Enterprise Technology Terminology: D

517 results · page 25 of 26

• Dual Licensing Model

  Dual licensing model is a software licensing approach in which a rights holder offers the same codebase under both an open source license and a commercial license, enabling enterprises to choose terms that align with compliance, distribution, and business requirements.

• Dual-Use Satellite Architecture

  Dual-use satellite architecture is the design of satellite, ground, and network components that support both civilian or commercial missions and military or security missions. It matters because shared space infrastructure affects security, availability, compliance, and procurement decisions for enterprise and public-sector users.

• Duplicate Record Detection

  Duplicate record detection is the process and toolset used to identify multiple database entries that represent the same real-world entity, helping enterprises maintain accurate, consistent, nonredundant data for analytics, operations, compliance, and master data management across systems and domains.

• Dynamic Access Policy

  Dynamic access policy is a context-aware access control approach that evaluates user, device, resource, and risk attributes in real time to determine authorization, supporting least privilege, zero trust architectures, and auditable, centrally managed access decisions across enterprise systems and data.

• Dynamic Application Placement

  Dynamic application placement is a policy- and telemetry-driven method for automatically deciding where enterprise applications or workloads run across distributed infrastructure, helping organizations align resource usage, resilience, performance, cost control, and compliance requirements in hybrid, multi-cloud, and edge environments.

• Dynamic Application Security Testing

  Dynamic application security testing (DAST) is a method that evaluates running applications from the outside in by simulating attacks over exposed interfaces, helping enterprises identify exploitable vulnerabilities in web applications and APIs for risk management, compliance, and remediation planning.

• Dynamic Baseline Detection

  Dynamic baseline detection is a monitoring and analytics method that continually learns normal behavior patterns from data and updates them over time, enabling enterprises to detect anomalies and deviations in security, IT operations, and business systems with adaptive thresholds.

• Dynamic Emission Factor

  Dynamic emission factor is a time-varying greenhouse gas emissions metric that expresses emissions per unit of energy or activity for specific time intervals, enabling enterprises to perform more granular emissions accounting, reporting, and operational planning than with static annual emission factors.

• Dynamic Fault Correlation

  Dynamic fault correlation is a real-time analytics capability that groups related faults and alarms across infrastructure, applications, or networks into unified incidents, helping enterprises manage alert volume, perform root cause analysis, and support more efficient IT and security operations.

• Dynamic Host Configuration Protocol

  Dynamic Host Configuration Protocol (DHCP) is a network protocol that automates IP address and network configuration assignment to devices, enabling centralized address management, reduced manual configuration errors, and more efficient operations across enterprise data centers, campus networks, and distributed environments.

• Dynamic Hybrid Controller

  Dynamic Hybrid Controller is a control architecture that coordinates multiple control modes or controllers in real time for systems with both continuous dynamics and discrete events, supporting stable, safe, and constraint-compliant operation across changing operating conditions in enterprise and industrial environments.

• Dynamic Inference Graph

  Dynamic inference graph is a runtime-constructed computational graph used to execute machine learning inference workloads with data-dependent control flow and variable structures. It matters in enterprise environments that require flexible, debuggable model serving across heterogeneous, context-dependent prediction use cases.

• Dynamic Job Scheduling

  Dynamic job scheduling is an automated method for assigning and executing jobs at runtime based on current resource conditions and policies, used in enterprises to coordinate workloads across clusters, clouds, and data platforms while supporting governance and operational control.

• Dynamic Kernel Fusion

  Dynamic kernel fusion is a runtime optimization that combines multiple compute kernels into a single kernel on accelerators such as GPUs, reducing memory traffic and launch overhead and affecting performance, cost efficiency, and service-level planning for enterprise AI workloads.

• Dynamic Latency Optimization

  Dynamic latency optimization is a runtime approach to managing distributed and networked systems that continuously adjusts routing, resource allocation, and workload or data placement to reduce end-to-end delay, support defined performance objectives, and maintain predictable service quality in enterprise environments.

• Dynamic Line Rating

  Dynamic line rating is a method for determining the real-time current-carrying capacity of overhead transmission lines based on measured or modeled environmental and operating conditions, which matters for grid operators seeking to use existing assets efficiently while maintaining reliability and safety limits.

• Dynamic Load Balancing

  Dynamic load balancing is a real-time traffic distribution method that uses current performance and health metrics to allocate workloads across multiple resources, helping enterprises maintain availability, predictable response times, and controlled access to applications and services across data center and cloud environments.

• Dynamic Model Partitioning

  Dynamic model partitioning is a method for distributing parts of a machine learning or deep learning model across multiple compute resources at runtime, enabling enterprises to run large models within heterogeneous infrastructure while meeting latency, capacity, and service-level requirements.

• Dynamic Network Slicing

  Dynamic network slicing is the automated creation and adjustment of multiple logical networks with defined performance and security characteristics on shared infrastructure, allowing enterprises and providers to align 5G and software-defined networks with distinct service, SLA, and isolation requirements.

• Dynamic Network Topology

  Dynamic network topology is a network architecture in which connections and routing relationships between nodes change over time based on mobility, link conditions, and control policies, which affects how enterprises design for reliability, performance, security, and automated operations.