Scientific Research

Large-Scale Scientific Instruments

Data Management and Analytics for Big Science Facilities

A one-stop platform for managing and analyzing massive time-series datasets — improving instrument reliability and research efficiency.

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Industry Background

From Operational Data to Scientific Knowledge

Large-scale research facilities — such as fusion reactors and particle accelerators — rely on industrial control systems like EPICS to monitor and collect data from tens of thousands of device measurement points in real time. This data must be deeply analyzed and retained over decades to support long-term scientific research and equipment diagnostics.

For any data platform, this represents an extreme technical challenge: managing time-series data at massive scale while supporting complex scientific computation.

Large-Scale Data Acquisition
EPICS-based control systems monitor tens of thousands of device measurement points at high frequency in real time.
Complex Scientific Computation
Deep analysis is required to support scientific research and equipment diagnostics spanning decades.
Long-Term Data Retention
Massive datasets must be preserved over extended periods, making lifecycle management a critical capability.
Challenges
Storage and Query Bottlenecks
Traditional EPICS Archiver Appliance stores data in a closed Protobuf format requiring middleware parsing for queries, resulting in slow response times. Inefficient compression and limited distributed scalability drive hardware and maintenance costs sharply upward as data grows into the TB and PB range.
Insufficient Advanced Analytics
Operational assessment relies on FFT, wavelet transforms, and other signal processing techniques. Traditional architectures require exporting raw data to external tools such as MATLAB or Python, creating fragmented workflows that cannot support rapid in-experiment diagnostics.
Weak Data Lifecycle Management
Scientific datasets routinely reach PB scale, making tiered storage essential for cost control. Traditional stacks like EPICS do not natively support tiered storage, requiring manual data migration and driving up operational overhead.

DolphinDB Solution

Seamless EPICS Integration and Efficient Storage

  • Native EPICS connectivity with built-in Protobuf parsing for efficient ingestion of large-scale PV data.
  • Columnar storage with lz4, delta-of-delta, and zstd compression reduces storage costs to as little as 10% of traditional solutions.
  • Flexible time-range and value-domain partitioning minimizes query scan scope, delivering millisecond query response even across trillion-row tables.

Built-In Engineering Analytics

  • A rich library of engineering functions — including FFT and wavelet transforms — enables signal processing and feature extraction directly within the database, eliminating data export overhead.
  • Multi-paradigm programming combines SQL usability, imperative flexibility, functional expressiveness, and vectorized performance to translate complex analytical logic into executable code efficiently.

Intelligent Data Lifecycle Management

  • Built-in hot/cold tiered storage policies and data retention rules support automatic migration and transparent access, significantly reducing long-term storage costs and operational complexity.

Key Benefits

Lower Total Cost

Unified management and efficient utilization of experimental and operational data dramatically reduces long-term storage and maintenance costs — transforming data from a cost burden into a continuously growing asset, with order-of-magnitude TCO improvement.

Accelerated Research Iteration

Analysis workflows that once required offline processing and cross-system coordination now run on a single platform, delivering up to 100x improvement in query and analytics performance. Researchers can validate hypotheses, locate anomalies, and adjust experiments in real time — shortening the path from data to insight.

Ready to Accelerate Your Large-Scale Research and Development?
Discover how DolphinDB can help your team:
pointConnect seamlessly with EPICS systems for efficient ingestion and querying of massive datasets
pointEnable real-time analysis, trend assessment, and performance comparison under high-frequency acquisition
pointRun fast cross-condition, cross-phase experimental data review and correlation analysis
pointAutomate data lifecycle management across petabyte-scale research archives
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