Monopedia

Schüle, Maximilian E.; Schliski, Pascal M. N.; Hutzelmann, Thomas; Rosenberger, Tobias; Leis, Viktor; Vorona, Dimitri; Kemper, Alfons; Neumann, Thomas

doi:10.14778/3137765.3137809

Cited by 5 publications

(2 citation statements)

References 5 publications

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“…Synchronizing multiple machines across the network to execute a Celonis PQL query adds overhead, which is against the low latency goal. The work of Schüle et al [14] supports our single-node approach by demonstrating that a single server can handle even large scale applications like Wikipedia. To support such applications, lightweight in-memory compression techniques have to be in place.…”

Section: Methodsmentioning

confidence: 83%

“…The ratio is calculated per purchase organization to discover the ones with the highest violation ratio. Comparing whether the activities "Request Approval" and "Grant Approval" were executed by the same department is done within the CASE WHEN statement (lines [4][5][6][7][8][9][10][11][12][13][14]. The statement contrasts the source event department to the target event department by using the SOURCE and TARGET operators (lines [5][6][7][8][9][10][11].…”

Section: Fraud Prevention By Identifying Segregation Of Duties Violat...mentioning

confidence: 99%

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Celonis PQL: A Query Language for Process Mining

Vogelgesang¹,

Ambrosy²,

Becher³

et al. 2021

Process Querying Methods

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Process mining studies data-driven methods to discover, enhance, and monitor business processes by gathering knowledge from event logs recorded by modern IT systems. To gain valuable process insights, it is essential for process mining users to formalize their process questions as executable queries. For this purpose, we present the Celonis Process Query Language (Celonis PQL), which is a domain-specific language tailored toward a special process data model and designed for business users. It translates process-related business questions into queries and executes them on a custom-built query engine. Celonis PQL covers a broad set of more than 150 operators, ranging from process-specific functions to machine learning and mathematical operators. Its syntax is inspired by SQL, but specialized for process-related queries. In addition, we present practical use cases and real-world applications, which demonstrate the expressiveness of the language and how business users can apply it to discover, enhance, and monitor business processes. The maturity and feasibility of Celonis PQL is shown by thousands of users from different industries, who apply it to various process types and huge amounts of event data every day.

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Section: Methodsmentioning

confidence: 83%

Section: Fraud Prevention By Identifying Segregation Of Duties Violat...mentioning

confidence: 99%

Celonis PQL: A Query Language for Process Mining

Vogelgesang¹,

Ambrosy²,

Becher³

et al. 2021

Process Querying Methods

View full text Add to dashboard Cite

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Recursive SQL and GPU-support for in-database machine learning

Schüle¹,

Lang²,

Springer³

et al. 2022

Distrib Parallel Databases

Self Cite

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In machine learning, continuously retraining a model guarantees accurate predictions based on the latest data as training input. But to retrieve the latest data from a database, time-consuming extraction is necessary as database systems have rarely been used for operations such as matrix algebra and gradient descent. In this work, we demonstrate that SQL with recursive tables makes it possible to express a complete machine learning pipeline out of data preprocessing, model training and its validation. To facilitate the specification of loss functions, we extend the code-generating database system Umbra by an operator for automatic differentiation for use within recursive tables: With the loss function expressed in SQL as a lambda function, Umbra generates machine code for each partial derivative. We further use automatic differentiation for a dedicated gradient descent operator, which generates LLVM code to train a user-specified model on GPUs. We fine-tune GPU kernels at hardware level to allow a higher throughput and propose non-blocking synchronisation of multiple units. In our evaluation, automatic differentiation accelerated the runtime by the number of cached subexpressions compared to compiling each derivative separately. Our GPU kernels with independent models allowed maximal throughput even for small batch sizes, making machine learning pipelines within SQL more competitive.

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