Supporting Complex Query Time Enrichment For Analytics

Dhrubajyoti Ghosh, Peeyush Gupta, Sharad Mehrotra, Shantanu Sharma

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Several application domains require data to be enriched prior to its use. Data enrichment is often performed using expensive machine learning models to interpret low-level data (e.g., models for face detection) into semantically meaningful observation. Collecting and enriching data offline before loading it to a database is infeasible if one desires online analysis on data as it arrives. Enriching data on the fly at insertion could result in redundant work (if applications require only a fraction of the data to be enriched) and could result in a bottleneck (if enrichment functions are expensive). Any scalable solution requires enrichment during query processing. This paper explores two different architectures for integrating enrichment into query processing - a loosely coupled approach wherein enrichment is performed outside of the DBMS and a tightly coupled approach wherein it is performed within the DBMS. The paper addresses the challenges of increased query latency due to query time enrichment.

Original languageEnglish (US)
Title of host publicationProceedings of the 26th International Conference on Extending Database Technology, EDBT 2023
Number of pages13
ISBN (Electronic)9783893180882
StatePublished - 2023
Event26th International Conference on Extending Database Technology, EDBT 2023 - Ioannina, Greece
Duration: Mar 28 2023Mar 31 2023

Publication series

NameAdvances in Database Technology - EDBT
ISSN (Electronic)2367-2005


Conference26th International Conference on Extending Database Technology, EDBT 2023

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Software
  • Computer Science Applications


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