StreamQCTree:efficient compression structure for streamcube

StreamCube,which responses OLAP queries fast and accurately,is in-memory and composed of Group-bys from DSMS.Becasue of limited capacity of memory,it needs an efficient structure to keep more information of StreamCube with more large time window.This paper presents a QCTree-based structure,StreamQCTree,with constructing,pruning and search algorithm.The upper bounds in QC-Tree are partitioned into two classes:Basic Upper Bounds(BUB) and Addition Upper Bounds(AUB),and cost model of AUB is analyzed.Using the cost model,a dynamic select approach is put forward to choose the AUBs with high cost-benefit in fixed memory,which gains less average response time for all queries in StreamQCTree.Experiments show that StreamQCTree performs well in compressing StreamCube and make queries efficiently.