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Demystifying Graph Data Science
With the growing maturity and stability of digitization and edge technologies, vast numbers of digital entities, connected devices, and microservices interact purposefully to create huge sets of poly-structured digital data. Corporations are continuously seeking fresh ways to use their data to drive business innovations and disruptions to bring in real digital transformation. Data science (DS) is proving to be the one-stop solution for simplifying the process of knowledge discovery and dissemination out of massive amounts of multi-structured data. Supported by query languages, databases, algorithms, platforms, analytics methods and machine and deep learning (ML and DL) algorithms, graphs are now emerging as a new data structure for optimally representing a variety of data and their intimate relationships. Compared to traditional analytics methods, the connectedness of data points in graph analytics facilitates the identification of clusters of related data points based on levels of influence, association, interaction frequency and probability. Graph analytics is being empowered through a host of path-breaking analytics techniques to explore and pinpoint beneficial relationships between different entities such as organizations, people and transactions. This edited book aims to explain the various aspects and importance of graph data science. The authors from both academia and industry cover algorithms, analytics methods, platforms and databases that are intrinsically capable of creating business value by intelligently leveraging connected data. This book will be a valuable reference for ICTs industry and academic researchers, scientists and engineers, and lecturers and advanced students in the fields of data analytics, data science, cloud/fog/edge architecture, internet of things, artificial intelligence/machine and deep learning, and related fields of applications. It will also be of interest to analytics professionals in industry and IT operations teams.
R 4,559.00
Applied Geospatial Data Science with Python
Intelligently connect data points and gain a deeper understanding of environmental problems through hands-on Geospatial Data Science case studies written in PythonThe book includes colored images of important conceptsKey FeaturesLearn how to integrate spatial data and spatial thinking into traditional data science workflowsDevelop a spatial perspective and learn to avoid common pitfalls along the wayGain expertise through practical case studies applicable in a variety of industries with code samples that can be reproduced and expandedBook DescriptionData scientists, when presented with a myriad of data, can often lose sight of how to present geospatial analyses in a meaningful way so that it makes sense to everyone. Using Python to visualize data helps stakeholders in less technical roles to understand the problem and seek solutions. The goal of this book is to help data scientists and GIS professionals learn and implement geospatial data science workflows using Python.Throughout this book, you'll uncover numerous geospatial Python libraries with which you can develop end-to-end spatial data science workflows. You'll learn how to read, process, and manipulate spatial data effectively. With data in hand, you'll move on to crafting spatial data visualizations to better understand and tell the story of your data through static and dynamic mapping applications. As you progress through the book, you'll find yourself developing geospatial AI and ML models focused on clustering, regression, and optimization. The use cases can be leveraged as building blocks for more advanced work in a variety of industries.By the end of the book, you'll be able to tackle random data, find meaningful correlations, and make geospatial data models.What you will learnUnderstand the fundamentals needed to work with geospatial dataTransition from tabular to geo-enabled data in your workflowsDevelop an introductory portfolio of spatial data science work using PythonGain hands-on skills with case studies relevant to different industriesDiscover best practices focusing on geospatial data to bring a positive change in your environmentExplore solving use cases, such as traveling salesperson and vehicle routing problemsWho this book is forThis book is for you if you are a data scientist seeking to incorporate geospatial thinking into your workflows or a GIS professional seeking to incorporate data science methods into yours. You'll need to have a foundational knowledge of Python for data analysis and/or data science.
R 1,889.00
Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track and Demo Track
This multi-volume set, LNAI 16013 to LNAI 16022, constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2025, held in Porto, Portugal, September 15-19, 2025. The 300 full papers presented here, together with 15 demo papers, were carefully reviewed and selected from 1253 submissions. The papers presented in these proceedings are from the following three conference tracks: The Research Track in Volume LNAI 16013-16020 refers about Anomaly & Outlier Detection, Bias & Fairness, Causality, Clustering, Data Challenges, Diffusion Models, Ensemble Learning, Graph Neural Networks, Graphs & Networks, Healthcare & Bioinformatics, Images & Computer Vision, Interpretability & Explainability, Large Language Models, Learning Theory, Multimodal Data, Neuro Symbolic Approaches, Optimization, Privacy & Security, Recommender Systems, Reinforcement Learning, Representation Learning, Resource Efficiency, Robustness & Uncertainty, Sequence Models, Streaming & Spatiotemporal Data, Text & Natural Language Processing, Time Series, and Transfer & Multitask Learning. The Applied Data Science Track in Volume LNAI 16020-16022 refers about Agriculture, Food and Earth Sciences, Education, Engineering and Technology, Finance, Economy, Management or Marketing, Health, Biology, Bioinformatics or Chemistry, Industry (4.0, 5.0, Manufacturing, ...), Smart Cities, Transportation and Utilities (e.g., Energy), Sports, and Web and Social Networks The Demo Track in LNAI 16022 showcased practical applications and prototypes, accepting 15 papers from a total of 30 submissions. These proceedings cover the papers accepted in the research and applied data science tracks.
R 3,558.72
Graph Neural Networks: Essentials and Use Cases
This book explains the technologies and tools that underpin GNNs, offering a clear and practical guide to their industrial applications and use cases. AI engineers, data scientists, and researchers in AI and graph theory will find detailed insights into the latest trends and innovations driving this dynamic field. With practical chapters demonstrating how GNNs are reshaping various industry verticals--and how they complement advances in generative, agentic, and physical AI--this book is an essential resource for understanding and leveraging their potential. The neural network paradigm has surged in popularity for its ability to uncover hidden patterns within vast datasets. This transformative technology has spurred global innovations, particularly through the evolution of deep neural networks (DNNs). Convolutional neural networks (CNNs) have revolutionized computer vision, while recurrent neural networks (RNNs) and their advanced variants have automated natural language processing tasks such as speech recognition, translation, and content generation. Traditional DNNs primarily handle Euclidean data, yet many real-world problems involve non-Euclidean data--complex relationships and interactions naturally represented as graphs. This challenge has driven the rise of graph neural networks (GNNs), an approach that extends deep learning into new domains. GNNs are powerful models designed to work with graph-structured data, where nodes represent individual data points and edges denote the relationships between them. Several variants have emerged: Graph Convolutional Networks (GCNs): These networks learn from a node's local neighborhood by aggregating information from adjacent nodes, updating the node's representation in the process.Graph Attentional Networks (GATs): By incorporating attention mechanisms, GATs focus on the most relevant neighbors during aggregation, enhancing model performance.Graph Recurrent Networks (GRNs): These networks combine principles from RNNs with graph structures to capture dynamic relationships within the data. GNNs are applied in a variety of advanced use cases, including node classification, link prediction, graph clustering, anomaly detection, recommendation systems, and also in natural language processing and computer vision. They help forecast traffic patterns, analyze molecular structures, verify programs, predict social influence, model electronic health records, and map brain networks.
R 8,661.69
Machine Learning and Knowledge Discovery in Databases: Applied Data Science Track
The 5-volume proceedings, LNAI 12457 until 12461 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020. The conference was planned to take place in Ghent, Belgium, but had to change to an online format due to the COVID-19 pandemic.The 232 full papers and 10 demo papers presented in this volume were carefully reviewed and selected for inclusion in the proceedings. The volumes are organized in topical sections as follows:Part I: Pattern Mining; clustering; privacy and fairness; (social) network analysis and computational social science; dimensionality reduction and autoencoders; domain adaptation; sketching, sampling, and binary projections; graphical models and causality; (spatio-) temporal data and recurrent neural networks; collaborative filtering and matrix completion.Part II: deep learning optimization and theory; active learning; adversarial learning; federated learning; Kernel methods and online learning; partial label learning; reinforcement learning; transfer and multi-task learning; Bayesian optimization and few-shot learning.Part III: Combinatorial optimization; large-scale optimization and differential privacy; boosting and ensemble methods; Bayesian methods; architecture of neural networks; graph neural networks; Gaussian processes; computer vision and image processing; natural language processing; bioinformatics.Part IV: applied data science: recommendation; applied data science: anomaly detection; applied data science: Web mining; applied data science: transportation; applied data science: activity recognition; applied data science: hardware and manufacturing; applied data science: spatiotemporal data.Part V: applied data science: social good; applied data science: healthcare; applied data science: e-commerce and finance; applied data science: computational social science; applied data science: sports; demo track.
R 2,226.07
Machine Learning and Knowledge Discovery in Databases. Research Track
This multi-volume set, LNAI 16941-16950, constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2026, held in Naples, Italy, during September 7–11, 2026.The papers inlcuded in these proceedings were carefully reviewed and selected; they were organised in different conference tracks: 280 full papers were accepted for the Research Track out of 1150 submissions, and 90 full papers for the Applied Data Science Track out of 381 submissions. The remaining short papers included in these proceedings are from the Demo Track (27 papers out of 38 submissions) and the Industrial Track (21 papers out of 42 submissions). The papers cover the following topical sections:The Research Track (LNAI 16941-16948): Anomaly & Outlier Detection; Anomaly Detection, Active Learning & Data-Centric Learning; AutoML, Data-Centric & Statistical Learning; Bias & Fairness; Biomedical AI; Causal Discovery & Causal Inference; Clustering & Pattern Mining; Continual Learning; Evaluation & Trustworthy AI; Federated Learning; Federated Learning & Machine Unlearning; Generative & Difusion Models; Graph Learning & Applications; Graph Learning & Graph Inference; Graph Neural Networks; Graph, Language & Decision Learning; Images & Computer Vision; Interpretability & Explainability; Knowledge Graphs & Graph Representation Learning; Large Language Models; Learning Theory & Probabilistic ML; Model Reliability & Evaluation; Multimodal, Vision & 3D Reconstruction; Neuro-Symbolic Learning; Optimization, Bandits & Online Learning; Recommender Systems & Ranking; Reinforcement Learning & Decision Making; Representation Learning; Representation, Alignment & Generative Learning; Robustness, Counterfactuals & Explanations; Robustness, Uncertainty & Trustworthy ML; Security, Privacy & Trustworthy AI; Structured Graphs, Causal & Relational Learning; Supervised & Weakly-Supervised Learning; Text Mining & Information Retrieval; Text Mining, Information Retrieval & Model Analysis; and Time Series & Streaming Data; and Vision Applications;The Applied Data Science Track (LNAI 16948-16950): AI for Sports, Mobility & Human-Centered Applications; Causal Inference, Decision Making & Counterfactual Learning; Explainability, Robustness & Responsible AI; Foundation Models & AI for Biology and Medicine; Graph Learning & Structured Representation Learning; Industrial AI, Monitoring & Digital Twins; Large Language Models, Agents & Reasoning; Natural Language Processing & Information Retrieval; Remote Sensing, Earth Observation & Environmental Monitoring; and Time Series, Forecasting & Anomaly Detection;The Demo Track and the Industial Track in LNAI 16950 showcased practical applications and prototypes.
R 3,587.83
Machine Learning and Knowledge Discovery in Databases. Research Track
This multi-volume set, LNAI 16941-16950, constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2026, held in Naples, Italy, during September 7–11, 2026.The papers inlcuded in these proceedings were carefully reviewed and selected; they were organised in different conference tracks: 280 full papers were accepted for the Research Track out of 1150 submissions, and 90 full papers for the Applied Data Science Track out of 381 submissions. The remaining short papers included in these proceedings are from the Demo Track (27 papers out of 38 submissions) and the Industrial Track (21 papers out of 42 submissions). The papers cover the following topical sections:The Research Track (LNAI 16941-16948): Anomaly & Outlier Detection; Anomaly Detection, Active Learning & Data-Centric Learning; AutoML, Data-Centric & Statistical Learning; Bias & Fairness; Biomedical AI; Causal Discovery & Causal Inference; Clustering & Pattern Mining; Continual Learning; Evaluation & Trustworthy AI; Federated Learning; Federated Learning & Machine Unlearning; Generative & Difusion Models; Graph Learning & Applications; Graph Learning & Graph Inference; Graph Neural Networks; Graph, Language & Decision Learning; Images & Computer Vision; Interpretability & Explainability; Knowledge Graphs & Graph Representation Learning; Large Language Models; Learning Theory & Probabilistic ML; Model Reliability & Evaluation; Multimodal, Vision & 3D Reconstruction; Neuro-Symbolic Learning; Optimization, Bandits & Online Learning; Recommender Systems & Ranking; Reinforcement Learning & Decision Making; Representation Learning; Representation, Alignment & Generative Learning; Robustness, Counterfactuals & Explanations; Robustness, Uncertainty & Trustworthy ML; Security, Privacy & Trustworthy AI; Structured Graphs, Causal & Relational Learning; Supervised & Weakly-Supervised Learning; Text Mining & Information Retrieval; Text Mining, Information Retrieval & Model Analysis; and Time Series & Streaming Data; and Vision Applications;The Applied Data Science Track (LNAI 16948-16950): AI for Sports, Mobility & Human-Centered Applications; Causal Inference, Decision Making & Counterfactual Learning; Explainability, Robustness & Responsible AI; Foundation Models & AI for Biology and Medicine; Graph Learning & Structured Representation Learning; Industrial AI, Monitoring & Digital Twins; Large Language Models, Agents & Reasoning; Natural Language Processing & Information Retrieval; Remote Sensing, Earth Observation & Environmental Monitoring; and Time Series, Forecasting & Anomaly Detection;The Demo Track and the Industial Track in LNAI 16950 showcased practical applications and prototypes.
R 3,588.56
R 458.00
Applied Data Science in Tourism
Access to large data sets has led to a paradigm shift in the tourism research landscape. Big data is enabling a new form of knowledge gain, while at the same time shaking the epistemological foundations and requiring new methods and analysis approaches. It allows for interdisciplinary cooperation between computer sciences and social and economic sciences, and complements the traditional research approaches. This book provides a broad basis for the practical application of data science approaches such as machine learning, text mining, social network analysis, and many more, which are essential for interdisciplinary tourism research. Each method is presented in principle, viewed analytically, and its advantages and disadvantages are weighed up and typical fields of application are presented. The correct methodical application is presented with a "how-to" approach, together with code examples, allowing a wider reader base including researchers, practitioners, and students entering the field. The book is a very well-structured introduction to data science – not only in tourism – and its methodological foundations, accompanied by well-chosen practical cases. It underlines an important insight: data are only representations of reality, you need methodological skills and domain background to derive knowledge from them - Hannes Werthner, Vienna University of Technology Roman Egger has accomplished a difficult but necessary task: make clear how data science can practically support and foster travel and tourism research and applications. The book offers a well-taught collection of chapters giving a comprehensive and deep account of AI and data science for tourism - Francesco Ricci, Free University of Bozen-Bolzano This well-structured and easy-to-read book provides a comprehensive overview of data science in tourism. It contributes largely to the methodological repository beyond traditional methods. - Rob Law, University of Macau
R 4,247.33
Machine Learning and Knowledge Discovery in Databases. Research Track and Applied Data Science Track
This multi-volume set, LNAI 16941-16950, constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2026, held in Naples, Italy, during September 7–11, 2026.The papers inlcuded in these proceedings were carefully reviewed and selected; they were organised in different conference tracks: 280 full papers were accepted for the Research Track out of 1150 submissions, and 90 full papers for the Applied Data Science Track out of 381 submissions. The remaining short papers included in these proceedings are from the Demo Track (27 papers out of 38 submissions) and the Industrial Track (21 papers out of 42 submissions). The papers cover the following topical sections:The Research Track (LNAI 16941-16948): Anomaly & Outlier Detection; Anomaly Detection, Active Learning & Data-Centric Learning; AutoML, Data-Centric & Statistical Learning; Bias & Fairness; Biomedical AI; Causal Discovery & Causal Inference; Clustering & Pattern Mining; Continual Learning; Evaluation & Trustworthy AI; Federated Learning; Federated Learning & Machine Unlearning; Generative & Difusion Models; Graph Learning & Applications; Graph Learning & Graph Inference; Graph Neural Networks; Graph, Language & Decision Learning; Images & Computer Vision; Interpretability & Explainability; Knowledge Graphs & Graph Representation Learning; Large Language Models; Learning Theory & Probabilistic ML; Model Reliability & Evaluation; Multimodal, Vision & 3D Reconstruction; Neuro-Symbolic Learning; Optimization, Bandits & Online Learning; Recommender Systems & Ranking; Reinforcement Learning & Decision Making; Representation Learning; Representation, Alignment & Generative Learning; Robustness, Counterfactuals & Explanations; Robustness, Uncertainty & Trustworthy ML; Security, Privacy & Trustworthy AI; Structured Graphs, Causal & Relational Learning; Supervised & Weakly-Supervised Learning; Text Mining & Information Retrieval; Text Mining, Information Retrieval & Model Analysis; and Time Series & Streaming Data; and Vision Applications;The Applied Data Science Track (LNAI 16948-16950): AI for Sports, Mobility & Human-Centered Applications; Causal Inference, Decision Making & Counterfactual Learning; Explainability, Robustness & Responsible AI; Foundation Models & AI for Biology and Medicine; Graph Learning & Structured Representation Learning; Industrial AI, Monitoring & Digital Twins; Large Language Models, Agents & Reasoning; Natural Language Processing & Information Retrieval; Remote Sensing, Earth Observation & Environmental Monitoring; and Time Series, Forecasting & Anomaly Detection;The Demo Track and the Industial Track in LNAI 16950 showcased practical applications and prototypes.
R 3,591.65
Data Science: Foundations and Applications
The two-volume set LNAI 15875 + 15876 constitutes the proceedings of the 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2025 Special Session, held in Sydney, NSW, Australia, during June 10–13, 2025.The 68 full papers included in this set were carefully reviewed and selected from 696 submissions. They were organized in topical sections as follows: survey track; machine learning; trustworthiness; learning on complex data; graph mining; machine learning applications; representation learning; scientific/business data analysis; and special track on large language models.
R 3,542.57
R 764.00
Applied Social Network Analysis With R
Understanding the social relations within the fields of business and economics is vital for the promotion of success within a certain organization. Analytics and statistics have taken a prominent role in marketing and management practices as professionals are constantly searching for a competitive advantage. Converging these technological tools with traditional methods of business relations is a trending area of research. Applied Social Network Analysis With R: Emerging Research and Opportunities is an essential reference source that materializes and analyzes the issue of structure in terms of its effects on human societies and the state of the individuals in these communities. Even though the theme of the book is business-oriented, an approach underlining and strengthening the ties of this field of study with social sciences for further development is adopted throughout. Therefore, the knowledge presented is valid for analyzing not only the organization of the business world but also for the organization of any given community. Featuring research on topics such as network visualization, graph theory, and micro-dynamics, this book is ideally designed for researchers, practitioners, business professionals, managers, programmers, academicians, and students seeking coverage on analyzing social and business networks using modern methods of statistics, programming, and data sets.
R 5,438.53
Machine Learning and Knowledge Discovery in Databases. Research Track and Applied Data Science Track
This multi-volume set, LNAI 16013 to LNAI 16022, constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2025, held in Porto, Portugal, September 15-19, 2025. The 300 full papers presented here, together with 15 demo papers, were carefully reviewed and selected from 1253 submissions. The papers presented in these proceedings are from the following three conference tracks: The Research Track in Volume LNAI 16013-16020 refers about Anomaly & Outlier Detection, Bias & Fairness, Causality, Clustering, Data Challenges, Diffusion Models, Ensemble Learning, Graph Neural Networks, Graphs & Networks, Healthcare & Bioinformatics, Images & Computer Vision, Interpretability & Explainability, Large Language Models, Learning Theory, Multimodal Data, Neuro Symbolic Approaches, Optimization, Privacy & Security, Recommender Systems, Reinforcement Learning, Representation Learning, Resource Efficiency, Robustness & Uncertainty, Sequence Models, Streaming & Spatiotemporal Data, Text & Natural Language Processing, Time Series, and Transfer & Multitask Learning. The Applied Data Science Track in Volume LNAI 16020-16022 refers about Agriculture, Food and Earth Sciences, Education, Engineering and Technology, Finance, Economy, Management or Marketing, Health, Biology, Bioinformatics or Chemistry, Industry (4.0, 5.0, Manufacturing, ...), Smart Cities, Transportation and Utilities (e.g., Energy), Sports, and Web and Social Networks The Demo Track in LNAI 16022 showcased practical applications and prototypes, accepting 15 papers from a total of 30 submissions. These proceedings cover the papers accepted in the research and applied data science tracks.
R 3,564.47
Spatio-Temporal Graph Data Analytics
This book highlights some of the unique aspects of spatio-temporal graph data from the perspectives of modeling and developing scalable algorithms. The authors discuss in the first part of this book, the semantic aspects of spatio-temporal graph data in two application domains, viz., urban transportation and social networks. Then the authors present representational models and data structures, which can effectively capture these semantics, while ensuring support for computationally scalable algorithms.In the first part of the book, the authors describe algorithmic development issues in spatio-temporal graph data. These algorithms internally use the semantically rich data structures developed in the earlier part of this book. Finally, the authors introduce some upcoming spatio-temporal graph datasets, such as engine measurement data, and discuss some open research problems in the area. This book will be useful as a secondary text for advanced-level students entering into relevant fields of computer science, such as transportation and urban planning. It may also be useful for researchers and practitioners in the field of navigational algorithms.
R 3,910.91
Spatio-Temporal Graph Data Analytics
This book highlights some of the unique aspects of spatio-temporal graph data from the perspectives of modeling and developing scalable algorithms. The authors discuss in the first part of this book, the semantic aspects of spatio-temporal graph data in two application domains, viz., urban transportation and social networks. Then the authors present representational models and data structures, which can effectively capture these semantics, while ensuring support for computationally scalable algorithms.In the first part of the book, the authors describe algorithmic development issues in spatio-temporal graph data. These algorithms internally use the semantically rich data structures developed in the earlier part of this book. Finally, the authors introduce some upcoming spatio-temporal graph datasets, such as engine measurement data, and discuss some open research problems in the area. This book will be useful as a secondary text for advanced-level students entering into relevant fields of computer science, such as transportation and urban planning. It may also be useful for researchers and practitioners in the field of navigational algorithms.
R 3,782.78
Advancing Recommender Systems with Graph Convolutional Networks
This book systematically examines scalability and effectiveness challenges related to the application of graph convolutional networks (GCNs) in recommender systems. By effectively modeling graph structures, GCNs excel in capturing high-order relationships between users and items, enabling the creation of enriched and expressive representations.The book focuses on two overarching problem categories: the first area deals with problems specific to GCN-based recommendation models, including over-smoothing, noisy neighboring nodes, and interpretability limitations. The second one encompasses broader challenges in recommendation systems that GCN-based methods are particularly well-suited to address as the attribute missing problem or feature misalignment. Through rigorous exploration of these challenges, this book presents innovative GCN-based solutions to push the boundaries of recommender system design. To this end, techniques such as interest-aware message-passing strategy, cluster-based collaborative filtering, semantic aspects extraction, attribute-aware attention mechanisms, and light graph transformer are presented.Each chapter combines theoretical insights with practical implementations and experimental validation, offering a comprehensive resource for researchers, advanced professionals, and graduate students alike.
R 4,762.00
Proceedings of 4th International Conference on Mathematical Modeling and Computational Science
This book aims to capture the interest of researchers and professionals in information technology, computer science, and mathematics. It covers fundamental and advanced concepts related to intelligent computing paradigms, data sciences, graph theory, and mathematical modeling. In high-performance computing, the need for intelligent, adaptive computing mechanisms and the integration of mathematical modeling in computational algorithms is becoming increasingly significant. Serving as a valuable resource for industry professionals, this book also supports beginners in gaining insights into enhanced computing paradigms and mathematical concepts, from foundational to advanced levels. Our objective is to provide a platform for researchers, engineers, academicians, and industry experts worldwide to share their findings on emerging trends. The authors believe this book not only presents innovative ideas but also fosters engaging discussions and inspires new perspectives.
R 9,319.59
Proceedings of the 5th International Conference on Mathematical Modeling and Computational Science
This book aims to capture the interest of researchers and professionals in Information Technology, Computer Science, and Mathematics. It covers fundamental and advanced concepts related to intelligent computing paradigms, data sciences, graph theory, and mathematical modeling. In high-performance computing, the need for intelligent, adaptive computing mechanisms and the integration of mathematical modeling in computational algorithms is becoming increasingly significant. Serving as a valuable resource for industry professionals, this book also supports beginners in gaining insights into enhanced computing paradigms and mathematical concepts, from foundational to advanced levels. Our objective is to provide a platform for researchers, engineers, academicians, and industry experts worldwide to share their findings on emerging trends. We believe this book not only presents innovative ideas but also fosters engaging discussions and inspires new perspectives.
R 8,059.00
Proceedings of 4th International Conference on Mathematical Modeling and Computational Science
This book aims to capture the interest of researchers and professionals in information technology, computer science, and mathematics. It covers fundamental and advanced concepts related to intelligent computing paradigms, data sciences, graph theory, and mathematical modeling. In high-performance computing, the need for intelligent, adaptive computing mechanisms and the integration of mathematical modeling in computational algorithms is becoming increasingly significant. Serving as a valuable resource for industry professionals, this book also supports beginners in gaining insights into enhanced computing paradigms and mathematical concepts, from foundational to advanced levels. Our objective is to provide a platform for researchers, engineers, academicians, and industry experts worldwide to share their findings on emerging trends. The authors believe this book not only presents innovative ideas but also fosters engaging discussions and inspires new perspectives.
R 7,326.00
Graph-Theoretic Concepts in Computer Science
This book reflects the scientific program of the annual workshop on Graph-theoretic Concepts in Computer Science in 1987. The purpose of this conference is to be the "missing link" between theory and application of graphs in as many branches of computer science as a conference scheduled for three days without parallel sessions can permit. So the organizers of WG '87 addressed a selected group of people with a strong interest in theory and practice. The proceedings include latest results on "classical" graph-theoretic problems (including formal language theory applied to graphs) and how to apply those results to practical problems, e.g. data bases, layout of graph operating systems, software engineering, chemistry, and modelling with graphs.
R 1,550.49
Machine Learning and Knowledge Discovery in Databases. Research Track
This multi-volume set, LNAI 16941-16950, constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2026, held in Naples, Italy, during September 7–11, 2026.The papers inlcuded in these proceedings were carefully reviewed and selected; they were organised in different conference tracks: 280 full papers were accepted for the Research Track out of 1150 submissions, and 90 full papers for the Applied Data Science Track out of 381 submissions. The remaining short papers included in these proceedings are from the Demo Track (27 papers out of 38 submissions) and the Industrial Track (21 papers out of 42 submissions). The papers cover the following topical sections:The Research Track (LNAI 16941-16948): Anomaly & Outlier Detection; Anomaly Detection, Active Learning & Data-Centric Learning; AutoML, Data-Centric & Statistical Learning; Bias & Fairness; Biomedical AI; Causal Discovery & Causal Inference; Clustering & Pattern Mining; Continual Learning; Evaluation & Trustworthy AI; Federated Learning; Federated Learning & Machine Unlearning; Generative & Difusion Models; Graph Learning & Applications; Graph Learning & Graph Inference; Graph Neural Networks; Graph, Language & Decision Learning; Images & Computer Vision; Interpretability & Explainability; Knowledge Graphs & Graph Representation Learning; Large Language Models; Learning Theory & Probabilistic ML; Model Reliability & Evaluation; Multimodal, Vision & 3D Reconstruction; Neuro-Symbolic Learning; Optimization, Bandits & Online Learning; Recommender Systems & Ranking; Reinforcement Learning & Decision Making; Representation Learning; Representation, Alignment & Generative Learning; Robustness, Counterfactuals & Explanations; Robustness, Uncertainty & Trustworthy ML; Security, Privacy & Trustworthy AI; Structured Graphs, Causal & Relational Learning; Supervised & Weakly-Supervised Learning; Text Mining & Information Retrieval; Text Mining, Information Retrieval & Model Analysis; and Time Series & Streaming Data; and Vision Applications;The Applied Data Science Track (LNAI 16948-16950): AI for Sports, Mobility & Human-Centered Applications; Causal Inference, Decision Making & Counterfactual Learning; Explainability, Robustness & Responsible AI; Foundation Models & AI for Biology and Medicine; Graph Learning & Structured Representation Learning; Industrial AI, Monitoring & Digital Twins; Large Language Models, Agents & Reasoning; Natural Language Processing & Information Retrieval; Remote Sensing, Earth Observation & Environmental Monitoring; and Time Series, Forecasting & Anomaly Detection;The Demo Track and the Industial Track in LNAI 16950 showcased practical applications and prototypes.
R 3,206.91
Database and Expert Systems Applications
The two-volume set LNCS constitutes the proceedings of the 37th International Conference on Database and Expert Systems Applications, DEXA 2026, held in Graz, Austria, in August 11–13, 2026.The 34 full and 20 short papers presented in this set together with 2 invited papers were carefully reviewed and selected from 123 submissions. They were organized in topical sections as follows:Part I: Knowledge Graphs and Graph Intelligence; Graph Data Management and Processing; Recommendation, Search and Information Retrieval; Large Language Models and Multi-Agent Systems; Data Governance, Quality and Integration; Database Systems and Query Processing.Part II: Storage Systems and Data Infrastructure; Machine Learning Foundations and Data Generation; Temporal Data Analytics and Forecasting; AI for IoT and Cybersecurity; Neural Networks and Applied AI; Systems, Security and Emerging Applications.
R 3,137.27
Database and Expert Systems Applications
The two-volume set LNCS constitutes the proceedings of the 37th International Conference on Database and Expert Systems Applications, DEXA 2026, held in Graz, Austria, in August 11–13, 2026.The 34 full and 20 short papers presented in this set together with 2 invited papers were carefully reviewed and selected from 123 submissions. They were organized in topical sections as follows:Part I: Knowledge Graphs and Graph Intelligence; Graph Data Management and Processing; Recommendation, Search and Information Retrieval; Large Language Models and Multi-Agent Systems; Data Governance, Quality and Integration; Database Systems and Query Processing.Part II: Storage Systems and Data Infrastructure; Machine Learning Foundations and Data Generation; Temporal Data Analytics and Forecasting; AI for IoT and Cybersecurity; Neural Networks and Applied AI; Systems, Security and Emerging Applications.
R 3,144.03
Network Models for Data Science
This text on the theory and applications of network science is aimed at beginning graduate students in statistics, data science, computer science, machine learning, and mathematics, as well as advanced students in business, computational biology, physics, social science, and engineering working with large, complex relational data sets. It provides an exciting array of analysis tools, including probability models, graph theory, and computational algorithms, exposing students to ways of thinking about types of data that are different from typical statistical data. Concepts are demonstrated in the context of real applications, such as relationships between financial institutions, between genes or proteins, between neurons in the brain, and between terrorist groups. Methods and models described in detail include random graph models, percolation processes, methods for sampling from huge networks, network partitioning, and community detection. In addition to static networks the book introduces dynamic networks such as epidemics, where time is an important component.
R 3,210.00
Data Science: Foundations and Applications
The two-volume set LNAI 15875 + 15876 constitutes the proceedings of the 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2025 Special Session, held in Sydney, NSW, Australia, during June 10–13, 2025.The 68 full papers included in this set were carefully reviewed and selected from 696 submissions. They were organized in topical sections as follows: survey track; machine learning; trustworthiness; learning on complex data; graph mining; machine learning applications; representation learning; scientific/business data analysis; and special track on large language models.
R 3,537.59
Algebraic Foundations for Applied Topology and Data Analysis
This book gives an intuitive and hands-on introduction to Topological Data Analysis (TDA). Covering a wide range of topics at levels of sophistication varying from elementary (matrix algebra) to esoteric (Grothendieck spectral sequence), it offers a mirror of data science aimed at a general mathematical audience. The required algebraic background is developed in detail. The first third of the book reviews several core areas of mathematics, beginning with basic linear algebra and applications to data fitting and web search algorithms, followed by quick primers on algebra and topology. The middle third introduces algebraic topology, along with applications to sensor networks and voter ranking. The last third covers key contemporary tools in TDA: persistent and multiparameter persistent homology. Also included is a user’s guide to derived functors and spectral sequences (useful but somewhat technical tools which have recently found applications in TDA), and an appendix illustrating a number of software packages used in the field. Based on a course given as part of a masters degree in statistics, the book is appropriate for graduate students.
R 2,354.58
Linear Algebra, Data Science, and Machine Learning
This text provides a mathematically rigorous introduction to modern methods of machine learning and data analysis at the advanced undergraduate/beginning graduate level. The book is self-contained and requires minimal mathematical prerequisites. There is a strong focus on learning how and why algorithms work, as well as developing facility with their practical applications. Apart from basic calculus, the underlying mathematics — linear algebra, optimization, elementary probability, graph theory, and statistics — is developed from scratch in a form best suited to the overall goals. In particular, the wide-ranging linear algebra components are unique in their ordering and choice of topics, emphasizing those parts of the theory and techniques that are used in contemporary machine learning and data analysis. The book will provide a firm foundation to the reader whose goal is to work on applications of machine learning and/or research into the further development of this highly active field of contemporary applied mathematics.To introduce the reader to a broad range of machine learning algorithms and how they are used in real world applications, the programming language Python is employed and offers a platform for many of the computational exercises. Python notebooks complementing various topics in the book are available on a companion GitHub site specified in the Preface, and can be easily accessed by scanning the QR codes or clicking on the links provided within the text. Exercises appear at the end of each section, including basic ones designed to test comprehension and computational skills, while others range over proofs not supplied in the text, practical computations, additional theoretical results, and further developments in the subject. The Students’ Solutions Manual may be accessed from GitHub. Instructors may apply for access to the Instructors’ Solutions Manual from the link supplied on the text’s Springer website.The book can be used in a junior or senior level course for students majoring in mathematics with a focus on applications as well as students from other disciplines who desire to learn the tools of modern applied linear algebra and optimization. It may also be used as an introduction to fundamental techniques in data science and machine learning for advanced undergraduate and graduate students or researchers from other areas, including statistics, computer science, engineering, biology, economics and finance, and so on.
R 2,931.00
Fuzzy Cognitive Maps for Applied Sciences and Engineering
Fuzzy Cognitive Maps (FCM) constitute cognitive models in the form of fuzzy directed graphs consisting of two basic elements: the nodes, which basically correspond to “concepts” bearing different states of activation depending on the knowledge they represent, and the “edges” denoting the causal effects that each source node exercises on the receiving concept expressed through weights. Weights take values in the interval [-1,1], which denotes the positive, negative or neutral causal relationship between two concepts. An FCM can be typically obtained through linguistic terms, inherent to fuzzy systems, but with a structure similar to the neural networks, which facilitates data processing, and has capabilities for training and adaptation.During the last 10 years, an exponential growth of published papers in FCMs was followed showing great impact potential. Different FCM structures and learning schemes have been developed, while numerous studies report their use in many contexts with highly successful modeling results. The aim of this book is to fill the existing gap in the literature concerning fundamentals, models, extensions and learning algorithms for FCMs in knowledge engineering. It comprehensively covers the state-of-the-art FCM modeling and learning methods, with algorithms, codes and software tools, and provides a set of applications that demonstrate their various usages in applied sciences and engineering.
R 4,030.00
Applied Categorical Data Analysis and Translational Research
An updated treatment of categorical data analysis in the biomedical sciences that now explores applications to translational research Thoroughly updated with the latest advances in the field, Applied Categorical Data Analysis and Translational Research, Second Edition maintains the accessible style of its predecessor while also exploring the importance of translational research as it relates to basic scientific findings within clinical practice. With its easy-to-follow style, updated coverage of major methodologies, and broadened scope of coverage, this new edition provides an accessible guide to statistical methods involving categorical data and the steps to their application in problem solving in the biomedical sciences. Delving even further into the applied direction, this update offers many real-world examples from biomedicine, epidemiology, and public health along with detailed case studies taken straight from modern research in these fields. Additional features of the Second Edition include: A new chapter on the relationship between translational research and categorical data, focusing on design study, bioassay, and Phase I and Phase II clinical trials A new chapter on categorical data and diagnostic medicine, with coverage of the diagnostic process, prevalence surveys, the ROC function and ROC curve, and important statistical considerations A revised chapter on logistic regression models featuring an updated treatment of simple and multiple regression analysis An added section on quantal bioassays Each chapter features updated and new exercise sets along with numerous graphs that demonstrate the highly visual nature of the topic. A related Web site features the book's examples as well as additional data sets that can be worked with using SAS® software. The only book of its kind to provide balanced coverage of methods for both categorical data and translational research, Applied Categorical Data Analysis and Translational Research, Second Edition is an excellent book for courses on applied statistics and biostatistics at the upper-undergraduate and graduate levels. It is also a valuable reference for researchers and practitioners in the biomedical and public health fields.
R 4,460.00
Data Science for Fake News
This book provides an overview of fake news detection, both through a variety of tutorial-style survey articles that capture advancements in the field from various facets and in a somewhat unique direction through expert perspectives from various disciplines. The approach is based on the idea that advancing the frontier on data science approaches for fake news is an interdisciplinary effort, and that perspectives from domain experts are crucial to shape the next generation of methods and tools.The fake news challenge cuts across a number of data science subfields such as graph analytics, mining of spatio-temporal data, information retrieval, natural language processing, computer vision and image processing, to name a few. This book will present a number of tutorial-style surveys that summarize a range of recent work in the field. In a unique feature, this book includes perspective notes from experts in disciplines such as linguistics, anthropology, medicine and politics that will help to shape the next generation of data science research in fake news.The main target groups of this book are academic and industrial researchers working in the area of data science, and with interests in devising and applying data science technologies for fake news detection. For young researchers such as PhD students, a review of data science work on fake news is provided, equipping them with enough know-how to start engaging in research within the area. For experienced researchers, the detailed descriptions of approaches will enable them to take seasoned choices in identifying promising directions for future research.
R 5,692.39
Graph-Theoretic Concepts in Computer Science
The 26th International Workshop on Graph-Theoretic Concepts in Computer Science (WG 2000) was held at Waldhaus Jakob, in Konstanz, Germany, on 15{ 17 June 2000. It was organized by the Algorithms and Data Structures Group of the Department of Computer and Information Science, University of K- stanz, and sponsored by Deutsche Forschungsgemeinschaft (DFG) and Univ- sit¨atsgesellschaft Konstanz. The workshop aims at uniting theory and practice by demonstrating how graph-theoretic concepts can be applied to various areas in computer science, or by extracting new problems from applications. The goal is to present recent research results and to identify and explore directions for future research. The workshop looks back on a remarkable tradition of more than a quarter of a century. Previous Workshops have been organized in various places in Europe, and submissions come from all over the world. This year, 57 attendees from 13 di erent countries gathered in the relaxing atmosphere of Lake Constance, also known as the Bodensee. Out of 51 submis- ons, the program committee carefully selected 26 papers for presentation at the workshop. This selection re?ects current research directions, among them graph and network algorithms and their complexity, algorithms for special graph cl- ses, communication networks, and distributed algorithms. The present volume contains these papers together with the survey presented in an invited lecture by Ingo Wegener (University of Dortmund) and an extended abstract of the invited lecture given by Emo Welzl (ETH Zuric ¨ h).
R 2,015.00
Applying Data Science
This book offers practical guidelines on creating value from the application of data science based on selected artificial intelligence methods. In Part I, the author introduces a problem-driven approach to implementing AI-based data science and offers practical explanations of key technologies: machine learning, deep learning, decision trees and random forests, evolutionary computation, swarm intelligence, and intelligent agents. In Part II, he describes the main steps in creating AI-based data science solutions for business problems, including problem knowledge acquisition, data preparation, data analysis, model development, and model deployment lifecycle. Finally, in Part III the author illustrates the power of AI-based data science with successful applications in manufacturing and business. He also shows how to introduce this technology in a business setting and guides the reader on how to build the appropriate infrastructure and develop the required skillsets. The book is ideal for data scientists who will implement the proposed methodology and techniques in their projects. It is also intended to help business leaders and entrepreneurs who want to create competitive advantage by using AI-based data science, as well as academics and students looking for an industrial view of this discipline.
R 3,715.94
Proceedings of 4th International Conference on Mathematical Modeling and Computational Science
This book aims to capture the interest of researchers and professionals in information technology, computer science, and mathematics. It presents fundamental and advanced concepts in intelligent computing paradigms, data science, graph theory, and mathematical modeling. As high-performance computing evolves, the emphasis on intelligent, adaptive computing mechanisms and the integration of mathematical modeling into computational algorithms is becoming increasingly vital. Serving as a valuable resource for professionals and newcomers alike, this book provides insights into enhanced computing paradigms and mathematical approaches, ranging from foundational to advanced levels. Our objective is to create a platform where researchers, engineers, academicians, and industry experts worldwide can exchange findings on emerging trends. Beyond introducing innovative concepts, the authors believe this book will spark meaningful discussions and inspire new ideas.
R 7,326.00
Applying Data Science
This book offers practical guidelines on creating value from the application of data science based on selected artificial intelligence methods. In Part I, the author introduces a problem-driven approach to implementing AI-based data science and offers practical explanations of key technologies: machine learning, deep learning, decision trees and random forests, evolutionary computation, swarm intelligence, and intelligent agents. In Part II, he describes the main steps in creating AI-based data science solutions for business problems, including problem knowledge acquisition, data preparation, data analysis, model development, and model deployment lifecycle. Finally, in Part III the author illustrates the power of AI-based data science with successful applications in manufacturing and business. He also shows how to introduce this technology in a business setting and guides the reader on how to build the appropriate infrastructure and develop the required skillsets. The book is ideal for data scientists who will implement the proposed methodology and techniques in their projects. It is also intended to help business leaders and entrepreneurs who want to create competitive advantage by using AI-based data science, as well as academics and students looking for an industrial view of this discipline.
R 4,192.83
Complete Guide to Graph Representation Learning with Case Studies
Comprehensive resource on graph representation learning (GRL), exploring fundamental principles, advanced methodologies, and case studies A Complete Guide to Graph Representation Learning with Case Studies provides a concise understanding of the subject of graph representation learning (GRL), a rapidly advancing field in the domain of machine learning. The book explores basic concepts to state-of-the-art techniques, enabling readers to progress from a fundamental understanding of the approach to mastering its application. The authors also cover the topics of graph embedding methods, graph neural network (GNN) -based approaches, and the latest trends in GRL such as deep learning, transfer learning, graph pooling, alignment, and matching, and graph machine learning. The book includes examples of applications of graph learning methods with real-world case studies in which the covered methods can be utilized. It also includes innovative solutions to graph machine learning problems such as node classification, link prediction, and unsupervised learning, and discusses neighborhood overlap visualization techniques and overlapping neighborhoods in heterogeneous graphs. Finally, the book provides an overview of open and ongoing research directions and student projects, providing a glimpse into potential avenues for future work. The book also includes information on: Node-level features such as node degree, node centrality, closeness, betweenness, eigenvector, page rank centrality, clustering coefficient, closed triangles, egograph, and motifsNeighborhood sampling techniques such as breadth-first sampling, depth-first sampling, snowball sampling, random walk, shallow walk, edge sampling, link-based sampling, and metapath-based samplingDeep learning models including Graph Autoencoder (GAE), Variational Graph Encoder (VGAE), and Graph Attention Network (GAN)Graph alignment and matching, covering subgraph matching and embedding for matching A Complete Guide to Graph Representation Learning with Case Studies is a thorough and up-to-date reference on the subject for engineers and researchers in data science and machine learning as well as graduate students in related programs of study.
R 4,579.00
Machine Learning and Knowledge Discovery in Databases. Research Track
This multi-volume set, LNAI 16941-16950, constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2026, held in Naples, Italy, during September 7–11, 2026.The papers inlcuded in these proceedings were carefully reviewed and selected; they were organised in different conference tracks: 280 full papers were accepted for the Research Track out of 1150 submissions, and 90 full papers for the Applied Data Science Track out of 381 submissions. The remaining short papers included in these proceedings are from the Demo Track (27 papers out of 38 submissions) and the Industrial Track (21 papers out of 42 submissions). The papers cover the following topical sections:The Research Track (LNAI 16941-16948): Anomaly & Outlier Detection; Anomaly Detection, Active Learning & Data-Centric Learning; AutoML, Data-Centric & Statistical Learning; Bias & Fairness; Biomedical AI; Causal Discovery & Causal Inference; Clustering & Pattern Mining; Continual Learning; Evaluation & Trustworthy AI; Federated Learning; Federated Learning & Machine Unlearning; Generative & Difusion Models; Graph Learning & Applications; Graph Learning & Graph Inference; Graph Neural Networks; Graph, Language & Decision Learning; Images & Computer Vision; Interpretability & Explainability; Knowledge Graphs & Graph Representation Learning; Large Language Models; Learning Theory & Probabilistic ML; Model Reliability & Evaluation; Multimodal, Vision & 3D Reconstruction; Neuro-Symbolic Learning; Optimization, Bandits & Online Learning; Recommender Systems & Ranking; Reinforcement Learning & Decision Making; Representation Learning; Representation, Alignment & Generative Learning; Robustness, Counterfactuals & Explanations; Robustness, Uncertainty & Trustworthy ML; Security, Privacy & Trustworthy AI; Structured Graphs, Causal & Relational Learning; Supervised & Weakly-Supervised Learning; Text Mining & Information Retrieval; Text Mining, Information Retrieval & Model Analysis; and Time Series & Streaming Data; and Vision Applications;The Applied Data Science Track (LNAI 16948-16950): AI for Sports, Mobility & Human-Centered Applications; Causal Inference, Decision Making & Counterfactual Learning; Explainability, Robustness & Responsible AI; Foundation Models & AI for Biology and Medicine; Graph Learning & Structured Representation Learning; Industrial AI, Monitoring & Digital Twins; Large Language Models, Agents & Reasoning; Natural Language Processing & Information Retrieval; Remote Sensing, Earth Observation & Environmental Monitoring; and Time Series, Forecasting & Anomaly Detection;The Demo Track and the Industial Track in LNAI 16950 showcased practical applications and prototypes.
R 3,587.11
Elements of Network Science
This book provides readers with a comprehensive guide to designing rigorous and effective network science tools using the statistical software platforms Stata, R, and Python.Network science offers a means to understand and analyze complex systems that involve various types of relationships. This text bridges the gap between theoretical understanding and practical application, making network science more accessible to a wide range of users. It presents the statistical models pertaining to individual network techniques, followed by empirical applications that use both built-in and user-written packages, and reveals the mathematical and statistical foundations of each model, along with demonstrations involving calculations and step-by-step code implementation. In addition, each chapter is complemented by a case study that illustrates one of the several techniques discussed.The introductory chapter serves as a roadmap for readers, providing an initial understanding of network science and guidance on the required packages, the second chapter focuses on the main concepts related to network properties. The next two chapters present the primary definitions and concepts in network science and various classes of graphs observed in real contexts. The final chapter explores the main social network models, including the family of exponential random graph models. Each chapter includes real-world data applications from the social sciences, using at least one of the platforms Stata, R, and Python, providing a more comprehensive understanding of the availability of network science methods across different software platforms. The underlying computer code and data sets are available online.The book will appeal to graduate students, researchers and data scientists, mainly from the social sciences, who seek theoretical and applied tools to implement network science techniques in their work.
R 5,197.96
Advanced Multimodal Compatibility Modeling and Recommendation
This Third Edition sheds light on state-of-the-art theories and practices in multimodal compatibility modeling and recommendation, offering comprehensive insights into this evolving field. This topic, and fashion compatibility modeling in particular, has garnered increasing research attention in recent years due to the significant economic impact of e-commerce. Building upon recent research and the prior edition, the authors present a series of graph-learning based multimodal compatibility modeling schemes, all of which have been proven to be effective over several public real-world datasets. This book introduces a number of advanced multimodal compatibility modeling and recommendation methods, including category-guided multimodal compatibility modeling and try-on-guided multimodal compatibility modeling. The authors also provide comprehensive solutions, including correlation-oriented graph learning, modality-oriented graph learning, unsupervised disentangled graph learning, partially supervised disentangled graph learning, and metapath-guided heterogeneous graph learning.
R 1,649.00
Geographic Data Science with Python
This book provides the tools, the methods, and the theory to meet the challenges of contemporary data science applied to geographic problems and data. In the new world of pervasive, large, frequent, and rapid data, there are new opportunities to understand and analyze the role of geography in everyday life. Geographic Data Science with Python introduces a new way of thinking about analysis, by using geographical and computational reasoning, it shows the reader how to unlock new insights hidden within data.Key Features:● Showcases the excellent data science environment in Python.● Provides examples for readers to replicate, adapt, extend, and improve.● Covers the crucial knowledge needed by geographic data scientists.It presents concepts in a far more geographic way than competing textbooks, covering spatial data, mapping, and spatial statistics whilst covering concepts, such as clusters and outliers, as geographic concepts.Intended for data scientists, GIScientists, and geographers, the material provided in this book is of interest due to the manner in which it presents geospatial data, methods, tools, and practices in this new field.
R 10,210.61
Managing and Mining Graph Data
Managing and Mining Graph Data is a comprehensive survey book in graph management and mining. It contains extensive surveys on a variety of important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy. It also studies a number of domain-specific scenarios such as stream mining, web graphs, social networks, chemical and biological data. The chapters are written by well known researchers in the field, and provide a broad perspective of the area. This is the first comprehensive survey book in the emerging topic of graph data processing. Managing and Mining Graph Data is designed for a varied audience composed of professors, researchers and practitioners in industry. This volume is also suitable as a reference book for advanced-level database students in computer science and engineering.
R 6,054.62
Transactions on Computational Science XXXVIII
The LNCS journal Transactions on Computational Science reflects recent developments in the field of Computational Science, conceiving the field not as a mere ancillary science but rather as an innovative approach supporting many other scientific disciplines. The journal focuses on original high-quality research in the realm of computational science in parallel and distributed environments, encompassing the facilitating theoretical foundations and the applications of large-scale computations and massive data processing. It addresses researchers and practitioners in areas ranging from aerospace to biochemistry, from electronics to geosciences, from mathematics to software architecture, presenting verifiable computational methods, findings, and solutions, and enabling industrial users to apply techniques of leading-edge, large-scale, high performance computational methods.This, the 38th issue of the Transactions on Computational Science, is devoted to research on modelling, optimization, and graphs, with applications in 3D and sketch modelling, engineering design, evolutionary computing, and networks.
R 2,150.97
R 916.00
Database Systems for Advanced Applications. DASFAA 2024 International Workshops
This 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)
R 2,947.32
Concepts, Methods and Practical Applications in Applied Demography
This textbook offers a comprehensive overview of applied demography by presenting both basic concepts and methodological techniques. It allows students from the social and human sciences, demographers, consultants and anyone interested in applied demography to gain an understanding of a wide range of practical applications of demographic concepts, methods and techniques to real- world problems. Featured sidebars highlight relevant terms and concepts and case studies and exercises throughout the book offer first-hand exposure to demographic applications. Charts and graphs supplement the presentation of demographic concepts and a glossary provides an inventory of relevant terms. The first section reviews basic components of applied demography as a context for understanding and addressing societal issues. It details the methods, techniques and data sources applied by demographers in a variety of areas. Coverage includes cohort analysis, data standardization, population estimation, and the use of geographic in- formation systems (GIS). The second section focuses on the substantive areas in which demography is currently applied. The topics covered include business demography, health demography, political demography, educational demography, and applications to urban and regional planning. The book illustrates the many ways in which demographers contribute to the formulation of public policy and the resolution of societal issues.
R 1,832.83
Advanced Data Mining and Applications
This six-volume set, LNAI 15387-15392, constitutes the refereed proceedings of the 20th International Conference on Advanced Data Mining and Applications, ADMA 2024, held in Sydney, New South Wales, Australia, during December 3-5, 2024. The 159 full papers presented here were carefully reviewed and selected from 422 submissions. These papers have been organized under the following topical sections across the different volumes: - Part I: Applications; Data mining. Part II: Data mining foundations and algorithms; Federated learning; Knowledge graph. Part III: Graph mining; Spatial data mining. Part IV: Health informatics. Part V: Multi-modal; Natural language processing. Part VI: Recommendation systems; Security and privacy issues.
R 3,152.68
