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