ICBDM 2026

Full Name: 2026 8th International Conference on Big Data Management

Abbreviation: ICBDM 2026

Place: Derby, UK

Date: March 10-12, 2026

Sponsor: University of Derby

Website: https://www.icbdm.org/

2026 8th International Conference on Big Data Management (ICBDM 2026) will be held in Derby, UK during March 10-12, 2026! It's sponsored by University of Derby. The conference aims to bring together leading academic scientists, researchers and research scholars to exchange and share their experiences and research results on all aspects of big data management.

PUBLICATION:

Submitted papers will be strictly reviewed by the technical program committee. Accepted papers after successful registration and presentation will be included in Conference Proceedings, and submitted to be indexed by Ei Compendex and Scopus. The authors of the papers will be invited to participate in ICBDM to display their research results.

SUBMISSION:

Submission Guide:

Word Template: https://icbdm.org/instruct8.5x11x2.docx

LaTex Template: https://icbdm.org/ieee-latex-conference-template.zip

Please prepare your submitted paper according to the above conference proceedings template. English is the official language of the conference, so the submitted paper is requested to be written in complete English and no less than 4 pages including figures and references. It may lead to direct rejection in the preliminary review if there is no enough content.

Originality:

Any act of plagiarism is totally unacceptable academic misconduct and cannot be tolerated. This is the responsibility of the author to check plagiarism before submitting the paper to ICBDM. In the first round of paper review, the submitted papers will be checked plagiarism, including self-plagiarism. If any plagiarism is found, the paper will be rejected directly.

Submission Method:

Welcome you to submit the paper by Electronic Submission System or Email (Email: icbdm_conf@outlook.com).

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