International Conference on Big Data Management ICBDM on June 24-26, 2026 in Derby, United Kingdom

CALL FOR PAPERS (Topics):

Topics of interest for submission include, but are not limited to:

Track 1: Big Data Analysis and Management

Data Acquisition, Integration, Cleaning, and Best Practices

Big Data Search Architectures, Scalability and Efficiency

Cloud/Grid/Stream Data Mining- Big Velocity Data

Semantic-based Data Mining and Data Pre-processing

Big Data as a Service

Data Lifecycle Management: From Collection to Archiving

Data Governance Frameworks and Best Practices

Data Management Standards (e.g., FAIR principles: Findable, Accessible, Interoperable, Reusable)

Ethical Considerations in Data Management

Algorithms and Systems for Big Data Search

Visualization Analytics for Big Data

Challenges in Managing Large-scale Datasets

Big Data Processing Frameworks (e.g., Apache Spark, Apache Flink)

Scalable Storage Solutions for Big Data

Mobility and Big Data

Methods for Data Collection: Surveys, Experiments, Sensors, Web Scraping

Data Integration Techniques: ETL (Extract, Transform, Load) Processes

Search and Mining of Variety of Data including Scientific and Engineering, Social, Sensor/IoT/IoE, and Multimedia Data

Track 2: Data Structures and Data Models

Multimedia and Multi-structured Data- Big Variety Data

Computational Modeling and Data Integration

Relational Databases (e.g., SQL) vs. NoSQL Databases (e.g., MongoDB, Cassandra)

Data Warehousing and Data Lake Architectures

Cloud-based Data Storage Solutions (e.g., AWS S3, Google BigQuery)

Distributed Storage Systems for Big Data (e.g., Hadoop HDFS)

Data Quality Metrics: Accuracy, Completeness, Consistency, and Timeliness

Techniques for Data Cleaning and Preprocessing

Handling Missing Data: Imputation Methods and Strategies

Outlier Detection and Treatment in Datasets

Real-Time Data Collection and Streaming Data Management

Importance of Metadata in Data Management

Metadata Standards and Schemas (E.G., Dublin Core, Schema.Org)

Tools for Metadata Extraction and Management

Role of Metadata in Data Discovery and Reuse

Visualization of High-Dimensional Data

Managing Unstructured Data (E.G., Text, Images, Videos)

Data Silos and Interoperability Issues

Track 3: Big Data Security and Privacy

Visualizing Large Scale Security Data

Threat Detection using Big Data Analytics

Privacy Threats of Big Data

Privacy Preserving Big Data Collection/Analytics

HCI Challenges for Big Data Security & Privacy

Sociological Aspects of Big Data Privacy

Trust Management in IoT and Other Big Data Systems

Data Encryption and Anonymization Techniques

Role-based Access Control (RBAC) and Data Permissions

Compliance with Data Protection Regulations (e.g., GDPR, CCPA)

Secure Data Sharing and Transfer Protocols

Visualizing Large Scale Security Data

Balancing Data Accessibility with Security

Trust Management in IoT and Other Big Data Systems

HCI Challenges for Big Data Security & Privacy

Track 4: Big Data Analysis Tools and Key Technologies

Healthcare: Managing Electronic Health Records (EHR) and Patient Data

Finance: Data Management for Fraud Detection and Risk Analysis

Environmental Science: Managing Climate and Satellite Data

Social Sciences: Handling Survey and Census Data

E-Commerce: Customer Data Management and Personalization

Complex Big Data Applications in Science, Engineering, Medicine, Healthcare, Finance, Business, Law, Education, Transportation, Retailing, Telecommunication

Big Data Analytics in Small Business Enterprises (SMEs)

Big Data Analytics in Government, Public Sector and Society in General

Real-Life Case Studies of Value Creation through Big Data Analytics

Experiences with Big Data Project Deployments

Big Data as a Service

Big Data Industry Standards

Track 5: Application of Big Data in Information Systems

Tools and Techniques for Exploratory Data Analysis (EDA)

Interactive Dashboards for Data Exploration (E.G., Tableau, Power BI)

Open-Source Data Management Tools (E.G., Apache Nifi, Talend)

Data Management Platforms (E.G., Snowflake, Databricks)

Cloud-Native Data Management Solutions

Automation Tools for Data Pipelines (E.G., Airflow, Prefect)

Data Pipelines for Machine Learning Workflows

Feature Engineering and Dataset Preparation

Managing Labeled and Unlabeled Data for Supervised and Unsupervised Learning

Data Versioning and Reproducibility in ML Experiments

Data Management for AI and Deep Learning

Blockchain for Secure and Decentralized Data Management

Federated Learning and Privacy-Preserving Data Management

Quantum Computing and Its Impact on Data Management

Name: etpub