Preserving Recomputability of Results from Big Data Transformation Workflows

Depending on External Systems and Human Interactions

Abstract

The ability to recompute results from raw data at any time is important for data-driven companies to ensure data stability and to selectively incorporate new data into an already delivered data product. However, data transformation processes are heterogeneous and it is possible that manual work of domain experts is part of the process to create a deliverable data product. Domain experts and their work are expensive and time consuming, a recomputation process needs the ability of automatically adding former human interactions. It becomes even more challenging when external systems are used or data changes over time. In this paper, we propose a system architecture which ensures recomputability of results from big data transformation workflows on internal and external systems by using distributed key-value data stores. Furthermore, the system architecture will contain the possibility of incorporating human interactions of former data transformation processes. We will describe how our approach significantly relieves external systems and at the same time increases the performance of the big data transformation workflows.

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Notes

  1. 1.

    https://accumulo.apache.org/

  2. 2.

    https://hbase.apache.org/

  3. 3.

    https://cassandra.apache.org/

  4. 4.

    https://hadoop.apache.org/

  5. 5.

    http://rocksdb.org/

References

  1. 1.

    Accumulo A (2017) Accumulo design – data model. http://accumulo.apache.org/1.8/accumulo_user_manual.html#_accumulo_design. Accessed 27 July 2017

    Google Scholar 

  2. 2.

    Chang F, Dean J, Ghemawat S, Hsieh WC, Wallach DA, Burrows M, Chandra T, Fikes A, Gruber RE (2008) Bigtable: A distributed storage system for structured data. ACM Trans Comput Syst 26(2):4

    Article  Google Scholar 

  3. 3.

    Corbett JC, Dean J, Epstein M, Fikes A, Frost C, Furman JJ, Ghemawat S, Gubarev A, Heiser C, Hochschild P et al (2013) Spanner: Google’s globally distributed database. ACM Trans Comput Syst 31(3):8

    Article  Google Scholar 

  4. 4.

    Dragojević A, Narayanan D, Nightingale EB, Renzelmann M, Shamis A, Badam A, Castro M (2015) No compromises: distributed transactions with consistency, availability, and performance. In: Proceedings 25th Symposium on Operating Systems Principles, ACM, pp 54–70

    Google Scholar 

  5. 5.

    Gray J, Reuter A (1992) Transaction processing: concepts and techniques. Elsevier, Amsterdam

    Google Scholar 

  6. 6.

    Jensen CS, Soo MD, Snodgrass RT (1994) Unifying temporal data models via a conceptual model. Inf Syst 19(7):513–547

    Article  Google Scholar 

  7. 7.

    Josefsson S (2006) The base16, base32, and base64 data encodings. https://tools.ietf.org/html/rfc4648. Accessed 9 June 2017

    Google Scholar 

  8. 8.

    Kulkarni K, Michels JE (2012) Temporal features in sql:2011. SIGMOD Rec 41(3):34–43. https://doi.org/10.1145/2380776.2380786

    Article  Google Scholar 

  9. 9.

    Lee J, Muehle M, May N, Faerber F, Sikka V, Plattner H, Krueger J, Grund M (2013) High-performance transaction processing in sap hana. IEEE Data Eng Bull 36(2):28–33

    Google Scholar 

  10. 10.

    Ozsoyoglu G, Snodgrass RT (1995) Temporal and real-time databases: a survey. IEEE Trans Knowl Data Eng 7(4):513–532

    Article  Google Scholar 

  11. 11.

    Rahm E, Do HH (2000) Data cleaning: problems and current approaches. IEEE Data Eng Bull 23(4):3–13

    Google Scholar 

  12. 12.

    Srivastava U, Widom J (2004) Flexible time management in data stream systems. In: Proceedings twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, ACM, pp 263–274

    Google Scholar 

Download references

Acknowledgements

This work was partly funded by the German Federal Ministry of Education and Research within the project Competence Center for Scalable Data Services and Solutions (ScaDS) Dresden/Leipzig (BMBF 01IS14014B) and Explicit Privacy-Preserving Host Intrusion Detection System EXPLOIDS (BMBF 16KIS0522K).

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Correspondence to Matthias Kricke.

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Kricke, M., Grimmer, M. & Schmeißer, M. Preserving Recomputability of Results from Big Data Transformation Workflows. Datenbank Spektrum 17, 245–253 (2017). https://doi.org/10.1007/s13222-017-0265-6

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Keywords

  • BigData
  • Recomputability
  • System architecture
  • Bitemporality
  • Time-to-consistency