HILDA '16- Proceedings of the Workshop on Human-In-the-Loop Data Analytics

Full Citation in the ACM Digital Library

Visual exploration of machine learning results using data cube analysis

Have a chat with clustine, conversational engine to query large tables

Bridging the gap between user intention and model parameters for human-in-the-loop data analytics

Towards a general-purpose query language for visualization recommendation

VisTrees: fast indexes for interactive data exploration

Clustering provenance facilitating provenance exploration through data abstraction

The exception that improves the rule

Interactive online learning for clinical entity recognition

Towards reliable interactive data cleaning: a user survey and recommendations

PFunk-H: approximate query processing using perceptual models

The case for interactive data exploration accelerators (IDEAs)

TrendQuery: a system for interactive exploration of trends

Data programming with DDLite: putting humans in a different part of the loop

ModelDB: a system for machine learning model management

A DeVIL-ish approach to inconsistency in interactive visualizations

Big data exploration requires collaboration between visualization and data infrastructures