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1
CI 2024 — Collective Intelligence Conference
27. Jun 2024 - 28. Jun 2024 • Boston , MA, Vereinigte Staaten
Veranstalter:
Association for Computing Machinery (ACM)
Zusammenfassung:
The ACM Collective Intelligence Conference Series is an interdisciplinary event sponsored by SIGCHI that brings together researchers from academia, business, nonprofits, governments and the world at large to share insights and ideas relevant to understanding and designing collective intelligence in its many forms thereby fostering the use of collective intelligence.
Eintrags-ID:
1605715
2
ICCBDC 2024 — The 8th International Conference on Cloud and Big Data Computing
15. Aug 2024 - 17. Aug 2024 • Oxford, Großbritannien
Veranstalter:
The South Asia Institute of Science and Engineering
Zusammenfassung:
We sincerely invite you to participate 2024 8th International Conference on Cloud and Big Data Computing. It will be held at Oxford Brookes University, Oxford, United Kingdom during August 15-17, 2024.
Kontakt:
Ms. Vera Lee;     Email: brandon@saise.org
Themen:
Algorithmic, experimental, prototyping and implementation; Big Data Algorithms, Applications and Services; Big Data Mining and Analytics; Big Data Processing and Querying; Cloud computing and semantic web technologies; Cloud Computing Techniques for Big Data Analysis; Cloud Solution Design Patterns; Cloud for Enterprise Business Transformation;
Eintrags-ID:
1582935
3
Massive Data Models and Computational Geometry
23. Sep 2024 - 27. Sep 2024 • Bonn, Deutschland
Veranstalter:
The Hausdorff Research Institute for Mathematics (HIM)
Zusammenfassung:
The last decade has seen an explosion of available data. To analyze the enormous amount of accrued data, classical algorithms are often not appropriate: the data may not be accessible in its entirety (but perhaps only in an aggregate form) and computations may need to be distributed or done in an online setting. The data often comes with an additional geometric flavor, either from applications or from mapping the data to feature spaces. The workshop will focus on three classes of algorithms for dealing with massive geometric data, namely, streaming, distributed, and sublinear algorithms. The aim of the workshop to identify and explore new research directions at the interface of massive data models and computational geometry, through fruitful discussions leading to potential collaborations.
Eintrags-ID:
1623286
4
CBIoT 2024 — 5th International Conference on Cloud, Big Data and IoT
28. Sep 2024 - 29. Sep 2024 • Toronto, Kanada
Zusammenfassung:
5th International Conference on Cloud, Big Data and IoT (CBIoT 2024) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the areas of Cloud, Big Data and IoT. It will also serve to facilitate the exchange of information between researchers and industry professionals to discuss the latest issues and advancement in the area of Cloud, Big Data and IoT. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in Cloud, Big Data and IoT.
Kontakt:
Email: cbiot@itcse2024.org
Themen:
Cloud Computing Cloud Application, Infrastructure and Platforms Design Tool for Cloud Computing Cloud Business Service-Oriented Architecture in Cloud Computing Cloud Based, Parallel Processing Virtualization on Platforms in the Cloud Location Based Services, Presence, Availability, and Locality Mobile Clouds for New Millennium, Mobile Devices Social Clouds (Social Networks in the Cloud) Maintenance and Management of Cloud Computing Architecture Cloud Applications in Vertical Industries Cloud Storage and File Systems Networks within Cloud systems, the Storage Area, and to the Outside Virtualization in the Context of Cloud Computing Platforms System Integration, Virtual Compute Clusters Consolidation NoSQL Data Stores Performance, SLA Management and Enforcement Resource Provisioning Security Techniques for the Cloud Energy Management and Programming Environments Data storage and Management in Cloud Computing Case Studies and Theories in Cloud Computing The Open Cloud: Cloud Computing and Open Source Big Data Big Data Techniques, models and algorithms Big Data Infrastructure and platform Big Data Search and Mining Big Data Security, Privacy and Trust Big Data Applications, Bioinformatics, Multimedia etc Big Data Tools and systems Big Data Mining Big Data Management Cloud and grid computing for Big Data Machine Learning and AI for Big Data Big Data Analytics and Social Media 5G and Networks for Big Data IoT Internet of Things Security and Privacy for IoT IoT-enabled Innovation and Entrepreneurship IoT Applications and Services Experimental Results and Deployment Scenarios Electronics and Signal processing for IoT Connectivity and Networking
Eintrags-ID:
1620156
5
Migrating applications to cloud technologies
14. Okt 2024 - 18. Okt 2024 • CIRM (Marseille Luminy), Frankreich
Veranstalter:
CIRM – Centre International de Rencontres Mathématiques
Eintrags-ID:
1574811
6
Dagstuhl-Seminar — Graph Algorithms: Distributed Meets Dynamic
17. Nov 2024 - 22. Nov 2024 • Schloss Dagstuhl, Wadern, Deutschland
Veranstalter:
Schloss Dagstuhl - Leibniz-Zentrum für Informatik
Zusammenfassung:
In modern computational systems, the need to handle large-scale inputs imposes interesting computational challenges. Two such challenges are (1) the need to distribute the computation over multiple units, and (2) the dynamic nature of the input, which may undergo changes over time. A particular class of problems studied in these settings is when the input to the computational task is a huge graph. The field of dynamic graph algorithms addresses efficiently processing edge/vertex insertions/deletions in the input graph. In distributed graph algorithms, the input resides across multiple machines, and the goal is to solve the problem while minimizing the number of rounds of communication. Both of these rich research areas have been extensively studied since at least the 1980’s. We know of efficient algorithms for a large variety of tasks, such as shortest paths problems, coloring, subgraph finding, symmetry breaking, approximations, and many more. However, there are still fundamental problems with no known efficient solutions in some of these models, and even more where the exact complexity of computation is yet to be determined. In the recent years, a number of influential works show how transferring ideas from one of these models to the other provides progress on some of the long-lasting open problems. The goal of this Dagstuhl Seminar is to build a bridge between the two research communities of dynamic graph algorithms and distributed computing, by working together on joint research frontiers.
Eintrags-ID:
1589390
7
ICMLSC 2025 — 2025 The 9th International Conference on Machine Learning and Soft Computing
24. Jan 2025 - 26. Jan 2025 • Tokyo, Japan
Veranstalter:
CZESND
Zusammenfassung:
The 9th International Conference on Machine Learning and Soft Computing (ICMLSC 2025) will be held on January 24-26, 2025. The conference is organized by Chuo University, Japan, co-organized by Keiai University, Japan, and supported by University of Muenster, Germany and Concordia University, Canada.
Kontakt:
Email: Nazal@sina.com
Themen:
Machine learning; Intelligent planning; Visual/linguistic perception; Evolutionary and swarm algorithms; Neural computing; Emotional intelligence; Applications;
Eintrags-ID:
1612534
Webseite:
8
NSDI '25 — 22nd USENIX Symposium on Networked Systems Design and Implementation
28. Apr 2025 - 30. Apr 2025 • Philadelphia, PA, Vereinigte Staaten
Veranstalter:
USENIX Association
Zusammenfassung:
Join us in Philadelphia, PA, USA, on April 28–30, 2025, for the 22nd USENIX Symposium on Networked Systems Design and Implementation. NSDI focuses on the design principles, implementation, and practical evaluation of networked and distributed systems. Our goal is to bring together researchers from across the networking and systems community to foster a broad approach to addressing overlapping research challenges.
Eintrags-ID:
1607495
Verwandte Fachgebiete:


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