DPM2MDM
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CEN Workshop Agreement
CEN WS XBRL Experts: Thierry Declerck (DFKI), Roland Hommes (Rhocon), Katrin Heinze (Deutsche Bundesbank)
Foreword
This document has been prepared by CEN/WS XBRL, the secretariat of which is held by NEN. CWA XBRL 001 consists of the following parts, under the general title Improving transparency in financial and business reporting — Harmonisation topics:
- Part 5: Mapping between DPM and MDM
General
The purpose of this document is to present and explain the mapping between the Data Point Model (DPM) and the Multidimensional Data Model (MDM) [1] [2] [3] [4] [5], through the Relational Model using ROLAP analytic tool. This document is based in work presented in Banca D’Italia, Rome, in September 2011 [20]. ROLAP is defined as Relational Online Analytical Processing. The DPM is a semantic model (Logical Model). Experts in the supervision in Europe have created the DPM, and as they are not software Engineers, and have important difficulties, especially with the XBRL specification [6]. XBRL taxonomies are metadata that provide a formal description of the data requirements to be used as data format in this case in the European reporting process. This document intends also to supply a description for IT experts about the linkage between a DPM as source and a relational data base as target of a transformation process.
These pages are hosting the guidelines to an European XBRL Taxonomy Architecture (EXTA). Comment-01
Objective The objective of the EXTA is to define a set of architecture guidelines that transform a European DPM without a loss in quality in a relational model. The taxonomy architecture provides a set of rules for this transformation to enable the creation of consistent and predicable set of definitions in an automated process in the relational model through MDM.
Target Audience EXTA is targeted at taxonomy authors and as par more, its implementation in a relational database (RDBMS, Relational Database Management System). Initially European Banking Authority (EBA), European Insurance and Occupational Pensions Authority (EIOPA), European Securities and Markets Authority (ESMA), European Central Bank (ECB) are the authors of these taxonomies. As a spin-off of these taxonomies, local (national) initiatives will emerge, hosted by National Supervisory Agencies (NSA’s). The audience of this document are financial or economic institutions, agencies, companies, or Universities that they want to implement the taxonomies in a relational model.
Relationship to other work The reader of this EXTA is expected to be familiar with the principles of data modelling and have an understanding of the XBRL family of the specification. But, also, it is necessary to have a knowledge in the relational model and in the MDM.
Contents |
Scope
The EXTA has been defined for the creation of XBRL Taxonomies in the context of European supervisory reporting XBRL taxonomies following this architecture are published by a European supervisory authority to reflect the data requirements based on a DPM in a machine-readable form. Comment-02
Terms and definitions
The terms definitions used in connection with Data Point modelling are inspired by vocabulary already known through their use for describing multidimensional databases and data warehouses. IT specialists originally introduced these terms. However, for an understanding and creation of Data Point Models they are now established in the language of business specialists as well. In this section are shown the set of definitions necessaries for mapping the DPM in ROLAP. The majority of the definitions are obtained of [6] [7] [8] [9] [10]. When the definition is in the area of CEN WS XBRL (“http:/xbrlWiki.info/index.php?title=XBRLWiki:About”) only is shown a hyperlink to definition.
Hypercube
The definition linkbase includes dimensional relationships valid in the context of the table. Valid combinations are defined using only positive (call) closed hypercubes obtained from the set of valid cells of the table following certain algorithm in order to optimize their number. A hypercube in the MDM is a set of pairs <dimension, attributes of dimension> and calculated attributes defining one or more facts [19].
Context
The context element contains information about the entity being described, the reporting period and the reporting scenario, all of which are necessary for understanding a business fact captured as an XBRL item [6]. In the MDM, the context is defined as a set of dimension of a fact or group of facts. A context belongs to an entity or financial institution, for a period, a meaning for the business (segment), and a scenario. The scenario shows the specific pairs of dimension and the dimension attribute of business logic [9].
Mapping from Data Point Model to Multidimensional Data Model
Introduction
This section presents the mapping between the DPM and the Relational model through ROLAP. It is not expected to mapping the XBRL specification, also any validation process. It is only mapped the DPM structure in the Relational model. However, it is expected that the reader of this document can understand better the DPM or even that the reader can store the DPM in a RDBMS (Relational Database Management System). There are a lot of bibliography about the mapping from different sources to a relational database as in [12] [13] [15] and about query in heterogeneous sources as [14]. However, the process of transformation of this section is based [15}. This section will go step to step with the different constructors that they are corresponding in the DPM. The transformation process from the DPM UML graph on the left hand side to UML class diagram to visualize the Relational model (ROLAP). The black arrows between both UML language but customized extensions which are use to describe the graph transformation. The square between two black arrows contains an abbreviation what is begin mapped. In this document are distinguished the next different types of mappings rules between the two graphs [15]:
- C2C is the automatic transformation between concepts.
- C2F is the automatic transformation between concepts and frameworks.
- C2T is the automatic transformation between concepts and taxonomy.
- A2T is the automatic transformation between attributes to taxonomy.
- T2T is the automatic transformation between taxonomies.
- A2A is the automatic transformation between attributes to attributes.
- C2D is the automatic transformation between classes of dimensions to dimensions.
- C2DA is the automatic transformation between classes to attributes of dimension.
- C2CTx is the automatic transformation between classes to contexts.
- C2CTxDM is the automatic transformation between classes to Contexts, dimensions, and attributes of dimension.
- C2Fact is the automatic transformation between classes and the fact table.
Framework
From [11] is obtained the figure 1. This figure shows the structural perspective only of the framework.
- Figure 1 — Structural perspective of the framework
In the figure 2 is shown the transformation of the Data Point Model in ROLAP.
Figure 2
Taxonomy
The next figure 3 shows the structural perspective of taxonomy. This obtained from [11].
Figure 3
The figure 4 depicts the transformation of the DPM to ROLAP model.
Figure 4
Dimensions
In this section is defined the mapping of the constructor dimension. The figure 5 shows a perspective of the structure of the dimension.
Figure 5
The figure 5 shows two type of dimension [9] [10], the implicit and the typed dimension. But in an upper level is the family or group of dimensions and they belong to a same domain. If the dimension is explicit then this contains a set of members. Each domain has one and only one member by default. An explicit dimension consists of a number of members, called domain-member, and therefore is enumerable. On the other hand, a typed dimension is not enumerable because its member are not known in advance.
Figure 6
However, in the Relational model both constructors are one. The entity Dimension entity will have an attribute for showing if the dimension is typed or explicit and another attribute with the data type. The figure 7 depicts the transformation of the DPM to ROLAP, and the reverse mapping of the typed dimension.
Figure 7
On the other hand, from the members defined or not are obtained the dimension attributes of the dimension. But in both cases they will defined in this constructor, although they are filled out when the taxonomy is defined or in run time of the document instance. The figure 8 shows the mapping of the dimension attributes with the members.
Figure 8
The figure 9 shows the mapping of Dimensions and domain-members and Dimensions/Dimension attributes in the Relational data model (ROLAP).
Figure 9
Context
The figure 10 shows the mapping of DPM to the Relational Model. There are two Context and another for the context with the dimensions and the domains-members. In the relational model the dimensions can be explicit or typed.
Figure 10
Primary Items
The primary item could be a domain-member of a dimension, however, is a little special, because is associated with this concept two attributes, the type of the data and the time period type. The figure 11 shows the mapping with the relational model. The set of primary items are grouped in the base dimension.
Figure 11
Fact table or Data points
The figure 12 shows the mapping between the DPM and the Relational model.
Figure 12
In the next graphic is presented the star diagram of the DPM in the relational model (ROLAP), figure 13.
Figure 13
Bibliography
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