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

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;

In today's digital world, effectively managing and utilizing knowledge is essential for organizational success. The "Knowledge Management: Transforming Information into Impact" conference focuses on how to turn data into actionable insights that drive decision-making, foster innovation, and enhance efficiency.Hosted on Zoom, this online event is tailored for the EMEA time zone, gathers industry leaders and experts to share strategies, tools, and best practices for effective knowledge management. Attendees will learn how to capture, share, and leverage information to achieve impactful results and align knowledge management with organizational goals​. Join us to explore how you can turn your organization's information into a strategic advantage and drive meaningful change.

Navigo Team;     Phone: [+91 91122 77710];     Email: info@navigoseminars.com

Knowledge Management, AI, GenAI, Machine Learning, Data Management, Data Analysis

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.

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.

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;

MIPRO Croatian Society

The term Business intelligence (BI) refers to applications and technologies used to gather, provide access to, and analyze data and information about company operations in order to help them to have a more comprehensive knowledge of the factors affecting their business. BI systems usually lean on data warehouses (DWs) to guarantee an easy and quick access to information. More than twenty years of research in the data warehousing field paved the way for new applications that goes beyond traditional OLAP analysis and concern real-time, spatiotemporal as well as distributed data. Furthermore, BI research is now dealing with the integration of BI with Corporate Performance Management (CPM), Business Process Management (BPM), Social Business Intelligence, Semantic Web, and Big Data Processing.

Boris Vrdoljak - conference chair;     Phone: [+38551423984];     Email: boris.vrdoljak@fer.hr

Data Warehouse and OLAP Business Intelligence Big Data Analytics - Algorithms, Techniques, Systems, User Interfaces Big Data Processing Big Data Analytics Applications in Science, Healthcare, Telecommunication, e-Business, etc., Data Science – Methods for Analyzing Scientific Data, Data Mining, Predictive and Descriptive Analytics and its Application in Science, Education, Healthcare, Telecommunications, Energy Providing, etc., Analytics for the Cloud Infrastructure, Social Network Analysis (SNA), CRM (Customer Relationship Management) and Social CRM, Analytics for Data Streams and Sensor Data, Edge Analytics, Analytics for Temporal, Spatial, Spatio-temporal and Mobile Data, Text, Web and Sentiment Analysis, Semantic Web and Semantic Data Integration, Education Management Information Systems, Business Information Systems, ERP and Supply Chain Management, Business Systems Integration, Business Process Management (BPM), Integration of Digital Twin and Big Data Technology

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;

Electric Power Transmission, Power Grid, Smart Grid,    Systems theory, Control and Automation

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.