Big Data Velocity Management–From Stream to Warehouse via High Performance Memory Optimized Index Join

Abstract

Efficient resource optimization is critical to manage the velocity and volume of real-time streaming data in near-real-time data warehousing and business intelligence. This article presents a memory optimisation algorithm for rapidly joining streaming data with persistent master data in order to reduce data latency. Typically during the transformation phase of ETL (Extraction, Transformation, and Loading) a stream of transactional data needs to be joined with master data stored on disk. To implement this process, a semi-stream join operator is commonly used. Most semi-stream join operators cache frequent parts of the master data to improve their performance, this process requires careful distribution of allocated memory among the components of the join operator. This article presents a cache inequality approach to optimise cache size and memory. To test this approach, we present a novel Memory Optimal Index-based Join (MOIJ) algorithm. MOIJ supports many-to-many types of joins and adapts to dynamic streaming data. We also present a cost model for MOIJ and compare the performance with existing algorithms empirically as well as analytically. We envisage the enhanced ability of processing near-real-time streaming data using minimal memory will reduce latency in processing big data and will contribute to the development of highperformance real-time business intelligence systems.

Intelligent Storage Data Classification System Based on the BP Neural Network

“…However, due to the scientific and technological means at that time, there were many problems in the overall software system, such as serious software closure, long inspection time, and poor real-time performance. e Bug-Sifter grain inspection system was developed by Naeem and others [5]. is system can achieve real-time monitoring of grain situation data and significantly mention the robustness and monitoring efficiency of the system.…”