Comparative Evaluation of Data Stream Indexing Models

TL;DR

This paper compares various data stream indexing models to evaluate their performance in online, real-time data processing environments, highlighting differences from traditional database indexing.

Contribution

It provides an analytical comparison of data stream indexing models, addressing their suitability and performance in data stream processing environments.

Findings

Different models have varying efficiency in time and space.

Analytical comparison highlights strengths and weaknesses of each model.

Guidelines for selecting appropriate indexing techniques for data streams.

Abstract

In recent years, the management and processing of data streams has become a topic of active research in several fields of computer science such as, distributed systems, database systems, and data mining. A data stream can be thought of as a transient, continuously increasing sequence of data. In data streams' applications, because of online monitoring, answering to the user's queries should be time and space efficient. In this paper, we consider the special requirements of indexing to determine the performance of different techniques in data stream processing environments. Stream indexing has main differences with approaches in traditional databases. Also, we compare data stream indexing models analytically that can provide a suitable method for stream indexing.