Publications: February 2013 Archives

We demonstrate TimeTravel, an efficient DBMS system for seamless integrated querying of past and (forecasted) future values of time series, allowing the user to view past and future values as one joint time series. This functionality is important for advanced application domain like energy. The main idea is to compactly represent time series as models. By using models, the TimeTravel system answers queries approximately on past and future data with error guarantees (absolute error and confidence) one order of magnitude faster than when accessing the time series directly. In addition, it efficiently supports exact historical queries by only accessing relevant portions of the time series. This is unlike existing approaches, which access the entire time series to exactly answer the query.
To realize this system, we propose a novel hierarchical model index structure. As real-world time series usually exhibits seasonal behavior, models in this index incorporate seasonality. To construct a hierarchical model index, the user specifies seasonality period, error guarantees levels, and a statistical forecast method. As time proceeds, the system incrementally updates the index and utilizes it to answer approximate and exact queries. TimeTravel is implemented into PostgreSQL, thus achieving complete user transparency at the query level. In the demo, we show the easy building of a hierarchical model index for a real-world time series and the effect of varying the error guarantees on the speed up of approximate and exact queries.

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.

Nowadays, Renewable Energy Sources (RES) are attracting more and more interest. Thus, many countries aim to increase the share of green energy and have to face with several challenges (e.g., balancing, storage, pricing). In this paper, we address the balancing challenge and present the MIRABEL project which aims to prototype an Energy Data Management System (EDMS) which takes benefit of flexibilities to efficiently balance energy demand and supply. The EDMS consists of millions of heterogeneous nodes that each incorporates advanced components (e.g., aggregation, forecasting, scheduling, negotiation). We describe each of these components and their interaction. Preliminary experimental results confirm the feasibility of our EDMS.