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Timehash: Hierarchical Time Indexing for Efficient Business Hours Search

Jinoh Kim, Jaewon Son

TL;DR

Timehash is presented, a novel hierarchical time indexing algorithm that achieves over 99% reduction in index size compared to minute-level indexing while maintaining 100% precision and is generalizable to various temporal filtering problems in search systems, e-commerce, and reservation platforms.

Abstract

Temporal range filtering is a critical operation in large-scale search systems, particularly for location-based services that need to filter businesses by operating hours. Traditional approaches either suffer from poor query performance (scope filtering) or index size explosion (minute-level indexing). We present Timehash, a novel hierarchical time indexing algorithm that achieves over 99% reduction in index size compared to minute-level indexing while maintaining 100% precision. Timehash employs a flexible multi-resolution strategy with customizable hierarchical levels. Through empirical analysis on distributions from 12.6 million business records of a production location search service, we demonstrate a data-driven methodology for selecting optimal hierarchies tailored to specific data distributions. We evaluated Timehash on up to 12.6 million synthetic POIs generated from production distributions. Experimental results show that a five-level hierarchy reduces index terms to 5.6 per document (99.1% reduction versus minute-level indexing), with zero false positives and zero false negatives. Scalability benchmarks confirm constant per-document cost from 100K to 12.6M POIs, while supporting complex scenarios such as break times and irregular schedules. Our approach is generalizable to various temporal filtering problems in search systems, e-commerce, and reservation platforms.

Timehash: Hierarchical Time Indexing for Efficient Business Hours Search

TL;DR

Timehash is presented, a novel hierarchical time indexing algorithm that achieves over 99% reduction in index size compared to minute-level indexing while maintaining 100% precision and is generalizable to various temporal filtering problems in search systems, e-commerce, and reservation platforms.

Abstract

Temporal range filtering is a critical operation in large-scale search systems, particularly for location-based services that need to filter businesses by operating hours. Traditional approaches either suffer from poor query performance (scope filtering) or index size explosion (minute-level indexing). We present Timehash, a novel hierarchical time indexing algorithm that achieves over 99% reduction in index size compared to minute-level indexing while maintaining 100% precision. Timehash employs a flexible multi-resolution strategy with customizable hierarchical levels. Through empirical analysis on distributions from 12.6 million business records of a production location search service, we demonstrate a data-driven methodology for selecting optimal hierarchies tailored to specific data distributions. We evaluated Timehash on up to 12.6 million synthetic POIs generated from production distributions. Experimental results show that a five-level hierarchy reduces index terms to 5.6 per document (99.1% reduction versus minute-level indexing), with zero false positives and zero false negatives. Scalability benchmarks confirm constant per-document cost from 100K to 12.6M POIs, while supporting complex scenarios such as break times and irregular schedules. Our approach is generalizable to various temporal filtering problems in search systems, e-commerce, and reservation platforms.
Paper Structure (36 sections, 2 theorems, 2 equations, 2 figures, 9 tables, 1 algorithm)

This paper contains 36 sections, 2 theorems, 2 equations, 2 figures, 9 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Current Approaches and Limitations
  3. Our Contributions
  4. Background and Related Work
  5. Temporal Indexing in Search Systems
  6. Hierarchical Data Structures
  7. Time Encoding Schemes
  8. Problem Definition
  9. Business Hours Search Problem
  10. Requirements
  11. Metrics
  12. Timehash Algorithm
  13. Overview: Hierarchical Multi-Resolution Approach
  14. Time Hierarchy Design
  15. Hash Key Generation Algorithm
  16. ...and 21 more sections

Key Result

theorem 1

Timehash guarantees zero false negatives. For any query time $t$ within a document's operating hours $[start, end]$, at least one query key will match at least one index key.

Figures (2)

  • Figure 1: Hierarchical decomposition of 08:00--21:00 into 4 keys.
  • Figure 2: Boundary handling for 11:40--21:00. Non-aligned boundaries require finer-level decomposition.

Theorems & Definitions (4)

  • theorem 1: Zero False Negatives
  • proof
  • theorem 2: Zero False Positives
  • proof