InsightNotes: summary-based annotation management in relational databases

In this paper, we address the challenges that arise from the growing scale of annotations in scientific databases. On one hand, end-users and scientists are incapable of analyzing and extracting knowledge from the large number of reported annotations, e.g., one tuple may have hundreds of annotations attached to it over time. On the other hand, current annotation management techniques fall short in providing advanced processing over the annotations beyond just propagating them to end-users. To address this limitation, we propose the InsightNotes system, a summary-based annotation management engine in relational databases. InsightNotes integrates data mining and summarization techniques into annotation management in novel ways with the objective of creating and reporting concise representations (summaries) of the raw annotations. We propose an extended summary-aware query processing engine for efficient manipulation and propagation of the annotation summaries in the query pipeline. We introduce several optimizations for the creation, maintenance, and zoom-in processing over the annotations summaries. InsightNotes is implemented on top of an existing annotation management system within which it is experimentally evaluated using real-world datasets. The results illustrate significant performance gain from the proposed techniques and optimizations (up to 100x in some operations) compared to the naive approaches.