Relevant bibliographies by topics / Modern data stack

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Modern data stack'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Modern data stack"

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Gandhari, Supriya. "The Modern CPG Data Stack: Building End-To-End Analytics on Azure, Snowflake, And Dbt." American Journal of Interdisciplinary Innovations and Research 7, no. 07 (2025): 91–100. https://doi.org/10.37547/tajiir/volume07issue07-08.

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Consumer Packaged Goods (CPG) firms encounter distinct obstacles in handling the extensive data produced from sales, supply chains, customer interactions, and market patterns. To tackle these obstacles, organizations are progressively implementing a contemporary data stack. This document examines how using Microsoft Azure, Snowflake, and DBT technologies can create a comprehensive analytics stack that improves scalability, data transformation, and decision-making processes. The paper outlines the technical framework, best practices, and real-world examples that showcase the implementation of t
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Simoes, J., and A. Cerciello. "SERVING GEOSPATIAL DATA USING MODERN AND LEGACY STANDARDS: A CASE STUDY FROM THE URBAN HEALTH DOMAIN." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4/W1-2022 (August 6, 2022): 419–25. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-w1-2022-419-2022.

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Abstract. The eMOTIONAL Cities project sets out to understand how the natural and built environment can shape the feelings and emotions of those who experience it. It does so with a cross-disciplinary approach which includes urban planners, doctors, psychologists, neuroscientists and engineers. At the core of this research project, lies a Spatial Data Infrastructure (SDI) which assembles datasets that characterise the emotional landscape and built environment, in different Cities across Europe and the US. The SDI is a key tool, not only to make the research data available within the project co
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Jain, Nehal. "Design and Development of a Full-Stack Book Store Application." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem43090.

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Full-stack development is essential for modern web applications, ensuring a seamless user experience and efficient data management. This project focuses on developing a full-stack book store application using the MERN (MongoDB, Express.js, React, Node.js) stack. The application supports CRUD operations (Create, Read, Update, Delete), allowing users to browse, purchase, and manage books. JWT-based authentication and authorization ensure user data security. The backend is built with Express.js and MongoDB, providing robust API endpoints, while the frontend is developed using React.js to create a
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Fitra, Muhammad Rizki Andrian, Ega Pratama, Muhammad Zidane Al Kautsar, Fatimah Asro Harahap, and Fanny Ramadhani. "Implementation of a Stack Data Structure for Browsing History Management in the Form of a Chrome Web Browser Extension." Indonesian Journal of Education and Development Research 3, no. 1 (2024): 239–48. https://doi.org/10.57235/ijedr.v3i1.4576.

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Browsing history is a fundamental feature in modern browsers, yet its management remains inefficient in many implementations. This research develops a Chrome extension utilizing the Stack data structure, leveraging its Last In, First Out (LIFO) principle to store, search, delete, and open browsing history URLs efficiently. The system is designed with Chrome's API to monitor tab activities, store data locally, and provide an interactive user interface. The findings demonstrate that the stack-based approach is more efficient than conventional methods. The system also offers users straightforward
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Mühlig, Jan, Michael Müller, Olaf Spinczyk, and Jens Teubner. "mxkernel: A Novel System Software Stack for Data Processing on Modern Hardware." Datenbank-Spektrum 20, no. 3 (2020): 223–30. http://dx.doi.org/10.1007/s13222-020-00357-5.

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Abstract Emerging hardware platforms are characterized by large degrees of parallelism, complex memory hierarchies, and increasing hardware heterogeneity. Their theoretical peak data processing performance can only be unleashed if the different pieces of systems software collaborate much more closely and if their traditional dependencies and interfaces are redesigned. We have developed the key concepts and a prototype implementation of a novel system software stack named mxkernel. For MxKernel, efficient large scale data processing capabilities are a primary design goal. To achieve this, heter
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Kushwaha, Aman, and Dr Subodhini Gupta. "Full Stack Web Development." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 10 (2024): 1–14. http://dx.doi.org/10.55041/ijsrem37848.

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Full-stack development encompasses the comprehensive design and implementation of both client-side and server-side components of web applications. Utilizing HTML for structuring content, CSS for styling and layout, and JavaScript for dynamic interactivity, developers create engaging and responsive user interfaces. On the server side, PHP serves as a robust scripting language for handling back-end logic, including data processing and application flow. Coupled with MongoDB, a NoSQL database, developers manage data storage and retrieval efficiently, leveraging its flexibility for complex data str
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Adnyana, I. Gede, Ayu Manik Dirgayusari, and Ketut Jaya Atmaja. "Data Visualization for Building a Cyber Attack Monitoring Dashboard Based on Honeypot." sinkron 8, no. 4 (2024): 2510–18. http://dx.doi.org/10.33395/sinkron.v8i4.14144.

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Computer networks are essential for modern life, enabling efficient global information exchange. However, as technology advances, network security challenges grow. To enhance security, honeypots are used alongside firewalls, mimicking legitimate systems to attract hackers and analyze their attack methods. In this research, Cowrie and Dionaea honeypots are implemented. Cowrie targets brute force attacks on SSH, while Dionaea detects port scanning and denial of service (DoS) attacks. These honeypots effectively capture and log malicious activities, providing insights into attack patterns. The co
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Kravchenko, Ilia, Risto Kosonen, Juha Jokisalo, and Simo Kilpeläinen. "Simulation of modern passive stack ventilation in a retrofitted Nordic apartment building." E3S Web of Conferences 362 (2022): 14003. http://dx.doi.org/10.1051/e3sconf/202236214003.

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Some heritage or protected residential buildings in Nordic conditions require retrofit; however, having limited or no access to the reconstruction of the ventilation system; thus, the native natural ventilation system must be preserved. Some retrofitted buildings are then equipped with modern passive stack ventilation systems implemented with self-regulated supply inlets. Although natural ventilation strongly depends on climate conditions, creating challenges in the design phase as the indoor air quality can not be guaranteed. In this study, such a building was modelled, and a novel self-regul
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Madhavan, Joel Paul. "Banking Operations Through Data Analysis." International Scientific Journal of Engineering and Management 04, no. 05 (2025): 1–7. https://doi.org/10.55041/isjem03519.

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Abstract— In the evolving landscape of personal finance, users often rely on multiple disjointed applications to manage expenses, monitor stock portfolios, and receive investment advice. This paper evaluates the usability and effectiveness of integrating these core functionalities into a single, unified platform. We developed a full-stack web application that combines an Expense Tracker, a Stock Visualizer, and a Stock Suggestor. The goal is to eliminate the need for switching between various financial tools, thereby improving convenience, reducing cognitive load, and enhancing financial decis
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Pedreira, Pedro, Deepak Majeti, and Orri Erling. "Composable Data Management: An Execution Overview." Proceedings of the VLDB Endowment 17, no. 12 (2024): 4249–52. http://dx.doi.org/10.14778/3685800.3685847.

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The trend of decomposing monolithic data management systems into a stack of reusable components has quickly gained momentum across the industry. Although a series of open-source projects have emerged targeting different layers of the stack, execution engines are of special importance due to the complexity they encapsulate, and the demand to optimize price-performance. In this tutorial, we will survey the space of composability in data management, focusing on the execution layer. We will discuss the main APIs, integration with existing and novel data management systems, and how specialized beha
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Dissertations / Theses on the topic "Modern data stack"

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Welin-Berger, Robert. "Return barriers and their application to stack tracing on modern VMs." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-260513.

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Virtual machines are today at the core of a very large portion of the code that runs our everyday lives. Their relative performance and characteristics are of high significance for society indirectly and few other are as impactful as the JVM. In this thesis, we examine the possibility to speed up stack tracing on HotSpot and OpenJDK by implementing a return barrier. The barrier is implemented by overwriting the return program counter on the top frame with a function that re-installs itself on the frame below every time it’s executed. Because of this, the barrier acts as a “low water mark” and
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PEREIRA, SAVANO SOUSA. "DURATION AND VOLATILITY MODELS FOR STOCK MARKET DATA." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2004. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=5868@1.

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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO<br>O presente trabalho visa generalizar a modelagem do tempo entre os negócios ocorridos no mercado financeiro, doravante chamado duração, e estudar os impactos destas duraçõoes sobre a volatilidade instântanea. O estudo foi realizado por meio do modelo linear ACD (autoregression conditional duration) proposto por Engel e Russel[3], os quais usaram a distribuição Exponencial e Weibull para as inovações, e o modelo GARCH-t para dados com alta freqüência para modelar a volatilidade instântanea, também usando a proposição d
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Kışınbay, Turgut. "Predictive ability or data snopping? : essays on forecasting with large data sets." Thesis, McGill University, 2004. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=85018.

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This thesis examines the predictive ability of models for forecasting inflation and financial market volatility. Emphasis is put on evaluation of forecasts and the usage of large data sets. Variety of models are used to forecast inflation, including diffusion indices, artificial neural networks, and traditional linear regressions. Financial market volatility is forecast using various GARCH-type and high-frequency based models. High-frequency data are also used to obtain ex-post estimates of volatility, which is then used to evaluate forecasts. All forecast are evaluated using recently p
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Hartmann, Daniel. "Stock markets and real-time macroeconomic data /." Hamburg : Kovač, 2007. http://d-nb.info/985325682/04.

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Northrop, Amanda Rosalind. "Importance of various data sources in deterministic stock assessment models." Thesis, Rhodes University, 2008. http://hdl.handle.net/10962/d1002811.

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In fisheries, advice for the management of fish populations is based upon management quantities that are estimated by stock assessment models. Fisheries stock assessment is a process in which data collected from a fish population are used to generate a model which enables the effects of fishing on a stock to be quantified. This study determined the effects of various data sources, assumptions, error scenarios and sample sizes on the accuracy with which the age-structured production model and the Schaefer model (assessment models) were able to estimate key management quantities for a fish resou
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Blazejewski, Adam. "Computational Models for Stock Market Order Submissions." Engineering, 2006. http://hdl.handle.net/2123/923.

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Doctor of Philosophy<br>The motivation for the research presented in this thesis stems from the recent availability of high frequency limit order book data, relative scarcity of studies employing such data, economic significance of transaction costs management, and a perceived potential of data mining for uncovering patterns and relationships not identified by the traditional top-down modelling approach. We analyse and build computational models for order submissions on the Australian Stock Exchange, an order-driven market with a public electronic limit order book. The focus of the thesis is o
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Blazejewski, Adam. "Computational Models for Stock Market Order Submissions." Thesis, The University of Sydney, 2005. http://hdl.handle.net/2123/923.

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The motivation for the research presented in this thesis stems from the recent availability of high frequency limit order book data, relative scarcity of studies employing such data, economic significance of transaction costs management, and a perceived potential of data mining for uncovering patterns and relationships not identified by the traditional top-down modelling approach. We analyse and build computational models for order submissions on the Australian Stock Exchange, an order-driven market with a public electronic limit order book. The focus of the thesis is on the trade implementati
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Téllez, De Vettori Giannio, and Chuchón Ricardo Najarro. "Duration models and value at risk using high-frequency data for the peruvian stock market." Master's thesis, Pontificia Universidad Católica del Perú, 2016. http://tesis.pucp.edu.pe/repositorio/handle/123456789/7890.

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Most empirical studies in nance use data on a daily basis which is obtained by retaining the last observation of the day and ignoring all intraday records. However, as a result of the increased automatization of nancial markets and the evolution of computational trading systems, intraday data bases that record every transaction along with their characteristics have been stablished. These data sets prompted the development of a new area of research ( nance with high frequency data), and in 1980 a literature based on the mechanisms of trading began (forms of trading, rules on securities
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Myburgh, Gustav. "Validation of the coherent market hypothesis using neural networks and JSE securities exchange data." Thesis, Stellenbosch : Stellenbosch University, 2001. http://hdl.handle.net/10019.1/52601.

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Thesis (MBA)--Stellenbosch University, 2001.<br>ENGLISH ABSTRACT: Much research effort has been spent over the past few decades in the field of capital market analysis and modelling. This research was mostly based on static linear models or derivatives thereof such as the Efficient Market Hypothesis, the Capital Asset Pricing Model and the Arbitrage Pricing Theory. This study project takes an interesting look at a contemporary capital market hypothesis, which is fundamentally based on a non-linear statistical model. The Coherent Market Hypothesis (CMH) was first formulated by Tonis Vaga in 19
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Corti, Rachele. "Benchmarking the ability of different stock-assessment models to capture the highly-fluctuating dynamics of small pelagics." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017.

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Small pelagics dynamics are characterised by extreme variability owing to environmental factors, fishing and natural mortality. Because of highly-fluctuating dynamics, it is difficult to evaluate the stock status through models. To assess these evaluation difficulties, a model comparison framework based on the Management Strategy Evaluation (MSE) approach has been developed and tested in the Gulf of Cadiz anchovy stock. We have used a minimum realistic model (MRM) as operating model, including well documented environmental drivers for this stock to simulate abundance indexes and catches, and
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Books on the topic "Modern data stack"

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Lee, Myung W. Applications of velocity-stack methods to seismic data processing. U.S. G.P.O., 1994.

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Pratomo, Eko Priyo. Reksa dana: Solusi perencanaan investasi di era modern. Gramedia Pustaka Utama, 2001.

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Tracy, Joseph S. Testing strategic bargaining models using stock market data. National Bureau of Economic Research, 1988.

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Maznev, Aleksandr, and Oleg Shatnev. Electric apparatus and circuits of rolling stock. INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1014641.

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Provides information about technical data and design of electrical apparatus of control, monitoring and protection of traction motors of electric rolling stock (EPS), the principles of speed control of locomotives and trains with contactor-resistor and semiconductor converters based on modern element base, a circuit diagram of various types of EPS with manifold and induction motors in modes of traction and braking.&#x0D; For students of institutions of secondary professional education. It may be useful to students of higher educational institutions, courses of improvement of qualification, the
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Cooper, Andrew B. A guide to fisheries stock assessment: From data to recommendations. University of New Hampshire, Sea Grant College Program, 2006.

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Lo, Andrew W. Trading volume: Definitions, data analysis, and implications of portfolio theory. National Bureau of Economic Research, 2000.

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Metrick, Andrew. Performance evaluation with transactions data: The stock selection of investment newsletters. National Bureau of Economic Research, 1998.

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S, Mariano Roberto, and Tse Yiu Kuen 1952-, eds. Econometric forecasting and high-frequency data analysis. World Scientific, 2008.

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Schiantarelli, Fabio. Form of ownership and financial contraints: Panel data evidence from leverage and investment equations. World Bank, Policy Research Dept., Finance and Private Sector Development Division, 1996.

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Agarwalla, Sobhesh Kumar. Whether cross-listing, stock-specific and market-wide calender events impact intraday volatility dynamics?: Evidence from the Indian stock market using high-frequency data. Indian Institute of Management, 2012.

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Book chapters on the topic "Modern data stack"

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Auliya, Mutiara, Abdul Aziz, and Wahyu Nurharjadmo. "Big Data Modern Stack for District Government." In Proceedings of the 1st International Conference on Demographics and Civil-registration (INCODEC 2021). Atlantis Press SARL, 2022. http://dx.doi.org/10.2991/978-2-494069-53-4_12.

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Huang, He, Yixin He, Longpeng Zhang, Zhicheng Zeng, Tu Ouyang, and Zhimin Zeng. "Leveraging Modern Big Data Stack for Swift Development of Insights into Social Developments." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_34.

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AbstractInsights of social development, presented in various forms, such as metrics, figures, text summaries, whose purpose is to summarize, explain, and predict the situations and trends of society, is extremely useful to guide organizations and individuals to better realize their own objectives in accordance with the whole society. Deriving these insights accurately and swiftly has become an interest for a range of organizations, including agencies governing districts, city even the whole country, they use these insights to inform policy-makings. Business investors who peak into statistical
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Haas, Gabriel, Adnan Alhomssi, and Viktor Leis. "Managing Very Large Datasets on Directly Attached NVMe Arrays." In Scalable Data Management for Future Hardware. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-74097-8_9.

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Abstract High-performance solid-state drives based on flash memory have replaced hard disks as the primary storage medium. Modern servers can host ten or more NVMe SSDs, and the aggregate bandwidth is approaching main memory bandwidth. In response to this evolution, this project evaluates the performance trade-offs posed by the abstraction layers and interfaces involved in I/O management. We experimentally slice through the software stack, exploring the limits of how efficient I/O can be done, and provide a discussion about durability. Finally, we discuss the implications of fast storage devic
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Vasilevich, Alena, and Michael Wetzel. "Multilingual Knowledge Systems as Linguistic Linked Open Data." In European Language Grid. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17258-8_23.

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AbstractCreation and re-usability of language resources in accordance with Linked Data principles is a valuable asset in the modern data world. We describe the contributions made to extend the Linguistic Linked Open Data (LLOD) stack with a new resource, Coreon MKS, bringing together concept-oriented, language-agnostic terminology management and graph-based knowledge organisation. We dwell on our approach to mirroring of Coreon’s original data structure to RDF and supplying it with a SPARQL endpoint. We integrate MKS into the existing ELG infrastructure, using it as a platform for making the p
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Wirth, Benjamin, Tizian Schwind, Marco Friedmann, and Jürgen Fleischer. "Automated Stack Singulation for Technical Textiles Using Sensor Supervised Low Pressure Suction Grippers." In Annals of Scientific Society for Assembly, Handling and Industrial Robotics 2022. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-10071-0_27.

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AbstractAutomated handling of technical textiles poses major challenges on modern handling systems. Previous research has shown that using suction grippers is feasible for handling processes involving textiles. However, separating individual sheets of air-permeable materials from a stack using such grippers is a nontrivial task. This paper details an automated stack singulation process using low pressure suction grippers leveraging online data from differential pressure sensors to control the singulation process. Subsystems are analyzed to derive a governing model representation of the process
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Hilborn, Ray, and Carl J. Walters. "Relating Models to Data: Parameter Estimation." In Quantitative Fisheries Stock Assessment. Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3598-0_6.

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Al Bakkari, Zakaria, Ikram El Azami, and Adil El Makrani. "A Bi-LSTM Neural Network to Forecast Stock Market Index." In Modern Artificial Intelligence and Data Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-33309-5_7.

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Mehta, Yash, Parth Singh, Dipak Ramoliya, Parth Goel, and Amit Ganatra. "Analysis of Stock Price-Prediction Models." In Advances in Data-driven Computing and Intelligent Systems. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0981-0_48.

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Yadav, Sourabh, and Nonita Sharma. "Homogenous Ensemble of Time-Series Models for Indian Stock Market." In Big Data Analytics. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04780-1_7.

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Wang, Hanchen. "Stock Price Prediction Based on Machine Learning Algorithms." In Modern Industrial IoT, Big Data and Supply Chain. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6141-6_12.

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Conference papers on the topic "Modern data stack"

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M, Supriya, Keshav Rathinavel, and Divya Radhakrishnan. "Development of a Comprehensive Learning Management System using Full Stack Technology: A Modern Approach to Educational Platforms." In 2024 5th International Conference on Data Intelligence and Cognitive Informatics (ICDICI). IEEE, 2024. https://doi.org/10.1109/icdici62993.2024.10810977.

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Fu, Yuanhao. "Research on Microsoft Stock Price Prediction Based on Various Models." In International Conference on Data Science and Engineering. SCITEPRESS - Science and Technology Publications, 2024. http://dx.doi.org/10.5220/0012807400004547.

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Liu, Lujia. "Exploring the Dynamics of Stock Markets: Mechanisms, Influences and Predictive Models." In International Conference on Data Analysis and Machine Learning. SCITEPRESS - Science and Technology Publications, 2024. https://doi.org/10.5220/0013487300004619.

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Koptyra, Bartłomiej, Marcin Oleksy, Ewa Dzięcioł, and Jan Kocoń. "Small Language Models for Emotion Recognition in Polish Stock Market Investor Opinions." In 2024 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2024. https://doi.org/10.1109/icdmw65004.2024.00065.

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Rathi, Snehal, Mahimna Bhuskute, Shardul Joshi, Shreyash Gawande, Harsh Anvekar, and Amol Dhumane. "A Methodological Evaluation of Machine Learning Models for Stock Price Predictions." In 2024 5th International Conference on Data Intelligence and Cognitive Informatics (ICDICI). IEEE, 2024. https://doi.org/10.1109/icdici62993.2024.10810789.

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Chi, Yiheng. "Predictive Analysis of Tesla's Stock Closing Prices Utilizing LSTM and GRU Deep Learning Models." In International Conference on Data Science and Engineering. SCITEPRESS - Science and Technology Publications, 2024. http://dx.doi.org/10.5220/0012807500004547.

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Dsouza, Lester David, Jamal Abdul Nasir, Muhammad Mohsin Kamal, and Lena Connolly. "Leveraging Large Language Models for Predicting Stock Option Valuation and Financial Risk Mitigation." In 2024 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2024. https://doi.org/10.1109/icdmw65004.2024.00019.

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Viedt, Isabell, Michel Gro�e (n� Mock), and Leon Urbas. "Development of a hybrid, semi-parametric Simulation Model of an AEM Electrolysis Stack Unit for large-scale System Simulations." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.129325.

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A key technology for integrating fluctuating renewable energy into the process industry is the production of green hydrogen through water electrolysis plants. Scaling up electrolysis plant capacity remains a significant challenge for the renewable energy transition. System simulation of large-scale electrolysis plants can support process design, monitoring, optimization, and maintenance scheduling. Hybrid modeling methods are promising for improving simulation reliability by combining process knowledge with process data, addressing gaps in understanding of the underlying processes. These hybri
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Elahi, Ali, and Fatemeh Taghvaei. "Combining Financial Data and News Articles for Stock Price Movement Prediction Using Large Language Models." In 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10825449.

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Chen, Tianai. "Advances in Artificial Intelligence for Stock Price Prediction: A Comprehensive Investigation of Models and Applications." In International Conference on Data Analysis and Machine Learning. SCITEPRESS - Science and Technology Publications, 2024. https://doi.org/10.5220/0013487200004619.

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Reports on the topic "Modern data stack"

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Boucadair, M., C. Jacquenet, and S. Sivakumar. A YANG Data Model for Dual-Stack Lite (DS-Lite). RFC Editor, 2019. http://dx.doi.org/10.17487/rfc8513.

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Tracy, Joseph. Testing Strategic Bargaining Models Using Stock Market Data. National Bureau of Economic Research, 1988. http://dx.doi.org/10.3386/w2754.

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Panek, McCarthy, and Drayton. PR-312-12208-R04 NO2-NOx Ratio Data Compilation and Assessment. Pipeline Research Council International, Inc. (PRCI), 2016. http://dx.doi.org/10.55274/r0011012.

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When modeling NO2 impacts from combustion sources, justification is required to receive approval of a representative in-stack NO2/NOx ratio, which is a key model input when using advanced modeling tiers that consider plume chemistry. A September 30, 2014 EPA memorandum requires approval by the Regional Office on a case-by-case basis regarding the selection and application of the in-stack NO2/NOx ratio. The EPA Tier 2 Ambient Ratio Method (ARM2) and Tier 3 Ozone Limiting Method (OLM) and Plume Volume Molar Ratio Method (PVMRM) NO2 methods require an in-stack NO2/NOx ratio (ISR) as an input to A
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Fyfe, Caroline, Phoebe Taptiklis, Dominic White, and Niven Winchester. Review of emissions data and modelling systems (Phase 1) Report. Motu Economic and Public Policy Research, 2023. http://dx.doi.org/10.29310/wp.2023.06.

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The purpose of this report is to review greenhouse gas (GHG) emissions and climate change mi􀆟ga􀆟on data and models. Building an evidence base to monitor and assess the impacts of ini􀆟a􀆟ves is explicitly men􀆟oned in Aotearoa New Zealand’s First Emissions Reduc􀆟on Plan (05/2022). Economic-Environmental modelling plays an important role in decision making to achieve emission reduc􀆟on targets. Data is hosted by a range of organisa􀆟ons and collected using different frameworks and methodologies. There is less awareness of data available through Stats NZ which may have led to it being underused. Main
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Sundelöf, Andreas, Massimilliano Cardinale, Hege Sande, Emmelie Hammenstig-Åström, and Henrik Pärn. Benchmark of European Lobster (Homarus gammarus) in Swedish areas of Skagerrak, Kattegat and Öresund : stock annex. Department of Aquatic Resources, Swedish University of Agricultural Science, 2025. https://doi.org/10.54612/a.4l1lrggjfs.

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This report summarizes the process of conducting a benchmark for the assessment of European lobster in the Swedish coastal areas of the Skagerrak, Kattegat and Öresund. Catch data, survey indices, commercial catch per unit of effort and life history data, on which the assessment is based on, are described in summary in the report and in detailed form in the attached appendices. Furthermore, the report summarizes the model structure and the sensitivity analyses done to generate an ensemble model that can be used to estimate the stock status of the lobster stock and provide advice. The current a
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Davis, S. C. UPDATING THE FREIGHT TRUCK STOCK ADJUSTMENT MODEL: 1997 VEHICLE INVENTORY AND USE SURVEY DATA. Office of Scientific and Technical Information (OSTI), 2000. http://dx.doi.org/10.2172/814526.

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MIlls, Stephanie E. The Intersection of Framework Geology and Mineral Potential Assessment in the Gold Hill Mining District. Utah Geological Survey, 2025. https://doi.org/10.34191/ss-175.

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The Gold Hill mining district (western Tooele County, Utah) was active from the late 1800s to the mid-1900s and is Utah’s leading historical producer of tungsten and arsenic. The district was also a past producer of other base and precious metals such as lead and silver. In the modern era of intense focus on domestic sources for critical minerals, the historical tungsten and arsenic production in Gold Hill stimulated renewed research interest in this district. The multi-generational geological history of the district includes Cordilleran hinterland Paleozoic bedrock of carbonate shelf to basin
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Kumar, Kaushal, and Yupeng Wei. Attention-Based Data Analytic Models for Traffic Flow Predictions. Mineta Transportation Institute, 2023. http://dx.doi.org/10.31979/mti.2023.2211.

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Traffic congestion causes Americans to lose millions of hours and dollars each year. In fact, 1.9 billion gallons of fuel are wasted each year due to traffic congestion, and each hour stuck in traffic costs about $21 in wasted time and fuel. The traffic congestion can be caused by various factors, such as bottlenecks, traffic incidents, bad weather, work zones, poor traffic signal timing, and special events. One key step to addressing traffic congestion and identifying its root cause is an accurate prediction of traffic flow. Accurate traffic flow prediction is also important for the successfu
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Hermansen, Anna, and Cailean Osborne. The Economic and Workforce Impacts of Open Source AI: Insights from Industry, Academia, and Open Source Research Publications. The Linux Foundation, 2025. https://doi.org/10.70828/itvq4899.

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In a literature review commissioned by Meta, LF Research found that open source AI (OSAI) is widely adopted, cost effective, highly performing, and leads to faster and higher-quality development of tools and models. The study included a comprehensive analysis of academic and industry literature as well as empirical data from previous LF Research surveys to determine existing evidence of the economic and workforce impacts of OSAI. The study assessed these impacts in four areas: Adoption rates: a significant majority (89%) of organizations are using some form of open source in their AI stack and
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Panek, Jeffrey, James McCarthy, Alan Krol, and Adrian Huth. PR-312-15201-Z01 Balko OK Compressor Station 102 Data Summary and Initial AERMOD Performance Assessment. Pipeline Research Council International, Inc. (PRCI), 2017. http://dx.doi.org/10.55274/r0011444.

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In 2010, the U.S. Environmental Protection Agency (EPA) adopted a 1-hour nitrogen dioxide (NO2) National Ambient Air Quality Standard (NAAQS) of 100 parts per billion (ppb) or approximately 188 micrograms per cubic meter (�g/m3) that is considerably more stringent than the longstanding annual standard of 53 ppb (100 �g/m3). New or modified compressor units may be encumbered by federal or state regulatory requirements to demonstrate compliance with the NO2 NAAQS using AERMOD, EPA's dispersion model, because the new NAAQS greatly reduces the compliance margin. AERMOD was developed and validated
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