  There is no unified definition of Data anomalies, which refers to the
specific data operation mode that may violate the consistency of the database.
Known data anomalies include Dirty Write, Dirty Read, Non-repeatable Read,
Phantom, Read Skew and Write Skew, etc. In order to improve the efficiency of
concurrency control algorithms, data anomalies are also used to define the
isolation levels, because the weak isolation level can improve the efficiency
of transaction processing systems. This paper systematically studies the data
anomalies and the corresponding isolation levels. We report twenty-two new data
anomalies that other papers have not reported, and all data anomalies are
classified miraculously. Based on the classification of data anomalies, two new
isolation levels systems with different granularity are proposed, which reveals
the rule of defining isolation levels based on data anomalies and makes the
cognition of data anomalies and isolation levels more concise.
