Big Data Use Cases

This page lists the use cases which BDA-IG is considering currently (when adding further use cases please use this template).

As the range of contributors has a very broad setup of expertise, and the field being highly diverse and complex, the Interest Group has adopted the ISO Reference Model for Open Distributed Processing (RM-ODP) model for structuring this analysis. RM-ODP appears particularly useful in situations where multiple stakeholders with divergent background need to converge. To facilitate information collection, RM-ODP comprises five different views on digital systems (Fig. 1). Notably, this does not anticipate or confine to any particular architecture, it merely serves for presenting contributions in a structured manner and avoiding lengthy discussions arising from different viewpoints. The RM-ODP approach allows each contributor to individually pick the appropriate viewpoint and make their case.

Fig. 1: RM-ODP view model (source: Wikipedia)

Each section below pertains to one RM-ODP viewpoint. First an explanatory introduction is given (in italics), then the Big Data Analytics use cases follow. (Please add your use cases by using the template and along this schema and fit it into the appropriate section!)

Enterprise viewpoint

The enterprise viewpoint focuses on the purpose, scope and policies for the system. It describes the scientific requirements and how to meet them, abstracting away from technical details.

Responsible section editor: Kuo Sen-Kwo

Science domain use cases found relevant for Big Data Analytics:

• Event Analysis (Dr. Tom Clune, Dr. Kuo GSFC/NASA)
• Particle physics and radiotherapy (Michael Simmons and Charles Boulton, University of Cambridge)
• Research on Data Analytics for Automated Quality Control of Measurement Data (Morris Riedel, Shiraz Memon, Shahbaz Memon - Juelich Supercomputing Centre; Robert Huber - MARUM Bremen)
• Spatio-Temporal Data in the Earth Sciences (Peter Baumann, Jacobs University)
• Classification of land cover types of remote sensing satellite image datasets (Gabriele Cavallaro - University of Iceland, Morris Riedel - Juelich Supercomputing Centre)

Information viewpoint

The information viewpoint focuses on the semantics of the information and the information processing performed. It describes the information managed by the system and the structure and content type of the supporting data.

Responsible section editor: Peter Baumann

The following information modelling approaches (in particular: data and service standards) have been found pertinent to Big Data Analytics:

• Science SQL (Peter Baumann, Jacobs University)
• Spatio-Temporal Data in the Earth Sciences (Peter Baumann, Jacobs University)

Computational viewpoint

The computational viewpoint enables distribution through functional decomposition on the system into objects which interact at interfaces. It describes the functionality provided by the system and its functional decomposition.

Responsible section editor: Morris Riedel

• Array Databases (Peter Baumann, Jacobs University)
• data mining (Peter Baumann - tbd: we might add this here as the underlying technology for several domain use cases above)

Engineering viewpoint

The engineering viewpoint focuses on the mechanisms and functions required to support distributed interactions between objects in the system. It describes the distribution of processing performed by the system to manage the information and provide the functionality.

Responsible section editor: tbd

• POST, REST, JSON Web protocols (Peter Baumann - just as an example, this still needs a champion)

Technology viewpoint

The technology viewpoint focuses on the choice of technology of the system. It describes the technologies chosen to provide the processing, functionality and presentation of information.

Responsible section editor: Peter Baumann, Morris Riedel

Technologies found relevant for Big Data Analytics:

• the rasdaman Array DBMS (contact: Peter Baumann, Jacobs University)