Conferences  >  Informatics  >  Information & Knowledge Management, Big Data Computing

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

Neural Networks and Artificial Intelligence, Machine Learning,    Scientific Computing and Data Science

ACMSC

Email: amychou2020@163.com

Big Data Science and Foundations; Big Data Infrastructure; Big Data Management; Big Data Search and Mining;

Istanbul Technical University

EELISA Summer School on Platform Urbanism: How do digital platforms read the city and intervene in urban space? (4 ECTS). Applications are open to MSc. / PhD students from the EELISA universities, Early Career Researchers (maximum of 10 years after obtaining their PhD) and Professionals from all over the world.

Email: eelisa@itu.edu.tr

urbanism, platform urbanism, big data, digital platforms, data mining, urban studies

The South Asia Institute of Science and Engineering

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.

Ms. Vera Lee;     Email: brandon@saise.org

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;

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

The central topic of our Dagstuhl Seminar is the development, implementation, and evaluation of new AI technologies to support decision-making in management. A distinguishing feature is therefore innovative algorithms from the field of AI (e.g. explainable AI, generative AI, large language models, probabilistic ML, causal ML, etc.) that enable new insights in practice and beyond. This holds great potential for informing and improving decision-making.

Association for Computing Machinery (ACM)

The annual ACM SIGKDD conference is the premier international forum for data mining researchers and practitioners from academia, industry, and government to share their ideas, research results and experiences. The KDD conferences feature keynote presentations, oral paper presentations, poster sessions, workshops, tutorials, panels, exhibits, demonstrations, and the KDD Cup competition.

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

Conversational Agents (CA) as frontends to Information Retrieval (IR) and Recommender Systems (RS) become more popular in everyday life, with a wider range of users and usages. The latest developments in Large Language Models (LLMs) will have tremendous consequences, especially for the workplace and education. In this Dagstuhl Perspectives Workshop, we want to focus on the evaluation of these conversational systems, as appropriate methods are still missing. The quality of these systems is limited in terms of personalization, veracity and correctness, bias, transparency, trustworthiness, and understandability. Thus, evaluation methods must address these shortcomings. Furthermore, user- and usage-oriented aspects should become a more prominent and integral component in evaluations, as the user population as well as the tasks these systems are used for become more heterogeneous. For this reason, the topic-centric view of relevance has to be extended to a broad range of facets which are important for the different usage scenarios. Therefore, suitable evaluation criteria have to be specified, which form the basis for defining appropriate measures. Most importantly, the range of evaluation methods must be revisited and extended, as popular methods like the Cranfield approach or crowdsourcing must be complemented by new evaluation methods and strategies specifically tailored to this new type of system.

Natural Language Processing, Computational Linguistics,    Implementation and Applications of Artificial Intelligence

International Academy of Computing Technology

2024 7th International Conference on Big Data and Machine Learning will be held in Xiamen, China during September 20-22, 2024. Machine Learning (ML) and Big Data Processing & Analytics (BDA) are both evolutionary fields of computing and the developments in these fields are complementing each other. The ever changing data landscape in modern digital world have resulted in newer ways of data processing frameworks in order to get meaningful insights which are unprecedented. ML based Big Data Processing has gained popularity and new developments are on the rise for efficient data processing. This field is witnessing unparalleled emergence of new methods and approaches for efficient data processing in order to discover interestingness for decision making. Thus, more and more ML based data processing approaches are being used for Big Data Processing. With the splurge data from different newer sources, heterogeneous nature of data, uncertain & unstructured data, the so called Big Data with all its characteristics (5 Vs) there is an ever increasing need to use approaches which aid in modelling and processing of these data, provide automated approach to data processing and so on. Thus the BDML conference series welcomes the scholars who are researching in this interesting & challenging domain of ML and Big Data Processing and provides an excellent international forum for sharing knowledge and results in theory, methodology and applications of Big Data and Machine Learning.

Email: Dunn@iact.net

Big Data; Machine learning;

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

Modern algorithms for data analysis require the use of advanced probabilistic methods to achieve desirable scalability and accuracy guarantees. At the same time, modern data-analysis tasks require the use of advanced statistics to handle challenges such as testing for multiple hypotheses or identifying dependencies among data points, such as in time series or graphs. Probabilistic methods are also at the core theoretical computer-science areas, such as sublinear algorithms and average-case analysis. To obtain efficient data-analysis algorithms, probabilistic methods require careful balancing of theoretical and practical aspects. This Dagstuhl Seminar brings together researchers interested in statistical and probabilistic methods to design and analyze scalable algorithms for discovering knowledge in large and rich datasets. We plan to cover the following topics, among others.

Probability and Statistics, Game Theory,    Scientific Computing and Data Science

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.

Email: cbiot@itcse2024.org

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

Distributed, Edge and Cloud Computing,    Internet of Things (IoT) and Sensor Networks

Abdus Salam International Centre for Theoretical Physics (ICTP), Trieste, Italy / IAEA

Nuclear Energy,    Didactics of Scientific Disciplines

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.

Database Technology and Design, Search Engines and Data Mining,    Soft Computing, Evolutionary Computation, Metaheuristic Optimization, Fuzzy Logic

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.

Database Technology and Design, Search Engines and Data Mining,    Soft Computing, Evolutionary Computation, Metaheuristic Optimization, Fuzzy Logic

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

Economics, Business and 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.

Neural Networks and Artificial Intelligence, Machine Learning,    Scientific Computing and Data Science

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 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.

Probability and Statistics, Game Theory,    Scientific Computing and Data Science

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.

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.

Events for IT Students and Early Career Researchers,    Database Technology and Design, Search Engines and 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

Events for IT Students and Early Career Researchers,    Database Technology and Design, Search Engines and 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.