<![CDATA[Data-Intensive Scalable Computing (DISC) systems are critical for managing large datasets while prioritizing fault tolerance, cost effectiveness, and user accessibility. However, the presence of input errors in processed data presents considerable hurdles to programmers. The Snowfall Analysis program, which is well-known for its anomalous data that causes forecasting failures, serves as a key case study in this research. To solve this problem, this study leverages Titian, an extended library designed to speed debugging by methodically tracing the provenance of incorrect data back to its original source. Through thorough analysis, we analyzed Titian's accuracy using confusion matrix and compared its efficiency to standard manual debugging approaches, showing solid evidence of its utility in improving data provenance in DISC systems.]]>
