Konferenzen  >  Informatik  >  Datenbanktechlologie, Suchmaschinen und Data-Mining

ECV International

With AI workloads driving exponential increases in data processing and storage needs, traditional data centers face significant pressure to scale up, enhance efficiency, and integrate advanced technologies. This summit will delve into the latest innovations in data center design, construction, cooling, energy management, and digital infrastructure tailored for AI applications. Participants will gain insights into how cutting-edge solutions, such as liquid cooling, modular construction, edge computing, and sustainable energy options, can be leveraged to optimize data centers for AI-driven workloads.

Asia AI Data Center Summit 2025 serves as a platform for networking, knowledge exchange, and collaboration, addressing pressing issues such as sustainability, resilience, and scalability. Attendees will have the opportunity to learn from industry experts, engage in interactive sessions, and explore pioneering strategies that enable data centers to meet the rising demands of AI technologies.

Email: marketing@ecvinternational.com

➤ The Impact of AI on Data Centers: Development and Trends ➤ Navigating Asia’s Regulatory Landscape: How National Policies Shape AI Data Center ➤ Chip Design for AI Acceleration ➤ Edge AI: Revolutionizing Data Center Architecture ➤ Liquid Cooling Solutions for AI Data Centers ➤ Small Modular Reactors (SMRs): Meeting Power Demands in the AI Era ➤ Energy Risk Management ➤ Regulatory Frameworks for AI Data Centers Across Asia: Key Compliance Strategies ➤ Prefabricated Modular Solutions for AI Data Centers ➤ Building a Sustainable Al Data Center: the Approach to Renewable Energy and Carbon Reduction ➤ How DCIM is Evolving to Meet AI Data Center Requirements ➤ Connectivity: The Importance of Low Latency Networks ......

Informations & Wissensmanagement, Big Data Computing,    Implementierung und Anwendungen von künstlicher Intelligenz

ICGDA 2025

On behalf of the organizing committee, we are pleased to announce that the 2025 8th International Conference on Geoinformatics and Data Analysis (ICGDA 2025) will be held from April 4 to 6, 2025 in Nice, France. The First two ICGDA conferences were held in Prague, Czech Republic successfully and ICGDA2020-2022 conferences were held as online form because of COVID-19 and travel restrictions.

Email: Dunn@iact.net

Acquisition and processing of remotely sensed data Analysis-oriented and standards-compliant WebGIS Applications of BeiDou navigation satellite system Automated object extraction and database updates from imagery Big Data analytics Cartographic theory and applications Climate changes and global environment Cyberinfrastructure and CyberGIS Data models in cartography Digital gazetteer Environmental criminology and crime simulation Environmental hazards and natural disasters Environmental health, disease, and wellbeing Errors and their measurements in spatial data Geo-event and geo-process Geographic information retrieval Geo-ontology Geospatial cloud computing Geospatial computation and simulation Geospatial data sharing and interoperability Geospatial education, such as virtual globes-based virtual education Geospatial intelligence Geospatial web portal and services GeoStatistics and applications GIS for environment and health GIS for Geosciences GIS for government and public services Indoors positioning and navigation Integration of RS, GIS and GNSS (3S) LIDAR technology for DEM generation and 3D modeling Land use and land cover change Map generalization Mobile data collection, management and analysis Monitoring of resources and environment Multi-dimensional (2D, 3D, and 4D) spatial data modeling and data quality New trends of technologies and research in GIS Processing and application of high-resolution imagery Representation and visualization of geospatial data Satellite positioning technology and location based service Semantic web and sensor web Smart city and sustainable development Space-time integration Spatial analysis of social web Spatial and spatiotemporal Big Data Spatial database Spatial data mining Spatial decision support systems Spatial and spatiotemporal uncertainties and data quality Spatial workflow Spatiotemporal dynamic analysis and simulation Spatiotemporal computing Surveying and mapping in geosciences Theories and algorithms in GIS Urban GIS and its applications Virtual Globes and their application to scientific research and daily life Wireless and mobile GIS solutions

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.

Society for Industrial and Applied Mathematics (SIAM)

Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH

Data structures are the science of organizing and accessing data, and their study is a core part of computer science. They underpin our computing infrastructure with efficiency being of critical importance. As the computing landscape changes with more demanding tasks arising, data structure research remains vibrant, with two aspects coming particularly in focus: scalability and adaptivity. Scalability means that data structures remain efficient as data sets increase, become more dynamic, and become more distributed. Adaptivity implies taking advantage of modern hardware, such as multicore computation or memory hierarchies, as well specific structure and biases in the operations performed. One seeks to create structures that maximally take advantage of such architectural and distributional details without any foreknowledge of them. General limits of adaptivity have long posed deep theoretical questions, which continue to inspire research. This Dagstuhl Seminar is part of a successful series begun in 1991. The series has contributed to shaping trends in data structures research.

Fuzhou University, China.

2025 3rd International Conference on Big Data and Privacy Computing will be held in Fuzhou, China during May 30-June 1, 2025. The event is sponsored by Fuzhou University, China. 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

# # Track 1 - Big Data Architecture and Systems Big Data Algorithms, Applications and Services Structured and Unstructured Data/Text/Web Mining Big Data Architectures Algorithms and Systems for Big Data Search Big Data Analysis Analytics and Metrics Big Data Analysis Architectures Big Data Open Platforms Big Data Quality and Provenance Control Big Data Toolkits Foundational Models for Big Data Experiences with Big Data Project Deployments Hardware/software infrastructure for big data Techniques, models and algorithms for big data Tools and systems for big data Algorithms and Programming Techniques for Big Data Processing Big Data Analytics and Metrics # # Track 2 -Data Encryption and Privacy Protection Hardware and Software solutions for Big Data Searching, Storing and Management Privacy and Security for Cloud and Big Data Virtualization Technologies, Cloud Data Management and Storage, Cloud Resource Management and QoS Big Data Analysis Encryption Big Data Analysis Management Big Data as a Service Big Data Encryption Big Data in Business Performance Management Big Data in Enterprise Management Models and Practices Big Data in Government Management Models and Practices Collaborative Threat Detection using Big Data Analysis Analytics Management Issues of Social Network Big Data Models and Languages for Big Data Protection Privacy Preserving Big Data Analytics Security and privacy for big data Big Data Management Big Data Protection, Integrity and Privacy Security Applications of Big Data Anomaly Detection in Very Large Scale Systems Big Data Persistence and Preservation Data quality issues: such as validation, metrics, optimizations and consistency # # Track 3 - Big Data Analysis and Computing Big Data Mining and Analytics Big Data Processing and Querying Data Visualization and Visual Analytics Evolutionary and Bio-inspired approaches for Big Data analysis Big Data Visualization Machine learning based on Big Data Data-driven innovation, computational modelling and data integration Data intensive computing theorems and technologies Big Data Analysis Search and Mining Big data analytics and social media Data mining, graph mining and data science Intelligent Data Processing Visualization Analytics for Big Data Big Data Models and Algorithms Cloud Computing Techniques for Big Data Big Data in Mobile and Pervasive Computing Big Data Search and Mining Novel Computational Intelligence approaches for data analysis # # Track 4 - Big Data Science and Applications Representation Formats for Multimedia Big Data New domains and novel applications related to Big Data technologies Scientific Applications of Big Data Scalable computational intelligence tools Cloud and Big Data for Internet of Things (IoT) Government and Industrial Experiences for Cloud and Big Data Green Computing and Networking Technologies for Cloud and Big Data Big Data Education Big Data Analysis for Business Model Innovation Big Data Analysis for Enterprise, Government and Society Big Data Analysis Economics Big data applications: Bioinformatics Big Data for Business Model Innovation Big Data for Enterprise Transformation Big Data for Vertical Industries (including Government, Healthcare, and Environment) Big Data in Smart Planet Solutions Machine learning and AI for big data Real-life Case Studies of Value Creation through Big Data Analytics SME-centric Big Data Analytics Software Engineering for Cloud Computing and Big Data Software, hardware and algorithm co-design, high-performance computing Software and tools for big data management # # Track 5 - Data Security and Blockchain Application Technology Blockchain Blockchain based lightweight data structures for IoT data Blockchain based IoT security solutions Blockchain in cyber physical systems Blockchain in social networking Bolckchain in crowdsourcing and crowdsensing Blockchain in 5G Blockchain in edge and cloud computing Blockchain and trust management Cryptocurrency Cryptographic hash function Cryptography Distributed ledger Distributed consensus algorithm Blockchain for business Blockchain for industries Blockchain for Cryptocurrency security Blockchain for supply chain solutions

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;     Tel.: [+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

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,    Informations & Wissensmanagement, Big Data Computing

International Association of Computer Science and Information Technology

The 8th International Conference on Machine Learning and Machine Intelligence (MLMI 2025) will be held in Kyoto, Japan during July 25-27, 2025. MLMI 2025 is organized by Ritsumeikan University, Japan.

Email: aimee@iacsit.net

Artificial Neural Networks; Genetic Algorithms; Machine Learning; Automata, Logic and Games; Intelligent Systems; Bayesian Networks; Logic and Proof; Commercial Software;

Neuronale Netze und Künstliche Intelligenz, Maschinelles Lernen,    Algorithmen und Datenstrukturen

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,    Informations & Wissensmanagement, Big Data Computing

Osaka University

Communication Chair;     Email: lwangct@connect.ust.hk

spatial data, temporal data, databases, data management

Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH

Retrieval-augmented generation (RAG) has proven effective in conditioning the output of large language models (LLMs) on relevant documents and for grounding LLM-generated statements, this way combatting the so-called confabulation or hallucination problem. Basically, RAG combines (1) a retrieval phase, where a search system identifies relevant documents for a user prompt, and (2) a generation phase, where an LLM synthesizes a tailored answer, probably linking to the retrieved sources.