CategoryIT & Technology
7th International Conference on Big Data and Applications (BDAP 2026)
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About this event
7th International Conference on Big Data and Applications (BDAP 2026)
About BDAP 2026
The 7th International Conference on Big Data and Applications (BDAP 2026) will be held in conjunction with the 13th International Conference on Computer Science, Engineering and Information Technology (CSEIT 2026) from July 16–17, 2026, in . This hybrid event (in-person and virtual) serves as a global platform for researchers, practitioners, and industry professionals to explore cutting-edge advancements in Big Data, analytics, machine learning, and related domains.
Key Highlights
- Dates: July 16–17, 2026
- Venue: London, United Kingdom (Exact venue: To Be Announced)
- Format: Hybrid (In-person + Virtual presentations)
- Focus Areas: Big Data technologies, data science, AI-driven analytics, IoT, cloud computing, and industrial applications.
- Target Audience: Academics, data scientists, IT professionals, enterprise technology leaders, and Ph.D. researchers.
Scope and Objectives
BDAP 2026 aims to foster innovation and collaboration by addressing theoretical and practical challenges in Big Data ecosystems. The conference invites submissions on (but not limited to):
- Big Data architectures and frameworks
- Real-time data processing and streaming analytics
- Machine learning for large-scale datasets
- Data privacy, security, and governance
- Industrial case studies and use cases
- Edge computing and distributed systems
Call for Papers
Researchers and practitioners are encouraged to submit original, unpublished work through the official CSEIT 2026 portal. Submissions should align with the conference themes and may include research papers, case studies, or industrial experiences.
- Submission Deadline: [To be confirmed – refer to official website]
- Notification of Acceptance: [To be confirmed]
- Registration Deadline: [To be confirmed]
Why Participate?
- Network with global experts in Big Data and emerging technologies.
- Present innovative solutions to a targeted audience of academia and industry.
- Access proceedings published in IEEE Xplore (pending) and indexed in Scopus/Google Scholar.
- Explore collaboration opportunities with technology leaders and startups.
Registration and Participation
Registration details, including fees and hybrid participation options, will be available on the official CSEIT 2026 website. Participants may register for either in-person (London) or virtual attendance.
Contact Us
For queries regarding BDAP 2026, please contact the organizing committee at bdap2026@cseit2026.org or visit the dedicated conference page.
Data sheet
7th International Conference on Big Data and Applications (BDAP 2026) – Event Attendee & Buyer Profile Analysis
Event date: July 16–17, 2026
Location: London, United Kingdom
Event status: Upcoming
Research date: June 30, 2026
Event Overview
| Event Name | 7th International Conference on Big Data and Applications (BDAP 2026) |
| Event Date | July 16–17, 2026 |
| Event Status | Upcoming |
| Venue | Venue not publicly specified in the provided official website excerpt. |
| City | London |
| State / Region | England |
| Country | United Kingdom |
| Organizer | Organizer name not clearly stated in the provided official excerpt. |
| Official Event Website | cseit2026.org/bdap/index |
| Event Type | International academic conference; hybrid conference track held in conjunction with CSEIT 2026 |
| Primary Category | IT & Technology |
| Secondary Applicable Categories | Education & Training; Science & Research |
| Audience Reach | Global |
| Estimated Attendance / Expected Footfall | Attendance figure not publicly confirmed by the organizer. |
| Attendance Data Reliability | Low for headcount; confirmed only for event timing, city, country, and hybrid format based on official website text. |
| Main Purpose of Event | Research presentation, technical knowledge exchange, publication-oriented submissions, cross-sector networking, and discussion of innovations in big data and related computing applications. |
About the Event
The 7th International Conference on Big Data and Applications (BDAP 2026) is positioned as a specialized conference track associated with the broader 13th International Conference on Computer Science, Engineering and Information Technology (CSEIT 2026). Based on the official conference website content provided, the event is scheduled for July 16–17, 2026 in London, United Kingdom, and operates in a hybrid format that allows registered authors to present online or face to face.
From a commercial and lead-generation perspective, BDAP 2026 is more relevant for thought leadership, research partnerships, data-technology networking, and identification of advanced technical practitioners than for high-volume procurement activity. The strongest attendee value is likely among academic researchers, applied data scientists, enterprise analytics teams, software and AI practitioners, and innovation-focused professionals evaluating methodologies, collaborations, and future technology adoption paths.
1. Who Attends: Buyers / Attendees
| Buyer / Attendee Segment | Typical Organizations | Buying Role or Influence | Relevance to Exhibitors / Suppliers |
|---|---|---|---|
| University researchers and faculty | Universities, research institutes, labs | Influence research software, cloud credits, datasets, tooling, and collaboration decisions | High for research platforms, data tools, publishing services, and grant-aligned technology outreach |
| PhD scholars and graduate researchers | Academic departments, doctoral programs | Emerging technical evaluators; low direct budget control but high adoption influence | Useful for community growth, pilot uptake, and developer evangelism |
| Industry data scientists and machine learning practitioners | Software companies, enterprise analytics teams, AI teams | Recommend platforms, evaluate model pipelines, influence tooling selection | High for data infrastructure, MLOps, analytics, and model deployment vendors |
| Software engineers and solution architects | Tech vendors, SaaS firms, integrators, enterprise IT groups | Technical evaluators and implementation stakeholders | Strong relevance for APIs, developer tools, platforms, and cloud services |
| IT and analytics leaders | Enterprises, public institutions, digital transformation teams | May hold or influence budget for analytics, infrastructure, and transformation initiatives | Moderate to high for enterprise data platforms and consulting offers |
| Research and innovation program managers | Innovation hubs, public research bodies, consortium programs | Coordinate projects, partnerships, and funding-linked technology adoption | Relevant for collaborative R&D, funded pilots, and knowledge partnerships |
| Consultants and advisory professionals | Data strategy consultancies, technology advisory firms | Referral and specification influence rather than direct purchase | Useful for channel expansion and solution recommendation networks |
| Industry professionals presenting case studies or applied work | Technology leaders, applied research teams, product groups | Can shape internal adoption roadmaps and vendor shortlists | Good fit for enterprise proof-of-concept outreach and peer-led credibility building |
2. Event Location and Attendee Geographic Origin
| Geographic Area | Likely Attendee Origin | Buyer Concentration | Notes |
|---|---|---|---|
| Host city | London | Moderate | London offers strong density of universities, data talent, enterprise technology teams, and consulting firms. |
| Host state / region | England | Moderate to high | Likely draw from universities, public research organizations, and enterprise IT teams across England. |
| Nearby business hubs | Cambridge, Oxford, Manchester, Birmingham, Reading | Moderate | These hubs are relevant for AI research, software engineering, advanced analytics, and cloud ecosystems. |
| National reach | United Kingdom-wide | High | Official branding as an international conference supports participation beyond the local market. |
| International reach | Europe, North America, Asia-Pacific, Middle East, and remote online presenters | Moderate | The event is explicitly international and hybrid, increasing potential cross-border participation. |
| Trade / collaboration corridors | Academic-industry research corridors in data science, AI, cloud, and software engineering | Moderate | Most valuable for research collaboration, technical recruiting, and solution evaluation rather than bulk purchasing. |
3. Audience Reach
| Reach Level | Assessment | Explanation |
|---|---|---|
| Global | Primary classification | The official conference positioning is international, and the hybrid format supports both in-person and online participation across geographies. |
| National | Secondary practical reach | For onsite networking and UK-based partnerships, the strongest concentration is likely to be national and regional around the United Kingdom. |
4. Sample Buyer Companies and Websites
| Buyer Company / Organization | Buyer Type | Why It Is Relevant | Website | Best Job Titles to Target | Evidence Level |
|---|---|---|---|---|---|
| No current-year attendee, sponsor, exhibitor, or speaker organization list was publicly confirmed in the provided official materials. | Data availability limitation | The event appears to be research- and paper-driven. Buyer-side organization confirmation for the 2026 edition was not available in the provided source text. | cseit2026.org | Conference Chair, Program Chair, Research Lead, Data Science Lead, IT Director | Confirmed current-year event website only; participant organizations not confirmed |
This event is not currently supported by a publicly verified buyer-company list in the provided official materials. For attendee list building, the best approach is title-based and institution-based prospecting around academic data research, enterprise analytics, AI engineering, and digital innovation functions rather than confirmed attendee-company extraction.
5. Job Profiles, Industries and Event Type
| Priority | Job Title / Function | Department | Seniority Level | Why This Role Matters |
|---|---|---|---|---|
| 1 | Director of Data Science | Data / Analytics | Director | Key decision-maker for analytics platforms, experimentation environments, and research collaboration tools. |
| 2 | Head of AI / Machine Learning | AI / Engineering | Head / VP | Strong fit for model development, data pipelines, MLOps, and innovation partnerships. |
| 3 | Chief Data Officer | Executive / Data Strategy | C-Level | Owns enterprise data strategy and may sponsor transformation projects. |
| 4 | Data Engineering Manager | Engineering | Manager | Important for implementation, stack evaluation, and workflow integration. |
| 5 | Research Scientist | R&D | Individual Contributor / Senior IC | Evaluates advanced methods and influences technical adoption in research settings. |
| 6 | Professor / Associate Professor | Academic / Research | Senior Academic | Influences lab tools, collaboration choices, and publication-related ecosystems. |
| 7 | IT Director | Information Technology | Director | Relevant for compute infrastructure, data governance, and institutional platform decisions. |
| 8 | Product Manager, Data Platform | Product | Manager / Director | Useful target where the event attracts applied industry product teams. |
| 9 | CTO | Executive | C-Level | High-value target for strategic solution vendors, though volume may be limited. |
| 10 | Program Manager, Research & Innovation | Program Management | Manager | Relevant for consortium projects, grants, pilots, and academic-industry partnerships. |
| Priority | Apollo Industry | Why It Fits the Event | Best Buyer Use Case |
|---|---|---|---|
| 1 | Information Technology & Services | Core fit for data platforms, engineering services, analytics, and digital transformation. | Enterprise analytics, implementation, and architecture buying |
| 2 | Computer Software | Strong fit for software vendors, platform builders, and engineering-led attendees. | Developer tooling, analytics products, SaaS partnerships |
| 3 | Research | Academic and applied research participation is central to the conference profile. | Research tools, data services, compute environments |
| 4 | Higher Education | Universities and academic departments are likely participant sources. | Lab software, institutional licenses, training and collaboration |
| 5 | Computer Networking | Relevant where big data intersects distributed systems and infrastructure. | High-performance data movement and infrastructure solutions |
| 6 | Computer Hardware | Useful for compute, storage, edge, and acceleration vendors. | Infrastructure evaluation and performance research |
| 7 | Telecommunications | Large-scale data applications and networked services align with telecom analytics teams. | Network analytics, capacity forecasting, AI operations |
| 8 | Financial Services | Data-intensive sectors often send analytics or innovation leaders to such events. | Risk analytics, fraud detection, customer intelligence |
| 9 | Hospital & Health Care | Healthcare analytics and data applications are common big-data use cases. | Clinical analytics, operational intelligence, research data management |
| 10 | Government Administration | Public-sector research and digital transformation teams may participate, especially via hybrid access. | Smart services, public data analytics, policy research support |
| 11 | Biotechnology | Big data is highly relevant to omics, discovery, and research-heavy biotech workflows. | Data-intensive scientific computing and ML applications |
| 12 | Management Consulting | Consulting firms often monitor emerging methods and tools for client delivery. | Referral partnerships and transformation advisory use cases |
6. Estimated Attendance
| Metric | Figure | Status | Source / Basis | Notes |
|---|---|---|---|---|
| Estimated total footfall | Attendance figure not publicly confirmed by the organizer. | Not Confirmed | Provided official website excerpt | No attendee count was stated in the source text. |
| Exhibitor count | Not publicly confirmed | Not Confirmed | Provided official website excerpt | The event appears conference-led rather than expo-led. |
| Buyer count | Not publicly confirmed | Not Confirmed | Provided official website excerpt | No formal hosted-buyer or procurement program was identified from the source text. |
| Speaker count | Not publicly confirmed | Not Confirmed | Provided official website excerpt | Program details were not included in the provided excerpt. |
| Sponsor count | Not publicly confirmed | Not Confirmed | Provided official website excerpt | No sponsor listing was visible in the provided content. |
| Historical attendance | Historical attendance not publicly confirmed in the provided source materials. | Historical / prior-year evidence unavailable in provided sources | Provided official website excerpt | No prior-year headcount data was available for verification. |
7. Key Focus Areas and Buyer Engagement
| Focus Area | Typical Buyer Need | Buyer Engagement Opportunity | Relevant Supplier Offering |
|---|---|---|---|
| Big data platforms | Scalable ingestion, storage, and analysis environments | Technical demos, benchmark case studies, proof-of-concept offers | Data infrastructure, lakehouse, distributed processing, storage acceleration |
| AI and machine learning applications | Model development, deployment, experimentation, and reproducibility | Workshops, applied research alignment, integration conversations | MLOps, feature stores, model monitoring, GPU-enabled compute |
| Cloud and distributed computing | Elastic resources, collaboration, cost control, high availability | Hybrid deployment discussions and cloud-credit incentives | Cloud platforms, orchestration, managed data services |
| Data analytics and visualization | Insight generation, dashboarding, interpretability, stakeholder reporting | Show practical impact with domain-specific use cases | BI tools, notebooks, visual analytics, collaborative reporting |
| Research collaboration | Academic-industry partnerships, funded pilots, publication support | Partner programs, data-sharing frameworks, co-authored studies | Research platforms, consortium participation, grant support services |
| Digital transformation | Operational modernization through data-led decision-making | Executive-level messaging around ROI and capability building | Consulting, data governance, architecture, enablement services |
| Education and skills development | Upskilling in advanced analytics and applied computing | Training partnerships, certification pathways, curriculum support | Learning platforms, bootcamps, academic support tools |
Lead Quality Assessment
| Factor | Assessment | Explanation |
|---|---|---|
| Buyer relevance | Medium | Strong for technical evaluators and research influencers; weaker for formal procurement buyers. |
| Decision-maker availability | Medium | Expect some senior technical and academic leaders, but likely fewer commercial budget owners than at enterprise trade shows. |
| Data collection potential | Low to Medium | Publicly confirmed participant-company data is limited in the provided materials. |
| Apollo targeting potential | High | Very workable through title-based and industry-based targeting around data science, AI, software, research, and higher education. |
| Geographic targeting potential | High | London and broader UK targeting is practical, with optional international expansion for hybrid participation profiles. |
| Best outreach approach | High | Use thought-leadership outreach, technical value messaging, pilot offers, and research-collaboration language rather than hard-sell procurement tactics. |
| Overall lead quality | Medium | Valuable for niche B2B technology, research tools, AI infrastructure, and collaboration-led sales cycles. |
| Best use case | High | Best suited to account-based outreach, technical partnership building, and data/AI product prospecting. |
| Limitations / risks | High | Limited official buyer visibility, uncertain attendance scale, and likely lower immediate purchase intent than at commercial expos. |
Apollo.io Targeting Recommendation
| Filter Type | Recommended Filters | Purpose |
|---|---|---|
| Apollo industries | Information Technology & Services; Computer Software; Research; Higher Education; Computer Networking; Computer Hardware; Telecommunications; Financial Services; Hospital & Health Care; Government Administration; Biotechnology; Management Consulting | Capture both academic and applied enterprise big-data audiences. |
| Departments | Engineering; Information Technology; Research; Data / Analytics; Product; Innovation | Focus on technical and innovation-led participants. |
| Seniority | C-Level; VP; Director; Head; Manager; Senior Individual Contributor | Balance budget authority with practical technical influence. |
| Job titles | Chief Data Officer; CTO; Director of Data Science; Head of AI; Head of Machine Learning; Data Engineering Manager; Analytics Director; Research Scientist; Professor; Associate Professor; IT Director; Product Manager Data Platform | Match likely participant and decision-influencer roles relevant to BDAP themes. |
| Geography | United Kingdom first; London, Cambridge, Oxford, Manchester, Birmingham, Reading; secondary expansion to Europe and North America | Prioritize practical event-proximate prospecting while allowing international hybrid relevance. |
| Employee size | 11–50; 51–200; 201–500; 501–1,000; 1,001–5,000; 5,001+ | Include startups, scale-ups, enterprises, and institutions with data initiatives. |
| Keywords | big data; analytics; machine learning; artificial intelligence; data engineering; data platform; MLOps; cloud data; distributed systems; research computing | Improve relevance for event-theme matching. |
| Technologies, if relevant | Cloud platforms, analytics stack, AI/ML tooling, distributed data processing, visualization tools | Useful for layered intent targeting where Apollo enrichment is available. |
| Revenue range, if relevant | $1M–$10M; $10M–$50M; $50M–$500M; $500M+ | Useful for segmenting startup innovation teams versus enterprise buyers. |
| Company type | Private companies; Public companies; Universities; Research institutions; Government-linked research bodies | Expands relevant market capture beyond strictly commercial firms. |
Suggested Apollo Search Logic: Target contacts with titles such as ("Chief Data Officer" OR "Director of Data Science" OR "Head of AI" OR "Head of Machine Learning" OR "Data Engineering Manager" OR "Research Scientist" OR Professor OR "IT Director") AND industries such as ("Information Technology & Services" OR "Computer Software" OR Research OR "Higher Education") AND geography focused on London or the United Kingdom. Add keyword layering for "big data", "analytics", "machine learning", "distributed systems", "cloud data", and "research computing".
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Sources & Verification Notes
| Source | Type | What It Verified | Reliability |
|---|---|---|---|
| BDAP 2026 Official Event Page | Official event website | Primary event identity and conference association with CSEIT 2026 | High |
| CSEIT 2026 Official Home Page | Official conference website | Confirmed July 16–17, 2026 dates; London, United Kingdom location; hybrid format statement | High |
| Provided official website excerpt in user prompt | Primary-source text supplied by user | Used to resolve conflicting location/date information and avoid unsupported venue or attendance claims | High |
Verification note: The user-supplied “known details” indicated Toronto, Canada and July 25–26, 2026, but the official website content provided in the prompt states July 16–17, 2026 in London, United Kingdom. This report follows the official website content as the primary source of truth and does not rely on the conflicting preliminary details.
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