English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Indexes provide a method to access data in databases quickly. It can improvethe response speed of subsequent queries by building a complete index inadvance. However, it also leads to a huge overhead of the continuous updatingduring creating the index. An in-memory database usually has a higher queryprocessing performance than disk databases and is more suitable for real-timequery processing. Therefore, there is an urgent need to reduce the indexcreation and update cost for in-memory databases. Database cracking technologyis currently recognized as an effective method to reduce the indexinitialization time. However, conventional cracking algorithms are focused onsimple column data structure rather than those complex index structure forin-memory databases. In order to show the feasibility of in-memory databaseindex cracking and promote to future more extensive research, this paperconducted a case study on the Adaptive Radix Tree (ART), a popular tree indexstructure of in-memory databases. On the basis of carefully examining the ARTindex construction overhead, an algorithm using auxiliary data structures tocrack the ART index is proposed.

Cracking In-Memory Database Index A Case Study for Adaptive Radix Tree Index