Towards Efficient Annotation Databases

Heinzl, René; Nissl, Markus; Sallinger, Emanuel

doi:10.34726/5427

Record link:

http://hdl.handle.net/20.500.12708/193572
https://doi.org/10.34726/5427

Title:

Citation:

Heinzl, R., Nissl, M., & Sallinger, E. (2023). Towards Efficient Annotation Databases. In B. Kimelfeld, M. V. Martinez, & R. Angles (Eds.), Proceedings of the 15th Alberto Mendelzon International Workshop on Foundations of Data Management (AMW 2023). CEUR-WS.org. https://doi.org/10.34726/5427

reposiTUm DOI:

10.34726/5427

CatalogPlus:

AC17203428

Publication Type:

Inproceedings - Full-Paper Contribution

Language:

English

Authors:

Heinzl, René
Nissl, Markus
Sallinger, Emanuel

Organisational Unit:

E192-02 - Forschungsbereich Databases and Artificial Intelligence

Published in:

Proceedings of the 15th Alberto Mendelzon International Workshop on Foundations of Data Management (AMW 2023)

Volume:

3409

Date (published):

2-Jun-2023

Event name:

AMW 2023 - 15th Alberto Mendelzon International Workshop on Foundations of Data Management

Event date:

22-May-2023 - 26-May-2023

Event place:

Santiago de Chile, Chile

Number of Pages:

Publisher:

CEUR-WS.org

Peer reviewed:

Yes

Keywords:

Databases; Machine Learning; Requirements; Data Management; Solution; Open Challenges (theory); Open Challenges (practice).; Real-time; Analytical; Base data ingestion; user devices; Storage; Metadata

Abstract:

Recent advances in machine learning have increased the demand for efficient annotation data management for machine learning applications by organizations. In this paper, we address this challenge through an industrial collaboration centered around the unification of data for training and prediction workflows by enabling fast analytical processing through summarization. Beyond this specific solution, we provide a very concrete real-world scenario and solution to the data management community as inspiration for further theoretical and practical research. Finally, we report on the open scientific challenges that remain in this field.

Project title:

Scalable Reasoning in Knowledge Graphs: VRG18-013 (WWTF Wiener Wissenschafts-, Forschu und Technologiefonds)
Knowledge Graph-driven Tour Management for Sustainable Waste Processing: NXT22-018 (WWTF Wiener Wissenschafts-, Forschu und Technologiefonds)
Decompose and Conquer: Fast Query Processing via Decomposition: ICT22-011 (WWTF Wiener Wissenschafts-, Forschu und Technologiefonds)

Research Areas:

Logic and Computation: 100%

Science Branch:

1020 - Informatik: 80%
1010 - Mathematik: 20%

License:

CC BY 4.0