Key Capabilities

• IoT Applications provide easy access to common IoT tasks, such as defining KPIs, via self-service tools.
• Visual Analytics provides powerful visualization to explore analytical results, highlight key relationships, spot anomalies, test hypotheses, and diagnose problems.
• Advanced Analytics provides a unified, comprehensive set of descriptive, predictive, and prescriptive analytics that can be executed across real-time data streams or historical data-sets. Machine learning tools support the generation of predictive models, and Automated Actions enable fast responses to opportunities and threats.
• Fast Data Ingestion & Integration leverages IoT protocols to process the huge speed and volume of data that typically flows in IoT.
• Open IoT Data Lake – stores, secures, and curates all of your IoT data, from raw device data to time-series analytic data, and provides an Elastic Query Service.

Advanced Analytics

VIA’s Advanced Analytics executes fast analytics in real-time to provide the context and insight needed for the fast decision-making required in IoT. VIA’s advanced analytics includes all the key types of analytics – real-time, historical, predictive, and prescriptive – needed for IoT.

Learn more about VIA Analytic Data Flow (ADF) which provides a comprehensive visual development environment that empowers developers and business analysts to rapidly create analytics solutions using visual models that require little or no coding.

Its key capabilities include:

• Fast Real Time Analytics (Streaming) providing contextual awareness and situational intelligence using descriptive analytics, multidimensional analysis, pattern matching, anomaly detection, trend analysis, and behavioral analytics.
• Historical Analytics (batch) providing historical KPIs and baselines, and autoregression and time-series analysis, yielding historical context for real-time analysis and machine learning.
• Machine learning algorithms over your Open IoT Data Lake for producing predictive and prescriptive analytics models.
• Rapid operationalization of Analytics Models to support real-time Predictive and Prescriptive Analytics, running in real-time or over historical data.
• Multi-dimensional time-series analytics for pattern detection and trend analysis.

Complex IoT analytics processing is specified via a visual “flow language” that empowers developers and analysts to rapidly create analytic processing flows graphically with no coding.

Visual Analytics

VIA’s Visual Analytics provide an easy and fast way for Operations and Business Analysts to explore and visualize analytical results, identify key relationships, spot anomalies, test hypotheses, and diagnose problems – ensuring the right decision and the right action at the right time.

Intelligent Actions & Automation

Timely action is the critical final step in creating value in IoT. Predictive and prescriptive analytics can trigger Intelligent Actions within VIA’s process automation suite, which supports both fully automated processes and human-guided workflows enabling intelligent business processes that are analytics-driven, situationally aware, and adaptive.

Open IoT Data Lake with Elastic Query Service

The volume and speed of data in IoT Analytics requires a level of scalability and elasticity that goes well beyond traditional enterprise data systems. VIA’s Open IoT Data Lake provides the open, scalable data services required to support the complete analytics life-cycle: raw data ingestion, data enrichment, data exploration, model building, and analytics processing. Data at all stages are captured, stored, secured, and curated, along with its appropriate metadata. An Elastic Query Service supports access by IoT applications, self-service analytics, and 3rd party data consumers via SQL standards.

Fast Data Ingestion & Integration

IoT Analytics solutions require advanced data ingestion and integration to process the huge speed and volume of data that typically flows in IoT. The platform’s Fast Data Ingestion provides a rich set of tools for accessing, parsing, transforming and integrating the three major types of data found in IoT scenarios:

• IoT data from sensors, devices, and other telemetry data
• Traditional Enterprise Data from systems like CRM and ERP
• Cloud & Public Data sources such as weather or traffic

Foundation

Underlying the VIA analytics platform are a mix of open source and proprietary technologies that support elastic scalable IoT Analytics. The platform’s Foundation includes Hadoop’s HDFS and Hive for storing massive amounts of data, Spark and Spark Streaming for scalable processing, and Kafka for scalable ingestion. Vitria has integrated these discrete technologies and added enterprise-class security and holistic lifecycle management to make this a robust Foundation to build, manage, and secure even the most mission-critical IoT applications. Vitria is constantly integrating new versions of foundational components seamlessly, so that your IoT solutions work better and faster – future-proofed for the rapid-pace of innovation.