Designing a Human-Machine Hybrid Computing System for Unstructured Data Analytics

TL;DR

This paper introduces a novel hybrid human-machine computing platform designed for unstructured data analysis, integrating service level objectives to optimize accuracy, budget, and completion time, with promising initial results.

Contribution

It presents the first hybrid platform that manages human and machine resources with integrated SLOs for complex, decomposable tasks.

Findings

Initial experimental results are highly encouraging.

First to support accuracy, budget, and time SLOs in such a platform.

Demonstrates potential for improved unstructured data analysis.

Abstract

Current machine algorithms for analysis of unstructured data typically show low accuracies due to the need for human-like intelligence. Conversely, though humans are much better than machine algorithms on analyzing unstructured data, they are unpredictable, slower and can be erroneous or even malicious as computing agents. Therefore, a hybrid platform that can intelligently orchestrate machine and human computing resources would potentially be capable of providing significantly better benefits compared to either type of computing agent in isolation. In this paper, we propose a new hybrid human-machine computing platform with integrated service level objectives (SLO) management for complex tasks that can be decomposed into a dependency graph where nodes represent subtasks. Initial experimental results are highly encouraging. To the best of our knowledge, ours is the first work that attempts to design such a hybrid human-machine computing platform with support for addressing the three SLO parameters of accuracy, budget and completion time.