Virtualized Data Transcends System Changes

Pelagic’s self-learning interfaces maintain system integrations as they evolve and grow creating virtualized data that transcends system changes, making data available to stakeholders and applications in real-time, optimizing storage and network performance.

Foundational Technology

The Pelagic Real-Time Data Platform is the foundation to solving data access and mission silo challenges. It is scalable and can be leveraged during system development and test to improve innovation and can be seamlessly transitioned to operational use, to reduced operational risk, to connect new platforms, and enhance mission capabilities. It is extensible and systems can be added to the platform over time, virtualizing more and more data, making it available across the enterprise, knocking down barriers. Pelagic’s foundational technology improves decision making, capabilities, and reduces costs and risks in the development, test, maintenance and operation of complex embedded systems like Military Platforms, Industrial Systems, and Defense Systems.

1v5 The Pelagic Real-Time Data Platform is the foundation to solving data access and mission silo challenges.

Access Embedded Systems Machine Data

The challenges in transferring, processing, and analyzing machine data is time-consuming, costly, and nearly impossible to maintain. Commercially-focused data integration and management solutions can be sufficient for business applications, but they cannot handle the volume, complexity, or speed required by embedded systems which produce massive amounts of machine data every minute of every day.

Traditional methods of integrating disparate systems has been to manipulate the data to conform to the application it is being sent to, manually compile/decompile, or develop proprietary interfaces that require endless and costly maintenance. FishEye Software flipped the process on its head and developed our patented MetaGen process to extract metadata from systems, allowing machine data to be immediately used in its natural form. This pairing of data and metadata allows Pelagic to provide virtualized, or self-describing, data for immediate utilization throughout the enterprise.

Pelagic solves the challenges to accessing machine data like data locked in disparate siloed systems, data overload, delays in processing and sharing data, hard to maintain or proprietary integrations, and limited access or usability of data by 3rd party applications.

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Connecting Data Silos

There is a widespread need for Artificial Intelligence (AI) Machine Learning (ML) model development and unified data access for analysis and insight. Operating many subsystems from diverse suppliers, built with multi-generation technology, emits complex and wide-ranging data flows at such a significant scale it makes data conversion impractical. There are constantly evolving data interfaces, diverse data formats, the need to move and analyze large-scale-data, and difficulty in accessing data.

Pelagic’s Intelligent Dynamic Data Logging automatically extracts meta-data from subsystems to dynamically link evolving data, with differing formats and time periods, into a common data repository (or Data Lake) for routine and impromptu analysis. The MetaGen process enables maximum performance by limiting data manipulation, and ensures integrity by keeping data in its original form. The Pelagic Tool Kits are used to create views into the repository so that the pools of heterogenous data appear homogeneous to a diverse analysis community and the custom or commercial tools they use.

Pelagic provides the highest performance by keeping data in its original form and in an enduring, sustainable, elegant and unified data repository. This provides integration of disparate data without the burden of unique tool interfaces, data conversion, and sensitivity to data content changes.

Connecting Data Silos

There is a widespread need for Artificial Intelligence (AI) Machine Learning (ML) model development and unified data access for analysis and insight. Operating many subsystems from diverse suppliers, built with multi-generation technology, emits complex and wide-ranging data flows at such a significant scale it makes data conversion impractical. There are constantly evolving data interfaces, diverse data formats, the need to move and analyze large-scale-data, and difficulty in accessing data.

Pelagic’s Intelligent Dynamic Data Logging automatically extracts meta-data from subsystems to dynamically link evolving data, with differing formats and time periods, into a common data repository (or Data Lake) for routine and impromptu analysis. The MetaGen process enables maximum performance by limiting data manipulation, and ensures integrity by keeping data in its original form. The Pelagic Tool Kits are used to create views into the repository so that the pools of heterogenous data appear homogeneous to a diverse analysis community and the custom or commercial tools they use.

Pelagic provides the highest performance by keeping data in its original form and in an enduring, sustainable, elegant and unified data repository. This provides integration of disparate data without the burden of unique tool interfaces, data conversion, and sensitivity to data content changes.

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Streamlining Sensor Analysis

Legacy sensors require batch processing and a delicate, multi-step data pipeline to translate and crunch data for meaningful mission analysis. Analysts consume expensive and custom embedded prime mission computing equipment to process gigabytes of complex radar data.

Problems

  • Hours or even days to get results
  • Maintenance builds disrupt teams and waste days repairing broken interfaces
  • Data structure changes require complex configuration management of interdependent or proprietary tools
  • Negative impacts on testing, simulation, sustainment, upgrade, and operations

Pelagic addresses such slow processing and fragile data pipelines to improve the productivity and responsiveness of sensor data analysis. It eliminates many of the complexities of data from real-time systems that include moving data between hardware platforms, managing large data sets, interpreting machine data, visualizing data, and maintaining data schema and data configuration.

Benefits

  • Data can be viewed in real-time, providing decision-makers with the necessary information to make judgements as the events unfold
  • Significant cost reductions and accelerated innovation through more efficient data flows that streamline integration
  • Provides an on-ramp to Machine Learning and Artificial Intelligence
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