An Audit-Oriented Method for Automatic Mining of Aggregate Algebraic Constraints in Relational Databases
An automatic mining and database technology, applied in relational databases, database models, electrical and digital data processing, etc., can solve the problems of high computing cost, huge computing scale, explosive growth, etc., to reduce computing costs and reduce the scale of computing space. Effect
Active Publication Date: 2022-04-12
ZHEJIANG UNIV
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Problems solved by technology
[0004] The existing technical methods have not been able to fully meet the needs of data-driven auditing. The reasons may be: 1) There are many types of audit rules, and it is difficult to use only one set of methods to cope with the diversification of auditing needs brought about by endless business changes; 2) The defect of the huge computing space brought by the data-driven method may lead to the huge scale of the intermediate results of the operation, and it will explode with the growth of the original data; 3) The huge computing scale and high computing cost make it impossible to Complete audit work in a meaningful time
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[0039] The following takes a simplified reimbursement data table in a relational database as an example, and introduces the process of discovering aggregation algebraic constraints as a supplement to the specific implementation. The reimbursement data table r existing in the relational database D contains 5 fields: (employee, department, transportation expenses, accommodation expenses, miscellaneous expenses), each record in the table is a reimbursement record containing the information of the above 5 fields; other data tables exist i , since its specific mode is irrelevant to this embodiment, it will not be described in detail. Auditors want to find out the illegal reimbursement records from the database. In addition to audit rules with relatively simple logic, which can be obtained from prior knowledge, fuzzy constraints with relatively complex logic, especially aggregation algebraic constraints, need to be discovered through the method of the present invention.
[0040] Fi...
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The invention discloses an audit-oriented relational database aggregation algebra constraint automatic mining method, which is a constraint relationship mining method hidden in massive data in smart audit applications. Existing audit rules are either constructed by experts or generated by database constraint discovery methods, which can achieve the purpose of auditing simple businesses, but are not enough to meet the challenges of massive data scale, and cannot meet the needs of complex calculations with statistical values in audit operations. Aiming at the above-mentioned deficiencies, the present invention formalizes the requirement into aggregate algebraic constraints, and provides an aggregated algebraic constraint mining method. The method enumerates table connections, grouping methods, and algebraic expressions in sequence according to the schema information of the database, and obtains aggregated algebraic constraints after calculation and analysis. constraint. The invention does not require manual participation in constraint mining, can cope with the space-time performance challenges brought about by the calculation of massive data, and meet the complex calculation requirements related to audit statistics.
Description
technical field [0001] The invention relates to the field of audit rule mining in smart audit applications, in particular to an audit-oriented automatic mining method for aggregation algebraic constraints in a relational database. Background technique [0002] In real-life auditing operations, auditors conduct auditing work in accordance with auditing practice guidelines and expert experience. Usually, the key to the smooth implementation of the audit process is whether the auditor can find the fuzzy constraints in the corresponding data table according to the business rules, that is, the constraints that act on most but not all records in the data table. These constraints can be As an audit rule, it plays an important role in the audit work. For example, "Constraint c 1 : Sum (income) = Sum (expenditure)" and "constraint c 2 : Transportation expenses + accommodation expenses<1000” is a relatively simple fuzzy constraint, which can be given by auditors based on expert ...
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IPC IPC(8): G06F16/28G06F16/2458
CPCG06F16/285G06F16/2465
Inventor江大伟陈珂张效伟陈刚寿黎但伍赛胡天磊
OwnerZHEJIANG UNIV