Processing Analytical Workloads Incrementally

TL;DR

This paper introduces a framework for incremental analysis of large data collections by reusing and updating models, significantly improving performance in machine learning workflows.

Contribution

It presents a novel approach to model materialization and incremental reuse, with specific techniques for linear regression, naive Bayes, and logistic regression.

Findings

Significant performance improvements demonstrated on real and synthetic datasets.

Effective techniques for incremental model maintenance and updates.

Trade-offs and optimization strategies analyzed in detail.

Abstract

Analysis of large data collections using popular machine learning and statistical algorithms has been a topic of increasing research interest. A typical analysis workload consists of applying an algorithm to build a model on a data collection and subsequently refining it based on the results. In this paper we introduce model materialization and incremental model reuse as first class citizens in the execution of analysis workloads. We materialize built models instead of discarding them in a way that can be reused in subsequent computations. At the same time we consider manipulating an existing model (adding or deleting data from it) in order to build a new one. We discuss our approach in the context of popular machine learning models. We specify the details of how to incrementally maintain models as well as outline the suitable optimizations required to optimally use models and their incremental adjustments to build new ones. We detail our techniques for linear regression, naive bayes and logistic regression and present the suitable algorithms and optimizations to handle these models in our framework. We present the results of a detailed performance evaluation, using real and synthetic data sets. Our experiments analyze the various trade offs inherent in our approach and demonstrate vast performance benefits.