| id |
oapen-20.500.12657-41294
|
| record_format |
dspace
|
| spelling |
oapen-20.500.12657-412942020-08-14T01:18:08Z Knowledge Graphs and Big Data Processing Janev, Valentina Graux, Damien Jabeen, Hajira Sallinger, Emanuel Database Management Information Systems Applications (incl. Internet) Logic in AI Computer Appl. in Administrative Data Processing Business Information Systems Computer and Information Systems Applications Computer Application in Administrative Data Processing artificial intelligence big data data analytics data handling data integration data mining databases digital storage domain knowledge graph theory information management information technology integrated data internet knowledge management knowledge-based system ontologies semantics Databases Database programming Information retrieval Internet searching Artificial intelligence Public administration Information technology: general issues Business mathematics & systems bic Book Industry Communication::U Computing & information technology::UN Databases bic Book Industry Communication::U Computing & information technology::UN Databases::UNH Information retrieval bic Book Industry Communication::U Computing & information technology::UY Computer science::UYQ Artificial intelligence bic Book Industry Communication::J Society & social sciences::JP Politics & government::JPP Public administration bic Book Industry Communication::K Economics, finance, business & management::KJ Business & management::KJQ Business mathematics & systems This open access book is part of the LAMBDA Project (Learning, Applying, Multiplying Big Data Analytics), funded by the European Union, GA No. 809965. Data Analytics involves applying algorithmic processes to derive insights. Nowadays it is used in many industries to allow organizations and companies to make better decisions as well as to verify or disprove existing theories or models. The term data analytics is often used interchangeably with intelligence, statistics, reasoning, data mining, knowledge discovery, and others. The goal of this book is to introduce some of the definitions, methods, tools, frameworks, and solutions for big data processing, starting from the process of information extraction and knowledge representation, via knowledge processing and analytics to visualization, sense-making, and practical applications. Each chapter in this book addresses some pertinent aspect of the data processing chain, with a specific focus on understanding Enterprise Knowledge Graphs, Semantic Big Data Architectures, and Smart Data Analytics solutions. This book is addressed to graduate students from technical disciplines, to professional audiences following continuous education short courses, and to researchers from diverse areas following self-study courses. Basic skills in computer science, mathematics, and statistics are required. 2020-08-13T11:54:12Z 2020-08-13T11:54:12Z 2020 book ONIX_20200813_9783030531997_36 https://library.oapen.org/handle/20.500.12657/41294 eng Lecture Notes in Computer Science; Information Systems and Applications, incl. Internet/Web, and HCI application/pdf n/a 2020_Book_KnowledgeGraphsAndBigDataProce.pdf https://www.springer.com/9783030531997 Springer Nature Springer 10.1007/978-3-030-53199-7 10.1007/978-3-030-53199-7 6c6992af-b843-4f46-859c-f6e9998e40d5 178e65b9-dd53-4922-b85c-0aaa74fce079 European Research Council (ERC) Springer 12072 209 734242 H2020 European Research Council H2020 Excellent Science - European Research Council open access
|
| institution |
OAPEN
|
| collection |
DSpace
|
| language |
English
|
| description |
This open access book is part of the LAMBDA Project (Learning, Applying, Multiplying Big Data Analytics), funded by the European Union, GA No. 809965. Data Analytics involves applying algorithmic processes to derive insights. Nowadays it is used in many industries to allow organizations and companies to make better decisions as well as to verify or disprove existing theories or models. The term data analytics is often used interchangeably with intelligence, statistics, reasoning, data mining, knowledge discovery, and others. The goal of this book is to introduce some of the definitions, methods, tools, frameworks, and solutions for big data processing, starting from the process of information extraction and knowledge representation, via knowledge processing and analytics to visualization, sense-making, and practical applications. Each chapter in this book addresses some pertinent aspect of the data processing chain, with a specific focus on understanding Enterprise Knowledge Graphs, Semantic Big Data Architectures, and Smart Data Analytics solutions. This book is addressed to graduate students from technical disciplines, to professional audiences following continuous education short courses, and to researchers from diverse areas following self-study courses. Basic skills in computer science, mathematics, and statistics are required.
|
| title |
2020_Book_KnowledgeGraphsAndBigDataProce.pdf
|
| spellingShingle |
2020_Book_KnowledgeGraphsAndBigDataProce.pdf
|
| title_short |
2020_Book_KnowledgeGraphsAndBigDataProce.pdf
|
| title_full |
2020_Book_KnowledgeGraphsAndBigDataProce.pdf
|
| title_fullStr |
2020_Book_KnowledgeGraphsAndBigDataProce.pdf
|
| title_full_unstemmed |
2020_Book_KnowledgeGraphsAndBigDataProce.pdf
|
| title_sort |
2020_book_knowledgegraphsandbigdataproce.pdf
|
| publisher |
Springer Nature
|
| publishDate |
2020
|
| url |
https://www.springer.com/9783030531997
|
| _version_ |
1771297424921329664
|
