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Related Experiment Video

Updated: May 7, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Identifying redundancy and exposing provenance in crowdsourced data analysis.

Wesley Willett¹, Shiry Ginosar, Avital Steinitz

¹INRIA.

IEEE Transactions on Visualization and Computer Graphics

|September 21, 2013

Summary

This summary is machine-generated.

This system helps analysts leverage crowd workers for data exploration. It identifies redundant explanations and prioritizes plausible insights, improving the efficiency of crowdsourced data analysis.

Related Experiment Videos

Last Updated: May 7, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Area of Science:

Human-Computer Interaction
Data Analysis
Crowdsourcing

Background:

Crowd workers can perform basic data analysis tasks.
Challenges include redundant explanations and varying worker expertise.
Efficiently consolidating and validating crowdsourced insights is crucial.

Purpose of the Study:

To present a system for analysts to effectively utilize paid crowd workers for data exploration.
To introduce crowd-assisted techniques for managing crowdsourced explanations.
To enhance the interactive examination and application of worker insights.

Main Methods:

Developed crowd-assisted strategies for detecting redundant explanations using color clustering with representative selection.
Implemented explanation provenance capture through highlighting tasks and embedded web browser tracking.
Refined provenance information via source-review tasks.
Created an explanation-management interface for filtering, sorting, and selecting plausible explanations.

Main Results:

Color clustering with representative selection achieved expert-level clustering quality for explanations.
The system enables analysts to efficiently identify and consolidate redundant and plausible crowdsourced explanations.
Provenance information aids in interactive filtering and selection of insights.

Conclusions:

The presented system significantly improves the utility of crowdsourced data exploration by addressing redundancy and plausibility.
Crowd-assisted techniques enhance analyst efficiency in managing and leveraging crowdsourced insights.
The system facilitates interactive analysis and builds upon worker-generated explanations effectively.