Abstract

In the age of big data, data analytics expertise is increasingly valuable. This expertise includes not only formal knowledge, such as algorithms and statistics, but also practical skills that are learned through practice and are difficult to teach in classroom settings: management and preparation of data sets, feature design, and iterative exploratory analysis. Semantic workflows are a valuable tool for empowering non-expert users to carry out systematic analytics on large datasets using sophisticated machine learning methods captured in the workflows and their semantic constraints. In this paper we motivate and illustrate the role of visualizations in the usability of workflows by non-experts as well as their role in learning practical data analytics skills to gain interesting insights into data and methods. This capability is particularly important when confronting large datasets, where the selection of appropriate methods and their configuration with the best parameter and algorithm selections can be crucial in obtaining useful results.

Date

2013

Authors

David C Kale, Samuel Di, Yan Liu, Yolanda Gil

Conference

AAAI Fall Symposia