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Hasso-Plattner-Institut
Hasso-Plattner-Institut
Prof. Dr. h.c. mult. Hasso Plattner
 

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Our team is giving a series of lectures and seminars with a focus on enterprise systems design and in-memory data management. Strong links to the industry ensure a close connection between theory and its implementation in the real world.

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If you are having questions regarding one of our publications, please contact the authors.

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Martin Lorenz, M.Sc.

Research Assistant, PhD Student

  Phone: +49 (331) 5509-1318
  Fax: +49 (331) 979 92-579
  Email: martin.lorenz(at)hpi.de
  Address: August-Bebel-Str. 88 Potsdam, Brandenburg, 14482 Germany
  Room: V-2.01

Overview

Enterprise information systems can be characterized as OnLine Transaction Processing (OLTP) or OnLine Analytical Processing (OLAP). In an enterprise information system landscape, OLTP systems are mostly systems that capture information either through single transactions (e.g., web forms) or batch operations (e.g., importing CSV-files). OLAP systems are business intelligence or decision support systems, which perform complex analytics (e.g., demand planning), based on the information captured by OLTP systems. Up to the present, the separation of both types of systems manifests itself in in the form of dedicated database technologies for either of the two. Systems with an OLTP database workload mainly rely on row-oriented systems, optimized for write operations. OLAP databases favor column-oriented data layout to optimize complex read operations. The data transfer from OLTP to OLAP systems is managed via an ETL process, which extracts, transforms, and loads the data. Drawbacks of that architecture include the inability to perform real-time analytics based on the most current data and an increased total cost of ownership (TCO), because two separate systems have to be managed and maintained.

One of the greatest promises of in-memory technology is the reunification of OLTP and OLAP. In-memory technology combines the advantages of hardware trends ( multi-core CPUs, cheap main memory) with suitable software concepts ( light-weight compression, column-orientation, differential buffer) to design a database system that allows run OLAP-style and OLTP-style queries on the same platform. A question that is to be answered is what is an appropriate data model design for a database system that supports mixed workloads. The data layout (row- vs. column-orientation) is only one characteristic that distinguishes OLAP and OLTP databases, the other one is the data model. Database schemas for OLTP systems are highly normalized to minimize the data volume and to speed up data manipulation queries. OLAP systems on the other hand are tuned to access large portions of data to analyze them in a timely manner. Star or snowflake schemas are the most prominent designs for OLAP database schemas.

In my research, I focus on data model designs and data access technologies that support the requirements of modern enterprise applications and at the same time are optimized for the concepts of an in-memory column-store.

Fig. 1 Mapping Strategies

Research Interests

Class inheritance is one of the major features of object-oriented programming languages. It provides for advanced conceptual modeling and code and structural reuse. However, fundamental differences between object-oriented programming and relational algebra obviate a straight forward mapping between domain models, designed for application logic, and relational data models, designed to persist the data. Research has proposed three different mapping strategies to persist inheritance hierarchies in relational databases (Figure 1).

 

What they all have in common is an inherent tradeoff between memory consumption and query performance. Depending on the actual characteristics of the inheritance structure at hand, each strategy has its strength and weaknesses. Consequently, the combination of inheritance characteristics and prioritization of non-functional requirements (memory consumption and query performance) leads to a decision for the appropriate strategy. Unfortunately, not all characteristics of the inheritance hierarchy can be defined in advance. Especially runtime characteristics, such as the distribution of objects along the hierarchy can rarely be determined at system's design time. This problem becomes even more serious for vendors of standard software, since they offer their software for a number of customers from different industries and various sizes.

In my research, I look at column-orientation, as a means to structure physically data in memory. In-memory column-oriented databases proof to be advantageous over traditional disk-based row-oriented storage technologies, both in terms of memory consumption and query performance. Additionally to that, it also seems to shift the tradeoff between those two requirements depending on the chosen mapping strategy. Both facts make column-orientation a promising fit for mapping inheritance hierarchies to relational databases.

Master's Thesis Topics

We offer different Master's Thesis topics in this field of research. If you are interested, please contact us for more information.

Teaching activities

Summer Term 2013

Winter Term 2012/13

Summer Term 2012

Winter Term 2011/2012

Summer Term 2011

Winter Term 2010/2011

Publications

  • 1.
    Hesse, G., Matthies, C., Perscheid, M., Uflacker, M., Plattner, H.: ESPBench: The Enterprise Stream Processing Benchmark. Proceedings of the ACM/SPEC International Conference on Performance Engineering. bll. 201–212 (2021).
    [ BibTeX ]
     
  • 2.
    Matthies, C., Hesse, G.: Towards Using Data to Inform Decisions in Agile Software Development: Views of Available Data. International Conference on Software Technologies (ICSOFT). bll. 552–559. SciTePress (2019).
    [ BibTeX ]
     
  • 3.
    Hesse, G., Reissaus, B., Matthies, C., Lorenz, M., Kraus, M., Uflacker, M.: Senska - Towards an Enterprise Streaming Benchmark. TPC Technology Conference (TPCTC). bll. 25–40. , Cham (2018).
    [ BibTeX ]
     
  • 4.
    Lorenz, M., Rudolph, J.-P., Hesse, G., Uflacker, M., Plattner, H.: Object-Relational Mapping Revisited - A Quantitative Study on the Impact of Database Technology on O/R Mapping Strategies. Hawaii International Conference on System Sciences (HICSS). bll. 4877–4886 (2017).
    [ BibTeX ]
     
  • 5.
    Hesse, G., Matthies, C., Reissaus, B., Uflacker, M.: A New Application Benchmark for Data Stream Processing Architectures in an Enterprise Context: Doctoral Symposium. ACM International Conference on Distributed and Event-based Systems (DEBS). bll. 359–362 (2017).
    [ BibTeX ]
     
  • 6.
    Richly, K., Lorenz, M., Oergel, S.: S4J - Integrating SQL into Java at Compiler-Level. To appear in Information and Software Technologies - 22st International Conference, ICIST 2016, Proceedings (Springer - Communications in Computer and Information Science) (2016).
    [ BibTeX ]
     
  • 7.
    Lorenz, M., Hesse, G., Rudolph, J.-P.: Object-relational Mapping Revised - Guideline Review and Consolidation. International Joint Conference on Software Technologies (ICSOFT). bll. 157–168 (2016).
    [ BibTeX ]
     
  • 8.
    Lorenz, M.: The impact of column-orientation on the quality of class inheritance mapping specifications. Software Analysis, Evolution and Reengineering (SANER), 2015 IEEE 22nd International Conference on (2015).
    [ BibTeX ]
     
  • 9.
    Hesse, G., Lorenz, M.: Conceptual Survey on Data Stream Processing Systems. IEEE International Conference on Parallel and Distributed Systems (ICPADS). 797–802 (2015).
    [ BibTeX ]
     
  • 10.
    Lorenz, M., Albrecht, J.: Object-Relational Mapping Strategies revised – A comparison of Row- and Column- oriented Database Systems. International Conference on Challenges in IT, Engineering and Technology (ICCIET) (2014).
    [ BibTeX ]
     
  • 11.
    Januschowski, T., Kolassa, S., Lorenz, M., Schwarz, C.: Forecasting With In-Memory Technology. Foresight, The International Journal of Applied Forecasting. (2013).
    [ BibTeX ]
     
  • 12.
    Folkerts, E., Heimburger, R., Simchi-Levi, D., Youssef, N., Schwarz, C., Lorenz, M., Januschowski, T., Akkas, A.: Demand Forecasting with partial POS Data using In-Memory Technology. 32nd International Symposium on Forecasting in Boston (2012).
    [ BibTeX ]
     
  • 13.
    Schapranow, M.-P., Zeier, A., Plattner, H., Müller, J., Lorenz, M.: Discovery Services in the EPC Network. Designing and Deploying RFID Applications, pp. 109-130, INTECH Press (2011).
    [ BibTeX ]
     
  • 14.
    Lorenz, M., Müller, J., Schapranow, M.-P., Zeier, A.: A Distributed EPC Discovery Service based on Peer-to-peer Technology. Proceedings of the RFID SysTech 2011, 7th European Workshop on Smart Objects: Systems, Technologies and Applications, Dresden. bll. 1–7. VDE (2011).
    [ BibTeX ]
     
  • 15.
    Schapranow, M.-P., Lorenz, M., Zeier, A., Plattner, H.: License-based Access Control in EPCglobal Networks. The 7th European Workshop on RFID Systems and Technologies, pp. 1-7 (2011).
    [ BibTeX ]
     
  • 16.
    Schapranow, M.-P., Geller, F., Lorenz, M., Müller, J., Kowark, T., Zeier, A.: Assessment of Communication Protocols in the EPC Network: Replacing Textual SOAP and XML with Binary Google Protocol Buffers Encoding. 17th IEEE International Conference on Industrial Engineering and Engineering Management (IE&EM), Xiamen, China (2010).
    [ BibTeX ]
     

News

22.09.2023 | Trends and Concepts in the Softwareindustry Seminar offered in WiSe 2023/2024

Trends and Concepts in the Softwareindustry Seminar offered in WiSe 2023/2024 > Go to article

22.05.2023 | Christopher Hagedorn Successfully Defended His PhD Thesis

Christopher Hagedorn Successfully Defended His PhD Thesis > Go to article

03.03.2023 | Last Trends and Concepts course of Prof. Hasso Plattner

After more than 20 years of teaching, our founder and benefactor Prof. Hasso Plattner visited the HPI this week for his … > Go to article

01.03.2023 | Jan Kossmann Successfully Defended His PhD Thesis

Last week, Jan Kossmann another PhD student of our EPIC group successfully defended his thesis on the topic of … > Go to article

26.02.2023 | Paper on Data Tiering in Hyrise Published in BTW Proceedings

Our latest paper on data tiering in Hyrise "Workload-Driven Data Placement for Tierless In-Memory Database Systems" by … > Go to article

24.02.2023 | Paper on EPIC Research Group Published in SIGMOD Record

Our report “Enterprise Platform and Integration Concepts Research at HPI” has been published in the December issue of … > Go to article

30.11.2022 | Paper on Database Optimizations for Spatio-Temporal Data published in PVLDB

Our paper “Robust and Budget-Constrained Encoding Configurations for In-Memory Database Systems” has been published in … > Go to article

04.10.2022 | Günter Hesse Successfully Defended His PhD Thesis

Last week, Günter Hesse another PhD student of our EPIC group successfully defended his thesis on the topic of "A … > Go to article

08.07.2022 | Successful PhD Defense by Markus Dreseler

Markus Dreseler has successfully defended his PhD thesis on Automatic Tiering for In-Memory Database Systems. > Go to article

Literature

"A Course in In-Memory Data Management" by Prof. Dr. h.c. Hasso Plattner. This book is the culmination of six years work of in-memory research. As such, it provides the technical foundation for combined transactional and analytical workloads inside one single database as well as examples of new applications that are now possible given the availability of the new technology. The book is available at Springer.

Contact

Dr. Michael Perscheid

Chair Representative

Tel.: +49 (331) 5509-566

E-Mail: michael.perscheid(at)hpi.de


Office:

Room: V-2.12

Tel.: +49 (331) 5509-560

Fax: +49 (331) 5509-579

E-Mail: office-epic(at)hpi.de

 

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