Query Performance Prediction For Concurrent Queries Using Graph Embedding - Its Overview
Predicting how well database queries will run is crucial for various uses (e.g., database monitoring and query scheduling). Because it is difficult to capture the correlations between distinct queries, such as lock conflict and buffer sharing, existing approaches concentrate on forecasting the performance of a single query. Still, they cannot successfully make query performance prediction for concurrent queries using graph embedding.
You can classify most current approaches to predicting query performance based on their predictions for the performance of individual queries. In the first place, there are statistically-based techniques. These techniques gather feature statistics from a small subset of data and then use those statistics to make predictions about the whole. Accuracy, however, is extremely sample-dependent, and it is not simple to collect many high-quality samples.
The second employs classic machine learning techniques. It starts by picking the highest-ranking query characteristics in termsof their correlations with performance indicators; then utilizes Support Vector Machines (SVMs) to forecast performance at the query level; and then suggests a hierarchical model at the operator level. These mixed machine-learning techniques strike a good compromise between precision and flexibility.
Estimating how successful a search will be in response to a question with no relevance judgments is the goal of the query performance prediction (QPP) job. The efficiency of a query in modeling the underlying information demand is not yet considered by existing QPP techniques.
Predicting how well a query will execute is an intriguing and critical problem in the field of Information Retrieval (IR). Relevance ratings are used in current predictors. However, they take a long time to calculate. As a result, present forecasting methods are inadequate.
Meeting service level agreements in database systems rely heavily on query performance prediction (SLAs). As a result, traditional prediction algorithms cannot accurately forecast the performance of several requests being processed simultaneously. The workload characteristics are first represented in GPredictor by a graph model, where operator features are vertices and query correlations and resource competitions are edges.
Passage Based Answer-Set Graph Approach for Query Performance Prediction
PerformancePredictor uses a deep learning model to forecast the performance of a query by embedding the network using a learning model.
Concurrent query performance may be predicted using various time-honored techniques. The query performance bottlenecks caused by disk and memory congestion are jointly modeled using the Buffer Access Latency (BAL) technique. It also made use of a linnear regression model to foretell how well queries will perform.
It has been suggested that a graph-embedding-based model may be used to forecast how well concurrent searches would execute. Query characteristics (vertices) and operator correlations (edges) are encoded in a graph-based model.
To predict how well each query will perform, a graph-embedding-based model is used. A graph-based model stores query characteristics as vertices and operator correlations as edges. After that, a three-layer deep learning model is used to forecast the query performance while the graph embedding network incorporates the attributes relevant to performance.
For the sake of accommodating shifting workloads and decreasing overall graph size, a graph update and compaction mechanism have been developed. The experimental findings of real-world datasets show that our strategy greatly outperformed the state-of-the-art methods.