Enterprise Technology Glossary

6,173 results · page 157 of 309

• Low-Latency Scheduling

  Low-latency scheduling is a set of scheduling methods and configurations that allocate compute or network resources with predictable, bounded delay, enabling deterministic response times for real-time and latency-sensitive enterprise workloads subject to strict performance, regulatory, or service-level requirements.

• Low-Latency Serving Stack

  Low-latency serving stack is a coordinated set of software and infrastructure components that deliver millisecond-level responses for online applications or machine learning inference, enabling real-time decisions while meeting strict service-level latency objectives in enterprise production environments.

• Low-Noise Amplifier

  Low-noise amplifier is an RF or microwave amplifier placed at the receiver front end to boost weak signals while adding minimal noise, which affects sensitivity, coverage, and spectrum efficiency in wireless, satellite, radar, and other enterprise communication systems.

• Low Power Indoor (LPI) Operation

  Low Power Indoor (LPI) operation is a regulatory mode for Wi‑Fi 6E and Wi‑Fi 7 devices in the 6 GHz band that restricts power and usage to indoor environments, enabling additional spectrum access while meeting coexistence and compliance requirements for enterprises.

• Low Power Mode

  Low Power Mode is an operating state in hardware or software that reduces energy consumption by limiting performance or disabling nonessential functions while maintaining core operation, which supports enterprise energy management, battery life targets, and sustainability objectives across infrastructure and devices.

• Low-Power Wide Area

  Low-power wide area (LPWA) is a family of wireless technologies that provide long-range, low-bandwidth connectivity for battery-powered devices, enabling large-scale IoT deployments in areas such as metering, tracking, and remote monitoring within enterprise and industrial environments.

• Low Power Wide Area Network

  Low power wide area network is a category of wireless technologies for long-range, low-bandwidth, low-energy connectivity among large numbers of internet of things devices, relevant to enterprises that deploy distributed sensing, monitoring, and telemetry across facilities, infrastructure, and field assets.

• Low-Rank Approximation

  Low-rank approximation is a linear algebra method that replaces a large matrix or tensor with a lower-rank representation that preserves most of its structure, enabling dimensionality reduction, compression, and more efficient analytics and machine learning in enterprise environments.

• LSO and NFV MANO

  LSO and NFV MANO are network automation and orchestration frameworks that coordinate service-level and resource-level lifecycle management of virtualized and connectivity services across multi-domain environments, supporting automated provisioning, assurance, and integration with OSS/BSS and SDN or cloud orchestration systems for service providers and large enterprises.

• LTE Advanced

  LTE Advanced is a 3GPP-standardized enhancement of LTE that meets IMT-Advanced requirements for 4G mobile broadband, providing higher data rates, spectral efficiency, and capacity for operators and enterprises using public networks, private mobile networks, and fixed wireless access solutions.

• Lunar Communications Relay

  Lunar communications relay is a cislunar communications capability that forwards signals between lunar assets and Earth or other spacecraft, enabling continuous or extended connectivity for telemetry, command, and data return when direct line-of-sight links to the lunar surface or orbit are not available.

• Lunar Surface Operations Center

  Lunar Surface Operations Center is a mission control facility concept that coordinates and manages human and robotic activities on the Moon’s surface. It matters for enterprises because it provides the operational, communications, and logistics framework that supports lunar missions and surface infrastructure.

• Lustre File System

  Lustre File System is an open-source parallel distributed file system used in high-performance computing to provide a POSIX-compliant shared file namespace across clustered servers and storage, relevant for organizations that run I/O-intensive scientific, engineering, or analytics workloads at large scale.

• M&A

  Mergers and acquisitions (M&A) are corporate transactions where one company combines with or acquires another to gain control of assets, equity, or operations. M&A matters to enterprises because it requires structured governance of technology, data, security, and integration across organizations.

• Machine Health Index

  Machine Health Index is a quantitative metric that aggregates multi-sensor and operational data into a normalized indicator of machine condition, used in enterprises to support condition monitoring, predictive maintenance decisions, asset performance management, and standardized reporting on equipment health across fleets.

• Machine Learning

  Machine learning is a field of computer science that uses data-driven statistical algorithms to learn patterns from data and improve task performance without explicit rules, enabling enterprises to build predictive and analytical capabilities into products, operations, and decision workflows.

• Machine Learning Acceleration

  Machine learning acceleration is the use of specialized hardware and software techniques to increase the performance and efficiency of machine learning training and inference, which supports enterprise requirements for throughput, latency, energy use, and cost across data center, cloud, and edge environments.

• Machine Learning Clinical Model

  Machine learning clinical model is a computational model that applies machine learning techniques to clinical data to support tasks such as diagnosis, prognosis, or risk prediction, and matters to enterprises for decision support, quality measurement, and governed clinical automation.

• Machine Learning–Driven Autotuning

  Machine learning–driven autotuning is an automated optimization method that uses machine learning models to adjust system or application configuration parameters based on observed telemetry, helping enterprises maintain performance objectives and reduce manual tuning effort under changing workloads and resource conditions.

• Machine Learning-Driven Scheduling

  Machine learning-driven scheduling is the application of machine learning models to construct and adjust schedules for tasks and resources under constraints, enabling data-based allocation decisions that support utilization, service levels, and cost control in enterprise operations and infrastructure environments.