Konferenzen  >  Informatik  >  Informations & Wissensmanagement, Big Data Computing

Mathematisches Forschungsinstitut Oberwolfach (MFO, Oberwolfach Research Institute for Mathematics)

Neuronale Netze und Künstliche Intelligenz, Maschinelles Lernen,    Wissenschaftliches Rechnen und Datenwissenschaft

ACMSC

Email: amychou2020@163.com

Big Data Foundations; Foundational theoretical or computational models for big data; Big Data Platforms and Technologies; Innovative, concurrent, and scalable big data platforms; Big Data Analytics and Services; Algorithms and systems for big data search, analytics and visualization; Big Data Applications and Experiences; Intelligent Computing Methodologies and Applications; Evolutionary Computing and Learning; Genetic Algorithms ;

The International Science Council’s Committee on Data (CODATA) and World Data System (WDS), and the Research Data Alliance (RDA)

International Data Week brings together a global community of data scientists and data stewards; researchers from all domains; data, interoperability and informatics experts from all fields; industry leaders, entrepreneurs and policymakers.

Qilu Normal University & Department of Education of Shandong Province &Purple Mountain Observatory, Chinese Academy of Sciences &National Supercomputing Jinan Center

Astronomy is facing the challenges and opportunities of big data. China played an important role in the development of basic disciplines of astronomy in the past two decades. Some large-scale ground-based and space-based astronomical equipments have been put into use, such as the ground-based 500-meter aperture spherical radio telescope FAST, the large-scale area multi-target optical fiber spectroscopic telescope LAMOST, the high-altitude cosmic ray observatory LHAASO, the space-based high-energy satellites DAMPE Wukong, HXMT Huiyan, GeCam Huairou-1, solar satellites Xihe and ASOS. And the chinese space station telescope CSST, Einstein EP and Sino-French cooperation satellite SVOM are preparing for launch. These large scientific equipments provide astronomical researchers with massive amounts of high-quality data, pushing astronomy research to the era of big data.

Local Organizing Committee;     Tel.: [+8613518615092];     Email: menquan@qlnu.edu.cn

Astronomical big data analysis processing and mining, construction of China's astronomical big data processing and sharing platform, astronomical big data algorithm, radio astronomy and compact star physics

Astronomie, Astrophysik und Kosmologie,    Computerphysik und Numerische Simulation

ICCBD 2023 is organized by University of Shanghai for Science and Technology, China and co-organized by Southern University of Science and Technology, China, Shanghai Jiao Tong University, China. And technical supported by IEEE Computer Society and Special Technical Communities on Big Data.

2023 6th International Conference on Computing and Big Data (ICCBD 2023) will be held in University of Shanghai for Science and Technology, China during October 27-29, 2023.

Email: geoff@iedrc.net

Identification of customer behavior; DER management and demand response; Transaction of power consumption right; Big Data Analytics; Novel Theoretical and Computational Models; Framework for Data Understanding and Quality; Big Data Science and Engineering: Big Data in Smart Planet Solutions; 5G network and communication; loud computing and edge computing; Internet of Things (IoT) integrated power systems; QoS Management in IoT Networks; Sensor Networks Protocols; IoT in 5G Networks;

ACMSC

Email: amychou2020@163.com

Big Data Science and Foundations; Big Data Infrastructure; Big Data Management; Big Data Search and Mining; Big Data Security, Privacy and Trust; Big Data Applications;

CIRM – Centre International de Rencontres Mathématiques

Wissenschaftliches Rechnen und Datenwissenschaft,    Netzgemeinschaften und Gesellschaftliche Aspekte der Informationstechnologie

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

Research data is essential to facilitate scientific progress, yet, many valuable datasets are hidden on web sites and small repositories or are hard to find due to insufficient metadata. Only a fraction of researchers pro-actively share dataset metadata through public portals, and curation of such metadata collections is costly. Unknown Data will provide means to automatically discover, extract, and publish metadata about research data that is hidden on the Web or in scholarly publications. Thus, the project’s goal is to improve findability and re-usability of research data by (a) improving metadata quality, in particular with respect to authority and use of existing datasets and (b) uncovering datasets that are not yet reflected in public data repositories and registries.

The event is sponsored by City University of Macau.

2024 the 2nd International Conference on Big Data will be held in Macau, China during January 10-12， 2024. This conference provides opportunities for the different areas delegates to exchange new ideas and application experiences face to face, to establish business or research relations and to find global partners for future collaboration. We hope that the conference results constituted significant contribution to the knowledge in these up-to-date scientific field.

Email: saraxc17@163.com

Big Data Architecture and Systems; Deep Learning architecture, representations, unsupervised and supervised algorithms; Algorithmic, experimental, prototyping and implementation; Data Security and Management; Blockchain; Privacy and Security for Cloud and Big Data; Big Data Analysis and Computing; Big Data Mining and Analytics; Big Data Processing and Querying;

YOUNG-nd

The Organizing Committee is pleased to announce that 2024 The 7th International Conference on Software Engineering and Information Management (ICSIM 2024) will still be held in Fiji on January 23-25, 2024, which is sponsored by the University of Fiji.

Email: emmaawang2019@sina.com

Software Agent Technology; Middleware Design Techniques; Reliability & Modeling Analysis; e-Government Engineering; Modeling with UML; Cost Modeling and Analysis; Information Management; Business strategy and information systems; Management Information Systems; Information systems planning and management; Adoption of information technology in organizations; Human factors in information systems;

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

Graphs and knowledge bases have been around for many decades, and research outcomes have tremendously impacted areas like mathematics, artificial intelligence, and databases. However, despite being already coined by the scientific community, technological developments and astronomical data growth make knowledge graph management a fundamental topic nowadays in various computer science areas, supporting novel applications at the science (e.g., biomedicine) and industry (e.g., Google’s Knowledge Graph) level.

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

Many big data sets in various application domains have complex relationships, which can be modelled as graphs, consisting of entities and relationships between them. Consequently, graphs are extensively studied in both Mathematics and Computer science. In particular, planar graphs, which can be drawn without edge crossings in the plane, form a distinguished role in Graph Theory and Graph Algorithms. Many structural properties of planar graphs are investigated, in terms of excluded minors, low density, and small separators, which lead to efficient algorithms for planar graphs. Consequently, fundamental algorithms for planar graphs have been discovered. However, most real-world graphs, such as social networks and biological networks, are nonplanar. For example, the scale-free networks, which are used to model web graphs, social networks and biological networks, are globally sparse nonplanar graphs, with locally dense clusters and low diameters. To understand such real-world networks, we need to solve fundamental mathematical and algorithmic research questions on beyond-planar graphs, which generalize the notion of planar graphs, in terms of topological constraints or forbidden edge crossing patterns.

Graphentheorie und Kombinatorik,    Wissenschaftliches Rechnen und Datenwissenschaft

The South Asia Institute of Science and Engineering

Warm welcome to the official website of 2024 the 10th International Conference on Information Management (ICIM2024). The conference will be held at the University of Cambridge, Cambridge, UK, from the 8th to the 10th of March, 2024.

Email: brandon@saise.org

Information Management & Systems; Information Systems and Technology; E-Business Engineering and Management; Marketing Intelligence & Digital Marketing; Green marketing & Sustainable Business; Human Resources;

Mathematisches Forschungsinstitut Oberwolfach (MFO, Oberwolfach Research Institute for Mathematics)

Analysis,    Infinitesimalrechnung, Differentialgleichungen, Integralrechnung

ICMS - International Centre for Mathematical Sciences

This workshop will gather researchers from the intersecting fields of inverse problems, uncertainty quantification, data analytics, machine learning, and related areas to discuss novel theory and new methods for big data challenges. A focused multifaceted research group gathered at one location is an excellent opportunity for established and early career researchers. The aim of this workshop is to foster a diverse, open, and friendly environment to advance this field, open new directions, and to stimulate new collaboration.

Wissenschaftliches Rechnen und Datenwissenschaft,    Angewandte Mathematik (allgem.)

The International Centre for Mathematical Sciences (ICMS)

This workshop will gather researchers from the intersecting fields of inverse problems, uncertainty quantification, data analytics, machine learning, and related areas to discuss novel theory and new methods for big data challenges. A focused multifaceted research group gathered at one location is an excellent opportunity for established and early career researchers. The aim of this workshop is to foster a diverse, open, and friendly environment to advance this field, open new directions, and to stimulate new collaboration. 

Department of Industrial Engineering, Faculty of Engineering, Chulalongkorn University

2024 9th International Conference on Big Data and Computing (ICBDC 2024) will be held during May 24-26, 2024 in Bangkok, Thailand . ICBDC 2024 is mainly organized by Department of Industrial Engineering, Faculty of Engineering, Chulalongkorn University.

Email: Dunn@iact.net

Algorithmic, experimental, prototyping and implementation; Big Data Algorithms, Applications and Services; Big Data Mining and Analytics; Big Data Processing and Querying; Cloud and Big Data for Internet of Things (IoT); Computing, scheduling and resource management for sustainability; Government and Industrial Experiences for Cloud and Big Data; Green Computing and Networking Technologies for Cloud and Big Data;

YOUNG-nd

2024 The 7th International Conference on Artificial Intelligence and Big Data (ICAIBD 2024) will take place on May 24-27, 2024 in Chengdu, China. It is sponsored by Sichuan Province Computer Federation and technically assisted by many local and international universities.

Email: emmaawang2019@sina.com

Intelligent information systems; Intelligent data mining and farming; Intelligent web-based business; AI and evolutionary algorithms; Big Data Analytics and Metrics; Cloud Computing Techniques for Big Data; Big Data Persistence and Preservation; Big Data Quality and Provenance Control; Big Data Economics; Neural networks and applications; Learning and adaptive sensor fusion; Management Issues of Social Network Big Data; Distributed, and Peer-to-peer Search; Machine learning based on Big Data;

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

Data-sharing across domain and infrastructure boundaries is becoming more common, and significant work has been done in aligning many of the standard model describing the information and data being exchanged. In order to guarantee the scalability and practicality of data-sharing implementations, these standard models need to be aligned with the emerging technology architectures designed to support exchange networks at this scale. This workshop will look at how the standard metadata models for cross-domain use - including DDI-CDI and related models - and architectural approaches can best be used to leverage the strengths of both.

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

Through a series of Dagstuhl hosted workshops, and the CODATA-DDI collaboration, we have developed a framework for cross-domain interoperability, published by the EC-funded WorldFAIR project. This framework is being employed both within data intensive science and and between the scientific world and the world of official data, collected to inform policy making, including in relation to the Sustainable Development Goals and other components of the UN Agenda. The effective use and uptake of such a framework will require methodologies to evaluate implementations. This workshop will explore such approaches, building on current work for FAIR assessment, and the experience gained from implementation in a range of case studies, including those in WorldFAIR. The workshop will focus on improvements to evaluation metrics, and to the metadata exchange frameworks themselves.

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

The vision of NFDI4DS is to support all steps of the complex and interdisciplinary research data lifecycle in Data Science and Artificial Intelligence. The overarching objective of NFDI4DS is the development, establishment, and sustainment of a national research data infrastructure for the Data Science and Artificial Intelligence community. The key idea is to work towards increasing the transparency, reproducibility and fairness of Data Science and Artificial Intelligence projects, by making all digital artifacts available, interlinking them, and offering innovative tools and services. Within this Dagstuhl Research Meeting, the NFDI4DS partners will reflect on their progress so far, and will work on their long-term strategy.

Neuronale Netze und Künstliche Intelligenz, Maschinelles Lernen,    Wissenschaftliches Rechnen und Datenwissenschaft