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Author: Bedir Tekinerdogan
Publisher: Academic Press
ISBN: 0128166509
Category : Computers
Languages : en
Pages : 344
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Book Description
Model Management and Analytics for Large Scale Systems covers the use of models and related artefacts (such as metamodels and model transformations) as central elements for tackling the complexity of building systems and managing data. With their increased use across diverse settings, the complexity, size, multiplicity and variety of those artefacts has increased. Originally developed for software engineering, these approaches can now be used to simplify the analytics of large-scale models and automate complex data analysis processes. Those in the field of data science will gain novel insights on the topic of model analytics that go beyond both model-based development and data analytics. This book is aimed at both researchers and practitioners who are interested in model-based development and the analytics of large-scale models, ranging from big data management and analytics, to enterprise domains. The book could also be used in graduate courses on model development, data analytics and data management. Identifies key problems and offers solution approaches and tools that have been developed or are necessary for model management and analytics Explores basic theory and background, current research topics, related challenges and the research directions for model management and analytics Provides a complete overview of model management and analytics frameworks, the different types of analytics (descriptive, diagnostics, predictive and prescriptive), the required modelling and method steps, and important future directions
Author: Bedir Tekinerdogan
Publisher: Academic Press
ISBN: 0128166509
Category : Computers
Languages : en
Pages : 344
Get Book
Book Description
Model Management and Analytics for Large Scale Systems covers the use of models and related artefacts (such as metamodels and model transformations) as central elements for tackling the complexity of building systems and managing data. With their increased use across diverse settings, the complexity, size, multiplicity and variety of those artefacts has increased. Originally developed for software engineering, these approaches can now be used to simplify the analytics of large-scale models and automate complex data analysis processes. Those in the field of data science will gain novel insights on the topic of model analytics that go beyond both model-based development and data analytics. This book is aimed at both researchers and practitioners who are interested in model-based development and the analytics of large-scale models, ranging from big data management and analytics, to enterprise domains. The book could also be used in graduate courses on model development, data analytics and data management. Identifies key problems and offers solution approaches and tools that have been developed or are necessary for model management and analytics Explores basic theory and background, current research topics, related challenges and the research directions for model management and analytics Provides a complete overview of model management and analytics frameworks, the different types of analytics (descriptive, diagnostics, predictive and prescriptive), the required modelling and method steps, and important future directions
Author: Boris Shishkov
Publisher: Springer Nature
ISBN: 3030523063
Category : Computers
Languages : en
Pages : 395
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Book Description
This book constitutes the refereed proceedings of the 10th International Symposium on Business Modeling and Software Design, BMSD 2020, which took place in Berlin, Germany, in July 2020. BMSD is a leading international forum that brings together researchers and practitioners interested in business modeling and its relation to software design. Particular areas of interest are: Business Processes and Enterprise Engineering; Business Models and Requirements; Business Models and Services; Business Models and Software; Information Systems Architectures and Paradigms; Data Aspects in Business Modeling and Software Development; Blockchain-Based Business Models and Information Systems; IoT and Implications for Enterprise Information Systems. The theme of BMSD 2020 was: Towards Knowledge-Driven Enterprise Information Systems.
Author: Ivan Mistrik
Publisher: CRC Press
ISBN: 1000387410
Category : Computers
Languages : en
Pages : 342
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Book Description
Data-intensive systems are software applications that process and generate Big Data. Data-intensive systems support the use of large amounts of data strategically and efficiently to provide intelligence. For example, examining industrial sensor data or business process data can enhance production, guide proactive improvements of development processes, or optimize supply chain systems. Designing data-intensive software systems is difficult because distribution of knowledge across stakeholders creates a symmetry of ignorance, because a shared vision of the future requires the development of new knowledge that extends and synthesizes existing knowledge. Knowledge Management in the Development of Data-Intensive Systems addresses new challenges arising from knowledge management in the development of data-intensive software systems. These challenges concern requirements, architectural design, detailed design, implementation and maintenance. The book covers the current state and future directions of knowledge management in development of data-intensive software systems. The book features both academic and industrial contributions which discuss the role software engineering can play for addressing challenges that confront developing, maintaining and evolving systems;data-intensive software systems of cloud and mobile services; and the scalability requirements they imply. The book features software engineering approaches that can efficiently deal with data-intensive systems as well as applications and use cases benefiting from data-intensive systems. Providing a comprehensive reference on the notion of data-intensive systems from a technical and non-technical perspective, the book focuses uniquely on software engineering and knowledge management in the design and maintenance of data-intensive systems. The book covers constructing, deploying, and maintaining high quality software products and software engineering in and for dynamic and flexible environments. This book provides a holistic guide for those who need to understand the impact of variability on all aspects of the software life cycle. It leverages practical experience and evidence to look ahead at the challenges faced by organizations in a fast-moving world with increasingly fast-changing customer requirements and expectations.
Author: Sherif Sakr
Publisher: CRC Press
ISBN: 1466581506
Category : Computers
Languages : en
Pages : 640
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Book Description
Large Scale and Big Data: Processing and Management provides readers with a central source of reference on the data management techniques currently available for large-scale data processing. Presenting chapters written by leading researchers, academics, and practitioners, it addresses the fundamental challenges associated with Big Data processing tools and techniques across a range of computing environments. The book begins by discussing the basic concepts and tools of large-scale Big Data processing and cloud computing. It also provides an overview of different programming models and cloud-based deployment models. The book’s second section examines the usage of advanced Big Data processing techniques in different domains, including semantic web, graph processing, and stream processing. The third section discusses advanced topics of Big Data processing such as consistency management, privacy, and security. Supplying a comprehensive summary from both the research and applied perspectives, the book covers recent research discoveries and applications, making it an ideal reference for a wide range of audiences, including researchers and academics working on databases, data mining, and web scale data processing. After reading this book, you will gain a fundamental understanding of how to use Big Data-processing tools and techniques effectively across application domains. Coverage includes cloud data management architectures, big data analytics visualization, data management, analytics for vast amounts of unstructured data, clustering, classification, link analysis of big data, scalable data mining, and machine learning techniques.
Author: Ananieva, Sofia
Publisher: KIT Scientific Publishing
ISBN: 3731512416
Category : Computers
Languages : en
Pages : 310
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Book Description
Developing variable systems faces many challenges. Dependencies between interrelated artifacts within a product variant, such as code or diagrams, across product variants and across their revisions quickly lead to inconsistencies during evolution. This work provides a unification of common concepts and operations for variability management, identifies variability-related inconsistencies and presents an approach for view-based consistency preservation of variable systems.
Author: Ashish Kumar Luhach
Publisher: Springer Nature
ISBN: 9811636605
Category : Computers
Languages : en
Pages : 698
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Book Description
This two-volume set (CCIS 1393 and CCIS 1394) constitutes selected and revised papers of the 4th International Conference on Advanced Informatics for Computing Research, ICAICR 2020, held in Gurugram, India, in December 2020. The 34 revised full papers and 51 short papers presented were carefully reviewed and selected from 306 submissions. The papers are organized in topical sections on computing methodologies; hardware; networks; security and privacy.
Author: M. A. Jabbar
Publisher: Springer Nature
ISBN: 3031342224
Category : Computers
Languages : en
Pages : 252
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Book Description
This book constitutes the refereed proceedings of the 5th International Conference on Applied Machine Learning and Data Analytics, AMLDA 2022, held in Reynosa, Tamaulipas, Mexico, during December 22–23, 2022. The 16 full papers and 4 short papers included in this book were carefully reviewed and selected from 89 submissions. They were organized in topical sections as follows: Machine learning, Healthcare and medical imaging informatics; biometrics; forensics; precision agriculture; risk management; robotics and satellite imaging.
Author: Silvia Chiusano
Publisher: Springer Nature
ISBN: 3031157435
Category : Computers
Languages : en
Pages : 675
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Book Description
This book constitutes the proceedings of the 26th European Conference on Advances in Databases and Information Systems, ADBIS 2022, held in Turin, Italy, in September 2022. The 29 short papers presented were carefully reviewed and selected from 90 submissions. The selected short papers are organized in the following sections: data understanding, modeling and visualization; fairness in data processing; data management pipeline, information and process retrieval; data access optimization; data pre-processing and cleaning; data science and machine learning. Further, papers from the following workshops and satellite events are provided in the volume: DOING: 3rd Workshop on Intelligent Data – From Data to Knowledge; K-GALS: 1st Workshop on Knowledge Graphs Analysis on a Large Scale; MADEISD: 4th Workshop on Modern Approaches in Data Engineering and Information System Design; MegaData: 2nd Workshop on Advanced Data Systems Management, Engineering, and Analytics; SWODCH: 2nd Workshop on Semantic Web and Ontology Design for Cultural Heritage; Doctoral Consortium.
Author: Önder Babur
Publisher: Springer Nature
ISBN: 3030581675
Category : Computers
Languages : en
Pages : 197
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Book Description
This book constitutes the refereed proceedings of the First International Conference on Systems Modelling and Management, ICSMM 2020, planned to be held in Bergen, Norway, in June 2020. Due to the COVID-19 pandemic the conference did not take place physically or virtually. The 10 full papers and 3 short papers were thoroughly reviewed and selected from 19 qualified submissions. The papers are organized according to the following topical sections: verification and validation; applications; methods, techniques and tools.
Author: National Research Council
Publisher: National Academies Press
ISBN: 0309287812
Category : Mathematics
Languages : en
Pages : 190
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Book Description
Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data. Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scale--terabytes and petabytes--is increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledge--from computer science, statistics, machine learning, and application disciplines--that must be brought to bear to make useful inferences from massive data.
