By Financial Executives Research Foundation, Inc.
This four-part record provides a business-centric and low-cost method of inner keep an eye on and possibility administration utilizing structures considering and structures. This strategy presents enterprise advantages and is helping permit compliance with the Sarbanes-Oxley Act of 2002, and different legislation and laws. This record is the fourth of the sequence, and it explores using tracking to check the functionality of controls. This FERF examine sequence is being subsidized through BWise B.V.
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Extra resources for A Top-Down Approach to Risk Management and Internal Control-Issue #4: Relying on Ongoing Monitoring to Test Controls Performance, to Reduce the Scope of Separate Testing
C ∈ decomp(c), s is frequent in c , 2. ∀c ∈ M decomp(c), s is not frequent in c . Proof: This result is obtained by directly applying Theorem 1 and Lemma 4 to the definition of a c-exclusive sequential pattern (Definition 3). Hence, a c-exclusive sequential pattern is a c-general sequential pattern s such that there does not exist a minimal context outside the decomposition of c where s is frequent. Hence, Theorems 1 and 2 show that both general and exclusive sequential patterns can be mined by considering minimal contexts only, while naive approaches require to consider all descendants of c to extract c-general sequential patterns, and all the contexts of the hierarchy to mine c-exclusive sequential patterns.
So, the process continues by outputting (a)(b) , and using it as a new prefix. We now present the Gespan algorithm that aims at mining general and exclusive sequential patterns in a context. The prefix-growth approach of PrefixSpan is used to extract general sequential patterns, relying on the anti-monotonicity of the c-generality property. From a prefix sequence s, the algorithm builds the s-projected database by making use of the method BuildPro jectedDatabase, and scans the projected database (method ScanDB) to find items i that can be assembled to form a new general sequential pattern s .
However, an expert 28 J. Rabatel, S. Bringay, and P. Poncelet studying context-dependent patterns in the whole database does not want a pattern being frequent in young customers only to be considered representative in the whole database. 2. A sequential pattern extracted in a given population does not bring any information about the rest of the population. For instance, an expert studying frequent behaviors in the young customers population will extract the sequence s = (a)(b) . However, the only information provided by this sequential pattern is that young customers frequently follow this behavior.