Time travel in column stores

Kaufmann, Martin; Manjili, Amin Amiri; Hildenbrand, Stefan; Kossmann, Donald; Tonder, A.

doi:10.1109/icde.2013.6544818

Cited by 3 publications

(1 citation statement)

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“…The first step is to study alternative approaches to represent temporal data in a main memory column store, as published in [7]. Here we focus on the physical storage of temporal data and scanbased algorithms rather than index data structures.…”

Section: Introductionmentioning

confidence: 99%

Storing and processing temporal data in a main memory column store

Kaufmann

Kossmann

2013

Proc. VLDB Endow.

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Managing and accessing temporal data is of increasing importance in industry. So far, most companies model the time dimension on the application layer rather than pushing down the operators to the database, which leads to a significant performance overhead. The goal of this PhD thesis is to develop a native support of temporal features for SAP HANA, which is a commercial inmemory column store database system. We investigate different alternatives to store temporal data physically and analyze the tradeoffs arising from different memory layouts which cluster the data either by time or by space dimension. Taking into account the underlying physical representation, different temporal operators such as temporal aggregation, time travel and temporal join have to be executed efficiently. We present a novel data structure called Timeline Index and algorithms based on this index, which have a very competitive performance for all temporal operators beating existing best-of-breed approaches by factors, sometimes even by orders of magnitude. The results of this thesis are currently being integrated into HANA, with the goal of being shipped to the customers as a productive release within the next few months.

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Section: Introductionmentioning

confidence: 99%