2026 The 7th International Conference on Computing and Big Data (ICCBD 2026)
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About this event
2026 The 7th International Conference on Computing and Big Data (ICCBD 2026)
Date: September 28–30, 2026
Venue: Guiyang, Guizhou Province, China
Hosted by: School of Big Data and Computer Science, Guizhou Normal University
Sponsored by: Guizhou Normal University
Event Overview
The 2026 IEEE 7th International Conference on Computing and Big Data (ICCBD 2026) will take place from September 28 to 30, 2026, in Guiyang, Guizhou Province, China. Organized by Guizhou Normal University and hosted by the School of Big Data and Computer Science, this conference is themed "Data-Centric and AI-Driven Computing for Real-World Intelligence." It aims to provide a global platform for researchers and practitioners to discuss recent advances in computing and big data, focusing on both theoretical foundations and practical applications.
Key Themes and Topics
ICCBD 2026 will address emerging topics such as:
- Big data analytics
- Artificial intelligence
- Cloud and edge computing
- Intelligent data-driven systems
The conference emphasizes data-intensive computing, intelligent analysis, and decision support systems, aligning with China's "Fourteenth Five-Year" plan and 2035 vision for high-quality development in big data and digital economy.
Target Audience
ICCBD 2026 welcomes experts, scholars, and professionals from academia, research institutions, and industry to exchange ideas, showcase research, and explore future trends in computing and big data.
Important Deadlines
Submission Deadline: July 25, 2026
Notification Date: August 15, 2026
Registration Deadline: September 1, 2026
Publication
Accepted papers will be published in IEEE conference proceedings and indexed by EI Compendex and Scopus. ICCBD 2026 is listed on the IEEE Official website.
Contact and Participation
For more information, visit the official website at https://www.iccbd.org/. Participants can submit full papers or abstracts via the online submission system. The conference emphasizes academic integrity and adheres to strict ethical standards.
Data sheet
2026 The 7th International Conference on Computing and Big Data (ICCBD 2026) – Event Attendee & Buyer Profile Analysis
Event date: September 28–30, 2026
Location: Guiyang, Guizhou Province, China
Event status: Upcoming
Research date: June 30, 2026
Event Overview
| Event Name | 2026 IEEE 7th International Conference on Computing and Big Data (ICCBD 2026) |
| Event Date | September 28–30, 2026 |
| Event Status | Upcoming |
| Venue | Guizhou Normal University |
| City | Guiyang |
| State / Region | Guizhou Province |
| Country | China |
| Organizer | Hosted by the School of Big Data and Computer Science, Guizhou Normal University; sponsored by Guizhou Normal University |
| Official Event Website | iccbd.org |
| Event Type | International academic conference / research and industry exchange forum |
| Primary Category | IT & Technology |
| Secondary Applicable Categories | Education & Training; Science & Research |
| Audience Reach | International academic and professional reach, with strong China-based participation expected |
| Estimated Attendance / Expected Footfall | Attendance figure not publicly confirmed by the organizer. |
| Attendance Data Reliability | Confirmed for dates, city, country, host, sponsor, theme, and deadlines. Attendance metrics not publicly confirmed. |
| Main Purpose of Event | To provide a high-level forum for researchers and practitioners to present advances in computing and big data, with emphasis on AI-driven, data-centric, cloud, edge, and intelligent systems research and applications. |
About the Event
ICCBD 2026 is an international conference focused on computing and big data, positioned around the theme “Data-Centric and AI-Driven Computing for Real-World Intelligence.” According to the official event website, the conference will be held in Guiyang, China, from September 28 to 30, 2026, and is sponsored by Guizhou Normal University and hosted by the School of Big Data and Computer Science at Guizhou Normal University.
From a market and lead-generation perspective, the event is most relevant for academic institutions, applied research groups, public-sector digitalization stakeholders, enterprise R&D teams, data platform providers, AI solution providers, and cloud or edge computing specialists. It matters less as a high-volume trade show and more as a qualified relationship-building environment for thought leadership, research partnerships, technical collaboration, innovation scouting, and selective B2B outreach into advanced computing and data ecosystems.
1. Who Attends: Buyers / Attendees
| Buyer / Attendee Segment | Typical Organizations | Buying Role or Influence | Relevance to Exhibitors / Suppliers |
|---|---|---|---|
| University faculty and academic researchers | Universities, laboratories, academic departments in computer science, AI, data science | Influence software selection, research tools, datasets, compute environments, publication partnerships | High relevance for research software, AI platforms, cloud credits, HPC tools, and academic collaboration |
| Research institution and lab teams | National or provincial research institutes, applied AI labs, digital economy institutes | Evaluate technical infrastructure, analytics tools, compute platforms, and collaboration models | Useful targets for pilot projects, grants-linked collaborations, and technical solution adoption |
| Industry R&D and engineering professionals | AI firms, software companies, cloud providers, enterprise data teams, industrial digitalization groups | Assess applied AI, analytics pipelines, cloud/edge architecture, integration capabilities | Relevant for technical product demos, enterprise pilots, and solution partnerships |
| Technology leaders | CIO, CTO, IT directors, architecture leaders, data platform heads | Shape architecture direction, platform spending, modernization, AI adoption | Good fit for cloud, data management, MLOps, cybersecurity, and infrastructure suppliers |
| Government and smart city digitalization stakeholders | Digital government units, public data initiatives, regional innovation agencies | Influence data governance, AI pilot adoption, and public-sector technology procurement | Selective relevance for digital infrastructure, analytics, governance, and public-sector consulting |
| Graduate researchers and doctoral candidates | Graduate schools, research programs, university labs | Early-stage users and influencers of tools, platforms, and publication ecosystems | Useful for community building, freemium adoption, and longer-term pipeline creation |
| Conference committee, invited speakers, and reviewers | Senior academics, recognized experts, research leaders | High influence over reputation, partnerships, and future collaboration opportunities | Strong fit for strategic partnerships, sponsorships, and high-value visibility initiatives |
2. Event Location and Attendee Geographic Origin
| Geographic Area | Likely Attendee Origin | Buyer Concentration | Notes |
|---|---|---|---|
| Guiyang | Local university, research, and digital economy participants | Medium | Host city benefits from proximity to the sponsoring and hosting institution |
| Guizhou Province | Regional academics, public-sector digital stakeholders, and local industry participants | Medium | Likely regional draw due to host university and provincial relevance of big data policy themes |
| Southwest China | Nearby academic and technology communities, including partner institutions | Medium to High | Official site names Southwest University as a patron, supporting wider regional relevance |
| China national market | Universities, research bodies, AI and data practitioners, enterprise technical teams | High | The event positions itself as an international forum and aligns with national big data and digital economy priorities |
| International | Researchers and practitioners from overseas institutions and industry | Selective | International reach is confirmed by positioning and visa information, but country-by-country attendee data is not publicly confirmed |
3. Audience Reach
| Reach Level | Assessment | Explanation |
|---|---|---|
| Global | Primary classification | The conference is presented as a high-level international forum, supports English manuscript submission, and includes visa information for overseas participants. |
| National | Secondary practical reach | In practical buyer targeting terms, China-based universities, research institutions, and enterprise technology teams are likely to form the strongest attendee concentration. |
4. Sample Buyer Companies and Websites
| Buyer Company / Organization | Buyer Type | Why It Is Relevant | Website | Best Job Titles to Target | Evidence Level |
|---|---|---|---|---|---|
| Guizhou Normal University | University / sponsor | Official sponsor and key institutional anchor for the event; relevant for research infrastructure, academic software, and partnership outreach | gznu.edu.cn | Dean, Professor, Director, Lab Director, IT Director, Research Program Lead | Confirmed Current-Year Participant |
| School of Big Data and Computer Science, Guizhou Normal University | Host academic department | Official host unit directly aligned with conference themes in computing, data science, and AI | gznu.edu.cn | Department Head, Professor, Associate Professor, Research Lead, Program Chair | Confirmed Current-Year Participant |
| Southwest University | University / patron | Named as patron on the official website, indicating formal event involvement and likely academic participation | swu.edu.cn | Professor, Research Director, Dean, Data Science Lead, Computing Faculty | Confirmed Current-Year Participant |
| IEEE | Professional association / conference publication ecosystem | The event is presented as an IEEE conference and is stated to be included in the IEEE official conference list | ieee.org | Conference Program Lead, Publication Liaison, Technical Committee Member | Confirmed Current-Year Participant |
| ICCBD 2023 participant organizations | Historical conference participant pool | Historical edition evidence may help identify recurring academic and research interest around ICCBD | iccbd.org | Professor, Researcher, Data Scientist, Lab Director | Prior-Year Participation Evidence |
| ICCBD 2022 participant organizations | Historical conference participant pool | Useful for backward-looking attendee profiling where current-year attendee lists are not public | iccbd.org | Professor, Principal Investigator, Research Manager | Prior-Year Participation Evidence |
| ICCBD 2021 participant organizations | Historical conference participant pool | Historical evidence supports continuity of the conference series and recurring subject-matter participation | iccbd.org | Research Director, Assistant Professor, Data Engineering Lead | Prior-Year Participation Evidence |
| ICCBD 2020 participant organizations | Historical conference participant pool | Relevant for identifying likely institutional continuity within the event series | iccbd.org | Chair, Research Scientist, Systems Architect | Prior-Year Participation Evidence |
| ICCBD 2019 participant organizations | Historical conference participant pool | Supports historical attendee research for academic list-building and series continuity analysis | iccbd.org | Professor, Lab Manager, Data Analytics Researcher | Prior-Year Participation Evidence |
| ICCBD 2018 participant organizations | Historical conference participant pool | Historical series evidence only. Not a confirmed attendee list for the current edition. | iccbd.org | Faculty Lead, Research Coordinator, Data Systems Specialist | Prior-Year Participation Evidence |
Prior-year participation evidence. Not a confirmed attendee list for the current edition.
5. Job Profiles, Industries and Event Type
| Priority | Job Title / Function | Department | Seniority Level | Why This Role Matters |
|---|---|---|---|---|
| 1 | Professor / Principal Investigator | Research / Faculty | Senior | Core influencers for research tools, collaboration, publications, and grant-linked technology use |
| 2 | Dean / Department Head | Academic Leadership | Executive / Senior | Decision influence over institutional partnerships, budget priorities, and strategic programs |
| 3 | Research Director / Lab Director | R&D / Laboratory | Director | Often owns tool evaluation, compute environments, and external technical collaborations |
| 4 | Chief Technology Officer | Technology | C-Level | Relevant where enterprise and applied research teams evaluate AI, infrastructure, and data platforms |
| 5 | IT Director | Information Technology | Director | Key contact for deployment, infrastructure compatibility, data systems, and security controls |
| 6 | Data Science Lead / AI Lead | Data / AI | Manager / Director | High-value role for analytics, model development, MLOps, and experimentation platforms |
| 7 | Research Scientist | Research | Mid-Senior | Hands-on evaluator of technical tooling, datasets, frameworks, and compute environments |
| 8 | Program Chair / Technical Committee Member | Conference / Research Governance | Senior | Influences ecosystem visibility, sponsorship quality, and future partnership pathways |
| Priority | Apollo Industry | Why It Fits the Event | Best Buyer Use Case |
|---|---|---|---|
| 1 | Higher Education | Direct fit for host, sponsor, faculty, and conference paper contributors | Research software, cloud credits, infrastructure, academic partnerships |
| 2 | Research | Matches institutes, labs, and advanced technical research groups | Specialized analytics, HPC, AI tooling, datasets, collaborative platforms |
| 3 | Information Technology & Services | Strong fit for IT implementation and enterprise data platform stakeholders | Data platform deployment, systems integration, managed AI solutions |
| 4 | Computer Software | Relevant to AI, analytics, developer tooling, and big data applications | ML platforms, visualization tools, data engineering software, APIs |
| 5 | Computer Hardware | Supports computing infrastructure, servers, accelerators, and edge workloads | Compute infrastructure, lab equipment, performance optimization |
| 6 | Computer Networking | Relevant for distributed systems, cloud-edge integration, and data movement | Network optimization, edge architecture, campus or lab connectivity |
| 7 | Computer & Network Security | Important where data-intensive systems, privacy, and trusted computing are involved | Security tooling, compliance, secure data sharing, research data protection |
| 8 | Government Administration | Applicable for digital government and public-sector big data initiatives referenced by the event themes | Public-sector AI pilots, analytics, data governance, smart city projects |
| 9 | Telecommunications | Relevant to intelligent data-driven systems and communications-sector analytics | Edge computing, network analytics, AI optimization |
| 10 | Industrial Automation | Useful where real-world intelligence and data-driven applications intersect with operational systems | Industrial analytics, predictive systems, intelligent control applications |
6. Estimated Attendance
| Metric | Figure | Status | Source / Basis | Notes |
|---|---|---|---|---|
| Estimated total footfall | Attendance figure not publicly confirmed by the organizer. | Not confirmed | Official website content reviewed | No public attendee count located in the provided official materials |
| Exhibitor count | Not publicly confirmed | Not confirmed | Official website content reviewed | This is a conference format rather than a conventional exhibition-first event |
| Buyer count | Not publicly confirmed | Not confirmed | Official website content reviewed | Academic and technical decision-makers are likely present, but no formal buyer program is disclosed |
| Speaker count | Not publicly confirmed in the provided content | Not confirmed | Official website menu includes keynote and invited speaker pages | Speaker pages exist, but no count was available in the supplied source text |
| Sponsor count | 1 confirmed sponsor; 1 confirmed patron | Confirmed | Official website content | Sponsor: Guizhou Normal University. Patron: Southwest University. |
| Historical attendance | Not publicly confirmed in the provided content | Historical evidence limited | Official history pages are referenced | Publication history is confirmed; attendee volumes are not |
7. Key Focus Areas and Buyer Engagement
| Focus Area | Typical Buyer Need | Buyer Engagement Opportunity | Relevant Supplier Offering |
|---|---|---|---|
| Big data analytics | Scalable analysis, modeling, visualization, and data pipeline optimization | Technical demos, benchmarking discussions, research collaborations | Analytics software, data engineering tools, visualization platforms |
| Artificial intelligence | Model development, applied AI use cases, intelligent decision systems | AI pilot conversations, model evaluation, applied research partnerships | AI platforms, MLOps tooling, training data solutions, model serving infrastructure |
| Cloud and edge computing | Distributed compute, storage, orchestration, latency-sensitive deployment | Architecture workshops, migration consulting, platform trials | Cloud services, edge platforms, storage, orchestration and observability tools |
| Intelligent data-driven systems | Operational intelligence, forecasting, optimization, decision support | Applied case study discussions and cross-sector implementation planning | Decision-support systems, optimization engines, custom AI applications |
| Academic publishing and research dissemination | Publication pathways, conference visibility, scholarly impact | Sponsorship, publication support, institutional branding | Publishing support, conference services, academic outreach solutions |
| Digital economy and public-sector data applications | Governance, public data utilization, AI-enabled public services | Policy-aligned engagement with public or university research stakeholders | Data governance software, analytics consulting, public-sector AI frameworks |
Lead Quality Assessment
| Factor | Assessment | Explanation |
|---|---|---|
| Buyer relevance | Medium | High relevance for research-tech, academic software, cloud, AI, and infrastructure suppliers; lower relevance for broad non-technical sales offers |
| Decision-maker availability | Medium to High | Faculty leaders, lab directors, department heads, and technical decision influencers are likely present |
| Data collection potential | Medium | Public attendee data appears limited; relationship-led collection is more realistic than mass contact harvesting |
| Apollo targeting potential | High | Strong alignment with Higher Education, Research, Software, IT Services, and AI-related roles |
| Geographic targeting potential | High | China and broader Asia-focused targeting is practical, with selective global outreach for international research audiences |
| Best outreach approach | High | Use thought-leadership messaging, academic collaboration language, technical value propositions, and publication or pilot support |
| Overall lead quality | Medium to High | Good quality for niche B2B and research-linked outreach; not ideal for high-volume transactional attendee list sales |
| Best use case | High | Best suited for strategic outreach into academic tech buyers, research partnerships, AI/cloud pilots, and conference sponsorship positioning |
| Limitations / risks | Medium | Limited publicly confirmed attendee volume and named participant data reduce certainty for list-building at scale |
Apollo.io Targeting Recommendation
| Filter Type | Recommended Filters | Purpose |
|---|---|---|
| Apollo industries | Higher Education; Research; Information Technology & Services; Computer Software; Computer Hardware; Computer Networking; Computer & Network Security; Government Administration; Telecommunications; Industrial Automation | Capture academic, technical, and applied data/AI buyers aligned with conference themes |
| Departments | Research, Information Technology, Engineering, Data, Innovation, Academic Affairs, Partnerships | Focus on users, technical evaluators, and institutional decision influencers |
| Seniority | C-Level, VP, Director, Head, Professor, Principal, Manager, Owner of Lab/Program | Balance strategic decision-makers with hands-on technical owners |
| Job titles | Professor, Principal Investigator, Dean, Department Head, Research Director, Lab Director, CTO, CIO, IT Director, Data Science Lead, AI Lead, Research Scientist, Program Chair | High-fit audience roles for this conference type |
| Geography | China first; Guizhou Province; Guiyang; Southwest China; broader Asia-Pacific for international academic outreach | Reflects strongest likely attendee concentration and practical outreach range |
| Employee size | 201–500; 501–1,000; 1,001–5,000; 5,001+ | Best fit for universities, institutes, established tech organizations, and public entities |
| Keywords | big data, data analytics, artificial intelligence, cloud computing, edge computing, intelligent systems, machine learning, data-driven, digital economy, research lab | Improve title and company-level relevance filtering |
| Technologies | Cloud infrastructure, analytics platforms, AI/ML tooling, data engineering stack, edge platforms | Useful if selling complementary technical products or services |
| Revenue range | Use selectively; not essential for higher education and research organizations | Revenue is less predictive than department, title, and institution type for this event |
| Company type | Universities, research institutes, enterprise R&D centers, government digital units, technical associations | Helps isolate realistic event-aligned prospects |
Suggested Apollo Search Logic: ("big data" OR "data analytics" OR "artificial intelligence" OR "cloud computing" OR "edge computing" OR "intelligent systems") AND (Professor OR "Research Director" OR "Lab Director" OR CTO OR CIO OR "IT Director" OR "Data Science Lead" OR "AI Lead") AND (China OR Guiyang OR Guizhou OR "Southwest China").
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Sources & Verification Notes
| Source | Type | What It Verified | Reliability |
|---|---|---|---|
| ICCBD 2026 Official Website | Official event website | Confirmed event title, dates, city, country, sponsor, host, conference theme, deadlines, publication claims, and event positioning | High |
| ICCBD 2026 Official Website – History references | Official historical series reference | Confirmed prior editions listed for 2018, 2019, 2020, 2021, 2022, and 2023, supporting historical continuity of the conference series | High |
| ICCBD 2026 Official Website – Registration and submission sections | Official registration / participation information | Verified that the event supports authors, presentation-only participants, listeners, invited speakers, committee registration, and sponsors | High |
| User-supplied event details | Provided input | Venue listed as Guizhou Normal University; this is consistent with the official host and sponsor information, though the supplied official text did not separately state a full venue line beyond Guiyang / Guizhou / China | Medium |
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