ETSI GR ENI 004 V3.1.1 (2023-07)

Experiential Networked Intelligence (ENI); Terminology

RGR/ENI-004v311_Terminology

General Information

• Status: Not Published
• Technical Committee: ENI - Experiential Networked Intelligence
• Current Stage: 12 - Completion
• Due Date: 04-Jul-2023
• Completion Date: 21-Jul-2023

Standards Content (Sample)

GROUP REPORT
Experiential Networked Intelligence (ENI);
Terminology
Disclaimer
The present document has been produced and approved by the Experiential Networked Intelligence (ENI) ETSI Industry
Specification Group (ISG) and represents the views of those members who participated in this ISG.
It does not necessarily represent the views of the entire ETSI membership.

2 ETSI GR ENI 004 V3.1.1 (2023-07)

Reference
RGR/ENI-004v311_Terminology
Keywords
artificial intelligence, network, terminology
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3 ETSI GR ENI 004 V3.1.1 (2023-07)
Contents
Intellectual Property Rights . 4
Foreword . 4
Modal verbs terminology . 4
1 Scope . 5
2 References . 5
2.1 Normative references . 5
2.2 Informative references . 5
3 Definition of terms, symbols and abbreviations . 6
3.1 Terms . 6
3.2 Symbols . 29
3.3 Abbreviations . 29
Annex A: Bibliography . 36
History . 37

ETSI
4 ETSI GR ENI 004 V3.1.1 (2023-07)
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ETSI in respect of ETSI standards", which is available from the ETSI Secretariat. Latest updates are available on the
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Foreword
This Group Report (GR) has been produced by ETSI Industry Specification Group (ISG) Experiential Networked
Intelligence (ENI).
Modal verbs terminology
In the present document "should", "should not", "may", "need not", "will", "will not", "can" and "cannot" are to be
interpreted as described in clause 3.2 of the ETSI Drafting Rules (Verbal forms for the expression of provisions).
"must" and "must not" are NOT allowed in ETSI deliverables except when used in direct citation.

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1 Scope
The present document provides terms and definitions used within the scope of the ETSI ISG ENI. The purpose is to
define a common lexicon for use across all deliverables of ENI.
2 References
2.1 Normative references
Normative references are not applicable in the present document.
2.2 Informative references
References are either specific (identified by date of publication and/or edition number or version number) or
non-specific. For specific references, only the cited version applies. For non-specific references, the latest version of the
referenced document (including any amendments) applies.
NOTE: While any hyperlinks included in this clause were valid at the time of publication, ETSI cannot guarantee
their long-term validity.
The following referenced documents are not necessary for the application of the present document but they assist the
user with regard to a particular subject area.
[i.1] ETSI GR NFV 003 (V1.7.1): "Network Functions Virtualisation (NFV); Terminology for Main
Concepts in NFV".
[i.2] MEF 95: "MEF Policy Driven Orchestration", July 2021.
[i.3] MEF 55.0.3: "Amendment to MEF 55: Service Orchestration Functionality", January 2018.
[i.4] MEF 55: "Lifecycle Service Orchestration (LSO): Reference Architecture and Framework", March
2016.
[i.5] MEF 78.1: MEF Core Model (MCM)", July 2020.
[i.6] Gamma E., Helm R. Johnson R. and Glissades J.: "Design Patterns: Elements of Reusable Object-
Oriented Software", Addison-Wesley, November 1994. ISBN 978-0201633610.
[i.7] ISO/IEC 2382-28: "Information technology -- Vocabulary".
[i.8] ISO/IEC/IEEE 42010: "Systems and software engineering -- Architecture description".
[i.9] The Atlan Data wiki definition of structured data.
[i.10] ETSI GR ENI 007 (V1.1.1): "Experiential Networked Intelligence (ENI); ENI Definition of
Categories for AI Application to Networks".
[i.11] IETF RFC 8321: "Alternate-Marking Method for Passive and Hybrid Performance Monitoring".
[i.12] Mitchell, Tom M.: "Machine Learning", McGraw-Hill, 978-0070428072.
[i.13] ETSI Directives.
[i.14] Gruber, Thomas R.: "Toward Principles for the Design of Ontologies Used for Knowledge
Sharing", International Journal of Human Computer Studies, Vol 43, pp 907-928, 1993.
[i.15] Strassner, J., Agoulmine, N., Lehtihet, E.: "FOCALE - A Novel Autonomic Networking
Architecture", ITSSA Journal 3(1), 64-79, 2007.
[i.16] ETSI TR 103 240 (V1.1.1): "Powerline Telecommunications (PLT); Powerline communication
recommendations for smart metering and home automation".
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[i.17] Strassner. J.: "Knowledge Representation, Processing, and Governance in the FOCALE
Autonomic Architecture", chapter 11 of Autonomic Network Management Principles, 2011,
Elsevier.
[i.18] Void.
[i.19] ETSI TR 102 748 (V1.1.1) "Electromagnetic compatibility and Radio spectrum Matters (ERM);
Impact of the trend towards flexibility in spectrum usage on the principles for drafting Harmonized
Standards and the ETSI work programme for Harmonized Standards".
[i.20] ETSI GR ENI 016 (V2.1.1) "Experiential Networked Intelligence (ENI); Functional Concepts for
Modular System Operation".
[i.21] ETSI GS ENI 019 (V3.1.1): "Experiential Networked Intelligence (ENI); Representing, Inferring,
and Proving Knowledge in ENI".
[i.22] ETSI GS ENI 005 (V3.1.1): "Experiential Networked Intelligence (ENI); System Architecture".
[i.23] ETSI GR ENI 018 (V2.1.1): "Experiential Networked Interlligence (ENI); Introduction to
Artificial Intelligence Mechanisms for Modular Systems".
[i.24] ETSI GR ENI 003 (V1.1.1): "Experiential Networked Intelligence (ENI); Context-Aware Policy
Management Gap Analysis".
[i.25] ETSI GS ENI 006 (V2.1.1): "Experiential Networked Intelligence (ENI); Proof of Concepts
Framework".
3 Definition of terms, symbols and abbreviations
3.1 Terms
0 to 9
Void.
A
abstraction: hiding of unnecessary details to focus on data and information that is relevant for defining a particular
concept or process
NOTE: ETSI GR ENI 003 [i.24] defined abstraction as the "process of focusing on the important characteristics
and behaviour of a concept, and realizing this as a set of one or more elements in an information or data
model". The above definition is introduced to emphasize the importance of hiding (not deleting)
unnecessary details, and more importantly, removing the constraint of use for a model. Abstraction is
fundamentally a mental process that may take the form of model elements, but does not have to.
action: set of operations that may be performed on a set of managed entities, it represents a transformation or
processing in the system being modelled
NOTE: An Action either maintains the state, or transitions to a new state, of the targeted managed entities. The
execution of an Action may be influenced by applicable attributes and metadata. As defined in MEF PDO
CfC [i.2].
actor: role, played by an external entity (human or machine), which interacts with the subject of a use case
NOTE: An actor is always a type of stakeholder (but not vice versa). See stakeholder.
agent: computational process that implements the autonomous, communicating functionality of an application:
• software agent: software that acts on behalf of a user or another program
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• software autonomous agent: software agent that acts on behalf of the entity that owns it without any
communication from the owning entity
• software intelligent agent: software agent that reasons about its environment and take the best set of actions
to satisfy a set of goals
NOTE: This has the connotation of containing AI mechanisms to provide the reasoning and decision-making
capabilities.
• software multi-agent: set of software agents that are physically separate that work together to satisfy a set of
goals
anomaly: measurable consequences of an unexpected change in state of a datum, or set of data, which is outside of its
local or global norm
API: set of communication protocols, code and tools that enable one set of software components to interact with either
a human or a different set of software components
NOTE: This is also known as an Application Programming Interface.
API Broker: software entity that mediates between two systems with different APIs, enabling the two different systems
to communicate transparently with each other
architecture: set of rules and methods that describe the functionality, organization, and implementation of a system:
• cognitive architecture: system that learns, reasons, and makes decisions in a manner resembling that of a
human mind
NOTE 1: Specifically, the learning, reasoning, and decision-making is performed using software that makes
hypotheses and proves or disproves them using non-imperative mechanisms that typically involve
constructing new knowledge dynamically during the decision-making process.
• deliberative architecture: symbolic world model that enables problem-solving components to be built using a
sense-plan-act paradigm
• functional architecture: model of the architecture that defines the major functions of each module and how
each module interacts with each other
• hybrid architecture: system made up of reactive and deliberative components that are combined into a
hierarchy of interacting layers, where each layer reasons at a different level of abstraction
• reactive architecture: system that is aware of changes that affect its computations and adjusts accordingly
NOTE 2: The adjustment is made by reacting to an event in real-time without centralized control. The availability
of new information drives program logic execution.
• software architecture: high-level structure and organization of a software-based system. This includes the
objects, their properties and methods and relationships between objects.
Artificial Intelligence (AI): computerized system that uses cognition to understand information and solve problems
NOTE 1: ISO/IEC 2382-28 [i.7] defines AI as "an interdisciplinary field, usually regarded as a branch of computer
science, dealing with models and systems for the performance of functions generally associated with
human intelligence, such as reasoning and learning".
NOTE 2: In computer science AI research is defined as the study of "intelligent agents": any device that perceives
its environment and takes actions to achieve its goals.
NOTE 3: This includes pattern recognition and the application of machine learning and related techniques.
NOTE 4: Artificial Intelligence is the whole idea and concepts of machines being able to carry out tasks in a way
that mimics the human intelligence and would be considered "smart".
assisted system: system that the ENI system is providing recommendations and/or management commands to is
referred to as the "assisted system"
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attention: part of a neural architecture that dynamically computes a weighted distribution on input text, assigning
higher values to more relevant elements
NOTE: Attention mimics cognitive attention as performed in the human brain. It selectively enhances some parts
of the input data while diminishing other parts. Instead of encoding the input sequence into a single fixed
context vector, the attention model develops a context vector that is filtered specifically for each output
time step. The model then predicts next word based on context vectors associated with these source
positions and all the previous generated target words.
autonomous network: set of self-governing programmable and explainable systems that seamlessly deliver secure,
context-aware, business-driven services that are created and maintained using model-driven engineering and
administered by using policies
Autonomous Network Responsibility Index (ANRI): level of responsibility delegated to the AN in all the Operational
Procedures bind to the lifecycle management of each Autonomous Domain and E2E Service
axiom: statement that is assumed to be true, in order to serve as a starting point for further reasoning
B
behaviour: way in which a set of objects function
NOTE: This includes how the object reacts in a particular situation given one or more events.
bias: systematic difference in treatment of certain objects, ideas, or people in comparison to others:
• algorithmic bias: algorithm that possesses systematic and repeatable errors that create unfair outcomes
• emergent bias: reliance on an algorithm that has not been adjusted to evaluate new forms of data
• inductive bias: set of assumptions that are used in a machine learning algorithm used to predict outputs for
inputs that it has not encountered
bidirectional encoder representations from transformers: unsupervised deep learning strategy that utilizes
bidirectional models that considers all words of the input sentence simultaneously and then uses an attention mechanism
to develop a contextual meaning of the words
blackboard: architecture that uses a shared workspace that a set of independent agents contribute to, which contains
input data along with partial, alternative and completed solutions
BSS-like functionality: functionality supporting customer-facing activities for the operator
NOTE: Examples include customer service, rating, order management, billing and settlement.
C
camelCase: a naming convention in which the first letter of each word in a compound word is capitalized, except for
the first word
NOTE: This is also called lowerCamelCase.
capability: type of metadata that represents a set of features that are available to be used from a managed entity
NOTE: These features may, but do not have to, be used. These features may represent all or a subset of the
functionality provided by a managed entity. Since a Functional Block is a type of managed entity,
Capabilities can be defined for Functional Blocks as well. A Capability provides information about the
functionality of a managed entity that enables management entities to decide whether that managed entity
is useful for a given task.
case-based reasoning: use of existing experiences and knowledge to understand and solve new problems
catastrophic forgetting: tendency of an artificial neural network to forget previously learned information when
learning new information
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choreography: set of processes that define how entities interact from a global point-of-view
NOTE: That is without a single point of control. Compare this definition to Orchestration.
class: template for defining a specific type of object that exhibits a common set of characteristics and behaviour:
• abstract class: class that cannot be directly instantiated
• concrete class: class that can be directly instantiated
classifier: procedure that predicts which elements of a set belong to which (pre-defined) classes:
NOTE: The classification is done using training data whose category membership is known, and can be thought
of as a function that assigns a new observation a class label.
• binary classifier: classifier that decides whether or not an input belongs to one of two groups (i.e. classes)
based on a classification function
• discriminative classifier: classifier that differentiates an object using class labels
NOTE: This directly estimates the conditional probability of P(Y|X). An example is logistic regression.
• generative classifier: classifier that learns a model of the joint probability of an input x and the label y, uses
Bayes rules to calculate p(Y|X), and then assigns the most likely label
NOTE: This estimates P(Y|X) by estimating P(Y) and P(X|Y). An example is Naïve Bayes classifier.
• hierarchical classifier: classifier that maps input data into a tree-like set of output categories by first,
classifying at a low level, and then iterating each lower-level classification into a higher-level classification
• linear classifier: classifier that assigns a label based on a linear combination of its features
• probabilistic classifier: classifier that assigns a label to an object based on a (conditional) probability
distribution
closed loop control: self-regulating mechanism in which outputs of a system are provided to a system that compares
the current state to a desired state (or set of states); the comparison is then used to adjust the behaviour of the system
NOTE 1: Positive feedback increases the correction value, while negative feedback reduces the correction value.
NOTE 2: Positive and negative feedback can be combined to achieve the needs of a system. In addition, more
complex forms of closed loop control exist, such as Proportional-Integral-Derivative (PID) control. See
control theory.
clustering: grouping of a set of objects such that objects in the same group are more similar to each other, by one or
more measures, than to other objects in other groups
cognition: process of understanding data and information and producing new data, information and knowledge:
• cognition model: computer model of how cognitive processes, such as comprehension, action and prediction
are performed and influence decisions
collaborating: two or more managed entities cooperate to accomplish a given task
column-oriented database: database that organizes data by field
NOTE: This type of database keeps all of the data associated with a field next to each other in memory, and is
optimized for online analytical processing. They are optimizes for reading and computing on columnar
data.
compiler: computer program that translates the content of a source programming language into a different, or target,
programming language
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component: part of a System that has operational and/or management significance
NOTE: A Software Component is an encapsulation of a set of related functions and/or data that perform a set of
specific purposes and have a set of associated semantics and behaviour.
compute node: object that performs a set of calculations according to a set of algorithms
concept drift: not taking changing data and its meanings into account when training an ML model [i.12]
condition: set of attributes, features and/or values that are to be compared with a set of known attributes, features,
and/or values in order to determine what decision to make
container: object that stores collections of other objects in an organized manner
context: collection of measured and inferred knowledge that describe the environment in which an entity exists or has
existed
control loop: mechanism that senses the performance of an object or process being controlled to achieve desired
behaviour:
• adaptive closed control loop: closed control loop whose controlling function adapts to the object or process
being controlled using parameter that are either unknown and/or vary over time
• closed control loop: control loop whose controlling action is dependent on feedback from the object or
process being controlled
NOTE 1: This type of control loop measures the difference between the actual and desired values of a set of
variables to adjust a set of parameters to change the behaviour of the system to bring the actual value
closer to that of the desired value.
• cognitive closed control loop: closed control loop that selects data and behaviours to monitor that can help
assess the status of achieving a set of goals, and produce new data, information, and knowledge to facilitate the
attainment of those goals
• distributed closed control loop: closed control loop whose components are physically distributed among
different locations
• federated closed control loop: set of semi-autonomous closed control loops that use formal agreements to
govern their interaction and behaviour
• hierarchical closed control loop: closed control loop that is organized in the form of a tree
• open control loop: control loop whose controlling action is independent of the output of the object or process
being controlled
NOTE 2: This type of control loop does not link the control action to the object or process being controlled (it
simply continues to apply the control action).
• peer closed control loop: two or more closed control loops that may interact, but are independent of each
other
control plane: communication between entities that enables forwarding and routing of traffic to work
NOTE: Control plane packets are destined to or locally originated, by entities themselves (e.g. they go to a
network entity and direct how traffic flows). Compare to Data Plane.
control theory: application of mechanisms to regulate the behaviour of a target system
NOTE: Control theory includes linear and nonlinear control mechanisms.
coupling: amount of interdependence between two components, modules or systems
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D
data: facts and statistics collected together for reference or analysis:
• database: structured set of data held in a computer, especially one that is accessible in various ways
• data lake: centralized storage repository that stores raw data that are in the form of structured, semi-structured
and unstructured format
• data mart: subset of a data warehouse focused on a particular line of business, department or subject area
• data mining: procedure that discovers patterns in, and extracts knowledge from, data sets
NOTE 1: For the purposes of ENI, these patterns are of two principal types:
1) patterns that cause the generation of data; and
2) patterns that relate data (typically in a semantic manner).
• data model: representation of concepts of interest to an environment that is dependent on data repository, data
definition language, query language, implementation language and/or protocol (typically, but not necessarily,
all five)
NOTE 2: As defined in MEF PDO CfC (MEF 95 [i.2]).
• data plane: path that the end-user traffic takes through a network
NOTE 3: It is made up of traffic that goes through network entities, not to a network entity. Compare to Control
Plane.
• data warehouse: repository used to connect, analyse, and report on historical and current data from
heterogeneous sources
NOTE 4: A data warehouse is designed for query and analysis as opposed to transaction processing. It analyses and
reports on data from operational systems as used in decision-support systems.
decidable: procedure that determines, by a mathematical formal means in a finite amount of time, whether a formula is
valid
decision making: set of processes that result in the selection of a set of actions to take from among several alternative
possible actions
declarative policy: type of policy that uses statements to express the goals of the policy, but not how to accomplish
those goals
NOTE 1: State is not explicitly manipulated, and the order of statements that make up the policy is irrelevant.
NOTE 2: In the present document, Declarative Policy will refer to policies that execute as theories of a formal
logic.
NOTE 3: As defined in MEF PDO CfC (MEF 95 [i.2]).
denormalisation: process of changing information from a canonical form to one specialized for a particular actor
and/or domain
design pattern: general, reusable solution in a given context to a commonly occurring software problem:
NOTE: This type of design pattern is not an architecture and not even a finished design; rather, it describes how
to build the elements of a solution that commonly occurs. It may be thought of as a reusable template.
• design pattern, architecture: general, reusable solution in a given context to a commonly occurring problem
in the design of the software architecture of a system
• design pattern, software: general, reusable solution in a given context to a commonly occurring problem in
the design of a software system
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designated entity: operator, nms, ems, controller or orchestrator acting on behalf of the assisted system
NOTE: The Designated Entity is a trusted entity.
digital twin: virtual representation of a physical object or system across its lifecycle, using real-time data to enable
understanding, learning and reasoning
domain: collection of entities that share a common purpose:
NOTE 1: Each constituent Entity in a Domain is both uniquely addressable and uniquely identifiable within that
Domain. This is based on the definition of an MCMDomain in MEF 78.1 [i.5].
• administrative domain: domain that employs a set of common administrative processes to manage the
behaviour of its constituent Entities
• management domain: domain that uses a set of common Policies to govern its constituent Entities
NOTE 2: A Management Domain refines the notion of a Domain by adding three important behavioural features:
1) it defines a set of administrators that govern the set of Entities that it contains;
2) it defines a set of applications that are responsible for different governance operations, such as
monitoring, configuration, and so forth;
3) it defines a common set of management mechanisms, such as policy rules, that are used to govern
the behaviour of MCMManagedEntities contained in the MCMManagementDomain.
This is based on the definition of an MCMDomain in MEF 78.1 [i.5].
domain technical expert: technical expert that has authority within a domain
E
ENI application programming interface: set of communication mechanisms applied between two or more software
components
NOTE: It consists of tools, object methods, and other elements of a model and/or code. APIs simplify producing
programs, since they abstract the underlying implementation and only expose objects and flow of
information, and the characteristics and behaviour of those objects. This prevents the unnecessary
exposure of objects.
ENI external reference point: reference point that is used to communicate between an ENI Functional Block and an
external functional block (e.g. a functional block of the OSS, BSS or assisted system)
NOTE: Where an ENI External Reference Point crosses between two organizational entities is not specified in
this release.
ENI framework: set of abstractions that provide reusable and extensible mechanisms to provide generic functionality
NOTE 1: The ISO/IEC/IEEE 42010 [i.8] defines the term architecture framework as: "An architecture framework
establishes a common practice for creating, interpreting, analysing and using architecture descriptions
within a particular domain of application or stakeholder community".
NOTE 2: The ENI Framework also uses its abstractions to enable the ENI System to dynamically adapt to changing
business goals, user needs, and environmental conditions. The ENI Framework hence provides a standard
way to build and deploy applications and application components.
ENI hardware interface: point across which electrical, mechanical, and/or optical signals are conveyed from a sender
to one or more receivers using one or more protocols
NOTE: A hardware interface decouples the hardware from other functional blocks in a system.
ENI interface: point across which two or more components exchange information:
• ENI API interface: ENI interface set of communication mechanisms through which a developer constructs a
computer program
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• ENI hardware interface: ENI interface across which electrical, mechanical, and/or optical signals are
conveyed from a sender to one or more receivers using one or more protocols
• ENI software interface: ENI interface point through which communication with a set of resources
(e.g. memory or CPU) of a set of objects is performed
ENI internal reference point: reference point that is used to communicate between two or more ENI Functional
Blocks
NOTE: This relationship stays within the ENI framework, and cannot be addressed by systems that are external to
the ENI framework.
ENI ISG PoC proposal: initial description of a PoC Project, submitted as a contribution for review and acceptance by
the ENI ISG before the PoC Project starts
ENI ISG PoC report: detailed description of the results and findings of a PoC project, submitted once the PoC Project
has finished
ENI Reference Point: logical point of interaction between specific Functional Blocks:
• ENI External Reference Point: ENI Reference Point that is used to communicate between an ENI Functional
Block and an external Functional Block of an external system
• ENI Internal Reference Point: ENI Reference Point that is used to communicate between two or more
Functional Blocks that belong to the ENI System
ENI software interface: point through which communication with a set of resources (e.g. memory or CPU) of a set of
objects is performed
NOTE: This decouples the implementation of a software function from the rest of the system.
ENI system: set of entities, based on the "observe-orient-decide-act" control loop model, that produces commands,
recommendations, and knowledge to assist or direct the management of another system
NOTE: The ENI system is an innovative, policy-based, model-driven entity that uses artificial intelligence and
other mechanisms to provide intelligent service operation and management. It is the enabler of intelligent
Infrastructure management, Network Operations Service Operation and Management, and Assurance. It
automates complex human-dependent decision-making processes. It also provides the ability to ensure
that automated decisions taken by the system are correct and are made to increase the reliability, security
and maintenance of the network and the applications that it supports. It also includes hardware and
software components, programs, and system and user documentation.
entity: object in the environment being managed that has a set of unique characteristics and behaviour
NOTE: Objects are represented by classes in an information model.
ethics: set of principles that govern the moral behaviour of a person or machine:
• consequentialist ethics: agent is ethical if and only if it considers the consequences of each decision and
chooses the decision that has the most moral outcome
• deontological ethics: agent is ethical if and only if it respects obligations, duties, and rights appropriate for a
given situation
• ethical dilemma: situation in which any available decision leads to infringing on one or more ethical
principles
• virtue ethics: agent is ethical if and only if it acts according to a set of moral values
evaluation dimension: viewpoint that can be divided into five dimensions such as ManMachine Interface
NOTE: This can be subdivided into Decision Making Participation, Data Collection and Analysis, Degree of
Intelligence and Environment Adaptability, as defined in ETSI GR ENI 007 [i.10].
evaluation object: AI application or a part of Network Lifecycle, defined from two dimensions: the subsystems and the
network lifecycle
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Event-Condition-Action (ECA): type of imperative policy in which actions can only execute if the event and
condition clauses are true
NOTE: An ECA policy rule is activated when its event clause is true; the condition clause is then evaluated and,
if true, enables the execution of one or more actions in the action clause. This type of policy explicitly
defines the current and desired states of the system being managed.
Experiential Networked Intelligence (ENI): processes associated with assimilating and understanding knowledge and
learning through experience
NOTE: Adding closed-loop artificial intelligence mechanisms based on context-aware, metadata-driven policies
enables the network to more quickly recognize and incorporate new and changed knowledge, and hence,
make actionable decisions. This enables the network functionality to evolve and become better able to
meet the demands of its operators with continued usage.
F
feature: (traditionally), individually measurable property of an object under observation
feature: (for ENI), individually measurable characteristic or behaviour of an object being observed:
NOTE 1: Traditionally, individually measurable characteristics were assigned numerical values. For ENI, these
individually measurable characteristics or behaviours may be allowed to be numeric or other types of
data.
• feature construction: feature that creates higher-level features from lower-level features (see feature
hierarchy)
• feature engineering: process of transforming raw data into features that better represent the underlying
problem to the predictive models, resulting in improved model accuracy on unseen data
NOTE 2: Feature engineering is concerned with determining the best representation of the sample data to learn a
solution for a given problem.
• feature hierarchy: tree-like structure of features, where a higher-level object represents the composition of its
lower-level objects
flow-oriented on-path telemetry: specific class of network forwarding-plane telemetry techniques, including IOAM
(In-situ OAM), EAM (Enhanced Alternate Marking), PBT (Postcard-based Telemetry) and HTS (Hybrid Two Steps)
formal: study of (typically linguistic) meaning of an object by constructing formal mathematical models of that object
and its attributes and relationships:
• formal grammar: set of structural rules that define how to form valid strings from a language's alphabet that
obey the syntax of the language
• formal logic: use of inference applied to the form, or content, of a set of statements
NOTE 1: The logic system is defined by a grammar that can represent the content of its sentences, so that
mathematical rules may be applied to prove whether the set of statements is true or false. Refer to MEF
PDO CfC [i.2].
• formal methods: set of mathematical theories, such as logic, automata, graph or set theory, that provide
associated notations for describing and analysing systems
NOTE 2: As used in MEF PDO CfC [i.2].
formula: finite sequence of symbols from an alphabet that is part of a formal language:
• atomic formula: formula that does not have logical connectives
• first-order logic formula: well-formed formula that has a subject and a predicate that can have quantifiers
NOTE 1: First-order logic restricts the predicate to refer to a single subject. Both the universal and existential
quantifiers may be used in constructing a first-order logic formula.
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• propositional formula: well-formed formula that has a unique truth value
• well-formed formula: formula used in logic that obeys the grammatical rules of its formal language
NOTE 2: Feature engineering is concerned with determining the best representation of the sample data to learn a
solution for a given problem.
functional block: modular unit that defines the properties, behaviour, and relationships of a part of a system
NOTE: With respect to ENI, functional blocks may be categorized as external (meaning that other systems
external to ENI can see them) and internal (meaning that the functional block is only visible to other ENI
functional blocks). External functional blocks use Reference Points to provide access to their
functionality. Internal functional blocks use private interfaces to provide access to their functionality. As
used in MEF 55.0.3 [i.3].
G
graph: collection of nodes, where some subset of the nodes is connected:
NOTE 1: Visually, a node is a "point" and a connection is a "line", called an "edge". For the purposes of ENI, any
graph may be directed, weighted or both.
• directed graph: graph where each connection, or edge, has an associated direction
• graph loop: edge of a graph that joins a vertex to itself
NOTE 2: For ENI, graph loops are not applicable.
• hypergraph: graph in which generalized edges may connect more than two nodes
• multigraph: graph in which multiple edges between nodes are permitted
• weighted graph: graph where each connection, or edge, has an associated weight
H
hadoop distributed file system: distributed fault-tolerant file system that stores data on commodity machines and
provides high throughput access
hyperparameter: learning parameter that is set before the learning process is started:
• algorithm hyperparameter: hyperparameter that affects only the speed and/or quality of the learning process,
and does not affect the mathematical or statistical model used in the learning process (e.g. learning rate)
• model hyperparameter: hyperparameter that selects the mathematical or statistical model used in the learning
process (e.g. size and topology of the ANN)
hypothesis: set of statements for explaining an observation that is not yet known to be true
I
identity: the set of data and information that allow an object to be disambiguated from all other objects in a system,
including objects of the same type:
• digital identity: the set of data and information used by a computer system to represent an actor, such as a
person, device, or application
• contextual identity: the digital identity of an object for a particular context
imperative policy: type of policy that uses statements to explicitly change the state of a set of targeted objects
NOTE 1: The order of statements that make up the policy is explicitly defined.
NOTE 2: In the present document, Imperative Policy will refer to policies that are made up of Events, Conditions,
and Actions. As defined in MEF PDO CfC [i.2].
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information model: representation of concepts of interest to an environment in a form that is independent of data
repository, data definition la
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