
TL;DR
This paper presents annotative indexing, a versatile framework unifying various database indexing methods, supporting diverse data types and complex queries, and enabling fully dynamic, transactional indexing for large-scale, concurrent data access.
Contribution
Introduces annotative indexing as a general framework that unifies multiple indexing paradigms and supports dynamic, transactional operations across diverse data types.
Findings
Supports retrieval augmented generation and knowledge graphs.
Enables fully dynamic, ACID-compliant indexing with high concurrency.
Demonstrates applicability to text, JSON, and other data types.
Abstract
This paper introduces annotative indexing, a novel framework that unifies and generalizes traditional inverted indexes, column stores, object stores, and graph databases. As a result, annotative indexing can provide the underlying indexing framework for databases that support retrieval augmented generation, knowledge graphs, entity retrieval, semi-structured data, and ranked retrieval. While we primarily focus on human language data in the form of text, annotative indexing is sufficiently general to support a range of other datatypes, and we provide examples of SQL-like queries over a JSON store that includes numbers and dates. Taking advantage of the flexibility of annotative indexing, we also demonstrate a fully dynamic annotative index incorporating support for ACID properties of transactions with hundreds of multiple concurrent readers and writers.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Text Analysis Techniques · Semantic Web and Ontologies
MethodsFocus
