A Processing of Top-k Aggregate Queries on Distributed Data

Abstract

Top-k queries are crucial tools on data analysis, data mining, and decision making. Investigating top-k processing on distributed data has been interested by many researchers which eliminates all the redundancy answers. In this paper, we review and evaluate the process of top-k queries on distributed data. The algorithms of top-k queries on distributed data are based on the data access methods namely random and sequential access which will be discussed and analyzed for effectiveness. Then, the execution of two algorithms, best position algorithm and no random access, is about the data access modes being presented with specific examples. In experiments, the results show that the top-k aggregate queries using the sequential access better than using the random access in terms of the run-time execution. The effect algorithm is no random access (NRA) using sequential access, while best position algorithm (BPA) using the random access to calculate the results.