Download or read book SSDBM 2018 written by Michael H. Böhlen and published by . This book was released on 2018 with total page 314 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Algorithms and Architectures for Parallel Processing written by Sheng Wen and published by Springer Nature. This book was released on 2020-01-21 with total page 725 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set LNCS 11944-11945 constitutes the proceedings of the 19th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2019, held in Melbourne, Australia, in December 2019. The 73 full and 29 short papers presented were carefully reviewed and selected from 251 submissions. The papers are organized in topical sections on: Parallel and Distributed Architectures, Software Systems and Programming Models, Distributed and Parallel and Network-based Computing, Big Data and its Applications, Distributed and Parallel Algorithms, Applications of Distributed and Parallel Computing, Service Dependability and Security, IoT and CPS Computing, Performance Modelling and Evaluation.
Download or read book Information Systems Security written by Salil Kanhere and published by Springer Nature. This book was released on 2020-12-05 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 16th International Conference on Information Systems Security, ICISS 2020, held in Jammu, India, during December 16-20, 2020. The 11 regular papers, 2 short papers and 3 work-in-progress papers included in this volume were carefully reviewed and selected from a total of 53 submissions. The papers were organized in topical sections named: access control; AI/ML in security; privacy and Web security; cryptography; and systems security.
Download or read book Advances in Knowledge Discovery and Management written by Rakia Jaziri and published by Springer Nature. This book was released on 2022-03-14 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a collection of high scientific novel contributions addressing several of these challenges. These articles are extended versions of a selection of the best papers that were initially presented at the French-speaking conferences EGC’2019held in Metz (France, January 21-25, 2019). These extended versions have been accepted after an additional peer-review process among papers already accepted in long format at the conference. Concerning the conference, the long and short papers selection were also the result of a double blind peer review process among the hundreds of papers initially submitted to each edition of the conference (acceptance rate for long papers is about 25%.
Download or read book Similarity Search and Applications written by Giuseppe Amato and published by Springer Nature. This book was released on 2019-09-24 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 12th International Conference on Similarity Search and Applications, SISAP 2019, held in Newark, NJ, USA, in October 2019. The 12 full papers presented together with 18 short and 3 doctoral symposium papers were carefully reviewed and selected from 42 submissions. The papers are organized in topical sections named: Similarity Search and Retrieval; The Curse of Dimensionality; Clustering and Outlier Detection; Subspaces and Embeddings; Applications; Doctoral Symposium Papers.
Download or read book Web and Big Data written by Xiangyu Song and published by Springer Nature. This book was released on with total page 539 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Data Profiling written by Ziawasch Abedjan and published by Springer Nature. This book was released on 2022-06-01 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data profiling refers to the activity of collecting data about data, {i.e.}, metadata. Most IT professionals and researchers who work with data have engaged in data profiling, at least informally, to understand and explore an unfamiliar dataset or to determine whether a new dataset is appropriate for a particular task at hand. Data profiling results are also important in a variety of other situations, including query optimization, data integration, and data cleaning. Simple metadata are statistics, such as the number of rows and columns, schema and datatype information, the number of distinct values, statistical value distributions, and the number of null or empty values in each column. More complex types of metadata are statements about multiple columns and their correlation, such as candidate keys, functional dependencies, and other types of dependencies. This book provides a classification of the various types of profilable metadata, discusses popular data profiling tasks, and surveys state-of-the-art profiling algorithms. While most of the book focuses on tasks and algorithms for relational data profiling, we also briefly discuss systems and techniques for profiling non-relational data such as graphs and text. We conclude with a discussion of data profiling challenges and directions for future work in this area.
Download or read book Advanced Data Mining and Applications written by Xiaochun Yang and published by Springer Nature. This book was released on 2023-12-06 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 19th International Conference on Advanced Data Mining and Applications, ADMA 2023, held in Shenyang, China, during August 21–23, 2023. The 216 full papers included in this book were carefully reviewed and selected from 503 submissions. They were organized in topical sections as follows: Data mining foundations, Grand challenges of data mining, Parallel and distributed data mining algorithms, Mining on data streams, Graph mining and Spatial data mining.
Download or read book Fundamentals written by Katharina Morik and published by Walter de Gruyter GmbH & Co KG. This book was released on 2022-12-31 with total page 506 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning is part of Artificial Intelligence since its beginning. Certainly, not learning would only allow the perfect being to show intelligent behavior. All others, be it humans or machines, need to learn in order to enhance their capabilities. In the eighties of the last century, learning from examples and modeling human learning strategies have been investigated in concert. The formal statistical basis of many learning methods has been put forward later on and is still an integral part of machine learning. Neural networks have always been in the toolbox of methods. Integrating all the pre-processing, exploitation of kernel functions, and transformation steps of a machine learning process into the architecture of a deep neural network increased the performance of this model type considerably. Modern machine learning is challenged on the one hand by the amount of data and on the other hand by the demand of real-time inference. This leads to an interest in computing architectures and modern processors. For a long time, the machine learning research could take the von-Neumann architecture for granted. All algorithms were designed for the classical CPU. Issues of implementation on a particular architecture have been ignored. This is no longer possible. The time for independently investigating machine learning and computational architecture is over. Computing architecture has experienced a similarly rampant development from mainframe or personal computers in the last century to now very large compute clusters on the one hand and ubiquitous computing of embedded systems in the Internet of Things on the other hand. Cyber-physical systems’ sensors produce a huge amount of streaming data which need to be stored and analyzed. Their actuators need to react in real-time. This clearly establishes a close connection with machine learning. Cyber-physical systems and systems in the Internet of Things consist of diverse components, heterogeneous both in hard- and software. Modern multi-core systems, graphic processors, memory technologies and hardware-software codesign offer opportunities for better implementations of machine learning models. Machine learning and embedded systems together now form a field of research which tackles leading edge problems in machine learning, algorithm engineering, and embedded systems. Machine learning today needs to make the resource demands of learning and inference meet the resource constraints of used computer architecture and platforms. A large variety of algorithms for the same learning method and, moreover, diverse implementations of an algorithm for particular computing architectures optimize learning with respect to resource efficiency while keeping some guarantees of accuracy. The trade-off between a decreased energy consumption and an increased error rate, to just give an example, needs to be theoretically shown for training a model and the model inference. Pruning and quantization are ways of reducing the resource requirements by either compressing or approximating the model. In addition to memory and energy consumption, timeliness is an important issue, since many embedded systems are integrated into large products that interact with the physical world. If the results are delivered too late, they may have become useless. As a result, real-time guarantees are needed for such systems. To efficiently utilize the available resources, e.g., processing power, memory, and accelerators, with respect to response time, energy consumption, and power dissipation, different scheduling algorithms and resource management strategies need to be developed. This book series addresses machine learning under resource constraints as well as the application of the described methods in various domains of science and engineering. Turning big data into smart data requires many steps of data analysis: methods for extracting and selecting features, filtering and cleaning the data, joining heterogeneous sources, aggregating the data, and learning predictions need to scale up. The algorithms are challenged on the one hand by high-throughput data, gigantic data sets like in astrophysics, on the other hand by high dimensions like in genetic data. Resource constraints are given by the relation between the demands for processing the data and the capacity of the computing machinery. The resources are runtime, memory, communication, and energy. Novel machine learning algorithms are optimized with regard to minimal resource consumption. Moreover, learned predictions are applied to program executions in order to save resources. The three books will have the following subtopics: Volume 1: Machine Learning under Resource Constraints - Fundamentals Volume 2: Machine Learning and Physics under Resource Constraints - Discovery Volume 3: Machine Learning under Resource Constraints - Applications Volume 1 establishes the foundations of this new field (Machine Learning under Resource Constraints). It goes through all the steps from data collection, their summary and clustering, to the different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Several machine learning methods are inspected with respect to their resource requirements and how to enhance their scalability on diverse computing architectures ranging from embedded systems to large computing clusters.
Download or read book Green Pervasive and Cloud Computing written by Zhiwen Yu and published by Springer Nature. This book was released on 2020-12-04 with total page 519 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 15th International Conference on Green, Pervasive, and Cloud Computing, GPC 2020, held in Xi'an, China, in November 2020. The 30 full papers presented in this book together with 8 short papers were carefully reviewed and selected from 96 submissions. They cover the following topics: Device-free Sensing; Machine Learning; Recommendation Systems; Urban Computing; Human Computer Interaction; Internet of Things and Edge Computing; Positioning; Applications of Computer Vision; CrowdSensing; and Cloud and Related Technologies.
Download or read book Knowledge Science Engineering and Management written by Christos Douligeris and published by Springer Nature. This book was released on 2019-08-20 with total page 868 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set of LNAI 11775 and LNAI 11776 constitutes the refereed proceedings of the 12th International Conference on Knowledge Science, Engineering and Management, KSEM 2019, held in Athens, Greece, in August 2019. The 77 revised full papers and 23 short papers presented together with 10 poster papers were carefully reviewed and selected from 240 submissions. The papers of the first volume are organized in the following topical sections: Formal Reasoning and Ontologies; Recommendation Algorithms and Systems; Social Knowledge Analysis and Management ; Data Processing and Data Mining; Image and Video Data Analysis; Deep Learning; Knowledge Graph and Knowledge Management; Machine Learning; and Knowledge Engineering Applications. The papers of the second volume are organized in the following topical sections: Probabilistic Models and Applications; Text Mining and Document Analysis; Knowledge Theories and Models; and Network Knowledge Representation and Learning.
Download or read book Foundations and Practice of Security written by Gabriela Nicolescu and published by Springer Nature. This book was released on 2021-02-26 with total page 213 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the revised selected papers of the 13th International Symposium on Foundations and Practice of Security, FPS 2020, held in Montréal, QC, Canada, in December 2020. The 11full papers and 1 short paper presented in this book were carefully reviewed and selected from 23 submissions. They cover a range of topics such as Analysis and Detection; Prevention and Efficiency; and Privacy by Design.
Download or read book Proceedings of the 30th International Conference on Scientific and Statistical Database Management written by Johann Gamper and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Transactions on Large Scale Data and Knowledge Centered Systems XLVII written by Abdelkader Hameurlain and published by Springer Nature. This book was released on 2021-01-16 with total page 247 pages. Available in PDF, EPUB and Kindle. Book excerpt: The LNCS journal Transactions on Large-Scale Data- and Knowledge-Centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing across different sites connected through networks has led to an evolution of data- and knowledge-management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability. This, the 47th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, constitutes a special issue focusing on Digital Ecosystems and Social Networks. The 9 revised selected papers cover topics that include Social Big Data, Data Analysis, Cloud-Based Feedback, Experience Ecosystems, Pervasive Environments, and Smart Systems.
Download or read book Model and Data Engineering written by Christian Attiogbé and published by Springer Nature. This book was released on 2021-06-14 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 10th International Conference on Model and Data Engineering, MEDI 2021, held in Tallinn, Estonia, in June 2021. The 16 full papers and 8 short papers presented in this book were carefully reviewed and selected from 47 submissions. Additionally, the volume includes 3 abstracts of invited talks. The papers cover broad research areas on both theoretical, systems and practical aspects. Some papers include mining complex databases, concurrent systems, machine learning, swarm optimization, query processing, semantic web, graph databases, formal methods, model-driven engineering, blockchain, cyber physical systems, IoT applications, and smart systems. Due to the Corona pandemic the conference was held virtually.
Download or read book Database Systems for Advanced Applications written by Jian Pei and published by Springer. This book was released on 2018-05-16 with total page 952 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set LNCS 10827 and LNCS 10828 constitutes the refereed proceedings of the 23rd International Conference on Database Systems for Advanced Applications, DASFAA 2018, held in Gold Coast, QLD, Australia, in May 2018. The 83 full papers, 21 short papers, 6 industry papers, and 8 demo papers were carefully selected from a total of 360 submissions. The papers are organized around the following topics: network embedding; recommendation; graph and network processing; social network analytics; sequence and temporal data processing; trajectory and streaming data; RDF and knowledge graphs; text and data mining; medical data mining; security and privacy; search and information retrieval; query processing and optimizations; data quality and crowdsourcing; learning models; multimedia data processing; and distributed computing.
Download or read book Runtime Verification written by Thao Dang and published by Springer Nature. This book was released on 2022-09-23 with total page 357 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 22nd International Conference on Runtime Verification, RV 2022, held in Tbilisi, Georgia, during September 28-30, 2022. The 12 regular papers and 10 short papers presented in this book were carefully reviewed and selected from 40 submissions. The RV conference is concerned with all aspects of monitoring and analysis of hardware, software and more general system executions. Runtime verification techniques are crucial for system correctness, reliability, and robustness; they provide an additional level of rigor and effectiveness compared to conventional testing, and are generally more practical than exhaustive formal verification.
