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In-Memory Data Management: An Inflection Point for Enterprise ApplicationsJune 2011
Publisher:
  • Springer Publishing Company, Incorporated
ISBN:978-3-642-19362-0
Published:23 June 2011
Pages:
254
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Abstract

In the last50 years the world has been completely transformed through the use of IT. We have now reached a new inflection point. Here we present, for the first time, how in-memory computing is changing the way businesses are run. Today, enterprise data is split into separate databases for performance reasons. Analytical data resides in warehouses, synchronized periodically with transactional systems. This separation makes flexible, real-time reporting on current data impossible. Multi-core CPUs, large main memories, cloud computing and powerful mobile devices are serving as the foundation for the transition of enterprises away from this restrictive model. We describe techniques that allow analytical and transactional processing at the speed of thought and enable new ways of doing business. The book is intended for university students, IT-professionals and IT-managers, but also for senior management who wish to create new business processes by leveraging in-memory computing.

Cited By

  1. Ivanova E and Sokolinsky L (2018). Parallel processing of very large databases using distributed column indexes, Programming and Computing Software, 43:3, (131-144), Online publication date: 1-May-2017.
  2. Eichinger F, Efros P, Karnouskos S and Böhm K (2015). A time-series compression technique and its application to the smart grid, The VLDB Journal — The International Journal on Very Large Data Bases, 24:2, (193-218), Online publication date: 1-Apr-2015.
  3. ACM
    Park M, Nam S, Choi C, Shin Y, Cho W and Lee K Performance Comparison of Real-time Spatial Data Processing Proceedings of the 2015 International Conference on Big Data Applications and Services, (237-241)
  4. Müller S, Butzmann L, Klauck S and Plattner H An adaptive aggregate maintenance approach for mixed workloads in columnar in-memory databases Proceedings of the Thirty-Seventh Australasian Computer Science Conference - Volume 147, (3-12)
  5. ACM
    Mühlbauer T, Rödiger W, Reiser A, Kemper A and Neumann T ScyPer Proceedings of the Second Workshop on Data Analytics in the Cloud, (11-15)
  6. ACM
    Färber F, Cha S, Primsch J, Bornhövd C, Sigg S and Lehner W (2012). SAP HANA database, ACM SIGMOD Record, 40:4, (45-51), Online publication date: 11-Jan-2012.
  7. ACM
    Sikka V, Färber F, Lehner W, Cha S, Peh T and Bornhövd C Efficient transaction processing in SAP HANA database Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, (731-742)
  8. ACM
    Boese J, Tosun C, Mathis C and Faerber F Data management with SAPs in-memory computing engine Proceedings of the 15th International Conference on Extending Database Technology, (542-544)
  9. ACM
    Müller S and Plattner H Aggregation strategies for columnar in-memory databases in a mixed workload Proceedings of the 4th workshop on Workshop for Ph.D. students in information & knowledge management, (51-58)
  10. Groß P, Lehner W, Weichert T, Färber F and Li W (2020). Bridging two worlds with RICE, Proceedings of the VLDB Endowment, 4:12, (1307-1317), Online publication date: 1-Aug-2011.
Contributors
  • Hasso Plattner Institute for Digital Engineering gGmbH

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