BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Software Center - ECPv6.16.2//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://www.software-center.se
X-WR-CALDESC:Events for Software Center
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Europe/Stockholm
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20200329T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20201025T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20210328T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20211031T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20220327T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20221030T010000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Europe/Stockholm:20210526T130000
DTEND;TZID=Europe/Stockholm:20210526T150000
DTSTAMP:20260525T122259
CREATED:20210425T173440Z
LAST-MODIFIED:20210426T060831Z
UID:2655-1622034000-1622041200@www.software-center.se
SUMMARY:Lic seminar: Data management and Data Pipelines
DESCRIPTION:Data management and Data Pipelines: An empirical investigation in the embedded systems domain \nCandidate: Aiswarya Raj Munappy \nContext: Companies are increasingly collecting data from all possible sources to extract insights that help in data-driven decision-making. Increased data volume\, variety\, and velocity and the impact of poor quality data on the development of data products are leading companies to look for an improved data management approach that can accelerate the development of high-quality data products. Further\, AI is being applied in a growing number of fields and thus it is evolving as a horizontal technology. Consequently\, AI components are increasingly been integrated into embedded systems along with electronics and software. We refer to these systems as AI-enhanced embedded systems. Given the strong dependence of AI on data\, this expansion also creates a new space for applying data management techniques. \nObjective: The overall goal of this thesis is to empirically identify the data management challenges encountered during the development and maintenance of AI-enhanced embedded systems\, propose an improved data management approach and empirically validate the proposed approach. \nMethod: To achieve the goal\, we conducted this research in close collaboration with Software Center companies using a combination of different empirical research methods: case studies\, literature reviews\, and action research. \nResults and conclusions: This research provides five main results. First\, it identifies key data management challenges specific to Deep Learning models developed at embedded system companies. Second\, it examines the practices such as DataOps and data pipelines that help to address data management challenges. We observed that DataOps is the best data management practice that improves the data quality and reduces the time to develop data products. The data pipeline is the critical component of DataOps that manages the data life cycle activities. The study also provides the potential faults at each step of the data pipeline and the corresponding mitigation strategies. Finally\, the data pipeline model is realized in a small piece of data pipeline and calculated the percentage of saved data dumps through the implementation. \nFuture work: As future work\, we plan to realize the conceptual data pipeline model so that companies can build customized robust data pipelines. We also plan to analyse the impact and value of data pipelines in cross-domain AI systems and data applications. We also plan to develop AI-based fault detection and mitigation system suitable for data pipelines. \nhttps://chalmers.zoom.us/j/69864251037
URL:https://www.software-center.se/event/lic-seminar-data-management-and-data-pipelines/
LOCATION:Virtual event\, Sweden
CATEGORIES:Event
ORGANIZER;CN="Helena Holmstr%C3%B6m Olsson":MAILTO:helena.holmstrom.olsson@mau.se
END:VEVENT
END:VCALENDAR