Trends in Design, Optimization, Languages, and Analytical Processing of Big Data (DOLAP 2020)

Data science requires the creation of complex data ecosystems to support data analysis, which we refer to as data-based information systems (DBIS). The diversity of techniques to manage and analyse data has contributed to a wide variety of DBIS. On the one hand, current data management solutions spa...

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Vydáno v:Information systems (Oxford) Ročník 104; s. 101929
Hlavní autoři: Hose, Katja, Romero, Oscar, Song, Il-Yeol
Médium: Journal Article
Jazyk:angličtina
Vydáno: Oxford Elsevier Ltd 01.02.2022
Elsevier Science Ltd
Témata:
Big Data
Data analysis
Data analytics
Data management
Data mining
Data processing
Data science
Deep learning
Design
Design analysis
Design optimization
Exact solutions
Information retrieval
Information systems
Information technology
Knowledge representation
Languages
Machine learning
Management
Management techniques
Mathematical analysis
Optimization
Processing
Relational data bases
Design
Data analytics
Big data
Data management
Optimization
Processing
ISSN:0306-4379, 1873-6076
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Abstract Data science requires the creation of complex data ecosystems to support data analysis, which we refer to as data-based information systems (DBIS). The diversity of techniques to manage and analyse data has contributed to a wide variety of DBIS. On the one hand, current data management solutions span classical relational databases, distributed (relational and non-relational) systems, document-oriented databases, column stores, in-memory databases, property and knowledge graph databases, stream processors, scientific databases, etc. On the other hand, data analytics techniques range from classical statistical-based data mining, to machine learning, process-oriented data analysis, stream and complex event processing, graph analytics, etc. On top of that, hardware-accelerated solutions, specially related to deep learning and CPU-intensive analytical solutions are complicating the big picture. Nowadays, a prominent research trend is to devise specific data management techniques to accelerate and improve the overall throughput and answer time of DBIS. DOLAP, the International Workshop On Design, Optimization, Languages and Analytical Processing of Big Data, has become a reference discussion forum where to witness the current advances in data management for modern data analytics needs. We summarize the advances presented in DOLAP 2020 and the best papers selected for the DOLAP 2020 Information Systems Special Issue.
AbstractList Data science requires the creation of complex data ecosystems to support data analysis, which we refer to as data-based information systems (DBIS). The diversity of techniques to manage and analyse data has contributed to a wide variety of DBIS. On the one hand, current data management solutions span classical relational databases, distributed (relational and non-relational) systems, document-oriented databases, column stores, in-memory databases, property and knowledge graph databases, stream processors, scientific databases, etc. On the other hand, data analytics techniques range from classical statistical-based data mining, to machine learning, process-oriented data analysis, stream and complex event processing, graph analytics, etc. On top of that, hardware-accelerated solutions, specially related to deep learning and CPU-intensive analytical solutions are complicating the big picture. Nowadays, a prominent research trend is to devise specific data management techniques to accelerate and improve the overall throughput and answer time of DBIS. DOLAP, the International Workshop On Design, Optimization, Languages and Analytical Processing of Big Data, has become a reference discussion forum where to witness the current advances in data management for modern data analytics needs. We summarize the advances presented in DOLAP 2020 and the best papers selected for the DOLAP 2020 Information Systems Special Issue.
Data science requires the creation of complex data ecosystems to support data analysis, which we refer to as data-based information systems (DBIS). The diversity of techniques to manage and analyse data has contributed to a wide variety of DBIS. On the one hand, current data management solutions span classical relational databases, distributed (relational and non-relational) systems, document-oriented databases, column stores, in-memory databases, property and knowledge graph databases, stream processors, scientific databases, etc. On the other hand, data analytics techniques range from classical statistical-based data mining, to machine learning, process-oriented data analysis, stream and complex event processing, graph analytics, etc. On top of that, hardware-accelerated solutions, specially related to deep learning and CPU-intensive analytical solutions are complicating the big picture. Nowadays, a prominent research trend is to devise specific data management techniques to accelerate and improve the overall throughput and answer time of DBIS. DOLAP, the International Workshop On Design, Optimization, Languages and Analytical Processing of Big Data, has become a reference discussion forum where to witness the current advances in data management for modern data analytics needs. We summarize the advances presented in DOLAP 2020 and the best papers selected for the DOLAP 2020 Information Systems Special Issue.
ArticleNumber 101929
Author Song, Il-Yeol
Hose, Katja
Romero, Oscar
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  surname: Song
  fullname: Song, Il-Yeol
  email: songiy@drexel.edu
  organization: Drexel University, Philadelphia, United States
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SubjectTerms Big Data
Data analysis
Data analytics
Data management
Data mining
Data processing
Data science
Deep learning
Design
Design analysis
Design optimization
Exact solutions
Information retrieval
Information systems
Information technology
Knowledge representation
Languages
Machine learning
Management
Management techniques
Mathematical analysis
Optimization
Processing
Relational data bases
Title Trends in Design, Optimization, Languages, and Analytical Processing of Big Data (DOLAP 2020)
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