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Revisiting Database Indexing for Parallel and Accelerated Computing: A Comprehensive Study and Novel Approaches

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datacite.subject.fosCiências Naturais::Ciências da Computação e da Informação
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
dc.contributor.authorAbbasi, Maryam
dc.contributor.authorBernardo, Marco V.
dc.contributor.authorANTUNES VAZ, PAULO JOAQUIM
dc.contributor.authorSilva, José
dc.contributor.authorMartins, Pedro
dc.date.accessioned2026-03-04T16:14:34Z
dc.date.available2026-03-04T16:14:34Z
dc.date.issued2024
dc.description.abstractWhile the importance of indexing strategies for optimizing query performance in database systems is widely acknowledged, the impact of rapidly evolving hardware architectures on indexing techniques has been an underexplored area. As modern computing systems increasingly leverage parallel processing capabilities, multi-core CPUs, and specialized hardware accelerators, traditional indexing approaches may not fully capitalize on these advancements. This comprehensive experimental study investigates the effects of hardware-conscious indexing strategies tailored for contemporary and emerging hardware platforms. Through rigorous experimentation on a real-world database environment using the industry-standard TPC-H benchmark, this research evaluates the performance implications of indexing techniques specifically designed to exploit parallelism, vectorization, and hardware-accelerated operations. By examining approaches such as cache-conscious B-Tree variants, SIMD-optimized hash indexes, and GPU-accelerated spatial indexing, the study provides valuable insights into the potential performance gains and trade-offs associated with these hardware-aware indexing methods. The findings reveal that hardware-conscious indexing strategies can significantly outperform their traditional counterparts, particularly in data-intensive workloads and large-scale database deployments. Our experiments show improvements ranging from 32.4% to 48.6% in query execution time, depending on the specific technique and hardware configuration. However, the study also highlights the complexity of implementing and tuning these techniques, as they often require intricate code optimizations and a deep understanding of the underlying hardware architecture. Additionally, this research explores the potential of machine learning-based indexing approaches, including reinforcement learning for index selection and neural network-based index advisors. While these techniques show promise, with performance improvements of up to 48.6% in certain scenarios, their effectiveness varies across different query types and data distributions. By offering a comprehensive analysis and practical recommendations, this research contributes to the ongoing pursuit of database performance optimization in the era of heterogeneous computing. The findings inform database administrators, developers, and system architects on effective indexing practices tailored for modern hardware, while also paving the way for future research into adaptive indexing techniques that can dynamically leverage hardware capabilities based on workload characteristics and resource availability.por
dc.description.sponsorship
dc.identifier.citationAbbasi, M., Bernardo, M. V., Váz, P., Silva, J., & Martins, P. (2024). Revisiting Database Indexing for Parallel and Accelerated Computing: A Comprehensive Study and Novel Approaches. Information, 15(8), 429. https://doi.org/10.3390/info15080429
dc.identifier.doihttps://doi.org/10.3390/info15080429
dc.identifier.eissn2078-2489
dc.identifier.urihttp://hdl.handle.net/10400.19/9728
dc.language.isoeng
dc.peerreviewedyes
dc.publisherMDPI
dc.relationCentre for Research in Digital Services
dc.relationUIDB/50008/2020
dc.relationCEECINST/00077/2021
dc.relation.hasversionhttps://www.mdpi.com/2078-2489/15/8/429
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectindexing strategies
dc.subjecthardware architectures
dc.subjectquery performance
dc.subjectparallel processing
dc.subjectmulti-core CPUs
dc.subjectGPU Acceleration
dc.titleRevisiting Database Indexing for Parallel and Accelerated Computing: A Comprehensive Study and Novel Approacheseng
dc.typetext
dspace.entity.typePublication
oaire.awardNumberUIDB/05583/2020
oaire.awardTitleCentre for Research in Digital Services
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05583%2F2020/PT
oaire.citation.issue8
oaire.citation.startPage429
oaire.citation.titleInformation
oaire.citation.volume15
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameANTUNES VAZ
person.familyNameSilva
person.givenNamePAULO JOAQUIM
person.givenNameJosé
person.identifier.ciencia-id351C-9899-0EE7
person.identifier.ciencia-id4A14-D3E7-5B32
person.identifier.orcid0000-0002-1745-8937
person.identifier.orcid0000-0001-7285-8282
person.identifier.scopus-author-id55447844100
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
relation.isAuthorOfPublication702e79ee-5b0b-47ff-989d-12e6d8ea1e89
relation.isAuthorOfPublicatione9d8719e-af47-4008-b854-817801bb3964
relation.isAuthorOfPublication.latestForDiscovery702e79ee-5b0b-47ff-989d-12e6d8ea1e89
relation.isProjectOfPublicationa2335235-05b4-404c-a71e-cc37ae7fbf2c
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