Class-based query-optimization for minimizing worst-case execution times of diagnostic queries in embedded real-time systems

Active diagnosis in real time embedded computer systems increases the overall reliability of the system by performing error detection and fault recovery. Real time databases and diagnostic queries are a common solution to realize active diagnosis. This paper presents a technique to optimize the diagnostic queries in a fault tolerant real time embedded system. A directed graph called the DMG (Diagnostic Multi-query Graph) based on the diagnostic symptoms and features is the input to the query optimization module for the processing of each query within a short worst case execution time. The diagnostic inference process is temporally and spatially decomposed by introducing intermediate inference steps called symptoms. These symptoms and diagnostic features extracted from the DMG are stored in an embedded database created in a Pervasive SQL server. The query execution is based on periods and each query node of the DMG has to finish within its time bound which is worst case execution time of the query. At first the estimated worst case execution time for each diagnostic query is calculated. After that the algorithm optimizes the diagnostic query using a class based query categorization technique. The access method for each query is selected on the basis of its type. For join queries the most optimized join order is calculated by estimating the selectivity factor based on the number of tuples present in each join order. Results presented in this context show that the diagnostic queries are optimized effectively and their estimated worst case execution time is minimized.