2019/10/17 by Wail Y. Alkowaileet, Alkowaileet, Wail Y., Sattam Alsubaiee +3 · 1 citation
Computer Science · Decision Sciences · #Advanced Database Systems and Queries #Cloud Computing and Resource Management #Data Quality and Management #Databases (cs.DB) #FOS: Computer and information sciences #Semantic Web and Ontologies
paper · pdf · doi:10.48550/arxiv.1910.08185
openalex publication_date 2019/10/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Document database systems store self-describing semi-structured records, such\nas JSON, "as-is" without requiring the users to pre-define a schema. This\nprovides users with the flexibility to change the structure of incoming records\nwithout worrying about taking the system offline or hindering the performance\nof currently running queries. However, the flexibility of such systems does not\nfree. The large amount of redundancy in the records can introduce an\nunnecessary storage overhead and impact query performance.\n Our focus in this paper is to address the storage overhead issue by\nintroducing a tuple compactor framework that infers and extracts the schema\nfrom self-describing semi-structured records during the data ingestion. As many\nprominent document stores, such as MongoDB and Couchbase, adopt Log Structured\nMerge (LSM) trees in their storage engines, our framework exploits LSM\nlifecycle events to piggyback the schema inference and extraction operations.\nWe have implemented and empirically evaluated our approach to measure its\nimpact on storage, data ingestion, and query performance in the context of\nApache AsterixDB.\n