User-friendly Financial OLAP (LMDQL)

I’ve been thinking about a financial OLAP system based on XBRL/XML/Json documents (free and open source) , in which components based on Front-End advanced technologies are implemented.

The state of the art of this kind of system (free and open source) is based on MDX queries (ex.: Forensic LMDQL, [if you know another one, please let me know]), in which the financial analyst or auditor must know about this computer language (MDX), as following:

1 SELECT
2    { [Measures].[Store Sales] } ON COLUMNS,
3    { [Date].[2002], [Date].[2003] } ON ROWS
4 FROM Sales
5 WHERE ( [Store].[USA].[CA] )

The system views are based on Mondrian JPivot (a JSP-based OLAP Client), in which the user have to insert MDX queries to initialize the forensic analysis, as following:

XBRL OLAP queries based on Z Test statistical calculation.

XBRL OLAP queries based on First Digit (or Benford’s Law) statistical calculation.

XBRL OLAP queries based on Empirical Rule statistical calculation.

XBRL OLAP queries based on Chi Squared Test statistical calculation.

So, this OLAP system has its processing based on statistical calculations for fraud detection over XBRL documents. All of those calculation are applied on non-computerized audit, henre, its feasibility is indisputable.

However, we can do a new approach about how the financial analyst or auditor can handle the XBRL documents through this tool. I mean, we have to figure out a user-friendly solution for XBRL data visualization for OLAP systems.

I have been created a prototype in which it’s possible to get the idea in which I try to improve the usability of both technology: LMDQL (and its financial OLAP operators) and Forensic LMDQL (and its financial forencic OLAP operators). This idea has used JavaScript technologies (as such: drag&drop), in which XBRL documents (and its data) can be a draggable object on web platform, as following figures:

Forensic LMDQL and its OLAP operators: FirstDigit, ZTest, ChiSquaredTest, EmpiricalRule.

LMDQL and its financial OLAP operators: Operator Definition, Horizontal Analysis, Vertical Analysis, N Nearest Values, Separatrix, N Nearest Values Percentual, Cross.

This is the analytical module of the XBRLFramework System.

Thoughts? If you have interest about this project, please contact me.