Swarm Quant’ Intelligence for Optimizing Multi-Node OLAP Systems

AbstrAct Globalization and market deregulation has increased business competition, which imposed OLAP data and technologies as one of the great enterprise's assets. Its growing use and size stressed underlying servers and forced new solutions. The distribution of multidimensional data through a number of serv-ers BLOCKINallows BLOCKINthe BLOCKINincreasing BLOCKINof BLOCKINstorage BLOCKINand BLOCKINprocessing BLOCKINpower BLOCKINwithout BLOCKINan BLOCKINexponential BLOCKINincrease BLOCKINof BLOCKINfinancial costs. However, this solution adds another dimension to the problem: space. Even in centralized OLAP, cube BLOCKINselection BLOCKINefficiency BLOCKINis BLOCKINcomplex, BLOCKINbut BLOCKINnow, BLOCKINwe BLOCKINmust BLOCKINalso BLOCKINknow BLOCKINwhere BLOCKINto BLOCKINmaterialize BLOCKINsubcubes. BLOCKINWe BLOCKINhave to BLOCKINselect BLOCKINand BLOCKINalso BLOCKINallocate BLOCKINthe BLOCKINmost BLOCKINbeneficial BLOCKINsubcubes, BLOCKINattending BLOCKINan BLOCKINexpected BLOCKIN(changing) BLOCKINuser BLOCKINprofile and constraints. We now have to deal with materializing space, processing power distribution, and communication costs. This chapter proposes new distributed cube selection algorithms based on discrete particle swarm optimizers; algorithms that solve the distributed OLAP selection problem considering a query BLOCKINprofile BLOCKINunder BLOCKINspace BLOCKINconstraints, BLOCKINusing BLOCKINdiscrete BLOCKINparticle BLOCKINswarm BLOCKINoptimization BLOCKINin BLOCKINits BLOCKINnormal(Di-PSO), cooperative (Di-CPSO), multi-phase (Di-MPSO), and applying hybrid genetic operators.