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Forecast Query Processing & Optimization in DBMS: Maximizing Accuracy & Efficiency

Explore the integration of sophisticated forecasting models inside a DBMS for high accuracy and efficiency in processing large quantities of time series data within runtime restrictions.

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Forecast Query Processing & Optimization in DBMS: Maximizing Accuracy & Efficiency

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  1. Ulrike Fischer Processing and Optimization of Forecast Queries

  2. Motivation • Time seriesdataappears in manydomains Salesandinventory Renewableenergyressources • High accuracypossible • Sophisticatedmodels • Sophisticatedestimators • Runtimerestrictions • Large numberof time series • Short amountof time available  Two Optimization Dimensions: Accuracy and Runtime Processing and Optimization of Forecast Queries

  3. Outline • Motivation • Integration ofForecastinginside a DBMS • Processing of Forecast Queries • Optimizationof Forecast Queries in Hierarchies • Summary Processing andOptimizationof Forecast Queries

  4. Model-based Time Series Forecasting • 1. Model Creation • Model Identification • Parameter Estimation • 3. Model Maintenance • Model Evaluation • Threshold-based, time-based … • Model Adaption • Parameter Re-estimation • 2. Model Usage • Forecasting Model • Triple Exponential Smoothing ! ! Processing andOptimizationof Forecast Queries

  5. Time Series Forecasting in DBMS  Transparency and Effienciency M M export M M SQL export M SQL  Reuse of models and results Processing and Optimization of Forecast Queries

  6. Project Overview EU FP7 project Scheduling quantity date 2012 34,000 SELECT date, quantity FROM sales WHERE … FORECAST … Forecasting Aggregation 38,000 2013 … … FlexOffers DWH Supply Demand Processing and Optimization of Forecast Queries

  7. Overview F2DB Forecast Queries Inserts Query Interface Model Usage Model Maintenance Model Index Query Processing & Optimization On-Demand Estimation QP in Hierarchies Hybrid Maintenance PublishSubscribeQueries Model Pool Model Model Model Model M1 Model Creation Time Series Time Series Time Series + := M1 M2 M3 Ensemble Models Base Tables Physical Design AR(2), BFGS, MSE … Processing andOptimizationof Forecast Queries

  8. Outline • Motivation • Integration ofForecastinginside a DBMS • Processing of Forecast Queries • Optimizationof Forecast Queries in Hierarchies • Summary Processing andOptimizationof Forecast Queries

  9. Forecast Query Processing SELECTdate, SUM(quantity) FROMsales WHEREproduct= ‘HTC‘ GROUP BYdate FORECAST 3 • Extension of SQL language • Horizon, measureand time column,model type andparameters, … • Logical query plan • Forecast operatorΨ Physicalquery plan Forecast MHTC Ψk=3 Forecast πdate, quantity BuildModel Aggregate γdate:AGG(sales) Scan σproduct= 'HTC' sales sales Processing andOptimizationof Forecast Queries

  10. Advanced Forecast Query Processing • Data warehousecontains multidimensional data SELECTdate, SUM(quantity) FROMsales WHEREproduct= ‘HTC‘ GROUP BYdate FORECAST 3 days Mobiles Aggregation 3. Disaggregation DisAgg Forecast Forecast 1. Direct MHD2 MSmart Forecast Key Nokia HTC 2. Aggregation MMobiles HD2 Smart Processing andOptimizationof Forecast Queries

  11. Aggregation vs. Disaggregation Top-Down (Disaggregation) Bottom-Up (Aggregation) Complete (Direct) Efficiency Accuracy Model creationeasier Noinformationloss Edwards andOrcuss (1969) Schwarzkopf et. al. (1988) Hubrich (2005) … GrunfeldandGriliches (1960) Grossand Sohl (1990) Zellner and Tobias (2000) ….  Depends on data set, quality of forecast model, correlation … Processing and Optimization of Forecast Queries

  12. Outline • Motivation • Integration ofForecastinginside a DBMS • Processing of Forecast Queries • Optimizationof Forecast Queries in Hierarchies • Summary Processing andOptimizationof Forecast Queries

  13. ConfigurationAdvisor Updates Forecast Queries Workload W Preference α • Problem: Exponentialsearchspace • GreedyAlgorithm(monotonicmaintenancecosts) • Start onemodelatthe top, addmodelsstep-by-step Query Interface Model Advisor Analyze Cost BW + Error EW Create Configuration CW DWH Model Pool Configuration + Strategy WeightedAccuracy WeightedEfficiency Processing and Optimization of Forecast Queries

  14. Performance Comparison • Complete (C) All models, onlydirectforecasts • Bottom-Up (B) Onlymodelsatlevelone, othersuseaggregation • Top-Down (T) Onlyonemodelfor top element, othersusedisaggregation • Greedy (G) Processing andOptimizationof Forecast Queries

  15. Extensions • Observation: aggregation(bottom-up) hardlyused in real datasets • Reason: large numberofchild time series • Sample Aggregation • Use sample ofchildmodels • Group Design • Relax fixedaggregationgroups ? ? Virtual Group ? • aggregation + estimation supportofdisjunctivequeries • Estimateusinghistoricalproportion • Weightedsampling Processing and Optimization of Forecast Queries

  16. Outline • Motivation • Integration ofForecastinginside a DBMS • Processing of Forecast Queries • Optimizationof Forecast Queries in Hierarchies • Summary Processing andOptimizationof Forecast Queries

  17. Summary • DBMS Integration • Sophisticatedmodelscomputationally expensive • DBMS integrationforreuse, transparencyandoptimization • Forecast Queries • New query type withforecasthorizon • Face twootimizationdimensions • HierarchicalForecasting • Reducemaintenancecostswithderivationschemes • Possibleincreaseofaccuracy • Large searchspace Processing andOptimizationof Forecast Queries

  18. Ulrike Fischer Processing and Optimization of Forecast Queries

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