F2DB: The Flash-Forward Database System

Fischer, Ulrike; Rosenthal, Frank; Lehner, Wolfgang

doi:10.1109/icde.2012.117

Cited by 9 publications

(15 citation statements)

References 7 publications

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“…Here, the time series are typically aggregated along the hierarchy based on dimensional attributes such as location or product [96,98,132]. However, besides the context information in the sense of external influences, there is also a further class of additional information provided for some time series.…”

Section: Exploiting Hierarchical Time Series Structuresmentioning

confidence: 99%

Energy Time Series Forecasting

Dannecker¹

2015

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Section: Exploiting Hierarchical Time Series Structuresmentioning

confidence: 99%

Energy Time Series Forecasting

Dannecker¹

2015

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“…TimeTravel [68], [69] Forecasting, AQP F 2 DB [70], [71] Forecasting, AQP Unamed [72], [73], [74] Interpolation, AQP In addition to the classification criteria shown in Table 1, the TSMSs are summarized with regard to their supported query functionality in Table 2. To describe the functionality of the surveyed TSMSs with a uniform set of well-known terms, we elected to primarily express it using SQL.…”

Section: Rdbms Extensionsmentioning

confidence: 99%

“…Another endeavor to extend PostgreSQL with the capability to forecast time series in a centralized RDBMS is F 2 DB proposed by Fischer et al [70]. The use case for F 2 DB is data analytics using data warehouse based business intelligence, and the system extends the SQL query language with statements for manually creating models and performing forecast on said models.…”

Section: Systemsmentioning

confidence: 99%

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Time Series Management Systems: A Survey

Jensen

Pedersen

Thomsen

2017

IEEE Trans. Knowl. Data Eng.

134

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The collection of time series data increases as more monitoring and automation are being deployed. These deployments range in scale from an Internet of things (IoT) device located in a household to enormous distributed Cyber-Physical Systems (CPSs) producing large volumes of data at high velocity. To store and analyze these vast amounts of data, specialized Time Series Management Systems (TSMSs) have been developed to overcome the limitations of general purpose Database Management Systems (DBMSs) for times series management. In this paper, we present a thorough analysis and classification of TSMSs developed through academic or industrial research and documented through publications. Our classification is organized into categories based on the architectures observed during our analysis. In addition, we provide an overview of each system with a focus on the motivational use case that drove the development of the system, the functionality for storage and querying of time series a system implements, the components the system is composed of, and the capabilities of each system with regard to Stream Processing and Approximate Query Processing (AQP). Last, we provide a summary of research directions proposed by other researchers in the field and present our vision for a next generation TSMS.Comment: 20 Pages, 15 Figures, 2 Tables, Accepted for publication in IEEE TKD

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“…Current research focuses on the integration of time series forecasting and prediction in general inside a DBMS [5,10,13]. These approaches allow for improved performance and additional functionality inside the database.…”

Section: Introductionmentioning

confidence: 99%

Sample-based forecasting exploiting hierarchical time series

Fischer¹,

Rosenthal²,

Lehner³

2012

Proceedings of the 16th International Database Engineering &Amp; Applications Sysmposium on - IDEAS '12

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Time series forecasting is challenging as sophisticated forecast models are computationally expensive to build. Recent research has addressed the integration of forecasting inside a DBMS. One main benefit is that models can be created once and then repeatedly used to answer forecast queries. Often forecast queries are submitted on higher aggregation levels, e.g., forecasts of sales over all locations. To answer such a forecast query, we have two possibilities. First, we can aggregate all base time series (sales in Austria, sales in Belgium ...) and create only one model for the aggregate time series. Second, we can create models for all base time series and aggregate the base forecast values. The second possibility might lead to a higher accuracy but it is usually too expensive due to a high number of base time series. However, we actually do not need all base models to achieve a high accuracy, a sample of base models is enough. With this approach, we still achieve a better accuracy than an aggregate model, very similar to using all models, but we need less models to create and maintain in the database. We further improve this approach if new actual values of the base time series arrive at different points in time. With each new actual value we can refine the aggregate forecast and eventually converge towards the real actual value. Our experimental evaluation using several real-world data sets, shows a high accuracy of our approaches and a fast convergence towards the optimal value with increasing sample sizes and increasing number of actual values respectively.

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F2DB: The Flash-Forward Database System

Cited by 9 publications

References 7 publications

Energy Time Series Forecasting

Energy Time Series Forecasting

Time Series Management Systems: A Survey

Sample-based forecasting exploiting hierarchical time series

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