Glossary
This glossary contains terms that are relevant to BMC Helix IoT Edge.
algorithm
A step-by-step set of instructions or rules used to solve a problem or perform a calculation. In machine learning, algorithms are the mathematical and computational procedures used to train models and make predictions.
analytics
Using advanced algorithms and machine learning models to uncover patterns, anomalies, and trends within collected data for proactive problem-solving.
anomaly detection
Identifying deviations from standard data patterns, allowing early detection of abnormal device behavior and timely intervention.
business services
IT and IoT systems often support core processes and activities contributing to an organization's primary goals and objectives.
classification
A type of supervised learning that categorizes data points into predefined classes or groups.
contextual data
Supplementary data provides additional context to the primary data set, enhancing the understanding and analysis of the collected information.
data collection
Gathering information from various IoT devices at the edge for analysis and insights.
data ingestion
Data processing involves importing and processing data from external sources into the edge platform for analysis and insights. It also involves transforming and manipulating collected data into meaningful insights at the edge, enabling real-time analysis and decision-making.
data source
A location or system from which data is collected within the IoT environment, supplying metric, event, contextual, and enrichment data.
device extensions
Custom enhancements or functionalities are added to IoT devices to extend their capabilities or compatibility with the edge environment.
device management
Configuring, monitoring, and controlling IoT devices, encompassing tasks like provisioning, firmware updates, and device settings management.
device services
BMC Helix IoT Edge provides services that enable device management, communication, and interaction within the IoT ecosystem.
downsampling
The process of reducing the frequency or resolution of data collected by node in the edge. This is often done to conserve storage space, reduce bandwidth usage during transmission, and speed up data processing. Downsampling can involve averaging data points over a set interval, selecting a subset of data points, or applying more complex filtering techniques.
edge computing
Processing data closer to the source (edge of the network) for real-time insights and rapid responses, avoiding the need to send data to centralized servers.
edge orchestration
Coordinating and managing activities, processes, and workflows among IoT devices and edge infrastructure for efficient and optimized operations.
enrichment data
Additional information is added to collected data to provide more insights and context, enhancing the value and utility of the data.
event data
Information generated by IoT devices indicates specific occurrences or changes in conditions that require attention or analysis.
integration
Connecting BMC Helix Edge with other systems, applications, or tools to enable data exchange and interoperability within the existing ecosystem.
IoT
Internet of Things refers to the network of interconnected devices that collect, transmit, and exchange data, enabling communication and automation in various contexts.
machine learning
A subset of artificial intelligence (AI) that enables systems to learn from data and improve their performance without explicit programming.
management workbench
A tool or interface within BMC Helix Edge that allows administrators to configure, monitor, and manage IoT devices and workflows.
metadata
Descriptive information about data, devices, or processes, providing context and facilitating management, organization, and analysis.
metric data
Numeric measurements and values collected from IoT devices provide insights into performance and status.
message bus
A communication system that enables data exchange between different components of an application or system, facilitating data flow and integration.
ML training
Machine Learning Training involves feeding algorithms with data to enable them to learn and improve their accuracy over time, facilitating predictive analytics.
node
A specific instance or endpoint within the BMC Helix Edge system.
node host
The hostname or IP address of the node.
on-premises
Refers to deploying software or hardware within an organization's physical location instead of cloud-based or remote hosting.
operational technology (OT)
Technology monitors, controls, and manages physical devices and processes typically found in industrial and infrastructure environments.
operational state
The current operational status of a.
predictive maintenance
Anticipating equipment or device failures through data analysis, optimizing maintenance schedules, reducing downtime, and lowering costs.
profile
A set of attributes or characteristics that define a group of devices.
regression
A type of supervised learning that predicts a continuous numerical value.
rules engine
A component that processes incoming data, applying predefined rules or conditions to trigger specific actions or alerts.
time series prediction
A type of machine learning that analyzes time-stamped data to forecast future values. Time series data has an inherent order, and the model learns from past patterns to predict future trends.
workflow
A sequence of triggered actions, including automated tasks, alerts, notifications, and data processing steps, enhances operational efficiency.