Global ETD Search

61	Developing Artificial Intelligence-Based Decision Support for Resilient Socio-Technical Systems Ali Lenjani (8921381) 15 June 2020 (has links) <div>During 2017 and 2018, two of the costliest years on record regarding natural disasters, the U.S. experienced 30 events with total losses of $400 billion. These exuberant costs arise primarily from the lack of adequate planning spanning the breadth from pre-event preparedness to post-event response. It is imperative to start thinking about ways to make our built environment more resilient. However, empirically-calibrated and structure-specific vulnerability models, a critical input required to formulate decision-making problems, are not currently available. Here, the research objective is to improve the resilience of the built environment through an automated vision-based system that generates actionable information in the form of probabilistic pre-event prediction and post-event assessment of damage. The central hypothesis is that pre-event, e.g., street view images, along with the post-event image database, contain sufficient information to construct pre-event probabilistic vulnerability models for assets in the built environment. The rationale for this research stems from the fact that probabilistic damage prediction is the most critical input for formulating the decision-making problems under uncertainty targeting the mitigation, preparedness, response, and recovery efforts. The following tasks are completed towards the goal.</div><div>First, planning for one of the bottleneck processes of the post-event recovery is formulated as a decision making problem considering the consequences imposed on the community (module 1). Second, a technique is developed to automate the process of extracting multiple street-view images of a given built asset, thereby creating a dataset that illustrates its pre-event state (module 2). Third, a system is developed that automatically characterizes the pre-event state of the built asset and quantifies the probability that it is damaged by fusing information from deep neural network (DNN) classifiers acting on pre-event and post-event images (module 3). To complete the work, a methodology is developed to enable associating each asset of the built environment with a structural probabilistic vulnerability model by correlating the pre-event structure characterization to the post-event damage state (module 4). The method is demonstrated and validated using field data collected from recent hurricanes within the US.</div><div>The vision of this research is to enable the automatic extraction of information about exposure and risk to enable smarter and more resilient communities around the world.</div> Mechanical Engineering Artificial intelligence Machine learning Convolutional neural networks Data-driven decision making Resilience Bayesian information fusion Automated data analysis Post-event reconnaissance Deep learning Socio-Technical Systems Dynamic programming Optimization Computer vision
62	Improving data-driven decision making through data democracy : Case study of a Swedish bank Amerian, Irsa January 2021 (has links) Nowadays, becoming data-driven is the vision of almost all organizations. However, achieving this vision is not as easy as it may look like and there are many factors that affect, enable, support and sustain the data-driven ecosystem in an organization. Among these factors, this study focuses on data democracy which can be defined as the intra-organizational open data that aims to empower the employees getting faster and easier access to data in order to benefit from the business insight they need without the interfere of external help. In the existing literature, while the importance of becoming data-driven has been widely discussed, when it comes to data democracy within organizations, there is a noticeable gap. As a result, this master’s thesis aims to justify the importance and role of the data democracy in becoming a data-driven organization, focusing on the case of a Swedish bank. Additionally, it intends to provide extra investigation on the role of data analytics tools in achieving data democracy. The results of the study show that there is a strong connection between the benefits of the empowering different actors of the organization with the needed data knowledge, and the speeding up of the data-driven transformation journey. Based on the study, shared data and the availability of data to a larger number of stakeholders inside an organization result into a better understanding of different aspects of the problems, simplify the data-driven decision making and make the organization more data-driven. In the process of becoming data-driven, the organizations should provide the analytics tools not only to the data specialists but even to the non-data technical people. And by offering the needed support, training and collaboration possibilities between the two groups of employees (data specialists and non-data specialists), it should be attempted to enable the second group to extract the insight from the data, independently from the help of the data scientists. An organization can succeed in the path of becoming data-driven when they invest on the reusable capabilities of its employees, by discovering the data science skills across various departments and turning their domain experts into citizen data scientists of the organization. Data-Driven Decision Making Data Democracy Data Analytics Tools Data-Driven Organization Citizen Data Scientist Share Data Share Data Skills Share Data Responsibility Democratization of Analytics Other Engineering and Technologies Annan teknik
63	Data-driven decision making and its effects on leadership practices and student achievement in K–5 public elementary schools in California Ceja, Rafael, Jr. 01 January 2012 (has links) The enactment of the NCLB Act of 2001 and its legislative mandates for accountability testing throughout the nation brought to the forefront the issue of data-driven decision making. This emphasis on improving education has been spurred due to the alleged failure of the public school system. As a result, the role of administrators has evolved to incorporate data-driven decision-making practices to help make educational choices. While the underlying assumption of implementing data-driven decision making is that it will lead to improvements in education, this has yet to be empirically proven. The purpose of the study was to analyze the relationships among school characteristics, principals' level of experience, principals' data-driven decision making practices, and student achievement. This census study addressed principals of k-5 public elementary schools. In this quantitative study, a web-based survey was used to measure principals' data-driven ion-making practices. The student achievement data examined were the California Standards Test results for English language arts and mathematics for the 2009–2010 and 2010–2011 school years. Through a series of multiple regression analyses, the study examined the relationships among school characteristics, principals' level of experience, principals' data-driven decision making practices, and student achievement. Specifically. this study explored the amount of variance in student achievement scores in language arts and mathematics that could be explained by school characteristics, principals' level of experience, and data-driven decision-making practices. The results showed principals are incorporating data-driven decision-making practices in k-5 public elementary schools in California. In addition, the results showed that principals believe the quality of their decision making has improved due to implementing data-driven decision making. Principals indicated they were incorporating practices identified in the four constructs used in the present study: (a) establishing a data-driven culture, (b) data-driven decision making by teachers to improve student achievement, (c) supporting systems for DDDM, and (d) collaboration among teachers using data-driven decision making. A strong negative correlation was found between the number of students on free and reduced lunch and student achievement. School administration Education Accountability California Data-driven decision-making Evidence-based practice K-5 Leadership No Child Left Behind Quantitative research Reform School improvement Education Educational Leadership
64	The role of decision-driven data collection on Northwest Ohio Local Education Agencies' intervention for first-time-in-college students' post-secondary outcomes: A quasi-experimental evaluation of the PK-16 Pathways of Promise (P³) Project Darwish, Rabab 20 May 2021 (has links) No description available. Higher Education Education college readiness PK-16 partnerships data-driven decision making decision-driven data collection sense of belonging post-secondary outcomes geographical location multilevel modeling first-time in college students
65	The Relationship Between Reading Coaches' Utilization Of Data Technology And Teacher Development Behrens, Cherie Allen 01 January 2012 (has links) The use of technology in assisting educators to use student data in well-devised ways to enhance the instruction received by students is gaining headway and the support of federal dollars across the nation. Since research has not provided insight as to whether or not reading coaches are using data technology tools with teachers, this mixed methods study sought to examine what behavioral intentions reading coaches have in using data technology tools with teachers, what variables may influence their behavioral intentions, and what trends may emerge in their views about using technology data tools with teachers. A mixed methods approach was deployed via a survey embedded in an email, and data from 61 Florida reading coaches from elementary, middle, and high schools in a large urban school district were examined using an adaptation of the Technology Acceptance Model (TAM). The results showed that collectively all reading coaches have a high level of behavioral intentions towards using a data technology tool with teachers. The study also showed that elementary, middle, and high school reading coaches vary in their degree of behavioral intentions in using a data technology tool based on different variables. Trends in data showed that reading coaches think data technology tools are helpful, but that trainings are needed and that technology tools should be user-friendly. Discussion is provided regarding the implications of the study results for all stakeholders. Reading coach elementary school middle school high school data technology tools assessment reading tests differentiated instruction progress monitoring content area teachers usage of technology student achievement data driven decision making teacher development behavioral intentions Education
66	Self Service Business Intelligence inom offentlig sektor : En kvalitativ studie om vilka utmaningar som den offentliga sektorn ställs inför vid användning av SSBI Eric, Törgren, Hugo, Jagaeus January 2023 (has links) Digitalisering sker idag både i privat som offentlig sektor där datadriven beslutsfattning är en av trenderna. En teknologi som vuxit fram i samband med digitaliseringen och som hjälper verksamheter utvecklas är Self Service Business Intelligence (SSBI). Offentliga verksamheters digitala utveckling går långsammare än för privata bolag. Studien syftar till att undersöka vilka utmaningar offentliga verksamheter ställs inför i sin användning av SSBI samt att presentera hanteringsförslag på dessa utmaningar. För att besvara studiens frågeställning och uppfylla dess syfte har en kvalitativ forskningsansats använts. Semistrukturerade intervjuer har genomförts där respondenterna har varit personer som arbetar på offentliga verksamheter alternativt mot offentliga verksamheter. Studien resulterade i fyra utmaningar som är vanligt förekommande inom offentlig verksamhet och som inte lyfts i tidigare litteratur. Dessa fyra är; diversifierade verksamheter, ledningen, lagar och säkerhet samt begränsad självständighet. För varje utmaning har förslag diskuterats för hur utmaningarna effektivt kan hanteras. Studiens slutsats kan vara hjälpsam för offentliga verksamheter i deras fortsatta utveckling mot att bli datadrivna i sin beslutsfattning. Med hjälp av datadriven beslutsfattning möjliggörs för offentliga verksamheter att arbeta mer hållbart och bli mer resurseffektiva. / Digitization is today taking place in both private and public sectors, wheredata driven decision making is one of the trends. Self Service BusinessIntelligence (SSBI) is a technology that has emerged in conjunction with thedigital development and is helping businesses to develop. However, thedigital development in public organizations tends to be slower than forprivate companies. Therefore, this study aims to examine the challengesfaced by public organizations in their use of SSBI and also to presentproposals for addressing these challenges.To answer the research question and fulfill the study's purpose, a qualitativeresearch approach has been used with an abductive thinking. Semistructured interviews have been conducted with respondents who work in orwith public organizations. The study resulted in four challenges that arecommon in public organizations and that have not been addressed inprevious literature. These four challenges are diversified organizations, themanagement, laws and security, limited self-reliance. For each challenge,proposals have been discussed for how the challenges can be effectivelyaddressed. This study conclusion can be helpful for public organizations inthe continued development towards becoming data driven decision making.With the help of data driven decision making, public organizations can workmore sustainably and become more resource efficient. Challenges Public Sector SSBI Usage Digital Development Data Driven Decision making. Utmaningar Offentlig verksamhet SSBI-användning Digital utveckling Datadriven beslutsfattning. Information Systems
67	The Relationship Between Students’ Performance On The Cognitive Abilities Test (Cogat) And The Fourth And Fifth Grade Reading And Math Achievement Tests In Ohio Warnimont, Chad 10 August 2010 (has links) No description available. Education Elementary Education School Administration Cognitive Abilities Test CogAT Ohio Achievement Test (OAT) fourth grade fifth grade reading achievement math achievement elementary education school administration data-driven decision making test data correlation regression
68	High Stakes Testing and Accountability Mandates: Impact on Central Office Leadership Carver, Susan D. 11 December 2008 (has links) No description available. Education Education History Elementary Education School Administration Secondary Education accountability case study central office administrators data driven decision making directives distributed leadership high stakes tests K-12 leadership mandates NCLB Ohio reform state report cards strategies superintendent.
69	Analytisk CRM för beslutsstöd : Faktorers påverkan på förmågan för beslutsfattande, samt dess genererade sociotekniska förändringar / Analytical CRM for decision support : Factors' impact on decision-making capability, and its generatedsociotechnical changes Salloum, Alexander, Yousef, Johan January 2023 (has links) Dagens samhälle genomgår förändringar av betydande karaktär som i stor utsträckningdrivs av digitalisering. En av de mest påtagliga förändringarna som påverkar företagenär förändringarna i konsumentbeteendet. Dessa dramatiska förändringar utgör enbetydande utmaning för företag då traditionella metoder för kundhantering inte längre ärtillräckliga.Betydelsen av Customer Relationship Management (CRM), utifrån ett analytiskttillvägagångssätt, blir då avgörande för att bättre hantera kundrelationer i dagens högtkonkurrerande arbetsmiljö. Analytisk CRM är ett IT-beroende arbetssystem däranvändaren av data och analys utför processer och aktiviteter som gör att erbjudnaprodukter och tjänster i högre grad möter kundernas behov. Studiens övergripande målär att genom insikter förstå hur beslutsfattare upplever olika faktorers påverkan påderas förmåga att använda analytisk CRM för att stödja deras beslutsfattande samt desociotekniska förändringar som genereras av det. För att uppnå detta antogs enkvalitativ forskningsmetod där djupintervjuer genomfördes. Sju respondenter, medvarierande roller som Business Analyst, Data Scientist, Marknadschef och CRMansvarig intervjuades för att få deras insikter och erfarenheter om analytisk CRM.Studiens resultat och slutsats visar på att beslutsfattare anser kundcentrering ochinformationsteknik som avgörande faktorer för användningen av analytisk CRM.Kundcentreringen skapar en datadriven miljö som främjar datadriven beslutsfattandegenom användningen av data och analys. Det genererar sociotekniska förändringar påbåde djupare och ytmässiga nivåer. Informationstekniken spelar en avgörande roll iinsamling, hantering och analys av data. Detta påverkar beslutsprocesserna till att blidatadrivna och stärker beslutsfattarnas förmåga att fatta välgrundade beslut.Sociotekniska förändringar som generades av informationstekniken var på ytliga nivåer. / Society today is undergoing significant changes largely driven by digitalization. Onetangible change that impacts businesses is the shift in consumer behaviour. Thesedramatic changes pose a significant threat for companies as traditional methods forcustomer management are no longer sufficient.The significance of Customer Relationship Management (CRM), based on an analyticalapproach, therefore becomes crucial to better manage customer relationships in today’shighly competing work environment. Analytical CRM is an IT-reliant work system whereparticipants of data and analysis perform processes and activities that enable offeredproducts and services to better meet the needs of customers. The overall goal of thestudy is to understand through insights how decision-makers' experience variousfactors' impact on their ability to utilize analytical CRM to support their decision making,as well as the sociotechnical changes generated by it. To achieve this, a qualitativeresearch method was adopted, where in-depth interviews were conducted. Sevenrespondents, with varying roles as Business Analyst, Data Scientist, Marketing Managerand CRM Manager, were interviewed to get their insights and experiences on analyticalCRM.The study’s results and conclusion show that decision-makers consider customercentricity and information technology (IT) as a pivotal factors' influencing the use ofanalytical CRM. Customer centricity fosters a data-driven environment that promotesdata-driven decision-making through the utilization of data and analysis. It generatessociotechnical changes on both deeper and surface structures. IT plays a critical role inthe collection, management, and analysis of data. This impacts decision-makingprocesses to become data-driven and enhances the decision-makers' ability to makedata-driven decisions. Sociotechnical changes generated by information technologywere at surface structures. Analytical CRM IT-reliant work system Critical Success Factors (CSF) Abilities to analyze data Data-driven decision making Information technology (IT) Analytisk CRM IT-beroende arbetssystem Kritiska framgångsfaktorer Förmågor för analys av data Datadrivet beslutsfattande Sociotekniska förändringar Kundcentrering Informationsteknik (IT) Information Systems
70	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 Tech Lithman, 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. UX HCD HCI AI Artificial Intelligence Generative AI Implementing AI LLM GPT RAG Chatbot Prompt engineering Prompting framework Hallucinations Transparency Interaction Technologies Usability Linguistics Innovation SME Tech Enterprise Qte Growth factors Documentation Communication Knowledge retention Competence development Education Training Retrieval Model Data Data Management Database Knowledge base Maintenance Data security Data driven decision making Data analytics Social sustainability Economical sustainability Figma Trello Github OpenAI GTPs VsCode Azure CoPilot Google Bard Zapier GCS API Pipeline Parsing data Sorting data Storing data Human Computer Interaction Interaction Technologies Interaktionsteknik

Search results