In-memory Technology Research Articles

Comments on “In-Memory Databases in Business Information Systems”

In-memory databases, which keep the full data set in the main memory for reading accesses, and their application in business information systems, receive more and more attention due to the development and marketing of SAP technology HANA. In issue 6/2011 of the journal Business & Information Systems Engineering, a total of 11 authors discuss potential applications of in-memory databases in business scenarios in 6 articles (Loos et al. 2011). Thereby, questions about the capability of in-memory technology, resulting consequences for OLTP and OLAP applications as well as implications on the architecture of business information systems, amongst others, were in the focus of the discussion. After publication, the first author received feedback and suggestions. Three of these responses resulted in written statements: Strohmeier focuses on the question whether or not future OLTP and OLAP applications should be integrated. He states that concepts for both an operative decision support and for the provision of operative real-time information need to be developed. As a result, he sees a demand for more research in the area “Operational Business Intelligence”. Piller provides more examples of application scenarios, which emerge with in-memory databases. For instance, operative reporting, adaptive planning, explorative mass data analysis or the analysis of consumer data from embedded systems. Schutte explains, after a description of implicit presumptions and requirements for in-memory technology, the potential of in-memory technology using a practical example of the EDEKA group, which he sees in batch processes, initial supply, and release change. If you like to comment on this topic or another article of the journal Business & Information Systems Engineering, please send your feedback to loos@iwi.uni-sb.de.

Innovation and Trends with In-Memory Technology

High performance in-memory computing will change how enterprises work. Currently, enterprise data is split into two databases for performance reasons. Usually, disk-based row-oriented database systems are used for operational data and column-oriented databases are used for analytics. Since hardware architectures have evolved dramatically during the past decade, this scenario has now changed. Multi-core architectures and the availability of large amounts of main memory at low costs are about to set new breakthroughs in the software industry. Traditional disks are one of the last remaining mechanical devices in a world of silicon and are about to become what tape drives are today: a device only necessary for backup. With in-memory computing and hybrid databases using both row and column-oriented storage where appropriate, transactional and analytical processing can be unified, allowing data analysis algorithms to run inside the database.