Forecasting is used as the basis for business planning in many application areas such as energy, sales and traffic management. Time series data used in these areas is often hierarchically organized and thus, aggregated along the hierarchy levels based on their dimensional features. Calculating forecasts in these environments is very time consuming, due to ensuring forecasting consistency between hierarchy levels. To increase the forecasting efficiency for hierarchically organized time series, we introduce a novel forecasting approach that takes advantage of the hierarchical organization. There, we reuse the forecast models maintained on the lowest level of the hierarchy to almost instantly create already estimated forecast models on higher hierarchical levels. In addition, we define a hierarchical communication framework, increasing the communication flexibility and efficiency. Our experiments show significant runtime improvements for creating a forecast model at higher hierarchical levels, while still providing a very high accuracy.

Continuous balancing of energy demand and supply is a fundamental prerequisite for the stability of energy grids and requires accurate forecasts of electricity consumption and production at any point in time. Today's Energy Data Management (EDM) systems already provide accurate predictions, but typically employ a very time-consuming and inflexible forecasting process. However, emerging trends such as intra-day trading and an increasing share of renewable energy sources need a higher forecasting efficiency. Additionally, the wide variety of applications in the energy domain pose different requirements with respect to runtime and accuracy and thus, require flexible control of the forecasting process. To solve this issue, we introduce our novel online forecasting process as part of our EDM system called pEDM. The online forecasting process rapidly provides forecasting results and iteratively refines them over time. Thus, we avoid long calculation times and allow applications to adapt the process to their needs. Our evaluation shows that our online forecasting process offers a very efficient and flexible way of providing forecasts to the requesting applications.

Forecasting is an important data analysis technique and serves as the basis for business planning in many application areas such as energy, sales and traffic management. The currently employed statistical models already provide very accurate predictions, but the forecasting calculation process is very time consuming. This is especially true since many application domains deal with hierarchically organized data. Forecasting in these environments is especially challenging due to ensuring forecasting consistency between hierarchy levels, which leads to an increased data processing and communication effort. For this purpose, we introduce our novel hierarchical forecasting approach, where we propose to push forecast models to the entities on the lowest hierarch level and reuse these models to efficiently create forecast models on higher hierarchical levels. With that we avoid the time-consuming parameter estimation process and allow an almost instant calculation of forecasts.

Investigating potential dependencies in data and their effect on future business developments can help experts to prevent misestimations of risks and chances. This makes correlation a highly important factor in risk analysis tasks. Previous research on correlation in uncertain data management addressed foremost the handling of dependencies between discrete rather than continuous distributions. Also, none of the existing approaches provides a clear method for extracting correlation structures from data and introducing assumptions about correlation to independently represented data. To enable risk analysis under correlation assumptions, we use an approximation technique based on copula functions. This technique enables analysts to introduce arbitrary correlation structures between arbitrary distributions and calculate relevant measures over thus correlated data. The correlation information can either be extracted at runtime from historic data or be accessed from a parametrically precomputed structure. We discuss the construction, application and querying of approximate correlation representations for different analysis tasks. Our experiments demonstrate the efficiency and accuracy of the proposed approach, and point out several possibilities for optimization.

With the SAP HANA database, SAP offers a high-performance in-memory hybrid-store database. Hybrid-store databases---that is, databases supporting row- and column-oriented data management---are getting more and more prominent. While the columnar management offers high-performance capabilities for analyzing large quantities of data, the row-oriented store can handle transactional point queries as well as inserts and updates more efficiently. To effectively take advantage of both stores at the same time the novel question whether to store the given data row- or column-oriented arises. We tackle this problem with a storage advisor tool that supports database administrators at this decision. Our proposed storage advisor recommends the optimal store based on data and query characteristics; its core is a cost model to estimate and compare query execution times for the different stores. Besides a per-table decision, our tool also considers to horizontally and vertically partition the data and manage the partitions on different stores. We evaluated the storage advisor for the use in the SAP HANA database; we show the recommendation quality as well as the benefit of having the data in the optimal store with respect to increased query performance.

With the amount of data in current data warehouse databases growing steadily, random sampling is continuously gaining in importance. In particular, interactive analyses of large datasets can greatly benefit from the significantly shorter response times of approximate query processing. Typically, those analytical queries partition the data into groups and aggregate the values within the groups. Further, with the commonly used roll-up and drill-down operations a broad range of group-by queries is posed to the system, which makes the construction of highly-specialized synopses difficult. In this paper, we propose a general-purpose sampling scheme that is biased in order to answer group-by queries with high accuracy. While existing techniques focus on the size of the group when computing its sample size, our technique is based on its standard deviation. The basic idea is that the more homogeneous a group is, the less representatives are required in order to give a good estimate. With an extensive set of experiments, we show that our approach reduces both the estimation error and the construction cost compared to existing techniques.

Random sampling is one of the most widely used means to build synopses of large datasets because random samples can be used for a wide range of analytical tasks. Unfortunately, the quality of the estimates derived from a sample is negatively affected by the presence of "outliers" in the data. In this paper, we show how to circumvent this shortcoming by constructing outlier-aware sample synopses. Our approach extends the well-known outlier indexing scheme to multiple aggregation columns.

Although approximate query processing is a prominent way to cope with the requirements of data analysis applications, current database systems do not provide integrated and comprehensive support for these techniques. To improve this situation, we propose an SQL extension---called SQL/S---for approximate query answering using random samples, and present a prototypical implementation within the engine of the open-source database system Derby---called Derby/S. Our approach significantly reduces the required expert knowledge by enabling the definition of samples in a declarative way; the choice of the specific sampling scheme and its parametrization is left to the system. SQL/S introduces new DDL commands to easily define and administrate random samples subject to a given set of optimization criteria. Derby/S automatically takes care of sample maintenance if the underlying dataset changes. Finally, samples are transparently used during query processing, and error bounds are provided. Our extensions do not affect traditional queries and provide the means to integrate sampling as a first-class citizen into a DBMS.