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Logicrunch
LogiCrunch is a sophisticated product, built from scratch primarily to process real-time streaming events from hospitals. LogiCrunch currently receives and processes events in various shape and form. LogiCrunch processing pipeline is metadata driven, tenant-specific patient messages are correlated and features dynamically generated, prior to pathway predictions and instant notification.
Interoperability
Platform is capable of handling and transforming sensor messages across versions. Also, capable of handling tenant specific semantics. All tenant specific encoded messages are transformed to a canonical form that is received and processed by all the downstream processing components.
Multi-Tenancy
Platform is broadly divided into 2 set of processing feeds. It receives Tenant agonistic feeds other feeds that involve tenant specific profiling and processes. Tenant-agnostic components receive, and process transformed canonical payloads.
RealTime-Streaming
Different predictive models are built offline on historical data and injected into the streaming pipeline. Sensor related events received in real time are passed through complex event processes(CEP) that transform, derive features and predict in real-time. Models built are segmented broadly based on high, low acuity areas. Ceptors that are clustered receives high severity events in real time at sub-second levels, while the low-acuity patient related events are received at relatively high levels. Platform receives processes, predicts on a continuous streaming data in real-time and relies heavily on clustered messaging brokers for handshakes
RealTime-Prediction
Cardiac and sepsis pathway predictive models are built offline on historical data and injected into the streaming pipeline. Device, ADT and Lab related events received in real time are passed through CEP processes that transform, derive features and predict in real-time. Models built are segmented broadly based on high, low acuity areas. Ceptors that are clustered receives high acuity patient related events in real time at sub-second levels, while the low-acuity patient related events are received at relatively high levels. Platform receives processes, predicts on a continuous streaming data in real-time and relies heavily on clustered messaging brokers for handshakes
Dynamic Metadata Generation
Platform generates and stores tenant-specific metadata, as it discovers dynamically in the ingestion process, while enriching and transforming from tenant-specific to canonical payloads. Metadata is also loaded onto a clustered cache, for data reads by the pipeline processes
Data Governance
Pipeline process stores the raw data as received, in the tenant specific data-lakes. Also, pipeline stores use-case specific data ponds per tenant, inclusive of the model features and predictions. Apache Atlas, Ranger and SOLR are configured over the data fabric to bolster data access, instrument, search and retrieval scenarios. Product has the complete Data fabric layer metadata driven.
Auto Scale
LogiCrunch is a sophisticated product that is pluggable, extensible and scalable. LogiCrunch has several moving parts – the streaming engine, messaging brokers, CEP engine, in-memory cache, the data fabric in HBase and PostgreSql. All these components are clustered, hosted in isolated and/or on shared nodes. Each of these pipeline components can be dynamically scaled based on resource usage.
Furthermore, streaming data is seen and used everywhere—from social networks, to mobile and web applications, IoT devices, instrumentation in data centres, and many other sources. As the speed and volume of this type of data increases, the need to perform data analysis in real time with machine learning algorithms. For example, you might want a continuous monitoring system to detect sentiment changes in a social media feed so that you can react to the sentiment in near real time.
LogiCrunch also has an avatar in AWS solution, which can be deployed with minor or no modification, in the form of cost-effective batch and real-time data analytics architecture on the AWS Cloud that leverages Apache Spark Streaming. In this edition, LogiCrunch enjoys the flexibility of AWS Cloud, streaming data, and data analysis. For more details please go through Cloud consulting case studies.
Our Valued Partners
Testimonials
Charles Boicey
Co-Founder, CIO,Clearsense
Team ElogicSquare is highly knowledgeable and well experienced in the field of Data Streaming and Analytics. The professionals at ElogicSquare took the time to understand Clearsense vision and technology needs to provide the most efficient solutions and highest quality products. They have been instrumental in shaping our portfolio of products, intensifying an analytics culture within and a key stakeholder in decision making. With Team ElogicSquare by our side, we are confident in growing Clearsense market presence in the competitive Healthcare Analytics domain.
Lisa Dahm, PhD
Director, Enterprise Data & Analytics, UC Irvine Health Information Services
ELogic has been a technology partner of the University of California, Irvine Health since 2011, initially building out the first Hadoop based technology solution in healthcare as well as developing the first Hadoop based system to retire healthcare information systems. Additionally, the team developed a surveillance system that detects the subtle changes in a patient’s physiology and predicts the potential for the patient to deteriorate in the near future. The eLogic team has been attentive to our needs and has provided innovative long term solutions for our complex healthcare data problems
Amy M. Andrade, MS, PMP
Senior Advisor to the President – Technology & Innovation,MMC,Nashville
In collaboration with Clearsense the eLogic team has been an integral component to the success of the development of the Data Science Institute at Meharry Medical College in Nashville, Tennessee, USA. The team developed the infrastructure to support the educational and the research needs of the institute. Our future work with eLogic centers on combining omic data sets with healthcare, exposome & social determinants data sets for clinical and research use cases. eLogic has been of great service in our quest to bring affordable solutions to serve the underserved.
