCKINbeneficial BLOCKINset, BLOCKINbased BLOCKINon BLOCKINthe BLOCKINprofile BLOCKINof BLOCKINthe BLOCKINqueries BLOCKINand BLOCKINobserving BLOCKINsome BLOCKINconstraints as materializing space and maintenance time, a problem denoted as cube views selection problem, is the condition for an effective OLAP system, with a variety of solutions for centralized approaches. When a distributed OLAP architecture is considered, the problem gets bigger, as we must deal with another dimension—space. Besides the problem of the selection of multidimensional structures, there's now a node allocation one; both are a condition for performance. This chapter focuses on distributed OLAP systems, recently introduced, proposing evolutionary algorithms for the selection and allocation of the distributed OLAP Cube, using a distributed linear cost model. This model uses an extended aggregation lattice as framework to capture the distributed semantics, and introduces processing nodes' power and real communication costs parameters, allowing the estimation of query and maintenance costs in time units. Moreover, as we have an OLAP environment, whit several nodes, we will have parallel processing and BLOCKINthen, BLOCKINthe BLOCKINevaluation BLOCKINof BLOCKINthe BLOCKINfitness BLOCKINof BLOCKINevolutionary BLOCKINsolutions BLOCKINis BLOCKINbased BLOCKINon BLOCKINcost BLOCKINestimation BLOCKINalgorithms that simulate the execution of parallel tasks, using time units as cost metric.