Optimizing Context-Enhanced Relational Joins
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866909490101616640 |
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| author | Sanca, Viktor Chatzakis, Manos Ailamaki, Anastasia |
| author_facet | Sanca, Viktor Chatzakis, Manos Ailamaki, Anastasia |
| contents | Collecting data, extracting value, and combining insights from relational and context-rich multi-modal sources in data processing pipelines presents a challenge for traditional relational DBMS. While relational operators allow declarative and optimizable query specification, they are limited to data transformations unsuitable for capturing or analyzing context. On the other hand, representation learning models can map context-rich data into embeddings, allowing machine-automated context processing but requiring imperative data transformation integration with the analytical query. To bridge this dichotomy, we present a context-enhanced relational join and introduce an embedding operator composable with relational operators. This enables hybrid relational and context-rich vector data processing, with algebraic equivalences compatible with relational algebra and corresponding logical and physical optimizations. We investigate model-operator interaction with vector data processing and study the characteristics of the E-join operator. Using an example of string embeddings, we demonstrate enabling hybrid context-enhanced processing on relational join operators with vector embeddings. The importance of holistic optimization, from logical to physical, is demonstrated in an order of magnitude execution time improvement. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_01476 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Optimizing Context-Enhanced Relational Joins Sanca, Viktor Chatzakis, Manos Ailamaki, Anastasia Databases Artificial Intelligence Machine Learning Collecting data, extracting value, and combining insights from relational and context-rich multi-modal sources in data processing pipelines presents a challenge for traditional relational DBMS. While relational operators allow declarative and optimizable query specification, they are limited to data transformations unsuitable for capturing or analyzing context. On the other hand, representation learning models can map context-rich data into embeddings, allowing machine-automated context processing but requiring imperative data transformation integration with the analytical query. To bridge this dichotomy, we present a context-enhanced relational join and introduce an embedding operator composable with relational operators. This enables hybrid relational and context-rich vector data processing, with algebraic equivalences compatible with relational algebra and corresponding logical and physical optimizations. We investigate model-operator interaction with vector data processing and study the characteristics of the E-join operator. Using an example of string embeddings, we demonstrate enabling hybrid context-enhanced processing on relational join operators with vector embeddings. The importance of holistic optimization, from logical to physical, is demonstrated in an order of magnitude execution time improvement. |
| title | Optimizing Context-Enhanced Relational Joins |
| topic | Databases Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2312.01476 |