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.
Author:Suleman ShahReviewer:Han JuOct 24, 202226 Shares676 Views
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.
What Is Query Performance Prediction?
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
People Also Ask
How Does Performance predictor Use Graph-based Learning?
PerformancePredictor uses a deep learning model to forecast the performance of a query by embedding the network using a learning model.
How To Predict Performance For Concurrent Queries?
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.
Can A Graph-embedding-based Model Predict Performance For Concurrent Queries?
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.
Final Words
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.
Suleman Shah
Author
Suleman Shah is a researcher and freelance writer. As a researcher, he has worked with MNS University of Agriculture, Multan (Pakistan) and Texas A & M University (USA). He regularly writes science articles and blogs for science news website immersse.com and open access publishers OA Publishing London and Scientific Times. He loves to keep himself updated on scientific developments and convert these developments into everyday language to update the readers about the developments in the scientific era. His primary research focus is Plant sciences, and he contributed to this field by publishing his research in scientific journals and presenting his work at many Conferences.
Shah graduated from the University of Agriculture Faisalabad (Pakistan) and started his professional carrier with Jaffer Agro Services and later with the Agriculture Department of the Government of Pakistan. His research interest compelled and attracted him to proceed with his carrier in Plant sciences research. So, he started his Ph.D. in Soil Science at MNS University of Agriculture Multan (Pakistan). Later, he started working as a visiting scholar with Texas A&M University (USA).
Shah’s experience with big Open Excess publishers like Springers, Frontiers, MDPI, etc., testified to his belief in Open Access as a barrier-removing mechanism between researchers and the readers of their research. Shah believes that Open Access is revolutionizing the publication process and benefitting research in all fields.
Han Ju
Reviewer
Hello! I'm Han Ju, the heart behind World Wide Journals. My life is a unique tapestry woven from the threads of news, spirituality, and science, enriched by melodies from my guitar. Raised amidst tales of the ancient and the arcane, I developed a keen eye for the stories that truly matter. Through my work, I seek to bridge the seen with the unseen, marrying the rigor of science with the depth of spirituality.
Each article at World Wide Journals is a piece of this ongoing quest, blending analysis with personal reflection. Whether exploring quantum frontiers or strumming chords under the stars, my aim is to inspire and provoke thought, inviting you into a world where every discovery is a note in the grand symphony of existence.
Welcome aboard this journey of insight and exploration, where curiosity leads and music guides.
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