The theme of BigData 2026 is:

Agentic AI as a Service for Big Data

Agentic AI systems can formulate plans, retrieve information, invoke analytical tools, coordinate workflows, and act on data-derived insights. Their effectiveness, however, depends fundamentally on the quality, accessibility, provenance, timeliness, and governance of the data they use.

For the BigData community, the central question is:

How can large-scale data systems provide reliable and governable foundations for agentic AI, and how can AI agents transform the big-data lifecycle?

This relationship is bidirectional. Big-data systems provide agents with data, context, memory, evidence, and knowledge. At the same time, agents can assist with data discovery, integration, transformation, quality control, analytics, and governance.

Agentic AI is transforming Big Data:

• From manually constructed pipelines to agent-assisted data engineering

• From passive repositories to agent-ready data and knowledge assets

• From schema-dependent access to semantic and natural-language interaction

• From periodic analysis to continuous and real-time intelligence

• From isolated datasets to federated data ecosystems

• From volume and processing speed to data quality, provenance, and fitness for use

• From reporting insights to supporting accountable actions

Realizing this vision requires advances in data architecture, data integration, metadata, indexing, query processing, vector and semantic retrieval, streaming analytics, data quality, provenance, privacy, security, and governance.

BigData 2026 invites researchers and practitioners to build scalable and trustworthy data foundations for the emerging agentic computing ecosystem.

BigData focuses on large-scale data architectures, systems, engineering, analytics, governance, and data-value realization.

Research whose primary contribution concerns AI model development, cloud-resource management, general service engineering, Web protocols, edge platforms, IoT infrastructure, cognitive systems, or blockchain should be submitted to the corresponding SCF conference unless it makes a clear contribution to large-scale data technologies.

Topics include, but are not limited to:

1. Data Foundations for Agentic AI

• Agent-ready data and knowledge architectures

• Data and memory systems for AI agents

• Retrieval-augmented generation data pipelines

• Vector, semantic, and hybrid indexing

• Natural-language-to-query and text-to-SQL systems

• Data quality, provenance, and grounding for agentic systems

2. Big-Data Architectures and Systems

• Distributed data storage and processing

• Data lakes, lakehouses, data fabrics, and data meshes

• Database architectures and large-scale query processing

• Streaming, event, and real-time data systems

• Graph, temporal, spatial, and multimodal data systems

• Open and federated data platforms

3. Data Engineering and Lifecycle

• Data acquisition, integration, transformation, and cleaning

• ETL, ELT, and continuous data pipelines

• Metadata management, catalogs, and data discovery

• Schema matching, mapping, and evolution

• Data quality, lineage, versioning, and observability

• Automated and AI-assisted data engineering

4. Big-Data Analytics and Intelligence

• ·Scalable data mining and machine learning

• Real-time and streaming analytics

• Graph, time-series, and spatiotemporal analytics

• Predictive, prescriptive, and causal analytics

• Large-scale anomaly and pattern detection

• Visual analytics and interactive data exploration

5. Data Security, Privacy, and Governance

• Big-data security and access control

• Privacy-preserving and federated data analysis

• Data governance, compliance, and sovereignty

• Data provenance, auditability, and accountability

• Responsible data sharing and data-use policies

• Bias, representativeness, and quality assessment

6. Data Services, Applications, and Value

• Big Data as a Service and data products

• Data marketplaces and data-value networks

• Enterprise and public-sector data platforms

• Healthcare, finance, science, manufacturing, and urban data

• Data-driven organizational and business transformation

Large-scale deployments, benchmarks, and industry experience

The BigData 2026 technical program includes four submission tracks.

Research Track

The Research Track welcomes original and technically rigorous contributions that advance large-scale data architectures, systems, algorithms, engineering methods, analytics, or governance.

Research papers should:

• Clearly define the big-data problem and contribution

• Explain the novelty relative to relevant literature

• Present technically sound methods or system designs

• Use appropriate datasets, workloads, baselines, and metrics

• Provide rigorous and reproducible evaluation

• Discuss scalability, limitations, and threats to validity

• Demonstrate a generalizable contribution to Big Data

Application and Industry Track

The Application and Industry Track focuses on deployed data systems, organizational data platforms, analytical solutions, governance practices, and evidence-based industry experience.

Papers should:

• Describe a significant real-world data problem

• Explain the data architecture, pipelines, and analytical methods

• Present deployment evidence or measurable outcomes

• Discuss scale, data quality, privacy, governance, and operational constraints

• Identify lessons transferable to other environments

• Clarify the practical or system-level innovation

Papers in this track do not need to introduce a new algorithm. However, they should contribute through system architecture, data engineering, deployment evidence, governance innovation, measurable value, or generalizable practical knowledge.

Short Paper Track

The Short Paper Track welcomes focused contributions, including:

• Promising early-stage research

• Novel data-system concepts or architectures

• Emerging challenges and research visions

• Datasets, benchmarks, tools, and reusable artifacts

• Focused empirical findings or negative results

• Concise deployment and industry reports

Short papers must clearly state their contribution, evidence, relationship to prior work, and limitations.

Special Paper Track

The Special Paper Track addresses designated emerging topics, including:

• Real-Time Big Data Analytics

• Big Data for Smart Cities

Depending on the corresponding call, Special Track submissions may include vision papers, technical research, datasets, benchmarks, survey papers, and application or experience reports.

All submissions must report original and unpublished work and must not be under review elsewhere at the time of submission.

Each manuscript will be reviewed by at least three program committee members. Papers will be evaluated according to:

• Relevance to BigData

• Originality and significance

• Technical or practical quality

• Strength of evaluation

• Clarity and organization

• Reproducibility

• Research or real-world impact

Papers should position their contributions accurately within the relevant international literature. Authors are encouraged to cite BigData and other conferences or journals when genuinely relevant. Citation of any particular venue is not required.

Research Track and Application and Industry Track manuscripts are limited to 15 pages in the Springer LNCS format. Short Paper Track manuscripts are limited to 8 pages. Authors may purchase up to two additional pages, subject to the applicable publication fee. Special Paper Track submissions should follow the limits specified in the corresponding call.

Authors must use the official Springer LNCS templates:

• Microsoft Word template

• LaTeX template

Electronic submission in PDF or Word format is required. Manuscripts that do not follow the required format or exceed the permitted page limit may be returned without review.

A Research Track submission that primarily presents an application or deployed solution may be recommended for consideration by the Application and Industry Track.

At least one author of each accepted paper must register for BigData 2026 and present the paper at the conference.

‍Research Track

Submit to the BigData 2026 Research Track: https://edas.info/newPaper.php?c=34929&track=136040

Application and Industry Track

Submit to the BigData 2026 Application and Industry Track: https://edas.info/newPaper.php?c=34929&track=136039

Short Paper Track

Submit to the BigData 2026 Short Paper Track: https://edas.info/newPaper.php?c=34929&track=136041

Special Paper Track

Submit to the BigData 2026 Special Paper Track: https://edas.info/newPaper.php?c=34929&track=136042

Detailed instructions for manuscript preparation, submission, review, panels, tutorials, and workshops are available on the SCF author-guidance pages.

Accepted and presented BigData 2026 papers will be published in the conference proceedings, which are scheduled to appear in Springer’s Lecture Notes in Computer Science (LNCS) series.

Springer makes LNCS proceedings available to major abstracting and indexing services. Final inclusion of a volume or individual paper is determined by the policies and selection criteria of each indexing service.

Under the current SCF publication arrangement, the BigData 2026 proceedings are expected to become freely accessible through SpringerLink four years after publication.

Extended versions of selected papers may be invited to relevant partner journals. Journal submissions must include substantial extensions and will undergo the journal’s independent editorial and peer-review process.

BigData 2026 will recognize outstanding contributions through:

• Best Paper Award

• Best Student Paper Award

For the Best Student Paper Award, the first author must be a full-time student.

BigData has developed alongside the evolution of data technologies—from distributed storage and batch processing to streaming analytics, data lakes, lakehouses, data fabrics, vector databases, and agent-ready data architectures.

Recent BigData conferences were held in Seattle in 2018, San Diego in 2019, virtually in 2020 and 2021, Hawaii in 2022 and 2023, Bangkok in 2024, and Hong Kong in 2025.

BigData 2026 continues this development by examining how large-scale data systems can support trustworthy Agentic AI and how agentic technologies can transform the data lifecycle.

BigData is a member of the Smart Computing Frontier, a federation of ten co-located international conferences.

Within SCF, BigData focuses on large-scale data architecture, storage, processing, engineering, analytics, governance, and value realization.

Its scope is complementary to the other SCF conferences:

• ICWS focuses on Web interfaces, APIs, protocols, and Web-based services.

• SCC focuses on service engineering, lifecycle management, and value creation.

• CLOUD focuses on cloud infrastructure, platforms, and resource management.

• AIMS focuses on AI models, multimodal intelligence, and AI-enabled services.

• EDGE focuses on computing systems located near data sources and users.

• ICIOT focuses on connected devices and Internet of Things systems.

• ICCC focuses on cognitive computing.

• ICBC focuses on blockchain technologies and trusted ecosystems.

• METAVERSE focuses on immersive and persistent digital environments.

This structure enables cross-conference collaboration while preserving BigData’s distinctive identity as the conference devoted to large-scale data systems and data-driven value.