A cost monitoring system can monitor a cost of queries executing in a complex event processing system, running on top of a pay-as-you-go cloud infrastructure. Certain embodiments may employ a generic, cloud-platform independent cost model, multi-query optimization, cost calculation, and/or operator placement techniques, in order to monitor and explain query cost down to an operator level. Certain embodiments may monitor costs in near real-time, as they are created. Embodiments may function independent of an underlying complex event processing system and the underlying cloud platform. Embodiments can optimize a work plan of the cloud-based system so as to minimize cost for the end user, matching the cost model of the underlying cloud platform.

Cost monitoring and cost-driven optimization of complex event processing system

The invention disclosed is a system for providing an aggregated econometric database with selectable sources of economic data. The econometric database is accessible to a system application that graphically displays econometric data over selected periods, and allows display of external economic data in conjunction with internal company metrics. The system applications further provide for identifying the features of indicators, economic and business forecasting, and providing alerts based on the available econometric data.

Business Performance Forecasting System and Method

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.

/pdf/towards-integrated-data-analytics-time-series-forecasting-in-261pri1rii.pdf

Towards Integrated Data Analytics: Time Series Forecasting in DBMS

The so-called smart grid is emerging in the energy domain as a solution to provide a stable, efficient and sustainable energy supply accommodating ever growing amounts of renewable energy like wind and solar in the energy production. Smart grid systems are highly distributed, manage large amounts of energy related data, and must be able to react rapidly (but intelligently) when conditions change, leading to substantial real-time business intelligence challenges. This paper discusses these challenges and presents data management solutions in the European smart grid project MIRABEL. These solutions include real-time time series forecasting, real-time aggregation of the flexibilities in energy supply and demand, managing subscriptions for forecasted and flexibility data, efficient storage of time series and flexibilities, and real-time analytical query processing spanning past and future (forecasted) data. Experimental studies show that the proposed solutions support important real-time business intelligence tasks in a smart grid system.

/pdf/real-time-business-intelligence-in-the-mirabel-smart-grid-actxg9eu18.pdf

Real-Time Business Intelligence in the MIRABEL Smart Grid System

Methods, systems, and computer-readable storage media for providing at least one parameter for use with a forecast model. Implementations include actions of receiving a first context vector, the first context vector including a plurality of context attributes that describe a first context, retrieving a first parameter vector from a repository based on the first context vector, the repository electronically storing a plurality of parameter vector, each parameter vector being associated with a respective context and including one or more parameters, parameterizing the forecast model based on parameters provided in the first parameter vector to provide a parameterized forecast model, optimizing the parameterized forecast model to provide an optimized forecast model, and forecasting one or more values using the optimized forecast model.

Context-aware parameter estimation for forecast models

Continuous balancing of energy demand and supply is a fundamental prerequisite for the stability and efficiency of energy grids. This balancing task requires accurate forecasts of future electricity consumption and production at any point in time. For this purpose, database systems need to be able to rapidly process forecasting queries and to provide accurate results in short time frames. However, time series from the electricity domain pose the challenge that measurements are constantly appended to the time series. Using a naive maintenance approach for such evolving time series would mean a re-estimation of the employed mathematical forecast model from scratch for each new measurement, which is very time consuming. We speed-up the forecast model maintenance by exploiting the particularities of electricity time series to reuse previously employed forecast models and their parameter combinations. These parameter combinations and information about the context in which they were valid are stored in a repository. We compare the current context with contexts from the repository to retrieve parameter combinations that were valid in similar contexts as starting points for further optimization. An evaluation shows that our approach improves the maintenance process especially for complex models by providing more accurate forecasts in less time than comparable estimation methods.

Context-aware parameter estimation for forecast models in the energy domain

Linear run length encoding is described. A system and method include storing a table of time series data in a database of a data platform, the table of time series data representing a set of time series blocks. Each time series block of the set of time series blocks has a time series of equally-incremented time intervals and a run length. Each time interval of the time series is associated with one or more values. The run length has a starting position with at least one starting value and an ending position with at least one ending value. The starting position and the at least one starting value is stored for each time series block in a column store of the database. Then, a compressed index is generated in the column store of the database for each time series block, the compressed index comprising the starting position and the at least one starting value.

Linear run length encoding: compressing the index vector

A system and method for faster access for compressed time series data. A set of blocks are generated based on a table stored in a database of the data platform. The table stores data associated with multiple sources of data provided as consecutive values, each block containing index vectors having a range of the consecutive values. A block index is generated for each block having a field start vector representing a starting position of the block relative to the range of consecutive values, and a starting value vector representing a value of the block at the starting position. The field start vector of the block index is accessed to obtain the starting position of a field corresponding to a first block and to the range of the consecutive values of the first block. The starting value vector is then determined from the block index to determine an end and a length of the field of the first block.

Faster access for compressed time series data: the bock index

Enabling experts to not only analyze current and historic data but also to evaluate the impact of decisions on the future state of the business greatly increased the value of decision support. However, the highly relevant aspect of representing and processing uncertain and temporally indeterminate data is often ignored in this context. Although the management of uncertainty has been researched intensely in the last decade, its role in decision support has not attracted much attention. We hold that not considering such information restricts the analyses users can run and the insights they can get into their data. In this paper, we complement large-scale data analyses with support for what-if analyses over uncertain and temporally indeterminate data. We use a histogram-based model to represent arbitrary uncertainty and temporal indeterminacy and allow its processing in a flexible manner using operators for analyzing, deriving, and modifying uncertainty in decision support tasks. We describe a prototypical implementation and approaches for parallelization on a commercial column store and present an initial evaluation of our solution.

Handling of Uncertainty and Temporal Indeterminacy for What-if Analysis

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