  In this paper we address the problem of rule-based stream data cleaning,
which sets stringent requirements on latency, rule dynamics and ability to cope
with the unbounded nature of data streams.
  We design a system, called Bleach, which achieves real-time violation
detection and data repair on a dirty data stream. Bleach relies on efficient,
compact and distributed data structures to maintain the necessary state to
repair data, using an incremental version of the equivalence class algorithm.
Additionally, it supports rule dynamics and uses a "cumulative" sliding window
operation to improve cleaning accuracy.
  We evaluate a prototype of Bleach using a TPC-DS derived dirty data stream
and observe its high throughput, low latency and high cleaning accuracy, even
with rule dynamics. Experimental results indicate superior performance of
Bleach compared to a baseline system built on the micro-batch streaming
paradigm.
