الصفحة 1
الصفحة 1
Information Retrieval for Music and Motion
The book then introduces a general and unified framework for motion analysis, retrieval, and classification, highlighting the design of suitable features, the notion of similarity used to compare data streams, and data organization.
Database Systems for Advanced Applications ; 13th International Conference, DASFAA 2008, New Delhi, India, March 19-21, 2008. Proceedings
This book constitutes the refereed proceedings of the 13th International Conference on Database Systems for Advanced Applications, DASFAA 2008, held in New Delhi, India, in March 2008.
Data Warehousing and Knowledge Discovery ; Vol.4081 ; 8th International Conference, DaWaK 2006, Krakow, Poland, September 4-8, 2006, Proceedings
DaWaK aimed at providing the right and logical balance between data warehousing and knowledge discovery. In data warehousing the papers cover different research problems, such as advanced techniques in OLAP visuali- tion and multidimensional modelling, innovation of ETL processes and integration problems, materialized view optimization, very large data warehouse processing, data warehouses and data mining applications integration, data warehousing for real-life applications, e. g. , medical applications and spatial applications. In data mining and knowledge discovery, papers are focused on a variety of topics from data streams analysis and mining, ontology-based mining techniques, mining frequent item sets, clustering, association and classification, patterns and so on.
Data Streams : Models and Algorithms
It primarily discusses issues related to the mining aspects of data streams rather than the database management aspect of streams. This volume covers mining aspects of data streams in a comprehensive style. Each contributed chapter, from a variety of well known researchers in the data mining field, contains a survey on the topic, the key ideas in the field from that particular topic, and future research directions.
Data science in theory and practice : Techniques for big data analytics and complex data sets
Delivers a comprehensive treatment of the mathematical and statistical models useful for analyzing data sets arising in various disciplines, like banking, finance, health care, bioinformatics, security, education, and social services. Written in five parts, the book examines some of the most commonly used and fundamental mathematical and statistical concepts that form the basis of data science. The authors go on to analyze various data transformation techniques useful for extracting information from raw data, long memory behavior, and predictive modeling. Readers will also learn from topics like: Analyses of foundational theoretical subjects, including the history of data science, matrix algebra and random vectors, and multivariate analysis A comprehensive examination of time series forecasting, including the different components of time series and transformations to achieve stationarity Introductions to both the R and Python programming languages, including basic data types and sample manipulations for both languages An exploration of algorithms, including how to write one and how to perform an asymptotic analysis A comprehensive discussion of several techniques for analyzing and predicting complex data sets
Machine learning for data streams : With practical examples in MOA
The book first offers a brief introduction to the topic, covering big data mining, basic methodologies for mining data streams, and a simple example of MOA. More detailed discussions follow, with chapters on sketching techniques, change, classification, ensemble methods, regression, clustering, and frequent pattern mining. Most of these chapters include exercises, an MOA-based lab session, or both. Finally, the book discusses the MOA software, covering the MOA graphical user interface, the command line, use of its API, and the development of new methods within MOA.
Learning from data streams : Processing techniques in sensor networks
The book provides the reader with a comprehensive overview of stream data processing, including famous prototype implementations like the Nile system and the TinyOS operating system. The set of chapters covers the state-of-art in data stream mining approaches using clustering, predictive learning, and tensor analysis techniques, and applying them to applications in security, the natural sciences, and education.
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 databases and information systems ; 25th European Conference, ADBIS 2021, Tartu, Estonia, August 24–26, 2021, Proceedings
This book constitutes the proceedings of the 25th European Conference on Advances in Databases and Information Systems, ADBIS 2021, held in Tartu, Estonia, in August 2021. The 18 full papers presented together with 3 keynotes were carefully reviewed and selected from 70 submissions. The selected papers span a wide spectrum of topics in databases and related technologies, tackling challenging problems and presenting inventive and efficient solutions. They are organized in 5 sessions: patterns and events, social media and text mining, indexes, queries and constraints, high-dimensional data and data streams, and data integration.
Advances in Big Data Analytics : Theory, Algorithms and Practices
Provides a comprehensive and cutting-edge study on big data analytics, based on the research findings and applications developed by the author and his colleagues in related areas. It addresses the concepts of big data analytics and/or data science, multi-criteria optimization for learning, expert and rule-based data analysis, support vector machines for classification, feature selection, data stream analysis, learning analysis, sentiment analysis, link analysis, and evaluation analysis. The book also explores lessons learned in applying big data to business, engineering and healthcare. Lastly, it addresses the advanced topic of intelligence-quotient (IQ) tests for artificial intelligence.
