CoClean: Collaborative Data Cleaning

High quality data is crucial for many applications but real-life data is often dirty. Unfortunately, automated solutions are often not trustable and are thus seldom employed in practice. In real-world scenarios, it is often necessary to resort to manual cleaning for obtaining pristine data. Existing human-in-the-loop solutions, such as Trifacta and OpenRefine, typically involve a single user. This is often error-prone, limited to a single-person expertise, and cannot scale with the ever growing volume, variety and veracity of data. We propose a crowd-in-the-loop cleaning system, called CoClean, built on top of Python Pandas dataframe, a widely used library for data scientists. The core of CoCleanis a new Python library called Collaborative dataframe (CDF) that allows one to share data represented as a dataframe with other users. CDF is responsible for synchronizing and aggregating annotations obtained from different users. The attendees will have the opportunity to experience the following features:(1)Data Assignment: Given a dataframe, the owner can assign it (or a subset of it) to different users. (2)Supporting both lay and power users: lay users can use a GUI for direct manual cleaning of the data, while power users can work on the assigned data through a Jupyter Notebook where they can write scripts to do batch cleaning. (3)Combining machines and humans: Possible errors and repairs generated by machine algorithms can be highlighted as annotations, which can make the life of users easier for manual cleaning. (4)Collaboration Modes: CoClean supports two modes: blind-on(no user can see the annotations from others) and blind-off.