الصفحة 1
الصفحة 1
Modeling and Management of Fuzzy Semantic RDF Data
Presents the latest research findings in fuzzy RDF data modeling and management. Fuzziness widely exist in many data and knowledge intensive applications. With the increasing amount of metadata available, efficient and scalable management of massive semantic data with uncertainty is of crucial importance. This book goes to great depth concerning the fast-growing topic of technologies and approaches of modeling and managing fuzzy metadata with Resource Description Framework (RDF) format. Its major topics include representation of fuzzy RDF data, fuzzy RDF graph matching, query of fuzzy RDF data, and persistence of fuzzy RDF data in diverse databases. The objective of the book is to provide the state-of-the-art information to researchers, practitioners, and postgraduates students who work on the area of big data intelligence and at the same time serve as the uncertain data and knowledge engineering professional as a valuable real-world reference.
Human–computer interaction ; International Workshop, HCI 2007 Rio de Janeiro, Brazil, October 20, 2007 Proceedings
Constitutes the refereed proceedings of the International Workshop on Human Computer Interaction, HCI 2007, held in Rio de Janeiro, Brazil, October 20, 2007. This book covers such topics as: Affective detection and recognition, Smart interfaces, Human motion tracking, Gesture recognition, and Multimedia data modeling and visualization.
Data-Driven Fault Detection and Reasoning for Industrial Monitoring
Assesses the potential of data-driven methods in industrial process monitoring engineering. The process modeling, fault detection, classification, isolation, and reasoning are studied in detail. These methods can be used to improve the safety and reliability of industrial processes. Fault diagnosis, including fault detection and reasoning, has attracted engineers and scientists from various fields such as control, machinery, mathematics, and automation engineering. Combining the diagnosis algorithms and application cases, this book establishes a basic framework for this topic and implements various statistical analysis methods for process monitoring.
Conceptual Modelling in Information Systems Engineering
Conceptual modeling has always been one of the cornerstones for information systems engineering as it describes the general knowledge of the system in the so-called conceptual schema.It contiant data modeling, goal-oriented modeling, agent-oriented modeling, and process-oriented modeling. Overall, the contributions reflect the most important developments and application areas of conceptual modeling in recent years, and they also pinpoint trends in conceptual modeling for the next decade.
Conceptual Modeling for Traditional and Spatio-Temporal Applications : The MADS Approach
This book shows that a conceptual design approach for spatio-temporal databases is both feasible and easy to apprehend. While providing a firm basis through extensive discussion of traditional data modeling concepts, the major focus of the book is on modeling spatial and temporal information. Parent, Spaccapietra and Zimányi provide a detailed and comprehensive description of an approach that fills the gap between application conceptual requirements and system capabilities, covering both data modeling and data manipulation features.
Conceptual Modeling - ER 2007 ; 26th International Conference on Conceptual Modeling, Auckland, New Zealand, November 5-9, 2007, Proceedings
Conceptual modeling is fundamental to the development of complex systems, because it provides the key communication means between systems developers, end-users and customers.Conceptua lmodeling provides languages,methods and tools to understand and represent the application domain;to elicitate,concepalize and formalize system requirements and user needs;to communicate systems designs to all stakeholders; to formally verify and validate system designs on high levels of abstractions; and to minimize ambiguities in system development. Initially, conceptual modeling mainly addressed data-intensive information s- tems and contributed to data modeling and database application engineering. The area of conceptual modeling has now matured to encompass all kinds of application areas such as e-applications (including e-business and e-learning), web-based systems (including the semantic web and ubiquitous systems), life science and geographic applications.
Logical Data Modeling : What it is and How to do it
LOGICAL DATA MODELING: What It Is and How To Do IT is directed toward three groups of people: (1) Business subject matter experts, (2) information technology professionals, (3) advanced students in Computer Science, Management Information Systems, and e-Business. Its purpose is to outline the basics of logical data modeling—specifically, data modeling for relational database management systems—in simple, practical terms and in a business context. The focus on relational data modeling is consciously made because it is superior in modeling real business activities.
Big Data – BigData 2020; 9th International Conference, Held as Part of the Services Conference Federation, SCF 2020, Honolulu, HI, USA, September 18-20, 2020, Proceedings
Constitutes the proceedings of the 9th International Conference on Big Data, BigData 2020, held as part of SCF 2020, during September 18-20, 2020. The conference was planned to take place in Honolulu, HI, USA and was changed to a virtual format due to the COVID-19 pandemic. The 16 full and 3 short papers presented were carefully reviewed and selected from 52 submissions. The topics covered are Big Data Architecture, Big Data Modeling, Big Data As A Service, Big Data for Vertical Industries (Government, Healthcare, etc.), Big Data Analytics, Big Data Toolkits, Big Data Open Platforms, Economic Analysis, Big Data for Enterprise Transformation, Big Data in Business Performance Management, Big Data for Business Model Innovations and Analytics, Big Data in Enterprise Management Models and Practices, Big Data in Government Management Models and Practices, and Big Data in Smart Planet Solutions.
Beginning relational data modeling
Data storage design, and awareness of how data needs to be utilized within an organization, is of prime importance in ensuring that company data systems work efficiently. 'Beginning Data Modeling' leads readers you step by step through the process of developing an effective logical data model for a relational database model.
Advances in spatial and temporal databases ; 9th International symposium, SSTD 2005, Angra dos Reis, Brazil, August 22-24, 2005, Proceedings
Constitutes the refereed proceedings of the introduce the papers of the proceedings of the 9th - ternational Symposium on Spatial and Temporal Databases – SSTD 2005. This year’s symposium continues the tradition of being the premier forum for the presentation of research results and experience reports on leading edge issues of spatial and temporal database systems, including data models, systems, applications and theory. ll the needs of novel applications and heterogeneous environments and identify new directions for future research and development. aspects of database systems for managing spatial and temporal data and for supporting their applications. A total of 77 papers were submitted this year from several countries. After a thorough review process, the program committee accepted 24 papers covering a variety of topics, including indexing techniques and query processing, mobile environments and moving objects, and spatial and temporal data streams.
Advances in intelligent data analysis XIX ; 19th International Symposium on Intelligent Data Analysis, IDA 2021, Porto, Portugal, April 26–28, 2021, Proceedings
Constitutes the proceedings of the 19th International Symposium on Intelligent Data Analysis, IDA 2021, which was planned to take place in Porto, Portugal. Due to the COVID-19 pandemic the conference was held online during April 26-28, 2021. The 35 papers included in this book were carefully reviewed and selected from 113 submissions. The papers were organized in topical sections named: modeling with neural networks; modeling with statistical learning; modeling language and graphs; and modeling special data formats.
