- Table View
- List View
Data-Driven Policy Impact Evaluation: How Access to Microdata is Transforming Policy Design
by Nuno Crato Paolo ParuoloIn the light of better and more detailed administrative databases, this open access book provides statistical tools for evaluating the effects of public policies advocated by governments and public institutions. Experts from academia, national statistics offices and various research centers present modern econometric methods for an efficient data-driven policy evaluation and monitoring, assess the causal effects of policy measures and report on best practices of successful data management and usage. Topics include data confidentiality, data linkage, and national practices in policy areas such as public health, education and employment. It offers scholars as well as practitioners from public administrations, consultancy firms and nongovernmental organizations insights into counterfactual impact evaluation methods and the potential of data-based policy and program evaluation.
Data-Driven Reproductive Health: Role of Bioinformatics and Machine Learning Methods
by Gaurav Majumdar Abhishek Sengupta Priyanka Narad Deepak ModiThis book provides insight into the transformative impact of data-driven approaches on reproductive health. Chapters cover a wealth of intricate algorithms of genomic analysis, predictive modeling, and personalized treatment strategies, providing an up-to-date view of the reproductive healthcare landscape. With more than 20 code-based examples, the book decodes complex biological data using bioinformatics and machine learning and provides valuable insights into fertility, genetic disorders, and personalized medicine. This book is relevant for healthcare professionals, researchers, and students in the fields of reproductive medicine, bioinformatics, and genetics.
Data-Driven SEO with Python: Solve SEO Challenges with Data Science Using Python
by Andreas VoniatisSolve SEO problems using data science. This hands-on book is packed with Python code and data science techniques to help you generate data-driven recommendations and automate the SEO workload. This book is a practical, modern introduction to data science in the SEO context using Python. With social media, mobile, changing search engine algorithms, and ever-increasing expectations of users for super web experiences, too much data is generated for an SEO professional to make sense of in spreadsheets. For any modern-day SEO professional to succeed, it is relevant to find an alternate solution, and data science equips SEOs to grasp the issue at hand and solve it. From machine learning to Natural Language Processing (NLP) techniques, Data-Driven SEO with Python provides tried and tested techniques with full explanations for solving both everyday and complex SEO problems.This book is ideal for SEO professionals who want to take their industry skills to the next level and enhance their business value, whether they are a new starter or highly experienced in SEO, Python programming, or both. What You'll LearnSee how data science works in the SEO contextThink about SEO challenges in a data driven wayApply the range of data science techniques to solve SEO issuesUnderstand site migration and relaunches areWho This Book Is ForSEO practitioners, either at the department head level or all the way to the new career starter looking to improve their skills. Readers should have basic knowledge of Python to perform tasks like querying an API with some data exploration and visualization.
Data-Driven, Nonparametric, Adaptive Control Theory (Lecture Notes in Control and Information Sciences #495)
by Andrew J. Kurdila Andrea L'Afflitto John A. BurnsData-Driven, Nonparametric, Adaptive Control Theory introduces a novel approach to the control of deterministic, nonlinear ordinary differential equations affected by uncertainties. The methods proposed enforce satisfactory trajectory tracking despite functional uncertainties in the plant model. The book employs the properties of reproducing kernel Hilbert (native) spaces to characterize both the functional space of uncertainties and the controller's performance. Classical control systems are extended to broader classes of problems and more informative characterizations of the controllers’ performances are attained. Following an examination of how backstepping control and robust control Lyapunov functions can be ported to the native setting, numerous extensions of the model reference adaptive control framework are considered. The authors’ approach breaks away from classical paradigms in which uncertain nonlinearities are parameterized using a regressor vector provided a priori or reconstructed online. The problem of distributing the kernel functions that characterize the native space is addressed at length by employing data-driven methods in deterministic and stochastic settings. The first part of this book is a self-contained resource, systematically presenting elements of real analysis, functional analysis, and native space theory. The second part is an exposition of the theory of nonparametric control systems design. The text may be used as a self-study book for researchers and practitioners and as a reference for graduate courses in advanced control systems design. MATLAB® codes, available on the authors’ website, and suggestions for homework assignments help readers appreciate the implementation of the theoretical results.
Data-Enabled Analytics: DEA for Big Data (International Series in Operations Research & Management Science #312)
by Joe Zhu Vincent CharlesThis book explores the novel uses and potentials of Data Envelopment Analysis (DEA) under big data. These areas are of widespread interest to researchers and practitioners alike. Considering the vast literature on DEA, one could say that DEA has been and continues to be, a widely used technique both in performance and productivity measurement, having covered a plethora of challenges and debates within the modelling framework.
Data-Intensive Radio Astronomy: Bringing Astrophysics to the Exabyte Era (Astrophysics and Space Science Library #472)
by Eleni Vardoulaki Marta Dembska Alexander Drabent Matthias HoeftRadio astronomy is irreversibly moving towards the exabyte era. In the advent of all-sky radio observations, efficient tools and methods to manage the large data volume generated have become imperative. This book brings together the knowledge of several different research fields to present an overview of current state-of-the-art methods in data-intensive radio astronomy. Its approach is comprehensive and data-centric, offering a coherent look at the four distinct parts of the data lifecycle: Data creation, storage and archivesData processingPost-processing and data analysisData access and reuse Large data management has been the topic of discussion within the astronomical community for decades. Some relevant areas explored in this volume are: ongoing technological innovations in interferometers and computing facilities; difficulties and possible solutions for the huge processing demands of radio telescope projects such as LOFAR, MeerKat, ASKAP; concepts for reliable and fast storage for archiving; and more. Written by experts across astrophysics, high-energy particle physics, data science, and computer science, this volume will help researchers and advanced students better understand the current state of data-intensive radio astronomy and tackle the major problems that may arise from future instruments.
Data-Intensive Science (Chapman And Hall/crc Computational Science Ser. #18)
by Terence Critchlow Kerstin Kleese Van DamData-intensive science has the potential to transform scientific research and quickly translate scientific progress into complete solutions, policies, and economic success. But this collaborative science is still lacking the effective access and exchange of knowledge among scientists, researchers, and policy makers across a range of disciplines. Bringing together leaders from multiple scientific disciplines, Data-Intensive Science shows how a comprehensive integration of various techniques and technological advances can effectively harness the vast amount of data being generated and significantly accelerate scientific progress to address some of the world's most challenging problems. In the book, a diverse cross-section of application, computer, and data scientists explores the impact of data-intensive science on current research and describes emerging technologies that will enable future scientific breakthroughs. The book identifies best practices used to tackle challenges facing data-intensive science as well as gaps in these approaches. It also focuses on the integration of data-intensive science into standard research practice, explaining how components in the data-intensive science environment need to work together to provide the necessary infrastructure for community-scale scientific collaborations. Organizing the material based on a high-level, data-intensive science workflow, this book provides an understanding of the scientific problems that would benefit from collaborative research, the current capabilities of data-intensive science, and the solutions to enable the next round of scientific advancements.
Data-Warehouse-Systeme kompakt: Aufbau, Architektur, Grundfunktionen (Xpert.press)
by Kiumars FarkischIn dem Buch werden Data-Warehouse-Systeme als einheitliche, zentrale, vollständige, historisierte und analytische IT-Plattform untersucht und ihre Rolle für die Datenanalyse und für Entscheidungsfindungsprozesse dargestellt. Dabei behandelt der Autor die einzelnen Komponenten, die für den Aufbau, die Architektur und den Betrieb eines Data-Warehouse-Systems von Bedeutung sind. Die multidimensionale Datenmodellierung, der ETL-Prozess und Analysemethoden werden erörtert und Maßnahmen zur Performancesteigerung von Data-Warehouse-Systemen diskutiert.
Data-centric Approaches to Industrial Decisions: Technology, Digitisation and Business Decisions (Asset Analytics)
by P. K. Kapur Omar Alhazmi Shakshi SinghalThis book explores mathematical modeling, predictive analytics, and interdisciplinary tools in formulating, implementing, and evaluating industrial strategies. This book presents cutting-edge research that intersects advanced computational techniques with practical decision-making across industries. This edited book showcases a curated collection of peer-reviewed contributions from international scholars and practitioners, addressing the growing complexity of business decisions in the era of big data, AI, and digital transformation. From cyber-physical systems to sustainability analytics and business intelligence, each chapter provides a robust academic foundation while offering real-world implications for policymakers, managers, and researchers. With a focus on actionable insights, the book is a vital reference for operations research, analytics, and digital strategy professionals and an enriching resource for postgraduate and doctoral scholars across business and engineering disciplines.
Data-driven Decision-making for Product Managers: A Primer to Data Literacy in Product Management (Business Guides on the Go)
by Gabriel SteinhardtThis book is an essential guide for product managers seeking to harness the power of data to drive their product decisions. It is a detailed resource for developing and maintaining data literacy, a core skill for product managers. Through a structured approach supported by practical insights and illustrative examples, readers will learn to prioritize decisions based on quantitative data rather than intuition, understand core data concepts, and analyze and leverage data effectively. Product managers will discover how data can transform decision-making processes, enabling evidence-based selection, prioritization, and resource allocation for product features. By using customer feedback, user behavior data, market research, and performance metrics, product managers can foster a culture that leverages data to create successful products. This introductory primer and reference guide is indispensable for product managers aiming to integrate data-driven methodologies into their practice, ensuring their decisions are informed, strategic, and impactful.
Data-driven Modelling and Scientific Machine Learning in Continuum Physics (Interdisciplinary Applied Mathematics #60)
by Krishna GarikipatiThis monograph takes the reader through recent advances in data-driven methods and machine learning for problems in science—specifically in continuum physics. It develops the foundations and details a number of scientific machine learning approaches to enrich current computational models of continuum physics, or to use the data generated by these models to infer more information on these problems. The perspective presented here is drawn from recent research by the author and collaborators. Applications drawn from the physics of materials or from biophysics illustrate each topic. Some elements of the theoretical background in continuum physics that are essential to address these applications are developed first. These chapters focus on nonlinear elasticity and mass transport, with particular attention directed at descriptions of phase separation. This is followed by a brief treatment of the finite element method, since it is the most widely used approach to solve coupled partial differential equations in continuum physics. With these foundations established, the treatment proceeds to a number of recent developments in data-driven methods and scientific machine learning in the context of the continuum physics of materials and biosystems. This part of the monograph begins by addressing numerical homogenization of microstructural response using feed-forward as well as convolutional neural networks. Next is surrogate optimization using multifidelity learning for problems of phase evolution. Graph theory bears many equivalences to partial differential equations in its properties of representation and avenues for analysis as well as reduced-order descriptions--all ideas that offer fruitful opportunities for exploration. Neural networks, by their capacity for representation of high-dimensional functions, are powerful for scale bridging in physics--an idea on which we present a particular perspective in the context of alloys. One of the most compelling ideas in scientific machine learning is the identification of governing equations from dynamical data--another topic that we explore from the viewpoint of partial differential equations encoding mechanisms. This is followed by an examination of approaches to replace traditional, discretization-based solvers of partial differential equations with deterministic and probabilistic neural networks that generalize across boundary value problems. The monograph closes with a brief outlook on current emerging ideas in scientific machine learning.
Data-driven Modelling of Structured Populations: A Practical Guide to the Integral Projection Model (Lecture Notes on Mathematical Modelling in the Life Sciences)
by Mark Rees Stephen P. Ellner Dylan Z. ChildsThis book is a "How To" guide formodeling population dynamics using Integral Projection Models (IPM) startingfrom observational data. It is written by a leading research team in this areaand includes code in the R language (in the text and online) to carry out allcomputations. The intended audience are ecologists, evolutionary biologists,and mathematical biologists interested in developing data-driven models foranimal and plant populations. IPMs may seem hard as they involve integrals. Theaim of this book is to demystify IPMs, so they become the model of choice forpopulations structured by size or other continuously varying traits. The bookuses real examples of increasing complexity to show how the life-cycle of thestudy organism naturally leads to the appropriate statistical analysis, whichleads directly to the IPM itself. A wide range of model types and analyses arepresented, including model construction, computational methods, and theunderlying theory, with the more technical material in Boxes and Appendices. Self-contained R code which replicates all of the figures and calculationswithin the text is available to readers on GitHub. Stephen P. Ellner is Horace White Professor of Ecology and Evolutionary Biology at Cornell University, USA; Dylan Z. Childs is Lecturer and NERC Postdoctoral Fellow in the Department of Animal and Plant Sciences at The University of Sheffield, UK; Mark Rees is Professor in the Department of Animal and Plant Sciences at The University of Sheffield, UK.
Data-driven Modelling of Wind Farm Flow Control Strategies
by Nassir Cassamo Jan-Willen van WingerdenThis book presents data-driven algorithms used in the context of wind farm modelling and exploits their relation with concepts from non-linear dynamical system theory. The algortihms include Input Output Dynamic Mode Decomposition and their combination with the Koopman Operator theory. The latter improves on modelling and analysis of the aerodynamic interaction between wind turbines in wind farms and assists in uncovering insights into the existing dynamics and improves models accuracy. The authors introduce the topic of wind farm flow control, illustrating current strategies devised to overcome power losses in wind plants due to the aerodynamic interaction between turbines. Although controlling wind farms as a whole is becoming increasingly important, the high dimensions and governing non-linear dynamics inherent of wind farm systems make the design of numerical optimal controllers computationally expensive. This book describes a possible pathway to circumvent this challenge through reduced order models that can embed the existing non-linearities. The authors make use of high fidelity open-source simulation datasets and developed algorithms to fully show the potential of this approach using visual results. The reader is motivated to use the datasets and algorithms and exploit the potential of the reduced order models.
Data-driven Retailing: A Non-technical Practitioners' Guide (Management for Professionals)
by Louis-Philippe KerkhoveThis book provides retail managers with a practical guide to using data. It covers three topics that are key areas of innovation for retailers: Algorithmic Marketing, Logistics, and Pricing. Use cases from these areas are presented and discussed in a conceptual and comprehensive manner. Retail managers will learn how data analysis can be used to optimize pricing, customer loyalty and logistics without complex algorithms.The goal of the book is to help managers ask the right questions during a project, which will put them on the path to making the right decisions. It is thus aimed at practitioners who want to use advanced techniques to optimize their retail organization.
Database Computing for Scholarly Research: Case Studies Using the Online Cultural and Historical Research Environment (Quantitative Methods in the Humanities and Social Sciences)
by Sandra R. Schloen Miller C. ProsserThis book discusses in detail a series of examples drawn from scholarly projects that use the OCHRE database platform (Online Cultural and Historical Research Environment). These case studies illustrate the wide range of data that can be managed with this platform and the wide variety of problems solved by OCHRE’s item-based graph data model. The unique features and design principles of the OCHRE platform are explained and justified, helping readers to imagine how the system could be used for their own data.Data generated by studies in the humanities and social sciences is often semi-structured, fragmented, highly variable, and subject to many interpretations, making it difficult to represent adequately in a conventional database. The authors examine commonly used methods of data management in the humanities and offer a compelling argument for a different approach that takes advantage of powerful computational techniques for organizing scholarly information.This book is a challenge to scholars in the humanities and social sciences, asking them to expect more from technology as they pursue their research goals. Written jointly by a software engineer and a research scholar, each with many years of experience in applying database methods to diverse kinds of scholarly data, it shows how scholars can make the most of their existing data while going beyond the limitations of commonly used software tools to represent their objects of study in a more accurate, nuanced, and flexible way.
Database Engineered Applications: 28th International Symposium, IDEAS 2024, Bayonne, France, August 26–29, 2024, Proceedings (Lecture Notes in Computer Science #15511)
by Richard Chbeir Yannis Manolopoulos Jorge Bernardino Peter Z. Revesz Carson K. Leung Sergio IlarriThis LNCS conference volume constitutes the proceedings of 28th International Symposium on Database Engineered Applications, IDEAS 2024, in Bayonne, France, in August 2024. The 21 full papers together with 5 short papers included in this volume were carefully reviewed and selected from 34 submissions. The symposium presents papers in the broader field of data management and were organized in seven sessions: (1) Theoretical Issues; (2) Classification; (3) Text and Languages; (4) Big Data; (5) Query Processing and Applications; (6) Machine Learning and Rules; (7) Recommendation, and (8) Indexing and Event Detection
Database Systems for Advanced Applications. DASFAA 2024 International Workshops: BDMS, GDMA, BDQM and ERDSE, Gifu, Japan, July 2-5, 2024, Proceedings (Lecture Notes in Computer Science #14667)
by Yoshiharu Ishikawa Atsuyuki Morishima Guoliang Li Sihem Amer-Yahia H. V. Jagadish Kejing LuThis volume constitutes the papers of several workshops which were held in conjunction with the 29th International Conference on Database Systems for Advanced Applications, held in Gifu, Japan, during July 2-5, 2024. The 26 full papers and 9 short papers included in this book were carefully reviewed and selected from 43 submissions. DASFAA 2024 presents the following four workshops: The 10th International Workshop on Big Data Management and Service (BDMS 2024) The 9th International Workshop on Big Data Quality Management (BDQM 2024) The DASFAA 2024 Workshop on Emerging Results in Data Science and Engineering (ERDSE 2024) The 8th International Workshop on Graph Data Management and Analysis (GDMA 2024)
Database Systems for Advanced Applications: 29th International Conference, DASFAA 2024, Gifu, Japan, July 2-5, 2024, Proceedings, Part VII (Lecture Notes in Computer Science #14856)
by Yoshiharu Ishikawa Yongxin Tong Jae-Gil Lee Chuan Xiao Sihem Amer-Yahia Makoto Onizuka H. V. Jagadish Kejing LuThe seven-volume set LNCS 14850-14856 constitutes the proceedings of the 29th International Conference on Database Systems for Advanced Applications, DASFAA 2024, held in Gifu, Japan, in July 2024. The total of 147 full papers, along with 85 short papers, presented together in this seven-volume set was carefully reviewed and selected from 722 submissions. Additionally, 14 industrial papers, 18 demo papers and 6 tutorials are included. The conference presents papers on subjects such as: Part I: Spatial and temporal data; database core technology; federated learning. Part II: Machine learning; text processing. Part III: Recommendation; multi-media. Part IV: Privacy and security; knowledge base and graphs. Part V: Natural language processing; large language model; time series and stream data. Part VI: Graph and network; hardware acceleration. Part VII: Emerging application; industry papers; demo papers.
Database Systems for Advanced Applications: 29th International Conference, DASFAA 2024, Gifu, Japan, July 2–5, 2024, Proceedings, Part I (Lecture Notes in Computer Science #14850)
by Yoshiharu Ishikawa Yongxin Tong Jae-Gil Lee Chuan Xiao Sihem Amer-Yahia Makoto Onizuka H. V. Jagadish Kejing LuThe seven-volume set LNCS 14850-14856 constitutes the proceedings of the 29th International Conference on Database Systems for Advanced Applications, DASFAA 2024, held in Gifu, Japan, in July 2024. The total of 147 full papers, along with 85 short papers, presented together in this seven-volume set was carefully reviewed and selected from 722 submissions. Additionally, 14 industrial papers, 18 demo papers and 6 tutorials are included. The conference presents papers on subjects such as: Part I: Spatial and temporal data; database core technology; federated learning. Part II: Machine learning; text processing. Part III: Recommendation; multi-media. Part IV: Privacy and security; knowledge base and graphs. Part V: Natural language processing; large language model; time series and stream data. Part VI: Graph and network; hardware acceleration. Part VII: Emerging application; industry papers; demo papers.
Database Systems for Advanced Applications: 29th International Conference, DASFAA 2024, Gifu, Japan, July 2–5, 2024, Proceedings, Part II (Lecture Notes in Computer Science #14851)
by Yoshiharu Ishikawa Yongxin Tong Jae-Gil Lee Chuan Xiao Sihem Amer-Yahia Makoto Onizuka H. V. Jagadish Kejing LuThe seven-volume set LNCS 14850-14856 constitutes the proceedings of the 29th International Conference on Database Systems for Advanced Applications, DASFAA 2024, held in Gifu, Japan, in July 2024. The total of 147 full papers, along with 85 short papers, presented together in this seven-volume set was carefully reviewed and selected from 722 submissions. Additionally, 14 industrial papers, 18 demo papers and 6 tutorials are included. The conference presents papers on subjects such as: Part I: Spatial and temporal data; database core technology; federated learning. Part II: Machine learning; text processing. Part III: Recommendation; multi-media. Part IV: Privacy and security; knowledge base and graphs. Part V: Natural language processing; large language model; time series and stream data. Part VI: Graph and network; hardware acceleration. Part VII: Emerging application; industry papers; demo papers.
Database Systems for Advanced Applications: 29th International Conference, DASFAA 2024, Gifu, Japan, July 2–5, 2024, Proceedings, Part III (Lecture Notes in Computer Science #14852)
by Yoshiharu Ishikawa Yongxin Tong Jae-Gil Lee Chuan Xiao Sihem Amer-Yahia Makoto Onizuka H. V. Jagadish Kejing LuThe seven-volume set LNCS 14850-14856 constitutes the proceedings of the 29th International Conference on Database Systems for Advanced Applications, DASFAA 2024, held in Gifu, Japan, in July 2024. The total of 147 full papers, along with 85 short papers, presented together in this seven-volume set was carefully reviewed and selected from 722 submissions. Additionally, 14 industrial papers, 18 demo papers and 6 tutorials are included. The conference presents papers on subjects such as: Part I: Spatial and temporal data; database core technology; federated learning. Part II: Machine learning; text processing. Part III: Recommendation; multi-media. Part IV: Privacy and security; knowledge base and graphs. Part V: Natural language processing; large language model; time series and stream data. Part VI: Graph and network; hardware acceleration. Part VII: Emerging application; industry papers; demo papers.
Database Systems for Advanced Applications: 29th International Conference, DASFAA 2024, Gifu, Japan, July 2–5, 2024, Proceedings, Part IV (Lecture Notes in Computer Science #14853)
by Yoshiharu Ishikawa Yongxin Tong Jae-Gil Lee Chuan Xiao Sihem Amer-Yahia Makoto Onizuka H. V. Jagadish Kejing LuThe seven-volume set LNCS 14850-14856 constitutes the proceedings of the 29th International Conference on Database Systems for Advanced Applications, DASFAA 2024, held in Gifu, Japan, in July 2024. The total of 147 full papers, along with 85 short papers, presented together in this seven-volume set was carefully reviewed and selected from 722 submissions. Additionally, 14 industrial papers, 18 demo papers and 6 tutorials are included. The conference presents papers on subjects such as: Part I: Spatial and temporal data; database core technology; federated learning. Part II: Machine learning; text processing. Part III: Recommendation; multi-media. Part IV: Privacy and security; knowledge base and graphs. Part V: Natural language processing; large language model; time series and stream data. Part VI: Graph and network; hardware acceleration. Part VII: Emerging application; industry papers; demo papers.
Database Systems for Advanced Applications: 29th International Conference, DASFAA 2024, Gifu, Japan, July 2–5, 2024, Proceedings, Part V (Lecture Notes in Computer Science #14854)
by Yoshiharu Ishikawa Yongxin Tong Jae-Gil Lee Chuan Xiao Sihem Amer-Yahia Makoto Onizuka H. V. Jagadish Kejing LuThe seven-volume set LNCS 14850-14856 constitutes the proceedings of the 29th International Conference on Database Systems for Advanced Applications, DASFAA 2024, held in Gifu, Japan, in July 2024. The total of 147 full papers, along with 85 short papers, presented together in this seven-volume set was carefully reviewed and selected from 722 submissions. Additionally, 14 industrial papers, 18 demo papers and 6 tutorials are included. The conference presents papers on subjects such as: Part I: Spatial and temporal data; database core technology; federated learning. Part II: Machine learning; text processing. Part III: Recommendation; multi-media. Part IV: Privacy and security; knowledge base and graphs. Part V: Natural language processing; large language model; time series and stream data. Part VI: Graph and network; hardware acceleration. Part VII: Emerging application; industry papers; demo papers.
Database Systems for Advanced Applications: 29th International Conference, DASFAA 2024, Gifu, Japan, July 2–5, 2024, Proceedings, Part VI (Lecture Notes in Computer Science #14855)
by Yoshiharu Ishikawa Yongxin Tong Jae-Gil Lee Chuan Xiao Sihem Amer-Yahia Makoto Onizuka H. V. Jagadish Kejing LuThe seven-volume set LNCS 14850-14856 constitutes the proceedings of the 29th International Conference on Database Systems for Advanced Applications, DASFAA 2024, held in Gifu, Japan, in July 2024. The total of 147 full papers, along with 85 short papers, presented together in this seven-volume set was carefully reviewed and selected from 722 submissions. Additionally, 14 industrial papers, 18 demo papers and 6 tutorials are included. The conference presents papers on subjects such as: Part I: Spatial and temporal data; database core technology; federated learning. Part II: Machine learning; text processing. Part III: Recommendation; multi-media. Part IV: Privacy and security; knowledge base and graphs. Part V: Natural language processing; large language model; time series and stream data. Part VI: Graph and network; hardware acceleration. Part VII: Emerging application; industry papers; demo papers.
Database and Expert Systems Applications - DEXA 2024 Workshops: IWCFS, AISys, CIU, Naples, Italy, August 26–28, 2024, Proceedings (Communications in Computer and Information Science #2169)
by Atif Mashkoor Johannes Sametinger Bernhard Moser Lukas Fischer Anna-Christina Glock Michael Mayr Sabrina LuftensteinerThis volume constitutes the refereed proceedings of the workshops held at the 35th International Conference on Database and Expert Systems Applications, DEXA 2024, which took place in Naples, Italy, in August 2024. The papers included in this book stem from the following workshops: - 9th International Workshop on Cyber-Security and Functional Safety inCyber-Physical Systems (IWCFS 2024), - 4th International Workshop on AI SystemEngineering: Math, Modelling and Software (AISys 2024), - 2nd InternationalWorkshop on Certainty in Uncertainty: Exploring Probabilistic Approaches in AI (CIU2024) The 10 full papers and 5 short papers were thoroughly reviewed and selected from a total of 24 submissions. They were organized in topical sections as follows: Cyber-security and functional safety in cyber-physical systems; AI system engineering: math, modelling, and software; certainty in uncertainty: exploring probabilistic approaches in AI.