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The Global ETD Search service is a free service for researchers
to find electronic theses and dissertations. This service is
provided by the
Networked Digital Library of Theses and Dissertations.
Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
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Showing 1 to 2 of 2 (0.09 seconds)Spelling suggestions: "subject:"atoring data"" "subject:"autoring data""
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Didelių duomenų kiekių saugojimas ir apdorojimas nutolusių interneto centrų stebėjimo ir administravimo sistemoje / Storing and processing big amount of data on tracking and management system for distant centers of internetAugulis, Nauris 16 July 2008 (has links)
Lietuvoje sparčiai plečiantis informacinių technologijų naudojimui, kuriama vis daugiau informacinių technologijų projektų, kuriuos remia Europos Sąjunga ir kitos įvairios organizacijos. Taip pat stengiamasi pasiekti, kad informacinės technologijos būtų pasiekiamos kuo platesniam vartotojų ratui. Todėl steigiami interneto centrai kaimiškose vietovėse ir ne tik. Tačiau įsteigus tokius centrus ir norint juos tinkamai administruoti, reikia atitinkamos programinės įrangos. Deja lietuviškų produktų skirtų nutolusių interneto centrų stebėsenai ir administravimui nėra. Todėl sukūrus šią sistemą, palengvėjo interneto centrų, kuriuose ji įdiegta, administravimas. / Project describes specifying, designing and implementing tracking and administration system for distant internet centers. Analysis of design and technology solutions were researched during this project development. Some basic goals of system realization and potential solutions were formulated, which were presented. The architecture of the software developed is based on three layer design. This software was installed over thousand of computers and successfully used by people. Some research of system usage and user experience was done after system installation. This was done with the purpose of software quality analysis, that showed system quality is evaluated as an average, but its functionality was high.
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Introducing Generative Artificial Intelligence in Tech Organizations : Developing and Evaluating a Proof of Concept for Data Management powered by a Retrieval Augmented Generation Model in a Large Language Model for Small and Medium-sized Enterprises in Tech / Introducering av Generativ Artificiell Intelligens i Tech Organisationer : Utveckling och utvärdering av ett Proof of Concept för datahantering förstärkt av en Retrieval Augmented Generation Model tillsammans med en Large Language Model för små och medelstora företag inom TechLithman, Harald, Nilsson, Anders January 2024 (has links)
In recent years, generative AI has made significant strides, likely leaving an irreversible mark on contemporary society. The launch of OpenAI's ChatGPT 3.5 in 2022 manifested the greatness of the innovative technology, highlighting its performance and accessibility. This has led to a demand for implementation solutions across various industries and companies eager to leverage these new opportunities generative AI brings. This thesis explores the common operational challenges faced by a small-scale Tech Enterprise and, with these challenges identified, examines the opportunities that contemporary generative AI solutions may offer. Furthermore, the thesis investigates what type of generative technology is suitable for adoption and how it can be implemented responsibly and sustainably. The authors approach this topic through 14 interviews involving several AI researchers and the employees and executives of a small-scale Tech Enterprise, which served as a case company, combined with a literature review. The information was processed using multiple inductive thematic analyses to establish a solid foundation for the investigation, which led to the development of a Proof of Concept. The findings and conclusions of the authors emphasize the high relevance of having a clear purpose for the implementation of generative technology. Moreover, the authors predict that a sustainable and responsible implementation can create the conditions necessary for the specified small-scale company to grow. When the authors investigated potential operational challenges at the case company it was made clear that the most significant issue arose from unstructured and partially absent documentation. The conclusion reached by the authors is that a data management system powered by a Retrieval model in a LLM presents a potential path forward for significant value creation, as this solution enables data retrieval functionality from unstructured project data and also mitigates a major inherent issue with the technology, namely, hallucinations. Furthermore, in terms of implementation circumstances, both empirical and theoretical findings suggest that responsible use of generative technology requires training; hence, the authors have developed an educational framework named "KLART". Moving forward, the authors describe that sustainable implementation necessitates transparent systems, as this increases understanding, which in turn affects trust and secure use. The findings also indicate that sustainability is strongly linked to the user-friendliness of the AI service, leading the authors to emphasize the importance of HCD while developing and maintaining AI services. Finally, the authors argue for the value of automation, as it allows for continuous data and system updates that potentially can reduce maintenance. In summary, this thesis aims to contribute to an understanding of how small-scale Tech Enterprises can implement generative AI technology sustainably to enhance their competitive edge through innovation and data-driven decision-making.
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