Scholarlink

<p>We study the problem of temporal database indexing, i.e., indexing versions of a database table in an evolving database. Although modern machines include large memory chips, data volumes quickly exceed resources, making it infeasible to keep the entire history in memory. Therefore we require temporal indices that optimize main memory usage while remaining scalable as the history grows. We depart from the classic indexing approach, where all data versions are indexed in a single data structure, and propose LIT, a hybrid index that decouples the management of the current and past states of the indexed column. LIT includes optimized indexing modules for current (i.e., live) and past (i.e., dead) records, supporting efficient queries and updates. Furthermore, our extended approach LIT<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(^+\)</EquationSource> <EquationSource Format="MATHML"><math> <mmultiscripts> <mrow /> <mrow /> <mo>+</mo> </mmultiscripts> </math></EquationSource> </InlineEquation> handles record versions in memory using LIT bounded by a memory budget, while managing older versions (fossils) that exceed the budget on disk. We show that LIT outperforms state-of-the-art solutions by orders of magnitude while using space linearly proportional to the number of indexed record versions, making it suitable for main-memory temporal data management. In addition, we also show that LIT<InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(^+\)</EquationSource> <EquationSource Format="MATHML"><math> <mmultiscripts> <mrow /> <mrow /> <mo>+</mo> </mmultiscripts> </math></EquationSource> </InlineEquation> efficiently indexes long database histories on disk while maintaining scalability and query performance.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Scalable lighting-fast temporal indexing

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月10日

查看全文
  • Panagiotis Simatis,
  • George Christodoulou,
  • Panagiotis Bouros,
  • Nikos Mamoulis

摘要

We study the problem of temporal database indexing, i.e., indexing versions of a database table in an evolving database. Although modern machines include large memory chips, data volumes quickly exceed resources, making it infeasible to keep the entire history in memory. Therefore we require temporal indices that optimize main memory usage while remaining scalable as the history grows. We depart from the classic indexing approach, where all data versions are indexed in a single data structure, and propose LIT, a hybrid index that decouples the management of the current and past states of the indexed column. LIT includes optimized indexing modules for current (i.e., live) and past (i.e., dead) records, supporting efficient queries and updates. Furthermore, our extended approach LIT \(^+\) + handles record versions in memory using LIT bounded by a memory budget, while managing older versions (fossils) that exceed the budget on disk. We show that LIT outperforms state-of-the-art solutions by orders of magnitude while using space linearly proportional to the number of indexed record versions, making it suitable for main-memory temporal data management. In addition, we also show that LIT \(^+\) + efficiently indexes long database histories on disk while maintaining scalability and query performance.