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Efficient Model-Relational Data Management: Challenges and Opportunities
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AI
DOI：10.1109/TKDE.2024.3384276.png)
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摘要
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As modern data pipelines continue to collect, produce, and store various data formats, extracting and combining value from traditional and context-rich sources becomes unsuitable for RDBMS. To tap into the dark data, domain experts analyze and extract insights and integrate them into various data repositories. This can involve out-of-DBMS processing with high manual effort and suboptimal performance. While AI systems based on ML models can automate the analysis, they can further generate context-rich answers. Using multiple data sources and models further exacerbates the problem of consolidating and analyzing the data of interest. We envision an analytical engine co-optimized with components that enable context-rich analysis. First, as all the data from different sources is expensive to clean ahead of time, we propose using online data integration via model-assisted similarity operations. Second, we aim for a holistic pipeline cost- and rule-based optimization across relational and model-based operators. Third, with increasingly heterogeneous hardware and workloads ranging from relational analytics to generative model inference, we envision a system that adapts to the complex query requirements at runtime. Composing ML-driven insights with established approaches aims to expand decades of research and systems-building effort in making complex functionality and performance effortless for the end user.
Keyword:
Data models
Task analysis
Analytical models
Optimization
Computational modeling
Context modeling
Adaptation models
Analytics
machine learning
data integration
query optimization
vector data
hardware-conscious processing
期刊
IF:
10.4
论文数:
6.9K
被引数:
3.2W
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