PocketView: A Concise and Informative Data Summarizer

Many tables on the web suffer from multi-level and multi-type quality problems, but existing cleaning systems cannot provide a comprehensive quality improvement for them. Most of these systems are designed for solving a specific type of error, so that we need to resort to a number of different cleaning tools (one per error type) to get a high quality table. In this demonstration, we propose a human-in-the-loop cleaning platform EasyDR for detecting and repairing multi-level&multi-type errors in tables. The attendees will experience the following features of EasyDR: 1) Holistic error detection&repair. Users are able to perform a holistic table cleaning in EasyDR where machine algorithms are responsible for error detection while human intelligence is leveraged for error repairing. 2) Human-in-the-loop table cleaning. EasyDR performs an all-round quality diagnosis for the table, and automatically generates crowdsourcing cleaning tasks for the detected errors. To simplify cleaning tasks for crowdsourcing workers, EasyDR provides two task optimization techniques including domain-aware table summarization and difficulty-aware task order optimization. 3) Customizable cleaning mode. EasyDR provides a declarative language for users to customize cleaning tasks flexibly, e.g., selecting target errors, restricting the cleaning scope, defining the cooperation mode for machine and crowd.

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PocketView: A Concise and Informative Data Summarizer

Cited by 2 publications

References 6 publications

Towards Scientific Data Synthesis Using Deep Learning and Semantic Web

Towards Scientific Data Synthesis Using Deep Learning and Semantic Web

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