2022

Farm Your ML-based Query Optimizer’s Food - Human-Guided Training Data Generation. van de Water, Robin; Ventura, Francesco; Kaoudi, Z; Quiane-Ruiz, J; Markl, Volker (2022).
[ BibTeX ] [ URL ]
 
@inproceedings{vandewaterFarmYourMLbased2022, author = {{van de Water}, Robin and Ventura, Francesco and Kaoudi, Z and {Quiane-Ruiz}, J and Markl, Volker}, booktitle = {Conference on {{Innovative Data Systems Research}} ({{CIDR}})}, keywords = {prior-hpi robinvandewater}, title = {Farm Your ML-based Query Optimizer's Food - Human-Guided Training Data Generation}, year = 2022 }

Farming Your ML-based Query Optimizer’s Food. Van De Water, Robin; Ventura, Francesco; Kaoudi, Zoi; Quiané-Ruiz, Jorge-Arnulfo; Markl, Volker (2022). 3186–3189.
[ Abstract ] [ BibTeX ] [ URL ]
 
Machine learning (ML) is becoming a core component in query optimizers, e.g., to estimate costs or cardinalities. This means large heterogeneous sets of labeled query plans or jobs (i.e., plans with their runtime or cardinality output) are needed. However, collecting such a training dataset is a very tedious and time-consuming task: It requires both developing numerous jobs and executing them to acquire ground-truth labels. We demonstrate Datafarm,a novel framework for efficiently generating and labeling training data for ML-based query optimizers to overcome these issues. Datafarmenables generating training data tailored to users' needs by learning from their existing workload patterns, input data, and computational resources. It uses an active learning approach to determine a subset of jobs to be executed and encloses the human into the loop, resulting in higher quality data. The graphical user interface of Datafarmallows users to get informative details of the generated jobs and guides them through the generation process step-by-step. We show how users can intervene and provide feedback to the system in an iterative fashion. As an output, users can download both the generated jobs to use as a benchmark and the training data (jobs with their labels).
@inproceedings{vandewaterFarmingYourMLbased2022, abstract = {Machine learning (ML) is becoming a core component in query optimizers, e.g., to estimate costs or cardinalities. This means large heterogeneous sets of labeled query plans or jobs (i.e., plans with their runtime or cardinality output) are needed. However, collecting such a training dataset is a very tedious and time-consuming task: It requires both developing numerous jobs and executing them to acquire ground-truth labels. We demonstrate Datafarm,a novel framework for efficiently generating and labeling training data for ML-based query optimizers to overcome these issues. Datafarmenables generating training data tailored to users' needs by learning from their existing workload patterns, input data, and computational resources. It uses an active learning approach to determine a subset of jobs to be executed and encloses the human into the loop, resulting in higher quality data. The graphical user interface of Datafarmallows users to get informative details of the generated jobs and guides them through the generation process step-by-step. We show how users can intervene and provide feedback to the system in an iterative fashion. As an output, users can download both the generated jobs to use as a benchmark and the training data (jobs with their labels).}, author = {Van De Water, Robin and Ventura, Francesco and Kaoudi, Zoi and Quiané-Ruiz, Jorge-Arnulfo and Markl, Volker}, keywords = {prior-hpi robinvandewater}, organization = {IEEE}, pages = {3186–3189}, title = {Farming Your ML-based Query Optimizer's Food}, year = 2022 }

2020

The Care2Report System: Automated Medical Reporting as an Integrated Solution to Reduce Administrative Burden in Healthcare. Maas, Lientje; Geurtsen, Mathan; Nouwt, Florian; Schouten, Stefan F; Van De Water, Robin; Van Dulmen, Sandra; Dalpiaz, Fabiano; Van Deemter, Kees; Brinkkemper, Sjaak in Information Technology in Healthcare: IT Architectures and Implementations in Healthcare Environments (2020).
[ Abstract ] [ BibTeX ] [ URL ]
 
Documenting patient medical information in the electronic medical record is a time-consuming task at the expense of direct patient care. We propose an integrated solution to automate the process of medical reporting. This vision is enabled through the integration of speech and action recognition technology with semantic interpretation based on knowledge graphs. This paper presents our dialogue summarization pipeline that transforms speech into a medical report via transcription and formal representation. We discuss the functional and technical architecture of our Care2Report system along with an initial system evaluation with data of real consultation sessions.
@incollection{maas2020care2report, abstract = {Documenting patient medical information in the electronic medical record is a time-consuming task at the expense of direct patient care. We propose an integrated solution to automate the process of medical reporting. This vision is enabled through the integration of speech and action recognition technology with semantic interpretation based on knowledge graphs. This paper presents our dialogue summarization pipeline that transforms speech into a medical report via transcription and formal representation. We discuss the functional and technical architecture of our Care2Report system along with an initial system evaluation with data of real consultation sessions.}, author = {Maas, Lientje and Geurtsen, Mathan and Nouwt, Florian and Schouten, Stefan F and Van De Water, Robin and Van Dulmen, Sandra and Dalpiaz, Fabiano and Van Deemter, Kees and Brinkkemper, Sjaak}, booktitle = {Information Technology in Healthcare: IT Architectures and Implementations in Healthcare Environments}, keywords = {prior-hpi robinvandewater}, publisher = {Hawaii International Conference on System Sciences (HICSS)}, title = {The Care2Report System: Automated Medical Reporting as an Integrated Solution to Reduce Administrative Burden in Healthcare}, year = 2020 }