Publications by Norman May

@inproceedings{abc,
	author = {Martin Kaufmann and Peter M. Fischer and Norman May and Chang Ge and Anil K. Goel and Donald Kossmann},
	booktitle = {31st IEEE International Conference on Data Engineering, ICDE 2015, Seoul, South Korea},
	title = {Bi-temporal Timeline Index: A data structure for Processing Queries on bi-temporal data.},
	url = {http://dx.doi.org/10.1109/ICDE.2015.7113307},
	year = {2015}
}

@inproceedings{abc,
	author = {Carsten Binnig and Stefan Hildenbrand and Franz F{\"a}rber and Donald Kossmann and Juchang Lee and Norman May},
	booktitle = {VLDB J.},
	title = {Distributed snapshot isolation: global transactions pay globally, local transactions pay locally.},
	url = {http://dx.doi.org/10.1007/s00778-014-0359-9},
	year = {2014}
}

@inproceedings{abc,
	author = {Martin Kaufmann and Peter M. Fischer and Norman May and Donald Kossmann},
	booktitle = {Proceedings of the 17th International Conference on Extending Database Technology, EDBT 2014, Athens, Greece},
	title = {Benchmarking Bitemporal Database Systems: Ready for the Future or Stuck in the Past?},
	url = {http://dx.doi.org/10.5441/002/edbt.2014.80},
	year = {2014}
}

@inproceedings{abc,
	abstract = {After more than a decade of a virtual standstill, the adoption of temporal data management features has recently picked up speed, driven by customer demand and the inclusion of temporal expressions into SQL:2011. Most of the big commercial DBMS now include support for bitemporal data and operators. 
In this paper, we perform a thorough analysis of these commercial temporal DBMS: We investigate their architecture, determine their performance and study the impact of performance tuning. This analysis utilizes our recent (TPCTC 2013) benchmark proposal, which includes a comprehensive temporal workload definition.
The results of our analysis show that the support for temporal data is still in its infancy:  All systems store their data in regular, statically partitioned tables and rely on standard indexes as well as query rewrites for their operations. As shown by our measurements, this causes considerable performance variations on slight workload variations and a significant effort for performance tuning. In some cases, there is considerable overhead for temporal operations even after extensive tuning.},
	author = {Martin Kaufmann and Peter M. Fischer and Norman May and Donald Kossmann},
	booktitle = {EDBT: 17th International Conference on Extending Database Technology},
	title = {Benchmarking Bitemporal Database Systems:  Ready for the Future or Stuck in the Past?},
	year = {2014}
}

@inproceedings{abc,
	author = {Martin Kaufmann and Peter M. Fischer and Norman May and Andreas Tonder and Donald Kossmann},
	booktitle = {Performance Characterization and Benchmarking - 5th TPC Technology Conference, TPCTC 2013, Trento, Italy},
	title = {TPC-BiH: A Benchmark for Bitemporal Databases.},
	url = {http://dx.doi.org/10.1007/978-3-319-04936-6_2},
	venue = {Revised Selected Papers},
	year = {2013}
}

@inproceedings{abc,
	author = {Martin Kaufmann and Amin Amiri Manjili and Panagiotis Vagenas and Peter M. Fischer and Donald Kossmann and Franz F{\"a}rber and Norman May},
	booktitle = {Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2013, New York, NY, USA},
	title = {Timeline index: a unified data structure for processing queries on temporal data in SAP HANA.},
	url = {http://doi.acm.org/10.1145/2463676.2465293},
	year = {2013}
}

@inproceedings{abc,
	author = {Martin Kaufmann and Peter M. Fischer and Donald Kossmann and Norman May},
	booktitle = {29th IEEE International Conference on Data Engineering, ICDE 2013, Brisbane, Australia},
	title = {A generic database benchmarking service.},
	url = {http://doi.ieeecomputersociety.org/10.1109/ICDE.2013.6544923},
	year = {2013}
}

@techreport{abc,
	abstract = {An increasing number of applications such as risk evaluation in banking or inventory management require support for temporal data.
After more than a decade of standstill, the recent adoption of some bitemporal features in SQL:2011 has reinvigorated the support among commercial database vendors, who incorporate an increasing number of relevant bitemporal features. Naturally, assessing the performance and scalability of temporal data storage and operations is of great concern for potential users.
The cost of keeping and querying history with novel operations (such as time travel, temporal joins or temporal aggregations) is not adequately reflected in any existing benchmark.
In this paper, we present a benchmark proposal which provides comprehensive coverage of the bitemporal data management.
It builds on the solid foundations of TPC-H but extends it with a rich set of queries and update scenarios.
This workload stems both from real-life temporal applications from SAP{\textquoteright}s customer base and a systematic coverage of temporal operators proposed in the academic literature.
In the accompanying paper we present preliminary results of our benchmark on a number of temporal database systems, also highlighting the need for certain language extensions.
In the appendix of this technical report we provide all details required to implement the benchmark.},
	author = {Martin Kaufmann and Peter M. Fischer and Norman May and Donald Kossmann},
	title = {Benchmarking Databases with History Support},
	url = {http://dx.doi.org/10.3929/ethz-a-009994978},
	year = {2013}
}