Embracing Opportunities of Livestock Big Data Integration with Privacy Constraints

Franz Papst, Olga Saukh, Kay Uwe Römer, Florian Grandl, Igor Jakovljevic, Franz Steiniger, Martin Mayerhofer, Juergen Duda, Christa Egger-Danner

Research output: Contribution to conferencePaperResearchpeer-review

Abstract

Today’s herd management undergoes a major transformation triggered by the penetration of cheap sensor solutions into cattle farms, and the promise of predictive analytics to detect animal health issues and product-related problems before they occur. The latter is particularly important to prevent disease spread, ensure animal health, animal welfare and product quality. Sensor businesses entering the market tend to build their solutions as end-to-end pipelines spanning sensors, proprietary algorithms, cloud services, and mobile apps. Since data privacy is an important issue in this industry, as a result, disconnected data silos, heterogeneity of APIs, and lack of common standards limit the value the sensor technologies could provide for herd management. In the last few years, researchers and communities proposed a number of data integration architectures to enable exchange between streams of sensor data. This paper surveys the existing efforts and outlines the opportunities they fail to address by treating sensor data as a black box. We discuss alternative solutions to the problem based on privacy-preserving collaborative learning, and provide a set of scenarios to show their benefits for both farmers and businesses.
Original languageEnglish
Number of pages4
Publication statusPublished - 22 Oct 2019
EventThe 9th International Conference on the Internet of Things - Bilbao, Spain
Duration: 22 Oct 201926 Oct 2019
https://iot-conference.org/iot2019/

Conference

ConferenceThe 9th International Conference on the Internet of Things
Abbreviated titleIoT 2019
CountrySpain
CityBilbao
Period22/10/1926/10/19
Internet address

Fingerprint

Data integration
Farms
Sensors
Animals
Health
Industry
Data privacy
Application programming interfaces (API)
Application programs
Big data
Pipelines

Fields of Expertise

  • Information, Communication & Computing

Cite this

Papst, F., Saukh, O., Römer, K. U., Grandl, F., Jakovljevic, I., Steiniger, F., ... Egger-Danner, C. (2019). Embracing Opportunities of Livestock Big Data Integration with Privacy Constraints. Paper presented at The 9th International Conference on the Internet of Things, Bilbao, Spain.

Embracing Opportunities of Livestock Big Data Integration with Privacy Constraints. / Papst, Franz; Saukh, Olga; Römer, Kay Uwe; Grandl, Florian; Jakovljevic, Igor; Steiniger, Franz; Mayerhofer, Martin; Duda, Juergen; Egger-Danner, Christa.

2019. Paper presented at The 9th International Conference on the Internet of Things, Bilbao, Spain.

Research output: Contribution to conferencePaperResearchpeer-review

Papst, F, Saukh, O, Römer, KU, Grandl, F, Jakovljevic, I, Steiniger, F, Mayerhofer, M, Duda, J & Egger-Danner, C 2019, 'Embracing Opportunities of Livestock Big Data Integration with Privacy Constraints' Paper presented at The 9th International Conference on the Internet of Things, Bilbao, Spain, 22/10/19 - 26/10/19, .
Papst F, Saukh O, Römer KU, Grandl F, Jakovljevic I, Steiniger F et al. Embracing Opportunities of Livestock Big Data Integration with Privacy Constraints. 2019. Paper presented at The 9th International Conference on the Internet of Things, Bilbao, Spain.
Papst, Franz ; Saukh, Olga ; Römer, Kay Uwe ; Grandl, Florian ; Jakovljevic, Igor ; Steiniger, Franz ; Mayerhofer, Martin ; Duda, Juergen ; Egger-Danner, Christa. / Embracing Opportunities of Livestock Big Data Integration with Privacy Constraints. Paper presented at The 9th International Conference on the Internet of Things, Bilbao, Spain.4 p.
@conference{41e8b02e0f31465e9abdb56f33b73880,
title = "Embracing Opportunities of Livestock Big Data Integration with Privacy Constraints",
abstract = "Today’s herd management undergoes a major transformation triggered by the penetration of cheap sensor solutions into cattle farms, and the promise of predictive analytics to detect animal health issues and product-related problems before they occur. The latter is particularly important to prevent disease spread, ensure animal health, animal welfare and product quality. Sensor businesses entering the market tend to build their solutions as end-to-end pipelines spanning sensors, proprietary algorithms, cloud services, and mobile apps. Since data privacy is an important issue in this industry, as a result, disconnected data silos, heterogeneity of APIs, and lack of common standards limit the value the sensor technologies could provide for herd management. In the last few years, researchers and communities proposed a number of data integration architectures to enable exchange between streams of sensor data. This paper surveys the existing efforts and outlines the opportunities they fail to address by treating sensor data as a black box. We discuss alternative solutions to the problem based on privacy-preserving collaborative learning, and provide a set of scenarios to show their benefits for both farmers and businesses.",
author = "Franz Papst and Olga Saukh and R{\"o}mer, {Kay Uwe} and Florian Grandl and Igor Jakovljevic and Franz Steiniger and Martin Mayerhofer and Juergen Duda and Christa Egger-Danner",
year = "2019",
month = "10",
day = "22",
language = "English",
note = "The 9th International Conference on the Internet of Things, IoT 2019 ; Conference date: 22-10-2019 Through 26-10-2019",
url = "https://iot-conference.org/iot2019/",

}

TY - CONF

T1 - Embracing Opportunities of Livestock Big Data Integration with Privacy Constraints

AU - Papst, Franz

AU - Saukh, Olga

AU - Römer, Kay Uwe

AU - Grandl, Florian

AU - Jakovljevic, Igor

AU - Steiniger, Franz

AU - Mayerhofer, Martin

AU - Duda, Juergen

AU - Egger-Danner, Christa

PY - 2019/10/22

Y1 - 2019/10/22

N2 - Today’s herd management undergoes a major transformation triggered by the penetration of cheap sensor solutions into cattle farms, and the promise of predictive analytics to detect animal health issues and product-related problems before they occur. The latter is particularly important to prevent disease spread, ensure animal health, animal welfare and product quality. Sensor businesses entering the market tend to build their solutions as end-to-end pipelines spanning sensors, proprietary algorithms, cloud services, and mobile apps. Since data privacy is an important issue in this industry, as a result, disconnected data silos, heterogeneity of APIs, and lack of common standards limit the value the sensor technologies could provide for herd management. In the last few years, researchers and communities proposed a number of data integration architectures to enable exchange between streams of sensor data. This paper surveys the existing efforts and outlines the opportunities they fail to address by treating sensor data as a black box. We discuss alternative solutions to the problem based on privacy-preserving collaborative learning, and provide a set of scenarios to show their benefits for both farmers and businesses.

AB - Today’s herd management undergoes a major transformation triggered by the penetration of cheap sensor solutions into cattle farms, and the promise of predictive analytics to detect animal health issues and product-related problems before they occur. The latter is particularly important to prevent disease spread, ensure animal health, animal welfare and product quality. Sensor businesses entering the market tend to build their solutions as end-to-end pipelines spanning sensors, proprietary algorithms, cloud services, and mobile apps. Since data privacy is an important issue in this industry, as a result, disconnected data silos, heterogeneity of APIs, and lack of common standards limit the value the sensor technologies could provide for herd management. In the last few years, researchers and communities proposed a number of data integration architectures to enable exchange between streams of sensor data. This paper surveys the existing efforts and outlines the opportunities they fail to address by treating sensor data as a black box. We discuss alternative solutions to the problem based on privacy-preserving collaborative learning, and provide a set of scenarios to show their benefits for both farmers and businesses.

UR - http://www.olgasaukh.com/paper/papst19d4dairyopportunities.pdf

M3 - Paper

ER -