MOIST: A Scalable and Parallel Moving Object Indexer with School Tracking

TL;DR

MOIST is a scalable, parallel spatial indexer for location-based services that efficiently manages updates and queries by clustering objects into schools and optimizing data storage.

Contribution

It introduces a dynamic clustering scheme and memory management techniques to improve update and query efficiency in spatial indexing.

Findings

Supports highly efficient nearest-neighbor queries

Scales well with increasing users and update frequency

Reduces update latency through clustering

Abstract

Location-Based Service (LBS) is rapidly becoming the next ubiquitous technology for a wide range of mobile applications. To support applications that demand nearest-neighbor and history queries, an LBS spatial indexer must be able to efficiently update, query, archive and mine location records, which can be in contention with each other. In this work, we propose MOIST, whose baseline is a recursive spatial partitioning indexer built upon BigTable. To reduce update and query contention, MOIST groups nearby objects of similar trajectory into the same school, and keeps track of only the history of school leaders. This dynamic clustering scheme can eliminate redundant updates and hence reduce update latency. To improve history query processing, MOIST keeps some history data in memory, while it flushes aged data onto parallel disks in a locality-preserving way. Through experimental studies, we show that MOIST can support highly efficient nearest-neighbor and history queries and can scale well with an increasing number of users and update frequency.