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Towards Integrated Data Analytics: Time Series Forecasting in DBMS

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Abstract

Integrating sophisticated statistical methods into database management systems is gaining more and more attention in research and industry in order to be able to cope with increasing data volume and increasing complexity of the analytical algorithms. One important statistical method is time series forecasting, which is crucial for decision making processes in many domains. The deep integration of time series forecasting offers additional advanced functionalities within a DBMS. More importantly, however, it allows for optimizations that improve the efficiency, consistency, and transparency of the overall forecasting process. To enable efficient integrated forecasting, we propose to enhance the traditional 3-layer ANSI/SPARC architecture of a DBMS with forecasting functionalities. This article gives a general overview of our proposed enhancements and presents how forecast queries can be processed using an example from the energy data management domain. We conclude with open research topics and challenges that arise in this area.

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References

  1. PredictTimeSeries–Microsoft SQL server 2008 books online (2012). http://msdn.microsoft.com/en-us/library/ms132167.aspx

  2. Agarwal D, Chen D, ji Lin L, Shanmugasundaram J, Vee E (2010) Forecasting high-dimensional data. In: SIGMOD conference, pp 1003–1012

    Google Scholar 

  3. Böhm M, Dannecker L, Doms A, Dovgan E, Filipic B, Fischer U, Lehner W, Pedersen TB, Pitarch Y, Siksnys L, Tusar T (2012) Data management in the MIRABEL smart grid system. In: EDBT/ICDT workshops, pp 95–102

    Google Scholar 

  4. Cohen J, Dolan B, Dunlap M, Hellerstein JM, Welton C (2009) MAD skills: new analysis practices for big data. Proc VLDB Endow 2(2):1481–1492

    Google Scholar 

  5. Dannecker L, Böhm M, Lehner W, Hackenbroich G (2011) Forcasting evolving time series of energy demand and supply. In: ADBIS, pp 302–315

    Google Scholar 

  6. Dannecker L, Böhm M, Lehner W, Hackenbroich G (2012) Partitioning and multi-core parallelization of multi-equation forecast models. In: SSDBM, pp 106–123

    Google Scholar 

  7. Dannecker L, Schulze R, Böhm M, Lehner W, Hackenbroich G (2011) Context-aware parameter estimation for forecast models in the energy domain. In: SSDBM, pp 491–508

    Google Scholar 

  8. Das S, Sismanis Y, Beyer KS, Gemulla R, Haas PJ, McPherson J (2010) Ricardo: Integrating R and hadoop. In: SIGMOD conference, pp 987–998

    Google Scholar 

  9. Deshpande A, Madden S (2006) MauveDB: supporting model-based user views in database systems. In: SIGMOD conference, pp 73–84

    Google Scholar 

  10. Duan S, Babu S (2007) Processing forecasting queries. In: VLDB’07, pp 711–722

    Google Scholar 

  11. Dunn D, Williams W, DeChaine T (1976) Aggregate versus subaggregate models in local area forecasting. J Am Stat Assoc 71:68–71

    Article  Google Scholar 

  12. Faerber F, Cha SK, Primsch J, Bornhoevd C, Sigg S, Lehner W (2011) SAP HANA database—data management for modern business applications. SIGMOD Rec 40:45–51

    Article  Google Scholar 

  13. Fischer U, Böhm M, Lehner W (2011) Offline design tuning for hierarchies of forecast models. In: BTW, pp 167–186

    Google Scholar 

  14. Fischer U, Rosenthal F, Böhm M, Lehner W (2010) Indexing forecast models for matching and maintenance. In: IDEAS, pp 26–31

    Google Scholar 

  15. Fischer U, Rosenthal F, Lehner W (2012) F2DB: the flash-forward database system. In: ICDE, pp 1245–1248

    Google Scholar 

  16. Ge T, Zdonik SB (2008) A skip-list approach for efficiently processing forecasting queries. Proc VLDB Endow 1(1):984–995

    Google Scholar 

  17. Gooijera JGD, Hyndman RJ (2006) 25 years of time series forecasting. Int J Forecast 22:443–473

    Article  Google Scholar 

  18. Große P, Lehner W, Weichert T, Färber F, Li WS (2011) Bridging two worlds with RICE integrating R into the SAP in-memory computing engine. Proc VLDB Endow 4(12):1307–1317

    Google Scholar 

  19. Hyndman RJ, Ahmed RA, Athanasopoulos G, Shang HL (2011) Optimal combination forecasts for hierarchical time series. Comput Stat Data Anal 55(9):2579–2589

    Article  MathSciNet  Google Scholar 

  20. Hyndman RJ, Khandakar Y (2008) Automatic time series forecasting: the forecast package for R. J Stat Softw 27:1–22

    Google Scholar 

  21. Hyndman RJ, Koehler AB, Snyder RD, Grose S (2000) A state space framework for automatic forecasting using exponential smoothing methods. Int J Forecast 18:439–454

    Article  Google Scholar 

  22. Jeung H, Yiu ML, Zhou X, Jensen CS (2010) Path prediction and predictive range querying in road network databases. VLDB J 19(4):585–602

    Article  Google Scholar 

  23. Koc ML, Ré C (2011) Incrementally maintaining classification using an RDBMS. Proc VLDB Endow 4(5):302–313

    Google Scholar 

  24. Lehner W (2003) Datenbanktechnologie für Data-Warehouse-Systeme. Konzepte und Methoden. dpunkt

  25. Oracle (2012) Oracle OLAP DML reference: FORECAST–DML statement

  26. Parisi F, Sliva A, Subrahmanian VS (2011) Embedding forecast operators in databases. In: Proceedings of the 5th international conference on scalable uncertainty management (SUM’11), pp 373–386

    Chapter  Google Scholar 

  27. Ramanathan R, Engle R, Granger CWJ, Vahid-Araghi F, Brace C (1997) Short-run forecasts of electricity loads and peaks. Int J Forecast 13(2):161–174

    Article  Google Scholar 

  28. Rosenthal F, Lehner W (2011) Efficient in-database maintenance of ARIMA models. In: SSDBM, pp 537–545

    Google Scholar 

  29. Rosenthal F, Volk PB, Hahmann M, Habich D, Lehner W (2009) Drift-Aware ensemble regression. In: Proceedings of the 6th international conference on machine learning and data mining in pattern recognition (MLDM’09), pp 221–235

    Chapter  Google Scholar 

  30. Roussopoulos N (1982) The logical access path schema of a database. IEEE Trans Softw Eng 8:563–573

    Article  MathSciNet  MATH  Google Scholar 

  31. Sánchez I (2008) Adaptive combination of forecasts with application to wind energy. Int J Forecast 24(4):679–693

    Article  Google Scholar 

  32. Taylor JW (2009) Triple seasonal methods for Short-term electricity demand forecasting. Eur J Oper Res 204:139–152

    Article  Google Scholar 

  33. Winter R, Kostamaa P (2010) Large scale data warehousing: trends and observations. In: ICDE, p 1

    Google Scholar 

  34. Xu B, Wolfson O (2003) Time-Series prediction with applications to traffic and moving objects databases. In: Proceedings of the 3rd ACM international workshop on data engineering for wireless and mobile access (MobiDe’03), pp 56–60

    Chapter  Google Scholar 

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Correspondence to Ulrike Fischer.

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Matthias Boehm is currently visiting IBM Almaden Research Center, San Jose, CA, USA.

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Fischer, U., Dannecker, L., Siksnys, L. et al. Towards Integrated Data Analytics: Time Series Forecasting in DBMS. Datenbank Spektrum 13, 45–53 (2013). https://doi.org/10.1007/s13222-012-0108-4

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  • DOI: https://doi.org/10.1007/s13222-012-0108-4

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