Matrix Multiplication with SQL Queries for Graph Analytics
Xiantian Zhou, CARLOS R. ORDÓÑEZ · 2020
Analyzing large data sets are challenging. Most data analytics research has proposed parallel algorithms that outside a DBMS because SQL is considered inadequate for complexity computations. R and Python are popular analysis systems that provide a vast collection of mathematical models and functions. However, they are limited by main memory and single computer. Recently, parallel DBMSs have significantly improved query processing performance. Moreover, SQL queries are elegant and efficient. This paper introduces a novel system architecture integrating a popular analysis system and parallel DBMSs, which has the matrix multiplication involving a large matrix evaluated inside a parallel DBMS and complex mathematical computations are done in R or Python. Many graph problems can be solved by matrix multiplication. In this paper, we show optimized queries which perform matrix multiplication in DBMSs to solve two fundamental graph problems, single-source reachability and transitive closure.