The Taxonomy

A shared vocabulary for describing technology.

Each category is a question people ask about a technology, and its elements are the answers. Choose a category, then an element, to see its icon and what it means. Press + to add elements to a datachain, then download it with its icons. New to the terms? Read the concepts.

Algorithm or Model

Elements 16

What algorithm or model processes the data?

What processes the input — the algorithm family, model class, or technique used to turn inputs into outputs.

About this category

Algorithm or Model

What algorithm or model processes the data?

What processes the input — the algorithm family, model class, or technique used to turn inputs into outputs.

Datachain

Answer: Some devices pass data on without processing it. Answer it when something turns inputs into outputs.
Icon shape: Data in motion: what goes in, what processes it, and what comes out.

Variables

Description Optional

Affect & Emotion AnalysisTechniques that infer emotional state, mood, or sentiment from text, speech, facial expressions, or physiological signals. Used to gauge crowd sentiment, evaluate service experience, or screen for distress. The accuracy and cultural fairness of affect inference is contested, and some jurisdictions restrict its use in workplaces and public services.

Anomaly DetectionTechniques that flag unusual events, patterns, or values that depart from a learned baseline of normal behavior. Used for equipment-failure prediction, fraud screening, intrusion detection, water-leak monitoring, and unusual crowd-flow alerts. Outputs are typically thresholded scores rather than direct decisions.

Biometric RecognitionTechniques that identify or verify a specific person from biological signals — face, fingerprint, voice, iris, or gait. Distinct from generic computer vision because the output is tied to a named individual. Many jurisdictions restrict or prohibit biometric recognition in publicly accessible spaces.

Classification & PredictionTechniques that assign a label to an input or estimate a numeric outcome — including classifiers (spam vs. not spam, risk tier), regressors (predicted demand, expected wait), and time-series forecasters (next-hour ridership, energy load). Built from labeled historical data using statistical or machine-learning models.

Clustering & SegmentationTechniques that group people, places, or events into clusters based on similarity, without using pre-defined labels. Used to segment audiences, mobility patterns, energy-use profiles, or service-demand zones. Outputs are cluster assignments that downstream systems may treat as categories — even though clusters are inferred, not authored.

Computer VisionTechniques that interpret images or video — counting people, detecting objects, reading text in pictures, tracking motion, or segmenting scenes. Common in occupancy sensing, traffic and crowd monitoring, accessibility assistance, and signage inspection. Distinct from biometric recognition, which identifies specific individuals.

Image GenerationTechniques that create or alter images from a prompt or a selection — generating artwork, filling in or removing parts of a photo, or extending a picture beyond its frame. Distinct from Computer Vision, which interprets images rather than producing them, and from Language Models, which work with text.

Language ModelsModels that read, write, summarize, translate, or hold a conversation in human language. Includes large language models (LLMs), chatbots, and generative-text systems. They work by predicting the most likely next tokens given the input and any prior context.

OptimizationTechniques that search for the best assignment, route, schedule, or allocation under constraints. Includes vehicle routing, transit signal timing, energy and HVAC scheduling, staff rostering, and resource allocation. Works by exploring possibilities to maximize or minimize a stated objective such as cost, time, or emissions.

Privacy-Preserving TransformationTechniques whose primary job is to transform input data so identifying details are removed, blurred, or replaced before any downstream use. Includes face blurring, license-plate masking, tokenization, k-anonymization, differential privacy, and synthetic-data generation. Acts as a barrier between raw collected data and the data used for analysis or decisions.

Recommendation & RankingTechniques that suggest or order items for a person — services, listings, news, wayfinding options — based on preferences, behavior, or similarity to other people. Includes collaborative filtering, content-based recommenders, and learned ranking. Personalizes what each person sees from the same underlying catalog.

Record Matching & LinkageTechniques that work out which records in one or more datasets refer to the same person, household, or place, using rules or statistical matching on names, addresses, and other fields. Used to remove duplicate case records, build a combined resident file, or attach contact details. A wrong match can merge two people or miss one, and the linked file can reveal more than any single source.

Rule-Based LogicTechniques that apply fixed rules written by people — a formula, a points table, a schedule, or eligibility criteria — to produce a result or score. Nothing is learned from data: the same input always gives the same output, and the rules can be read and audited.

Search & RetrievalTechniques that find and surface relevant content from a body of documents, records, or media in response to a query. Includes semantic search, vector retrieval, and retrieval-augmented generation (RAG) pipelines that feed relevant passages into a language model. Used in document QA, public-records lookup, and library or archive search.

Speech & AudioTechniques that convert between spoken words and other formats, or that classify non-speech sound. Includes speech-to-text (transcription), text-to-speech (synthetic voice), keyword spotting, and audio event detection. Voice-as-identity (recognizing who is speaking) belongs in Biometric Recognition.

Statistical ModelingTechniques that weigh how likely different explanations of the data are, using a model of how the data arise rather than one trained on past outcomes. Includes likelihood and Bayesian methods — for example, working out whose DNA is in a mixed sample, or building a family tree of pathogen strains from their genomes. Distinct from Classification & Prediction, which learns from labeled historical data.

Statistical Modeling

Techniques that weigh how likely different explanations of the data are, using a model of how the data arise rather than one trained on past outcomes. Includes likelihood and Bayesian methods — for example, working out whose DNA is in a mixed sample, or building a family tree of pathogen strains from their genomes. Distinct from Classification & Prediction, which learns from labeled historical data.

Variables

Description Optional