Sap Se

Ianywhere Solutions, Inc.

Sybase, Inc.

Hasso-plattner-institut Fur Softwaresystemtechnik Gmbh

Ias Solutions, Inc.

Anisoara Nica

Mihnea Andrei

Reza Sherkat

Anil Kumar Goel

Peter Bumbulis

Christian Bensberg

Ismail Oukid

Alexander Boehm

Xun Cheng

Daniel James Farrar

Gregor Hackenbroich

Zbigniew Jerzak

Adrian Dragusanu

Yuanzhen Ji

Thomas Willhalm

Steven A Kirk

Hasso Plattner

Wolfgang Lehner

Martin Heidel

Rolando Blanco

Ivan T Bowman

Muhammed Sharique

Jens Krueger

Jeffrey Pound

Matthew Young-Lai

Juergen Mueller

Nathan Auch

David Edward DeHaan

Stephan Mueller

Prabhas Kumar Samanta

Mark Delafranier

Jeffrey J Plager

Vipul Sharma

Stephen Chou

Ian Lorne Charlesworth

Xun Chen

Daniel Scott Brotherston

David William Hillis

Evguenia Eflov

Sergiu Pocol

Yogi Joshi

Wanxin Li

Kaleb Paul Alway

Pooya Saadat-Panah

Johan Lasperas

Disclosed herein are system, method, and computer program product embodiments for generating synthetic data records with database constraints using generative adversarial networks (GAN). The method can include training, by using a generator loss function, a generator neural network of a generator model of the GAN to generate a scaling factor and a cluster vector for a datum of a continuous variable of a continuous column of a data table, and a datum for a categorical variable of a categorical column of the data table. The generator loss function includes a penalty component determined based on a set of data constraints related to the continuous column or the categorical column.

Supporting database constraints in synthetic data generation based on generative adversarial networks

Systems, methods, and computer media are described for index-based join size estimation. For a join operation between two tables, a filter is applied to the first table, resulting in a filter output. The filter output is then sampled. For each sample, an index for a second table is accessed and counts of records in the second table that match the sample are retrieved. Using the sample size and the retrieved counts from the index of the second table, a data size for the join operation can be efficiently and accurately estimated. Statistical confidence in the estimate can also be assessed using variance-based calculations.

Index-based, adaptive join size estimation

Disclosed herein are system, method, and computer program product embodiments for using a data statistic as a dynamic data integrity constraint. An embodiment operates by defining a data statistic for a column or a set of columns of a partition of a plurality of partitions of a database table. The embodiment creates a constraint data statistics object based on the data statistic. The embodiment receives a query for the database table. The embodiment determines the constraint data statistics object is consistent with a data state of the partition. The embodiment derives an implied constraint based on the constraint data statistics object. The embodiment processes the query for the partition based on the implied constraint.

Constraint data statistics

Disclosed herein are system, method, and computer program product embodiments for performing dynamic partition pruning using data statistic objects as data integrity constraints. An embodiment operates by partitioning a database table into a plurality of partitions based on a partition criterion. The embodiment creates a data statistics object for a partition in the plurality of partitions. The embodiment receives a query for the database table. The embodiment determines the data statistics object is consistent with data in the partition. The embodiment processes the query for the partition based on the data statistics object.

Constraint data statistics for dynamic partition pruning

Disclosed herein are system, method, and computer program product embodiments for determining a constraint data statistics object is consistent. An embodiment operates by receiving a query for a database table. The embodiment determines a constraint data statistic object associated with a partition of the database table. The embodiment determines the constraint data statistics object is consistent with a data state of the partition of the database table based on consistency metadata of the constraint data statistics object. The embodiment processes the query for the database table based on the determining the constraint data statistics object is consistent with the data state of the partition of the database table.

Synopsis based advanced partition elimination

Persisting data in logical tiers of an in-memory database based on aging rules of the database system. The database system can be configured to age data by moving the data from a first logical tier supporting high performance data access to a second logical tier supporting lower performance data access, thereby improving the operational efficiency of the database system.

Providing database storage to facilitate the aging of database-accessible data

A system, computer-implemented method, and a computer-readable storage medium for determining estimations to statistical questions. Data statistics used in evaluating estimations to statistical questions are gathered from multiple sources in a database management system. A statistical question comprising, for example, cardinality estimations is received, wherein the question is posed by different database components, such as query optimizer, or execution engine. Estimation for the statistical question is computed, where the estimation is based on multiple data statistics and without using the data referred to in the question.

Data statistics in data management systems

Cracking page-loadable columns for in-memory data management is described herein. An embodiment operates by accessing a column according to a received query, determining that the received query requires a non-critical data structure associated with the column, and rebuilding the non-critical data structure from data associated with the column.

Cracking page-loadable columns for in-memory data management

Disclosed herein are system, method, and computer program product embodiments for generating join histograms. An embodiment operates by a computer implemented method that includes determining, by at least one processor, a first interval associated with a first histogram of a first data structure and a first estimate frequency associated with the first interval. The method further includes determining, by the at least one processor, a second interval associated with a second histogram of a second data structure and a second estimate frequency associated with the second interval. The method further includes determining, by the at least one processor, a join interval based on the first and second intervals by calculating an intersection of the first and second intervals. The method further includes calculating, by the at least one processor, a join estimate frequency based on the first and second estimate frequencies.

Constructing join histograms from histograms with Q-error guarantees

The present disclosure involves systems, software, and computer implemented methods for providing quality-driven processing of out-of-order data streams based on a configurable quality metric. In one example, the method includes identifying a configurable quality metric defined by a user or application for executing continuous queries over a sliding window, the metric specifying a user requirement for the quality of the continuous query result. A quality threshold metric is calculated based on the configurable quality metric, the configurable quality metric associated with a size of an adaptation buffer for incoming tuples of the query. In response to receiving a signal indicating a new query result, a sliding window prior to a current time is identified. A runtime quality metric associated with the incoming tuples for the sliding window is measured, and the adaptation buffer is modified based on a difference between the quality threshold metric and the measured runtime quality metric.

Quality-driven processing of out-of-order data streams

The present disclosure involves systems, software, and computer implemented methods for optimizing continuous queries for hybrid execution over a stream processing engine and an in-memory database. In one example, a method may include identifying a continuous query executed at a stream processing engine, the continuous query including a plurality of operators. An optimal plan for execution of the identified continuous query at the stream processing engine is determined. For each of the plurality of operators in the determined optimal plan, an optimized placement decision for executing a particular operator in the stream processing engine or at a database system is determined. An optimized continuous query is generated from the identified continuous query based on the determined optimal placement decisions for each of the plurality of operators in the determined optimal plan. The optimized continuous query is then executed at the stream processing engine and the database system.

Optimization of continuous queries in hybrid database and stream processing systems

A method and system, the system including a plurality of machines each having a processor and a main memory component; a shared distributed storage facility storing a set of data and accessible by the plurality of machines over a communication network; a controller to select, in response to a state of a query execution plan comprising a plurality of executable jobs for the set of data, which one of a set of scheduling algorithms to execute; an execution engine to execute the selected scheduling algorithm to determine, for each job in the plurality of jobs, which server to schedule to execute the respective job; and providing an indication of the scheduling of the servers determined to be schedules for the execution of the jobs.

Distributed resource-aware task scheduling with replicated data placement in parallel database clusters

Disclosed herein are system, method, and computer program product embodiments for accessing and managing storage class memory (SCM) enabled main-memory database structures. An embodiment operates by traversing a first node to find a location of a second node corresponding to a search key, calculating a hash value for the search key, comparing the calculated hash value with at least one fingerprint value stored in the second node, wherein the fingerprint value is determined by hashing a stored key, accessing at least one key-value pair having a matching hash value, and returning a value associated with the matching key-value pair, wherein at least one of the traversing, calculating, comparing, accessing, and returning are performed by one or more computers.

Secondary data structures for storage class memory (scm) enables main-memory databases

Systems, methods and computer program product embodiments for providing table parameterized function (TPF) functionality in a database are disclosed herein. An embodiment includes defining a TPF in a database, wherein the definition of the TPF defines one or more parameters of the TPF, at least one of the parameters being a table parameter. A query is received that operates to call the TPF, wherein the query defines one or more input parameters, the input parameters comprising at least one table input parameter to be used as input for calling the TPF. The query is processed to produce a result, and the result of the query is returned.

Table parameterized functions in database

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Waterloo, Canada

Anisoara Nica