Experiments to try: 1  Turn on show energy. 2  Reduce elasticity (bounciness) to less than 1, for example 0.9. 3  Make a small gap between each ball by increasing the offset to above zero, for example 0.1. 4  Try using simultaneous collision handling method. 5  Turn on show forces to see the contact forces and collision impacts.
Data cleaning involves the detection and removal (or correction) of errors and inconsistencies in a data set or database due to the corruption or inaccurate entry of the data. Incomplete, inaccurate or irrelevant data is identified and then either replaced, modified or deleted.
We classify data quality problems that are addressed by data cleaning and provide an overview of the main. solution approaches. Data cleaning is especially required when integrating heterogeneous data sources and. should be addressed together with schema-related data transformations. In data warehouses, data cleaning is a major part of the so-called ETL process. We also discuss current tool support for data cleaning. 1 Introduction Data cleaning, also called data cleansing or scrubbing, deals with detecting and removing errors and