Big Data has become a reality that cannot be ignored. In one of my previous posts, I explained the reason for the adjective big that sits before data to create big data. I mentioned that big is not just refereeing to volume, but also to variety and velocity of growth. Big data not only is big in size, but is fast in getting bigger and it covers a variety of data sources that exceed the boundary of the existing relational systems such as CRM and ERP.
I thought may be a post to give some examples of unstructured big data may interest many readers, so below I have bullet pointed some and I may update this post in future when I come across more examples.
- Detailed machine generated data, such as equipment logs, RFID tags
- Sensor generated data such as in manufacturing, metering, condition monitors
- Web related data, such as visitors, hits, keywords, times, etc
- Social Media data such as twitter, facebook comments, feedbacks, etc
- books, journals and text base documents
- Scanned records
- Audio files
- Video files
- GIS and map related data files
- Body of email messages
- Web page contents such as static pages or blogs and wikis
- Image data such as spatial and auto cad images
Enterprise Resource Management (ERP) systems are organisational platforms for coordination of organisational processes and supporting data in order to provide cohesive and timely services by providing integration of HR, Finance, Manufacturing, Supply Chain and Customer Services as core activities. These could be extended further to contain other entities such as Project Management, Asset and Maintenance Management, etc. ERP systems normally are supported by a Relational Database Management System (RDBMS).
Business Intelligence Systems are created to provide timely decision making power for organisations. More and more organisations use Business Intelligence to gain ability to access the correct information in the format that is easy to understand and analyse, or even in the form of applications providing answers to specific business queries.
As both ERP and BI systems are complex, supporting vast number of business processes and related information, to model and communicate the relation between business processes in ERP systems and related Business Intelligence Objects, an extension to OMG (Object Management Group) UML 2.o Activity Diagram for Business Intelligence and Data Warehouse is suggested in the white paper “Extending UML 2 Activity Diagrams with Business Intelligence Objects” by Veronika Stefanov, Beate List, Birgit Korher. In this paper a BI profile is introduced by defining new object stereotypes “DataRepository”, “DataObject” and “PresentationObject”. “DataRepository” covers “OperationalDataStore”, “DataWarehouse” and “Datamart”. “DataObject” covers “Entity” and “Fact”, and finally “PresentationObject” covers “Report” and “InteactiveAnalysis”. To view the extended meta model see Fig 2.0 in the above paper.
With the corresponding notation for the extended stereotype, the profile is a powerful tool for modelling the BI Objects in UML 2.0 activity diagram. As in the above diagram, the diamonds are notation for “Fact” Objects, representing multidimensional data models, showing “Customer” and “Policy Transactions”, as well as “insurance company” Data Warehouse.
