Sap Se

Lars Dannecker

Gordon Gaumnitz

Robert Schulze

Ivan T Bowman

Dan Farrar

Yu Cheng

Daniel James Farrar

Gregor Hackenbroich

Johannes Meinecke

Philipp Roesch

Boyi Ni

Dave Parsons

Paul Binks

Simon Moscrop

Jeremie Brunet

Juchang Lee

Mihnea Andrei

Norman May

Thorsten Glebe

Yong Sik Kwon

Jonathan Bregler

Jaeyoung Choi

Wolfgang Lehner

Alexander Boehm

Meinolf Block

Frank Renkes

Hyunjun Kim

Eunsang Kim

Henning Zahn

Matthias Boehm

Martin Schindewolf

Patrick Voelker

Martin Kittel

Holger Mack

Uwe Hahn

Urs Klingsporn

Melanie Handreck

Robert Lorenz

Systems and methods include creation of a first instance of a tenant object in a database instance, association of the first instance of the tenant object with a first plurality of database artifacts including first data associated with the first instance of the tenant object, creation of a second instance of the tenant object in the database instance, association of the second instance of the tenant object with a second plurality of database artifacts including second data associated with the second instance of the tenant object, and reception and response to queries on the first data associated with the first instance of the tenant object and to queries on the second data associated with the second instance of the tenant object.

Native multi-tenancy for database system

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 block index

Disclosed is a system and method for improving database memory consumption and performance using compression of time stamp columns. A number of time stamps of a time series is received. The time stamps have a start time, and are separated by an equal increment of time that defines an interval. The start time and interval are stored in a dictionary of a column store of a database. An index is generated in the column store of the database, the index having a number of index vectors. Using the index vectors, each time stamp of the number of time stamps can be calculated from the start time stored in the dictionary and the position in the time series based on the interval stored in the dictionary.

Compressing time stamp columns

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

Time series data may be represented with multiple representations, optionally using a variety of storage approaches, and the plurality of representations may be indexed using a representation index, which includes a start row identifier, a representation identifier, and an offset within the representation for each segment of one or more rows in the time series data column.

Indexing flexible multi-representation storages for time series data

An advisor creates configurations for generating multi-representations of time series data based upon detected characteristics such as length, interval, minimums, data types, etc., as well as configurations provided by a user. In an offline mode the advisor may further consider a previous time series workload. In an on-line mode the advisor may adapt multi-representation configurations with respect to ongoing changes in a current time series workload. The advisor may reference a cost model including values quantifying various dimensions (e.g., compression technique, accuracy, covered time period, storage medium, memory consumption, speed) of the multi-representations for optimization purposes. Configurations created by the advisor may be input to a storage engine to generate and store the multi-representations according to goals for data aging, operation execution pattern optimization, and ease of access to time series data located in hot zones. The advisor may be implemented with an engine of an in-memory database.

Advisor generating multi-representations of time series data

A flexible approach for storing time series data, utilizes multiple representations in order to achieve optimization amongst various dimensions such as covered time period, accuracy, compression model, and storage medium. A first aspect selectively provides different representations for various portions of a time series along the time axis (horizontal). In this horizontal aspect, a first compression model may be applied to store data occurring during one period in a time series, while a different compression model is applied to store data during another period. In a separate (vertical) aspect, time series data over a same time period may be saved in multiple representations using different compression models. While increasing demands on memory, such vertical storage can afford efficient access for specific purposes (i.e., analytical). Storing time series data according to horizontal and/or vertical distributions, may be useful for applications such as data aging and the optimization of operator execution patterns.

Multi-representation storage of time series data

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.

A system includes multiple memory modules arranged and configured to store data and at least one processor that is operably coupled to the memory modules. The at least one processor is arranged and configured to select an access pattern of a forecast model, determine a storage layout model based on the identified access pattern of the forecast model, and store values in an order defined by the storage layout model using at least one of the memory modules. The order of the stored values enables sequential access to the stored values for use in the forecast model. Implementations of one or more features of the system may be performed by a computer-implemented method and/or a computer program product.

Forecast-model-aware data storage for time series data

Allocation within a database environment is performed by processing a sequence of steps, each of which is described by an allocation processing block. The allocation processing block receives three inputs (input tables or input view) and computes a result. Related apparatus, systems, techniques and articles are also described.

Flexibly performing allocations in databases

Examples of energy forecasting in hierarchical energy systems are provided herein. A global forecast model instance for a hierarchical energy system can be determined through aggregation of energy forecast model data from individual energy smart meters. Energy forecast model data can include values for energy forecast model parameters used by the individual smart meters. The energy smart meters include measurement, forecasting, and calculation capabilities. The smart meters locally determine a forecast model instance used by the smart meter and provide corresponding information to higher levels in the energy system hierarchy. A global forecast model instance is determined based on the provided information.

Efficient forecasting for hierarchical energy systems

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Dresden, Germany

Lars Dannecker