DPM2MDM
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| - | 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. <span style="background-color:yellow">[[Talk:DPM2MDM#Comment-02|Comment-02]]</span> | + | |
| + | The DPM has been defined for the creation of Data Point Models in the context of European supervisory reporting. Data Point Model by an European supervisory authority. To reflect the defined structures in a machine-readable from the can be accompanied by an XBRL taxonomy. It is also possible to extend the described methodology to other environments as Database. <span style="background-color:yellow">[[Talk:DPM2MDM#Comment-02|Comment-02]]</span> | ||
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| =Terms and definitions= | =Terms and definitions= | ||
Revision as of 08:33, 17 September 2013
CEN Workshop Agreement
CEN WS XBRL Experts: Ignacio Santos (Bank of Spain)
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 a 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, they 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.
The objective of the European XBRL Taxonomy Architecture (EXTA), is to set a framework or set of architecture guidelines that enables a Discoverable Taxonomy Set (DTS) author to create an XBRL taxonomy. However in this document expect to help to design the taxonomy through the DPM and in turn, it can implement the DPM in a Database. But, moreover, to help to Information System (IS) to have a better comprehension of the metadata. Comment-01
This project is funded by the EU commission to support the standardization process for supervisory reporting in Europe.
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 XBRL format. The taxonomy architecture provides a set of rules for this transformation to enable the creation of consistent and predicable XBRL meta definitions in an automated process. On the other hand CEN WS has built a guidelines for mapping the DPM in the MDM. In such a way that the architect of IS can understand and/or mapping the DPM to ROLAP.
Target Audience
EXTA is targeted at taxonomy authors. Initially organizations like 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 DPM has been defined for the creation of Data Point Models in the context of European supervisory reporting. Data Point Model by an European supervisory authority. To reflect the defined structures in a machine-readable from the can be accompanied by an XBRL taxonomy. It is also possible to extend the described methodology to other environments as Database. 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.
- XBRL specific terms like context, unit, period, entity see XBRL 2.1
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
[1] Inmon W. H. (2005) Buildind the Data Warehouse, 4th Edition. John Wiley & Sons 2005.
[2] Kimball R. (2004) The Data Warehouses Toolkit series. John Willey & Sons 1996-2004.
[3] Jarke M., Lenzerini M., Vassilion I. and Vassiliou Y, (2003). Fundamentals of Data Warehouses, 2nd edition, Springer.
[4] Kimball Group (2012). http://www.rkimball.com/html/articles.html.
[5] Data Warehouse Institute (2013). http://www.tdwi.org”.
[6] Engel P, Hamscher W., Shuetrim G., Vun Kannon D., Wallis H. (2008). Extensible Business Reporting Language (XBRL) 2.1. July 2nd, 2008. XBRL International. http://www.xbrl.org/Specification.
[7] Schmehl K (2009) Data Model and Matrix Schemas. November 16th, 2009. http://www.eurofiling.info . XI European Banking Supervisor, XBRL Workshop hosted by the Oesterreichische Nationalbank, Vienna.
[8] Santos I, Castro E (2011) XBRL and the Multidimensional Data Model. In Proceedings of the 7th International Conference on Web Information Systems and Technologies, WEBIST 2011, pages 161-164, Noordwijkerhout. The Netherlands, May 6th-9th, 2011.
[9] Santos I, Castro E (2011) XBRL Interoperability through a Multidimensional Data Model. IADIS International Conference on Internet Technologies & Society (ITS2011), Shanghai, China. December 8th-10th, 2011.
[10] Hernandez-Ros I, Wallis H (2006) XBRL Dimensions 1.0 XBRL International. April 26th, 2006. http://www.xbrl.org/Specification/XDT-CR3-2006-04-26.rtf.
[11] Improving transparency in financial and business reporting-Harmonisation topics-Part 3: European XBRL Taxonomy Architecture. Date June. 2003. www.xbrlwiki.info/index.php?title=European_XBRL_Taxonomy_Architecture_V2.0.
[12] Bernstein P A, Halevy A Y, Pottinger RA (2000). A vision for management of complex models, SIGMOD Record 29 (4), 2000, 55-63.
[13] Chewathe S, García-Molina H, Hammer J, Ireland K, Papakonstantinou Y, Ullman J, and Widom J (1994) The TSIMMIS Project: Integration of heterogeneous information sources. In Proc. 10th Meeting of the Information Prrocessing Soc of Japan, pages 7-18, 1994.
[14] Levi A, Rajaraman A, and Ordille J (1996) Querying heterogeneous information sources using source descriptions. VLDB’96, Proceedings of Twenty-second International Conference on Very Large Data Bases.
[15] Taentzer G, Ehrig K, Guerra E, Lara J, Lengyel L, Levendovszky T, Prange U, Varro D, and Varro-Gaypay S (2005) Model Transformation by Graph Transformation: A comparative Study. Model Transformation in Practice Workshop 2005 (MIIP2005).
[16] EBA (2013) Update on the technical standards on supervisory reporting requirements. http://www.eba.europa.eu/-/update-on-the-technical-standards-on-supervisory-reporting-requirements.
[17] Santos I (2013) Data Point Model (DPM) versus Multidimensional Data Model (MDM). Contribution for DPM chapter in CEN WS XBRL Plenary Session, Dublin, April 19th 2013. Hosted by Central Bank of Ireland.
[18] Academy works (2013) Openfiling. http://www.oprmfiling.info?page_id=286.
[19] Santos I, Castro E, Velasco M (2013) Conceptual and Logical Models in the Design of Economic and Financial Reports Using the XBRL Specification. In the journal Business & Information Systems Engineering (BISE), under review.
[20] Santos I, Castro E (2011) Proof of concept of mapping a XBRL report versus a RDBMS. Openfiling 1st General Assembly, organized by XBRL Operational Network of the European Banking Authority, and hosted by Bank of Italy. September 5th, 2011. Banca d’Italia, Rome, Italy. http://www.openfiling.info/?page_id=286.
