Sökning: "Data Model"

Visar resultat 1 - 5 av 8730 avhandlingar innehållade orden Data Model.

1. 1. Data Stream Mining and Analysis : Clustering Evolving Data

   Författare :Christian Nordahl; Håkan Grahn; Veselka Boeva; Marie Netz; Plamen Angelov; Blekinge Tekniska Högskola; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Data Stream Mining; Clustering; Data Streams; Data Mining; Computer Science; Datavetenskap;

   Sammanfattning : Streaming data is becoming more prevalent in our society every day. With the increasing use of technologies such as the Internet of Things (IoT) and 5G networks, the number of possible data sources steadily increases. Therefore, there is a need to develop algorithms that can handle the massive amount of data we now generate. LÄS MER

3. 2. Data Privacy for Big Automotive Data

   Författare :Boel Nelson; Chalmers tekniska högskola; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; data privacy; differential privacy; big data; vehicular data; privacy;

   Sammanfattning : In an age where data is becoming increasingly more valuable as itallows for data analysis and machine learning, big data has become ahot topic. With big data processing, analyses can be carried out onhuge amounts of user data. LÄS MER

4. 3. Design of Business Process Model Repositories : Requirements, Semantic Annotation Model and Relationship Meta-model

   Författare :Mturi Elias; Ilia Bider; Paul Johannesson; Marlon Dumas; Stockholms universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; business process; business process model; business process repository; semantic annotation; Computer and Systems Sciences; data- och systemvetenskap;

   Sammanfattning : Business process management is fast becoming one of the most important approaches for designing contemporary organizations and information systems. A critical component of business process management is business process modelling. LÄS MER

5. 4. Data management and Data Pipelines: An empirical investigation in the embedded systems domain

   Författare :Aiswarya Raj Munappy; Chalmers tekniska högskola; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; data management; empirical investigation; artificial intelligence; data pipelines; embedded systems; software engineering; machine learning;

   Sammanfattning : Context: 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. LÄS MER

7. 5. Learning from Complex Medical Data Sources

   Författare :Jonathan Rebane; Panagiotis Papapetrou; Isak Samsten; Myra Spiliopoulou; Stockholms universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Machine Learning; Data Science; Healthcare; Complex Data; Explainable AI; Deep Learning; data- och systemvetenskap; Computer and Systems Sciences;

   Sammanfattning : Large, varied, and time-evolving data sources can be observed across many domains and present a unique challenge for classification problems, in which traditional machine learning approaches must be adapted to accommodate for the complex nature of such data. Across most domains, there is also a need for machine learning models that are both well-performing and interpretable, to help provide explanations of a model's decisions that stakeholders can trust and take appropriate actions with. LÄS MER