TRACE: A Time-Relational Approximate Cubing Engine for Fast Data Insights
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913193265201152 |
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| author | Sivakumar, Suharsh Shen, Jonathan Monga, Rajat |
| author_facet | Sivakumar, Suharsh Shen, Jonathan Monga, Rajat |
| contents | A large class of data questions can be modeled as identifying important slices of data driven by user defined metrics. This paper presents TRACE, a Time-Relational Approximate Cubing Engine that enables interactive analysis on such slices with a low upfront cost - both in space and computation. It does this by materializing the most important parts of the cube over time enabling interactive querying for a large class of analytical queries e.g. what part of my business has the highest revenue growth ([SubCategory=Sports Equipment, Gender=Female]), what slices are lagging in revenue per user ([State=CA, Age=20-30]). Many user defined metrics are supported including common aggregations such as SUM, COUNT, DISTINCT COUNT and more complex ones such as AVERAGE. We implemented and deployed TRACE for a variety of business use cases. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_06336 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TRACE: A Time-Relational Approximate Cubing Engine for Fast Data Insights Sivakumar, Suharsh Shen, Jonathan Monga, Rajat Information Retrieval Databases H.3.3 A large class of data questions can be modeled as identifying important slices of data driven by user defined metrics. This paper presents TRACE, a Time-Relational Approximate Cubing Engine that enables interactive analysis on such slices with a low upfront cost - both in space and computation. It does this by materializing the most important parts of the cube over time enabling interactive querying for a large class of analytical queries e.g. what part of my business has the highest revenue growth ([SubCategory=Sports Equipment, Gender=Female]), what slices are lagging in revenue per user ([State=CA, Age=20-30]). Many user defined metrics are supported including common aggregations such as SUM, COUNT, DISTINCT COUNT and more complex ones such as AVERAGE. We implemented and deployed TRACE for a variety of business use cases. |
| title | TRACE: A Time-Relational Approximate Cubing Engine for Fast Data Insights |
| topic | Information Retrieval Databases H.3.3 |
| url | https://arxiv.org/abs/2401.06336 |