On a model of indexability and its bounds for range queries

Author:

Hellerstein Joseph M.1,Koutsoupias Elias2,Miranker Daniel P.3,Papadimitriou Christos H.1,Samoladas Vasilis3

Affiliation:

1. University of California, Berkeley, CA

2. University of California, Los Angeles, CA

3. University of Texas, Austin, TX

Abstract

We develop a theoretical framework to characterize the hardness of indexing data sets on block-access memory devices like hard disks. We define an indexing workload by a data set and a set of potential queries. For a workload, we can construct an indexing scheme, which is a collection of fixed-sized subsets of the data. We identify two measures of efficiency for an indexing scheme on a workload: storage redundancy, r (how many times each item in the data set is stored), and access overhead, A (how many times more blocks than necessary does a query retrieve).For many interesting families of workloads, there exists a trade-off between storage redundancy and access overhead. Given a desired access overhead A , there is a minimum redundancy that any indexing scheme must exhibit. We prove a lower-bound theorem for deriving the minimum redundancy. By applying this theorem, we show interesting upper and lower bounds and trade-offs between A and r in the case of multidimensional range queries and set queries.

Publisher

Association for Computing Machinery (ACM)

Subject

Artificial Intelligence,Hardware and Architecture,Information Systems,Control and Systems Engineering,Software

Cited by 26 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Data Structures for Data-Intensive Applications: Tradeoffs and Design Guidelines;Foundations and Trends® in Databases;2023

2. The next 50 years in database indexing or;Proceedings of the VLDB Endowment;2021-11

3. I/O-efficient 2-d orthogonal range skyline and attrition priority queues;Computational Geometry;2021-02

4. On the I/O Complexity of the k-Nearest Neighbors Problem;Proceedings of the 39th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems;2020-05-29

5. Efficient Evaluation of Multi-Column Selection Predicates in Main-Memory;IEEE Transactions on Knowledge and Data Engineering;2019-07-01

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3