2026 8th International Conference on Big Data Engineering (BDE 2026)
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About this event
2026 8th International Conference on Big Data Engineering (BDE 2026)
Event Details
Dates: August 5–7, 2026
Venue: Keio University (Yagami Campus), Yokohama, Japan
Type: Academic & Industry Forum on Big Data Engineering
Target Audience: Researchers, Practitioners, PhD Students, Postdoctoral Fellows, Industry Experts, and Young Scientists
Objective
BDE 2026 serves as a global platform for sharing theoretical advancements, methodological innovations, and practical applications in Big Data Engineering. The conference aims to foster collaboration between academia and industry, emphasizing emerging research, educational development, and real-world implementations. Special focus will be given to nurturing early-career researchers and addressing open challenges in the field.
Key Features
- World-class keynote speakers from academia and industry
- Peer-reviewed paper presentations and interactive discussions
- Special sessions and tracks on emerging Big Data Engineering topics
- Opportunities for PhD students and young researchers to showcase work
- Networking with global experts and potential collaborators
Publication
Accepted and registered papers will be published in the ACM Conference Proceedings (ISBN: 979-8-4007-2525-8) and submitted for indexing in Ei Compendex and Scopus. Submissions must be original and unpublished, covering theoretical, practical, or industrial experiences in Big Data Engineering.
Review Process
All submissions undergo a rigorous double-blind peer-review by at least two Technical Committee members. Reviews focus on scientific quality, validity, and clarity. Revised papers may undergo a second review cycle if required. Anonymized submissions are matched to reviewers based on research expertise.
Special Sessions & Tracks
Researchers can propose special sessions by contacting Ms. Josie SHEN before the submission deadline. Track chairs are also invited to contribute to specialized focus areas within Big Data Engineering.
Important Dates
- Submission Deadline: [To be confirmed - check official website]
- Notification of Acceptance: [To be confirmed]
- Registration Deadline: [To be confirmed]
- Conference Dates: August 5–7, 2026
Past Edition Highlights
Previous BDE conferences have achieved indexing in Ei Compendex and Scopus, with proceedings archived in the ACM Digital Library. Notable past events include:
- BDE 2025: Held at University of the Ryukyus, Okinawa, Japan
- BDE 2024: Hosted at Qinghai Minzu University, Xining, China
Venue
Keio University (Yagami Campus)
Yokohama, Japan
A leading academic institution known for its contributions to science, technology, and engineering.
Call for Papers
Authors are invited to submit original research articles, survey papers, and industrial case studies. Topics of interest include but are not limited to:
- Big Data Algorithms and Systems
- Data Mining and Analytics
- Cloud Computing for Big Data
- Real-Time Data Processing
- Machine Learning and AI Integration
- Data Privacy and Security
- Internet of Things (IoT) and Big Data
Contact
For inquiries, contact the conference team at bde.conference@gmail.com.
Visit the official website for updates: https://www.bde.net/
Data sheet
2026 8th International Conference on Big Data Engineering (BDE 2026) – Event Attendee & Buyer Profile Analysis
Event date: August 5–7, 2026
Location: Keio University (Yagami Campus), Yokohama, Kanagawa, Japan
Event status: Upcoming
Research date: June 29, 2026
Event Overview
| Event Name | 2026 8th International Conference on Big Data Engineering (BDE 2026) |
| Event Date | August 5–7, 2026 |
| Event Status | Upcoming |
| Venue | Keio University, Japan (Yagami Campus) |
| City | Yokohama |
| State / Region | Kanagawa |
| Country | Japan |
| Organizer | BDE 2026 Organizing Committee / Conference Secretariat |
| Official Event Website | bde.net |
| Event Type | Academic & Industry Forum / Research Conference |
| Primary Category | IT & Technology |
| Secondary Applicable Categories | Science & Research; Education & Training |
| Audience Reach | Global academic and professional conference audience |
| Estimated Attendance / Expected Footfall | Attendance figure not publicly confirmed by the organizer. |
| Attendance Data Reliability | Low for total attendance; confirmed for dates, city, country, venue, and publication details via official event website. |
| Main Purpose of Event | To present peer-reviewed research, industrial experiences, and methodological advances in Big Data Engineering, while supporting collaboration among researchers, practitioners, industry experts, and early-career scientists. |
About the Event
BDE 2026 is the 8th edition of an international conference focused on Big Data Engineering. According to the official event website, the conference will take place at Keio University (Yagami Campus) in Yokohama, Japan, on August 5–7, 2026. The program is positioned as a forum for theory, methodology, applied research, peer-reviewed paper presentations, keynote talks, and exchange between academia and industry.
From a commercial and partnership perspective, the event matters most for organizations selling advanced data infrastructure, AI/analytics tools, research computing, cloud platforms, engineering software, consulting, and university-industry collaboration services. While it is not a classic procurement expo with a published buyer directory, it is relevant for lead generation among technical decision-makers, lab leaders, applied researchers, innovation teams, and enterprise data stakeholders who influence technology evaluation, partnerships, and future purchasing decisions.
1. Who Attends: Buyers / Attendees
| Buyer / Attendee Segment | Typical Organizations | Buying Role or Influence | Relevance to Exhibitors / Suppliers |
|---|---|---|---|
| University researchers and faculty | Universities, engineering schools, data science institutes | Influence software selection, lab tools, data platforms, research collaborations | High relevance for analytics tools, cloud credits, HPC, data platforms, publications, and training |
| PhD students and postdoctoral fellows | Graduate schools, doctoral programs, research labs | End users and future technical buyers; strong product evaluators | Useful for adoption, trial usage, advocacy, and early community building |
| Industry data scientists and machine learning practitioners | Technology firms, R&D teams, enterprise analytics units | Recommend platforms, validate technical fit, shape pilot requirements | Strong fit for AI, big data processing, model deployment, and data engineering suppliers |
| Engineering and platform architects | Cloud, software, telecom, finance, manufacturing enterprises | Influence architecture, integration, scalability, and vendor shortlist decisions | High-value for infrastructure software, storage, APIs, observability, and cybersecurity vendors |
| Innovation leaders and R&D managers | Corporate research units, applied AI teams, digital transformation groups | Sponsor pilots, fund collaborations, evaluate commercialization opportunities | Relevant for joint research, proof-of-concept programs, and strategic partnerships |
| Academic program leaders and department heads | Computer science, information systems, engineering faculties | Budget influence for educational software, labs, events, and institutional partnerships | Relevant for curriculum tools, learning platforms, and sponsored programs |
| Conference speakers, session chairs, and committee members | Academic institutions, industry labs, specialist research groups | High influence, high credibility, often shape adoption and referrals | Best for partnership outreach, speaking sponsorship, and technical co-marketing |
| Publishers, indexing, and research ecosystem stakeholders | Proceedings publishers, indexing services, scholarly infrastructure providers | Influence dissemination, credibility, and conference positioning | Relevant for sponsorship, publishing services, and academic ecosystem partnerships |
2. Event Location and Attendee Geographic Origin
| Geographic Area | Likely Attendee Origin | Buyer Concentration | Notes |
|---|---|---|---|
| Yokohama | Local university community, nearby labs, regional enterprise technology teams | Medium | Host city supports strong academic and corporate technology participation. |
| Kanagawa Prefecture | Regional universities, engineering groups, industrial R&D stakeholders | Medium | Convenient access for nearby institutions and corporate innovation hubs. |
| Greater Tokyo / Kanto corridor | Tokyo-based universities, tech companies, cloud providers, research institutes | High | This is likely the highest-value nearby business and research catchment area. |
| Japan nationwide | Faculty, researchers, graduate students, enterprise data teams | High | National pull is likely due to the international conference positioning and paper presentation format. |
| Asia-Pacific | Regional academic delegates and industry experts | Medium to High | Likely based on prior editions in Japan and China and the conference’s international positioning. |
| Global | International researchers, authors, committee members, keynote speakers | Medium | Confirmed as an international conference; exact country mix not publicly confirmed. |
3. Audience Reach
| Reach Level | Assessment | Explanation |
|---|---|---|
| Global | Primary classification | The event is explicitly positioned as an international conference and targets researchers, practitioners, and young scientists from all over the world. |
| National | Secondary practical reach | A strong proportion of in-person attendees is likely to come from Japan due to host-country convenience and academic travel patterns. |
| Regional | Secondary practical reach | The Kanto region, including Tokyo and Kanagawa, is likely the densest nearby cluster for universities and technology buyers. |
4. Sample Buyer Companies and Websites
Current-year attendee and buyer lists were not publicly available in the reviewed official materials. The table below therefore shows officially evidenced institutions linked to BDE 2026 or prior-year participation evidence. Prior-year participation evidence is not a confirmed attendee list for the current edition.
| Buyer Company / Organization | Buyer Type | Why It Is Relevant | Website | Best Job Titles to Target | Evidence Level |
|---|---|---|---|---|---|
| Keio University | Host academic institution | Confirmed venue host; relevant for research collaboration, data infrastructure, and academic technology outreach. | keio.ac.jp | Professor, Lab Director, Research Administrator, IT Director, Department Chair | Confirmed Current-Year Participant |
| BDE 2026 Organizing Committee | Conference organizer | Relevant for sponsorship, partnerships, mailing visibility, speaking access, and event ecosystem intelligence. | bde.net | Conference Chair, Program Chair, Sponsorship Lead, Secretariat | Confirmed Current-Year Participant |
| ACM Digital Library / ACM proceedings ecosystem | Publication ecosystem stakeholder | BDE 2026 accepted papers are stated to be published in ACM conference proceedings, making ACM relevant to publication and research dissemination. | acm.org | Publishing Partnerships Manager, Digital Library Manager, Research Program Lead | Confirmed Current-Year Participant |
| Elsevier Scopus | Research indexing stakeholder | Official site states proceedings will be submitted for indexing to Scopus, making it relevant to research visibility and conference credibility. | scopus.com | Indexing Program Manager, Research Solutions Director, Academic Partnerships Lead | Confirmed Current-Year Participant |
| Ei Compendex | Research indexing stakeholder | Official site states proceedings will be submitted for indexing to Ei Compendex, relevant to academic quality and author demand. | engineeringvillage.com | Indexing Manager, Product Director, Academic Relations Lead | Confirmed Current-Year Participant |
| University of the Ryukyus | Academic institution | Official BDE site states BDE 2025 was held there successfully; relevant as prior-edition institutional participation evidence. | u-ryukyu.ac.jp | Professor, Research Center Director, IT Administrator, Dean | Prior-Year Participation Evidence |
| Qinghai Minzu University | Academic institution | Official BDE site states BDE 2024 was successfully held there; useful as historical institutional participation evidence. | qhmu.edu.cn | Professor, Lab Director, International Programs Lead, Dean | Prior-Year Participation Evidence |
5. Job Profiles, Industries and Event Type
| Priority | Job Title / Function | Department | Seniority Level | Why This Role Matters |
|---|---|---|---|---|
| 1 | Professor / Principal Investigator | Research / Academic | Senior | Core audience for conference papers, lab technology decisions, grants, and partnerships. |
| 2 | Research Scientist | R&D / Data Science | Manager to Senior IC | Direct evaluator of data engineering tools, analytics workflows, and experimental platforms. |
| 3 | Director of Data Engineering | Engineering / Data Platform | Director | Owns platform architecture, tooling decisions, and enterprise adoption priorities. |
| 4 | Data Scientist | Analytics / AI | IC to Manager | Key product user and technical recommender for big data and ML tooling. |
| 5 | Machine Learning Engineer | Engineering / AI | IC to Manager | Important for model pipelines, compute, deployment, and data infrastructure use cases. |
| 6 | Chief Technology Officer | Executive / Technology | C-Level | High-value target for strategic partnerships, sponsored research, and platform selection. |
| 7 | IT Director | IT / Infrastructure | Director | Relevant for campus systems, compute, storage, security, and network procurement. |
| 8 | Dean / Department Chair | Academic Administration | Executive | Budget and partnership influence for labs, programs, and research initiatives. |
| 9 | Program Manager, Research Partnerships | Partnerships / Innovation | Manager | Useful for grant-aligned collaborations, pilots, and institutional partnerships. |
| 10 | Conference Chair / Program Chair | Events / Research Leadership | Senior | High-value contact for sponsorships, visibility, and speaking placements. |
| Priority | Apollo Industry | Why It Fits the Event | Best Buyer Use Case |
|---|---|---|---|
| 1 | Information Technology & Services | Core fit for enterprise data teams, solution providers, and digital transformation leaders. | Data platforms, implementation services, analytics modernization |
| 2 | Computer Software | Strong fit for analytics, AI, data pipeline, and developer tooling vendors. | SaaS outreach, developer tool adoption, platform demos |
| 3 | Research | Direct fit for research institutes and applied scientific teams. | Lab software, HPC, data management, collaborative research tools |
| 4 | Higher Education | Universities are a primary attendee segment. | Academic partnerships, campus technology, data science education |
| 5 | Computer Networking | Relevant for large-scale data transfer, distributed systems, and infrastructure support. | Networking hardware, throughput optimization, distributed compute |
| 6 | Computer Hardware | Relevant for servers, accelerators, edge compute, and storage systems. | GPU/CPU systems, storage arrays, edge analytics hardware |
| 7 | Telecommunications | Telecom operators use big data for network analytics and optimization. | Streaming analytics, infrastructure monitoring, AI operations |
| 8 | Financial Services | Financial institutions are active users of data engineering and analytics. | Risk analytics, fraud monitoring, data governance |
| 9 | Industrial Automation | Industrial data, sensors, and manufacturing analytics connect strongly to big data engineering use cases. | Predictive maintenance, IoT pipelines, plant optimization |
| 10 | Government Administration | Public research institutions and government-backed innovation programs may be relevant. | Research funding, smart city data, public analytics initiatives |
| 11 | Management Consulting | Consulting firms often monitor data engineering trends and solution ecosystems. | Transformation advisory, data strategy, implementation partner outreach |
| 12 | E-Learning | Relevant for training vendors serving data science and technical education audiences. | Certification, courseware, academic skill development |
6. Estimated Attendance
| Metric | Figure | Status | Source / Basis | Notes |
|---|---|---|---|---|
| Estimated total footfall | Attendance figure not publicly confirmed by the organizer. | Unconfirmed | Official website review | No public attendance number found in the reviewed source material. |
| Exhibitor count | Not publicly confirmed | Unconfirmed | Official website review | Conference appears content-led rather than trade-exhibition-led. |
| Buyer count | Not publicly confirmed | Unconfirmed | Official website review | No published procurement or hosted-buyer program identified. |
| Speaker count | Not publicly confirmed in reviewed text | Unconfirmed | Official website review | Website references world-class keynote speakers, but no reviewed count was available in the supplied source text. |
| Sponsor count | Not publicly confirmed | Unconfirmed | Official website review | Sponsorship page exists, but no sponsor roster was verified in the supplied material. |
| Historical attendance | Not publicly confirmed by the organizer | Historical data unavailable | Official website review | Prior-year locations are referenced, but attendance figures were not found in the reviewed text. |
7. Key Focus Areas and Buyer Engagement
| Focus Area | Typical Buyer Need | Buyer Engagement Opportunity | Relevant Supplier Offering |
|---|---|---|---|
| Big Data Engineering research | Scalable data processing methods and new engineering approaches | Technical demos, workshops, research collaboration proposals | Data processing engines, workflow orchestration, benchmarking tools |
| AI and analytics enablement | Model-ready data pipelines, training data quality, experimentation infrastructure | ML pipeline case studies, pilot programs, academic licenses | MLOps, feature stores, data quality, model monitoring |
| Cloud and compute infrastructure | Elastic compute, storage, collaboration environments | Cloud credits, migration planning, HPC partnership discussions | Cloud platforms, GPU compute, storage systems, container platforms |
| Data governance and quality | Reliable, reusable, auditable research and enterprise datasets | Consultative outreach to lab heads and enterprise data owners | Metadata, lineage, governance, cataloging, observability tools |
| Industry-academia collaboration | Applied research partners and commercialization pathways | Joint research programs, sponsored tracks, innovation challenges | Co-development services, grants support, innovation partnership programs |
| Education and talent development | Training for students, researchers, and technical teams | Curriculum partnerships, workshops, student competitions | Courseware, certifications, labs-in-a-box, training subscriptions |
| Publication and research visibility | Credible proceedings, indexation, scholarly exposure | Academic ecosystem sponsorship and thought leadership | Publishing support, indexing services, research dissemination platforms |
Lead Quality Assessment
| Factor | Assessment | Explanation |
|---|---|---|
| Buyer relevance | High | Strong for technical products, research tools, infrastructure, AI software, and academic-industry collaboration services. |
| Decision-maker availability | Medium | Good access to influencers and technical evaluators; direct budget owners may be fewer than at pure trade expos. |
| Data collection potential | Medium | Useful via paper authors, speakers, committee members, and host institution mapping; limited by lack of public attendee roster. |
| Apollo targeting potential | High | Strong because industries, departments, and technical titles map well in Apollo for follow-up campaigns. |
| Geographic targeting potential | High | Japan, Kanto, and broader APAC can be segmented effectively. |
| Best outreach approach | High | Thought-leadership messaging, research partnership positioning, product trial offers, and conference-aligned technical outreach work best. |
| Overall lead quality | High | Best for niche B2B technology, research computing, AI infrastructure, and academic-commercial partnership pipelines. |
| Best use case | High | Account-based outreach to universities, labs, enterprise data teams, and speaker/committee ecosystems. |
| Limitations / risks | Medium | Not a classic hosted-buyer event; public attendee verification is limited, so list-building must rely on official speaker, committee, paper, and institution data when available. |
Apollo.io Targeting Recommendation
| Filter Type | Recommended Filters | Purpose |
|---|---|---|
| Apollo industries | Information Technology & Services; Computer Software; Research; Higher Education; Computer Hardware; Computer Networking; Telecommunications; Financial Services; Industrial Automation; Government Administration; Management Consulting; E-Learning | Focus on the most relevant sectors participating in or adjacent to big data engineering. |
| Departments | Engineering; Information Technology; Research; Data/Analytics; Innovation; Academic Affairs; Partnerships | Reach technical buyers, evaluators, and partnership stakeholders. |
| Seniority | C-Level; VP; Director; Head; Professor; Manager; Owner of Lab / Program where available | Balance strategic decision-makers with practical adopters and recommenders. |
| Job titles | CTO, Chief Data Officer, Director of Data Engineering, Head of AI, Data Scientist, Machine Learning Engineer, Research Scientist, Professor, Principal Investigator, IT Director, Dean, Department Chair, Program Manager Research Partnerships | Direct mapping to likely conference attendees and adjacent buying personas. |
| Geography | Japan first; then Kanagawa, Tokyo, Osaka, Kyoto, Aichi; expand to APAC and global research hubs as needed | Prioritize near-event and high-density research markets. |
| Employee size | 51–200; 201–500; 501–1,000; 1,001–5,000; 5,001+ | Captures serious institutions and companies with research or enterprise data budgets. |
| Keywords | big data, data engineering, machine learning, distributed systems, analytics, cloud computing, data platform, research computing, AI infrastructure, data science | Narrows toward conference-theme-aligned organizations and people. |
| Technologies | Cloud data stack, distributed analytics, ML tooling, HPC, storage, orchestration technologies where Apollo enrichment supports it | Useful for intent-style targeting around technical stacks. |
| Revenue range | Optional: mid-market to enterprise for commercial targets; exclude if prioritizing universities and research institutions | Helps separate enterprise outreach from academic outreach. |
| Company type | Public; Private; Educational Institution; Nonprofit Research; Government-linked research entities where available | Supports segmentation by account strategy. |
Suggested Apollo Search Logic: ("big data" OR "data engineering" OR "machine learning" OR "distributed systems" OR analytics OR "research computing") AND (Professor OR "Research Scientist" OR "Director of Data Engineering" OR CTO OR "IT Director" OR "Head of AI" OR "Principal Investigator") AND (Japan OR Yokohama OR Tokyo OR Kanagawa).
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Sources & Verification Notes
| Source | Type | What It Verified | Reliability |
|---|---|---|---|
| BDE 2026 Official Website | Official event website | Confirmed event title, dates, Yokohama location, Japan country, Keio University (Yagami Campus) venue, conference positioning, target audience, and publication statement. | High |
| Keio University Official Website | Official institution website | Verified Keio University as a legitimate host institution in Japan; useful for institutional validation of the venue host. | High |
| ACM Digital Library | Official publisher platform | Used as supporting source for ACM publication ecosystem context referenced on the official BDE site. | Medium |
| Verification Note | Research note | The supplied prompt included conflicting dates. The official event website clearly states August 5–7, 2026; this report uses the official website as the primary source of truth. | High |
| Verification Note | Research limitation | No public current-year attendee count, buyer list, exhibitor list, or complete current-year speaker roster was verified in the reviewed source material. The event is suitable for B2B attendee list building mainly through speakers, committees, paper authors, host institutions, and official agenda-linked entities once published. | High |
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Tell us your work email and our AI instantly builds a buyer list matched to 2026 8th International Conference on Big Data Engineering (BDE 2026) — the best‑fit companies to target, the exact decision‑maker job titles, and the industry filters that fit what you sell.
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