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

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.

The 8th International Conference on Algorithmic Decision Theory (ADT 2024) focuses on algorithmic decision theory broadly defined, seeking to bring together researchers and practitioners coming from diverse areas of Computer Science, Economics, and Operations Research in order to improve the theory and practice of modern decision support.

Datenbanktechlologie, Suchmaschinen und Data-Mining,    Soft Computing, Evolutionäre Algorithmen, metaheuristische Optimierung, Fuzzylogik

Asia Society of Researchers

The conference is sponsored by Southern University of Science and Technology, China, co-sponsored by Chongqing University, China.

Email: adam@asr.org

Communication and Information Theory; Networking and Security; Electronic Design and Technologies; Signal Processing and Digital Communications;

The 15th Multidisciplinary Workshop on Advances in Preference Handling (M-PREF 2024) addresses all computational aspects of preference handling. This includes methods for the elicitation, learning, modeling, representation, aggregation, and management of preferences and for reasoning about preferences. The workshop studies the usage of preferences in computational tasks from decision making, database querying, web search, personalized human-computer interaction, personalized recommender systems, e-commerce, multi-agent systems, game theory, social choice, combinatorial optimization, planning and robotics, automated problem solving, perception and natural language understanding and other computational tasks involving choices. The workshop seeks to improve the overall understanding of and best methodologies for preferences in order to realize their benefits in the multiplicity of tasks for which they are used. Another important goal is to provide cross-fertilization between the numerous sub-fields that work with preferences.

Datenbanktechlologie, Suchmaschinen und Data-Mining,    Soft Computing, Evolutionäre Algorithmen, metaheuristische Optimierung, Fuzzylogik

Association for Computing Machinery (ACM)

The Conference on Information and Knowledge Management (CIKM) provides an international forum for presentation and discussion of research on information and knowledge management, as well as recent advances on data and knowledge bases. The purpose of the conference is to identify challenging problems facing the development of future knowledge and information systems, and to shape future directions of research by soliciting and reviewing high quality, applied and theoretical research findings. An important part of the conference is the Workshops program which focuses on timely research challenges and initiatives. CIKM has a strong tradition of workshops devoted to emerging areas of database management, IR, and related fields. Workshops vary from year to year.

Institute for Computational and Experimental Research in Mathematics (ICERM)

The unprecedented amount of genomic data that has become readily available presents specific challenges for the field of phylogenetic inference, which is concerned with estimating the evolutionary relationships among collections of species, populations, or sequences. These challenges include the development of evolutionary models that are sufficiently complex to be biologically realistic while remaining computationally tractable; deriving and implementing algorithms to efficiently estimate phylogenetic relationships that use models whose theoretical properties are well-understood and therefore interpretable; and devising ways to scale novel methodology developed to handle datasets that are increasingly large and complex. This workshop focuses on statistical modeling and the scaling of phylogenetic methods. Topics will include modeling (e.g. multispecies coalescent model with extension to networks; diversification models) and inference with speed to scale to genomic datasets, consistency, and robustness using statistical, combinatorial, and algebraic approaches. This workshop will present the latest advances in these areas and serve as a forum to spark new ideas and collaborations.

ICERM;     Email: info@icerm.brown.edu

statistical modeling, phylogenetics, combinatorics, algebra

Henry Stewart Events

Henry Stewart is delighted to announce the inaugural event Semantic Data: Taxonomy, Ontology and Knowledge Graphs taking place in New York, USA on October 23, 2024. The event will be co-located with DAM New York 2024. Chaired by Madi Weland Solomon, Head of Client Solutions, Graphifi, join us for a one-day conference focused on the critical role of data in shaping corporate advantage amidst the rise of AI technologies. This event delves into practical approaches to managing and leveraging data resources effectively, with a particular emphasis on taxonomies, ontologies, and knowledge graphs.

Email: JennyM@henrystewart.co.uk

Semantic Data, Semantic Layer, Linked Data, knowledge graph, semantic architect, NLP, natural language processing, semantic analysis, semantic taxonomy, taxonomies, ontology, ontologies, Ontologist, knowledge graphs, semantic web, RDF, Resource Description Framework, OWL, Ontology Web Language, Ontology, Ontologies, Semantic Technology, SPARQL, Interoperability, Information Science, Artificial Intelligence, Data Integration, Semantic Web, Taxonomies, Taxonomist

Wirtschaftswissenschaft, Unternehmen und Marketing,    e-Business, e-Government, Software as a service (SaaS)

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

Neo4j Inc

NODES 2024 is a free online developer conference for anyone interested in knowledge graphs, AI, and intelligent applications. This skills-building event, which will run for 24 hours and include technical presentations by Neo4j and community experts, will have four tracks and 145+ sessions. Check out the agenda.

Community Manager;     Tel.: [408.315.2514];     Email: Yolande.Poirier@neo4j.com

Artificial Intelligence, Software, Machine learning, Graph, RAG, GraphRAG, Algorithms, Visualization, Application Development, Graph Database, Knowledge Graphs, Data Science

Computerprogramme,    Neuronale Netze und Künstliche Intelligenz, Maschinelles Lernen

Asia Society of Researchers

2024 4th International Conference on Advanced Enterprise Information System (AEIS 2024) will be held in Cambridge, UK during December 06-08, 2024.

Email: adam@asr.org

Enterprise Engineering; Models and Frameworks; Enterprise Knowledge Engineering and Management; Business Modelling and Business Process Management; EA Adoption and Governance;

Asia Society of Researchers

2024 8th International Conference on Natural Language Processing and Information Retrieval (NLPIR 2024) will bring together researchers, which devoted their work to progress in the above described timely tasks. It will be held in Okayama, Japan during December 13-15, 2024.

Email: adam@asr.org

Automated knowledge aquisition and representation; Natural language understanding; Topic recognition and topic tracking, subject indexing; Event and anomaly detection - Sentiment analysis;

Institute for Artificial Intelligence, Peking University

The Ninth International Conference on Data Mining and Big Data ([*ACRONYM*]) serves as an international forum for researchers and practitioners to exchange latest advantages in theories, algorithms, models, and applications of data mining and big data as well as artificial intelligence techniques. Data mining refers to the activity of going through big data sets to look for relevant or pertinent information. Big data contains huge amount of data and information and is worth researching in depth. [*ACRONYM*] is the ninth event after the preceding Sanya, Beijing, Guangzhou, Belgrade, Chiang Mai, Shanghai, Fukuoka and Bali events where more than hundreds of delegates from all over the world to attend and share their latest achievements, innovative ideas, marvelous designs and excel implementations.

Email: dmbd2024@iasei.org

Institute of Mathematical Statistics (IMS)

The objective of ICSDS is to bring together researchers in statistics and data science from academia, industry, and government in a stimulating setting to exchange ideas on the developments of modern statistics, machine learning, and broadly defined theory, methods, and applications in data science. There will be a student paper award competition, in addition to plenary sessions, and invited, contributed and poster sessions. Young researchers are particularly encouraged to participate, as a portion of the invited sessions will be designated for young researchers.

Statistik, Spieltheorie ,    Wissenschaftliches Rechnen und Datenwissenschaft

YOUNG

2025 3rd International Conference on Big Data and Privacy Computing will be held in Fuzhou, China during January 10-12, 2025. The event is sponsored by Fuzhou University, China.

Email: saraxc17@163.com

Big Data Architecture and Systems; Data Encryption and Privacy Protection; Big Data Analysis and Computing; Big Data Science and Applications; Data Security and Blockchain Application Technology;

Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH

Process mining and visual analytics are separate disciplines with the common goal of helping humans gain insight into and extract knowledge about relevant phenomena from complex data. Process mining (PM) is a rapidly growing discipline blending machine learning and data mining concepts with ideas taken from the field of business process management (BPM). It utilizes event data recorded by IT systems that support business process execution for a variety of tasks, from the automated discovery of graphical process models to operational support. Visual Analytics (VA) is a multidisciplinary approach that combines interactive, visual, and analytical methods to make complex phenomena more comprehensible, facilitate new insights, and enable knowledge discovery. VA research happens at the intersection of data mining and knowledge discovery, information visualization, human-computer interaction, and cognitive science.

Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH

In today’s digital landscape, complexity grows with increasing data volume and degree of interconnection. A suitable data abstraction is crucial for comprehending and navigating this dense network of connections. Starting from Euler’s pioneering work on The Bridges of Konigsberg in 1735, graphs have steadily evolved as a robust and adaptable conceptual framework. Graphs are universal representations of concepts, where nodes are markers for distinct entities and edges delineate their interrelations, further enriched with detailed annotations when necessary. Graphs are successful in various domains, like bioinformatics, e-commerce, logistics and transportation networks, urban planning, and even pandemic analysis or vaccine development (e.g., during COVID-19).

ETPUB

2025 the 3rd International Conference on Intelligent Control and Computing (IC&C 2025) will take place in Changchun, China, from April 25 to 27, 2025. The goal of this conference is to foster pure and applied researches in recognition of academic and industry, and to encourage interdisciplinary and international collaboration for the participants.The conference would be an ideal platform for keeping up with advances and changes to a consistently morphing field, we sincerely look forward to some exemplary and inspirational project experiences and innovations.

Email: helen@academic.net

Autonomous Systems; Decision Support Systems; Learning and Adaptive Control Systems; Intelligent Fault Detection and Identification; Intelligent Control Theory and Applications; Network Intelligence and Network Control; Hybrid Intelligent Systems; Neural Networks Based Control Systems; Fuzzy Control;

Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH

The increasing amount of data being collected, stored, and analyzed induces a need for efficient, scalable, and robust methods to handle this data. Representation learning, i.e. the practice of leveraging neural networks to obtain generic representations of data objects, has been shown effective for various applications over data modalities such as images and text. More recently, representation learning has shown initial impressive capabilities on structured data (e.g. relational tables in databases), for a limited set of tasks in data management and analysis, such as data cleaning, insight retrieval, and data analytics. Most applications traditionally relied on heuristics and statistics, which are limited in robustness, scale, and accuracy. The ability to learn abstract representations across tables unlocked new opportunities, such as pretrained models for data augmentation and machine learning, that address these limitations. This emerging research area, which we refer to as Table Representation Learning (TRL), receives increasing interest from industry as well as academia , in particular in the communities of data management, machine learning, and natural language processing. This growing interest is a result of the high potential impact of TRL in industry given the abundance of tables in the organizational data landscape, the large range of high-value applications relying on tables, and the early state of TRL research so far.

ICBDC2025 is technically sponsored by Sensors and System Society of Singapore, Newcastle Australia Institute of Higher Education Pte Ltd, Singapore, supported by Harbin Engineering University, China.

2025 10th International Conference on Big Data and Computing (ICBDC 2025) will be held during May 16-18, 2025 in Singapore. The aim of ICBDC 2025 is to present the latest research and results of scientists related to Big Data and Computing topics.

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;

CZESND

2025 9th International Conference on Power Energy Systems and Applications (ICoPESA) will be held at Nanjing, China during June 20-22, 2025. ICoPESA 2025 is sponsored by Nanjing University of Aeronautics and Astronautics. The conference continues to retain its tradition of high-quality conference and will open up an opportunity for academics and industrial professionals worldwide to exchange their knowledge of the state-of-the-art power energy systems and applications, the future role of power energy systems in ecological revolution will be explored at ICoPESA.

Email: Nazal@sina.com

Power system solutions towards a net-zero future; Energy efficiency and low carbon emission; Big data & AI in energy internet; New-type power system; Power electronics application in power systems; Aerospace power electronics; Renewable energy and energy storage systems; Electric power sustainable technologies; Transportation electrification and more-electric-aircraft; Power equipment planning & asset management;

Stromversorgungsnetz, Intelligentes Stromnetz,    Systemtheorie, Regelung und Automatisierung

Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH

This summer school is an intensive course on software techniques for data management and database management. Topics include database command translation and query optimization, query execution and indexing, row- and column-oriented storage structures and algorithms, optimistic and pessimistic concurrency control, logging and recovery, replication and high availability, scalability and cloud computing, and more. The goal of lectures and presentations is to understand classic techniques and to link them to industrial reality and to research opportunities. The target audience are recent MS graduates who have already taken 2-3 courses covering data management software and who are embarking on a career in industrial development or in academic research in related topics.

Veranstaltungen für Studenten der Informatik,    Datenbanktechlologie, Suchmaschinen und Data-Mining

Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH

The Autumn School for Information Retrieval and Information Foraging (ASIRF) focuses on recent developments in Information Retrieval and Information-Seeking behavior and their applications. Information Retrieval deals with searching in huge amounts of unstructured data. In many professional fields of work and everyday life, access to text data via search engines is one of the most important forms of access to knowledge. Information Foraging aims to model the entire process from the awareness of an information problem through selecting appropriate information sources to the actual search.

Students and lecturers will live together for a week at Schloss Dagstuhl, which guarantees the school's traditionally highly communicative and interactive atmosphere. The intended audience of ASIRF are doctoral students and young researchers working in Information Retrieval, Information Foraging, or using concepts or methods from these fields in their research

Veranstaltungen für Studenten der Informatik,    Datenbanktechlologie, Suchmaschinen und Data-Mining

Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH

More and more, we find that the provenance of data is a key missing ingredient in the information set made available to us. Provenance - the origination and intention of data as collected - provides significant challenges for modern networks. While standards exist for describing data and its analysis - and even for parts of the provenance chain - there is no standard mechanism for tying together all relevant information across the series of steps which fully describe the origination of data. This workshop explores how the many contextualising factors needed to support responsible and accurate data reuse can be described, allowing for the needed chaining of provenance steps.