Exploring Data Warehouse Engineering: Development Process

This presentation provides an overview of data warehouse engineering, focusing on the development process, diagrams, and requirements. It highlights that a data warehouse is a large data store accumulated from wide sources used for managing decisions, emphasizing its subject-oriented and non-volatile nature. The presentation covers various aspects of DW development, including the importance of efficient engineering methods, visual modeling languages, and the Unified Development Process using UML. It discusses the lifecycle of a DW project, divided into phases and workflows, including analysis, design, implementation, testing, maintenance, and post-development reviews. The presentation also touches on the use of diagrams, such as Data Warehouse Conceptual Schema, CCS, and Data Mapping, along with Object Constraint Language for specifying constraints. It concludes by summarizing the key topics discussed, including data warehouse development, diagrams, engineering processes, requirements, and the various phases of the development lifecycle.

Data Warehouse Engineering

Data Warehouse Engineering
Large data store accumulated from wide source used managing decisions [3].
Data warehouse can be considered as subject-oriented, not volatile data
collection..
Dw development method is presented.
Dw building is complex in nature, because it contains numerous
organizational units.
Dw engineers makes development process efficient.
The proposals can be defined as a object oriented approch.

Data Warehouse Development
DW engineering makes developing process efficient .
Methods are based on visual modelling language.
Clear and seamless method are exoected.
The method should provide different levels.
The methods should be able to tackle all the dw designs in
integrated fashion.

Data Warehouse Diagrams
 Diagrams of DW are overlapping
 Large portion is created based on another
diagram.
 Common Warehouse Met model can be
considered as a kind of open industrial
standard of that of the Object Managing
Group.
 Standard based on Meta Object Facility,
UML and XML Metadata Interchange

Data Warehouse Engineering
Process It is basically defined as Unified Development Process [1].
 UML is a visual modelling language whereas UP specifies
development of software using UML.
 Life cycle of project can be divided into four phases and
five workflows [4].
 Addition of two workflows , Maintenance and Post-
development reviews

Analysis, Design &
Implementation
Refining and structuring of the requirements
based on the output.
Source Conceptual used Schema used for
modelling data sources at different levels.
In design part the structure of DW defined.
Output is a conceptual model of DW.
Main diagram include Data warehouse
Conceptual Schema, CCS and Data Mapping.
Object Constraint Language is used in specifying
the constraints.

Requirements, Test, Maintenance and Post-
Development-review
 Specification of most interesting measures and
aggregations must be provided .and UML provides
using the case diagram for use case specification.
 After clearing the requirements DW project must be
established and jobs are designed [2].
 UML is used for providing use case diagram for visual
modelling.
 Tracking of time and effort on each phases data is
useful in time estimation and meeting the requirements
of staff for any kind of future projects.

Conclusion
In this presentation the discussion was related to
Data Warehouse Engineering. The discussion
was related to Data Warehouse Development.
The presentation also shows Data Warehouse
Diagram. The slide also shows the processes
related to Data Warehouse Engineering. The
requirements are discussed in details. Analysis,
Design and Implementation are mentioned in
the same slide. Testing and maintenance and
post-development reviews are also mentioned in
the slide.

References
[1] Carreteiro, Paulo, José Braga de Vasconcelos, Alexandre Barão, and Álvaro Rocha.
"A knowledge management approach for software engineering projects development."
In New advances in information systems and technologies, pp. 59-68. Springer, Cham, 2016.
[2] Prakash, Deepika, and Naveen Prakash. "A multifactor approach for elicitation of
Information requirements of data warehouses." Requirements Engineering 24, no. 1 (2019): 103-117.
[3] Bhardwaj, Hanu, and Naveen Prakash. "Eliciting and structuring business indicators in data
warehouse requirements engineering." Expert Systems 33, no. 4 (2016): 405-413.
[4] Luján-Mora, Sergio, and Juan Trujillo. "A data warehouse engineering process." In International
Conference on Advances in Information Systems, pp. 14-23. Springer, Berlin, Heidelberg, 2004.