Efficient Incremental Model Learning on Data Streams

abstract: With the development of modern technological infrastructures, such as social networks or the Internet of Things (IoT), data is being generated at a speed that is never before seen. Analyzing the content of this data helps us further understand underlying patterns and discover relationships among different subsets of data, enabling intelligent decision making. In this thesis, I first introduce the Low-rank, Win-dowed, Incremental Singular Value Decomposition (SVD) framework to inclemently maintain SVD factors over streaming data. Then, I present the Group Incremental Non-Negative Matrix Factorization framework to leverage redundancies in the data to speed up incremental processing. They primarily tackle the challenges of using factorization models in the scenarios with streaming textual data. In order to tackle the challenges in improving the effectiveness and efficiency of generative models in this streaming environment, I introduce the Incremental Dynamic Multiscale Topic Model framework, which identifies multi-scale patterns and their evolutions within streaming datasets. While the latent factor models assume the linear independence in the latent factors, the generative models assume the observation is generated from a set of latent variables with various distributions. Furthermore, some models may not be accessible or their underlying structures are too complex to understand, such as simulation ensembles, where there may be thousands of parameters with a huge parameter space, the only way to learn information from it is to execute real simulations. When performing knowledge discovery and decision making through data- and model-driven simulation ensembles, it is expensive to operate these ensembles continuously at large scale, due to the high computational. Consequently, given a relatively small simulation budget, it is desirable to identify a sparse ensemble that includes the most informative simulations, while still permitting effective exploration of the input parameter space. Therefore, I present Complexity-Guided Parameter Space Sampling framework, which is an intelligent, top-down sampling scheme to select the most salient simulation parameters to execute, given a limited computational budget. Moreover, I also present a Pivot-Guided Parameter Space Sampling framework, which incrementally maintains a diverse ensemble of models of the simulation ensemble space and uses a pivot guided mechanism for future sample selection.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Multikonferenz Wirtschaftsinformatik (MKWI) 2016: Technische Universität Ilmenau, 09. - 11. März 2016; Band III

Übersicht der Teilkonferenzen Band III • Service Systems Engineering • Sicherheit, Compliance und Verfügbarkeit von Geschäftsprozessen • Smart Services: Kundeninduzierte Kombination komplexer Dienstleistungen • Strategisches IT-Management • Student Track • Telekommunikations- und Internetwirtschaft • Unternehmenssoftware – quo vadis? • Von der Digitalen Fabrik zu Industrie 4.0 – Methoden und Werkzeuge für die Planung und Steuerung von intelligenten Produktions- und Logistiksystemen • Wissensmanagemen

An aesthetic for sustainable interactions in product-service systems?

Copyright @ 2012 Greenleaf PublishingEco-efficient Product-Service System (PSS) innovations represent a promising approach to sustainability. However the application of this concept is still very limited because its implementation and diffusion is hindered by several barriers (cultural, corporate and regulative ones). The paper investigates the barriers that affect the attractiveness and acceptation of eco-efficient PSS alternatives, and opens the debate on the aesthetic of eco-efficient PSS, and the way in which aesthetic could enhance some specific inner qualities of this kinds of innovations. Integrating insights from semiotics, the paper outlines some first research hypothesis on how the aesthetic elements of an eco-efficient PSS could facilitate user attraction, acceptation and satisfaction

Друга міжнародна конференція зі сталого майбутнього: екологічні, технологічні, соціальні та економічні питання (ICSF 2021). Кривий Ріг, Україна, 19-21 травня 2021 року

Second International Conference on Sustainable Futures: Environmental, Technological, Social and Economic Matters (ICSF 2021). Kryvyi Rih, Ukraine, May 19-21, 2021.Друга міжнародна конференція зі сталого майбутнього: екологічні, технологічні, соціальні та економічні питання (ICSF 2021). Кривий Ріг, Україна, 19-21 травня 2021 року