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Key contributors: Ahmad Assaf, Corentin Follenfant, Petko Ivanov, Eric Peukert, Alexandr Savinov, Magali Seguran, Aline Senart, David Trastour and Konrad Voigt.

In most of the companies, we observe the following challenges :

  • Information workers want access to all relevant data sources, wherever the information is located (externally or internally). The data may be in different form: raw data (e.g. database tables, spreadsheets) or aggregated data in the form of data analytics (e.g. charts, reports, dashboards).
  • Information workers want easy ways to reuse existing Business Intelligence (BI) content:  it is always simpler and faster to reuse existing content rather than starting from scratch. However, current BI solutions make this difficult for a non-technical user. 
  • Information workers want to be self-sufficient and make faster decisions. Current BI solutions still require technical experts for many tasks.  For example, the task of connecting to and modelling new data sources or customizing reports or dashboards need the help of an expert.
is a self-service BI application that allows business users to easily reuse and combine analytics. The application enables mashups of enterprise and external data/analytics. Business users are able to share their analytics allowing other users to quickly build new analytics. Business users do not write queries but simply graphically manipulate data and analytics in a single user interface. It’s a direct and intuitive way to build and reuse analytics without requiring the user to understand IT concepts.
In the following demo, remix capabilities are illustrated with a healthcare scenario. The hospital is running SAP IS-H and the ERP data is automatically synchronized with HANA.
Two doctors, Doctor House and Doctor Cuddy, share and combine any internal or external analytics with remix in order to solve a medical problem. The application offers them different ways to combine analytics: it is possible to append data from one analytic to another. The resulting analytic will keep the same visualization but the two data sets will be concatenated. They can also replace the data of an analytic by another data set while keeping the original visualization. Finally, they can overlay two analytics that share common dimensions such as time or geography.
This demo was finalist in the 2012 TechEd DemoJam in Madrid.
Demo is available on YouTube:
Our solution is built on SAP Netweaver Cloud, making the application suitable for cloud or on-premise deployment. For scalability, mashups are performed in SAP HANA DB, leveraging the in-memory calculation engine. Finally, the solution is built as a web application, working on desktop and mobile browsers (targeted to chrome/desktop and safari/ipad) using HTML5 and SAP UI5.
  1. Magali Seguran, Aline Senart and David Trastour, remix: a Semantic Mashup Application.  In Proceedings of the Meta4esociety workshop , OTM conferences 2012, 10th of September, Roma, Italy.
  2. Ahmad Assaf and Aline Senart, Data Quality Principles in the Semantic Web, In Proceedings of the International Workshop on Data Quality Management and Semantic Technologies (DQMST 2012), September 2012, Palermo, Italy.
  3. Ahmad Assaf, Eldad Louw, Aline Senart, Corentin Follenfant, Raphael Troncy, David Trastour, RUBIX: A Framework for Improving Data Integration with Linked Data, to be published in ICP Series of the ACM Digital Library.
  4. Ahmad Assaf, Eldad Louw, Aline Senart, Corentin Follenfant, Raphael Troncy, David Trastour, Improving Schema Matching with Linked Data, In Proceedings of the 1st International Workshop on Open Data (WOD), Nantes, France, May 2012.
  5. Corentin Follenfant, David Trastour and Olivier Corby, A Model for Assisting Business Users along Analytical Sessions, In Proceedings of the 2nd Workshop on Semantic Personalized Information Management: Retrieval and Recommendation, SPIM, held with ISWC, October 2011, Bonn, Germany.
  6. Eric Peukert and Julian Eberius and Erhard Rahm. A Self-Configuring Schema Matching System. In Proceedings of the 28th IEEE International Conference on Data Engineering, April 2012, Washington, DC, USA.
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