{ "model": { "rank": 16388, "code": "thing-q6661985", "model_id": "vr.tr.data-processing", "name": "data processing", "purpose": "Let an agent explain data processing and its operations, relay methods, tools and standards from computing sources, describe the operations the registry aliases name, and distinguish data processing from data analysis, data science, information processing broadly and data protection in law.", "family": "Thing Registry", "category": "Activities and processes", "status": "research-draft", "kind": "thing", "plane": "ACT", "domain": "ACT.ACT", "industry": "", "version": "", "url": "/models/thing/q6661985/", "tier": 2, "score": 32, "payload": { "layer": "wikidata", "aliases": [ "data roundtripping", "serialization", "ingest", "data deduplication", "denormalization", "access operation", "extract, transform, load", "neural network training", "OSM data processing", "electronic data processing", "text processing", "JSON streaming", "extract, load, transform", "stream processing", "borehole imaging log", "canonicalization", "data blending", "unmarshalling", "deserialization", "marshalling", "industrial data processing", "end-user computing", "data scraping", "event monitoring", "encode", "transaction processing", "copy elision", "single-instance storage", "indexing", "spatial ETL", "Punch card technology", "processing mode", "event stream processing", "Unicode normalization", "URI normalization", "text normalization", "one-hot encoding", "Czech encoding", "return-value optimization", "semantic indexing" ], "aliasCount": 49, "merged": 50, "knownIn": 32, "facets": null, "markers": [], "lexicalClass": "", "senseRank": null, "alsoRegisteredAs": null, "source": { "dataset": "wikidata", "item": "Q6661985", "url": "https://www.wikidata.org/wiki/Q6661985", "license": "CC0 1.0" } }, "research": { "vercy": "1.0-draft", "publication": { "status": "research-draft", "adjudicationStatus": "unreviewed", "publishableCanonical": false, "generatedAt": "2026-09-13T15:39:07Z", "providers": [ "Claude" ], "breadth": "written by Claude from model knowledge without web access - no source was read, every claim is a lead to verify", "pass": 2, "wave": 4, "engine": "claude" }, "metaModel": { "id": "THING-Q6661985", "registryId": "vr.tr.data-processing", "name": "data processing", "version": "0.2.0-wave.4", "entryKind": "thing", "family": "Thing Registry", "domain": [ "ACT.ACT" ], "status": "research-draft" }, "canonicalUrl": "https://ver.cy/models/thing/q6661985/", "model": { "registry_id": "vr.tr.data-processing", "name": "data processing", "purpose": "Let an agent explain data processing and its operations, relay methods, tools and standards from computing sources, describe the operations the registry aliases name, and distinguish data processing from data analysis, data science, information processing broadly and data protection in law.", "definition": "The collection, transformation and management of data by computers, from ingesting and validating input through operations such as serialization to storable formats, deduplication, normalisation and denormalisation of databases, access operations and round-tripping data between formats without loss, to output and reporting; data processing is both a set of processes and a field of work, historically associated with batch processing of business records.", "what_it_is_for": "Turning raw data into usable information.", "affordances": [ "explain the field", "relay operations and tools", "describe named operations", "distinguish related fields" ], "distinguishing_features": [ "Transformation of data", "Pipeline operations", "Field of work", "Batch and streaming" ], "appearance": "Not a visible object; computational processes.", "visual_identification": [ "Collection and transformation of data by computers", "Ingest, serialization, deduplication, denormalisation, access operation, data round-tripping", "Analysis interprets data; data science models it; information processing is broader; data protection is legal" ], "physical_properties": [ { "quantity": "historical form", "typical_range": "batch processing on punched cards and mainframes", "unit": "note", "note": "" }, { "quantity": "modern pipelines", "typical_range": "ETL and streaming", "unit": "list", "note": "" }, { "quantity": "legal sense", "typical_range": "processing of personal data under GDPR", "unit": "note", "note": "distinct" } ], "families_and_kinds": [ "batch processing", "real-time and stream processing", "ETL and data integration", "database operations such as normalisation and denormalisation", "serialization and format conversion", "deduplication and cleansing" ], "related_models": [ { "relation": "is a kind of", "target": "information processing", "why": "in registry terms" }, { "relation": "is a kind of", "target": "field of work", "why": "in registry terms" }, { "relation": "is contrasted with", "target": "data analysis", "why": "which interprets data" }, { "relation": "is exemplified by", "target": "extract, transform, load", "why": "a common pipeline" } ], "identifiers": [], "standards_and_regulation": [ "Data format standards such as JSON, XML and Parquet", "GDPR definition of processing of personal data", "ISO 8000 data quality standards" ], "failure_modes_and_hazards": [ "Data loss in round-tripping", "Confusing the computing and legal senses", "Registry aliases mixing operations at different levels" ], "in_scope": [], "out_of_scope": [], "characteristics": [] }, "sources": [], "structure": { "bundles": [ { "id": "understand", "name": "Understand", "description": "What data processing is.", "rationale": "Definition.", "layers": [ { "id": "definition", "name": "Definition", "description": "Definition.", "findings": [ { "id": "definition-finding", "name": "Definition", "description": "Definition.", "questions": [ { "text": "What is data processing, and how does it differ from data analysis, data science, information processing and data protection?", "kind": "definition" }, { "text": "Is the question about the field, an operation, tools or the legal sense?", "kind": "boundary" } ] } ] }, { "id": "operations", "name": "Operations", "description": "Operations.", "findings": [ { "id": "operations-finding", "name": "Operations", "description": "Operations.", "questions": [ { "text": "What are ingest, serialization, deduplication, denormalisation, access operations and round-tripping?", "kind": "definition" }, { "text": "Which entry fits the specific operation?", "kind": "action" } ] } ] } ] }, { "id": "methods", "name": "Methods", "description": "Methods.", "rationale": "Science.", "layers": [ { "id": "pipelines", "name": "Pipelines", "description": "Pipelines.", "findings": [ { "id": "pipelines-finding", "name": "Pipelines", "description": "Pipelines.", "questions": [ { "text": "How are batch, streaming and ETL pipelines designed?", "kind": "provenance" }, { "text": "Which references are standard?", "kind": "provenance" } ] } ] }, { "id": "quality", "name": "Quality", "description": "Data quality.", "findings": [ { "id": "quality-finding", "name": "Quality", "description": "Quality.", "questions": [ { "text": "How are validation, cleansing and deduplication done?", "kind": "provenance" }, { "text": "Which sources are cited?", "kind": "provenance" } ] } ] } ] }, { "id": "practice", "name": "Practice", "description": "Practice.", "rationale": "Application.", "layers": [ { "id": "tools", "name": "Tools", "description": "Tools.", "findings": [ { "id": "tools-finding", "name": "Tools", "description": "Tools.", "questions": [ { "text": "What tools and platforms are used for data processing?", "kind": "provenance" }, { "text": "Which entry fits data engineering?", "kind": "action" } ] } ] }, { "id": "law", "name": "Law", "description": "Legal sense.", "findings": [ { "id": "law-finding", "name": "Law", "description": "Law.", "questions": [ { "text": "What does processing mean under data protection law?", "kind": "provenance" }, { "text": "Which entry fits data protection?", "kind": "action" } ] } ] } ] }, { "id": "context", "name": "Context", "description": "History.", "rationale": "Context.", "layers": [ { "id": "history", "name": "History", "description": "History.", "findings": [ { "id": "history-finding", "name": "History", "description": "History.", "questions": [ { "text": "How did data processing develop from tabulating machines to cloud pipelines?", "kind": "provenance" }, { "text": "Which entry fits the history of computing?", "kind": "action" } ] } ] }, { "id": "careers", "name": "Careers", "description": "Careers.", "findings": [ { "id": "careers-finding", "name": "Careers", "description": "Careers.", "questions": [ { "text": "What roles work in data processing today?", "kind": "provenance" }, { "text": "Which entry fits data engineer?", "kind": "action" } ] } ] } ] } ] }, "openQuestions": [ "Should serialization and deduplication be separate primary entries?", "How should computing sources be linked?", "The registry entry has merged aliases naming specific operations; should they be split off?" ], "statistics": { "bundles": 4, "layers": 8, "findings": 8, "questions": 16 } }, "draft": { "generator": "vr.draft.v3", "status": "draft-generated", "researched": false, "archetype": "abstract concept", "method": "Written from the archetype playbook - what this kind of thing needs beyond identity and provenance - and from the structure that recurred across 6,333 models already researched by two engines. Applied to this entry by rule. No source was read for this thing and no claim here is researched. This entry carries no facets of its own, so they were inferred from its domain - a guess about a whole domain applied to one thing.", "facetsInferred": true, "nextPass": "A researcher replaces this draft with a sourced specification. Treat every sentence below as a proposal to argue with.", "purpose": "Give an agent a durable, checkable way to recognise a data processing, record what state it is in, and decide what may be done with it.", "whatItIs": "Let an agent explain data processing and its operations, relay methods, tools and standards from computing sources, describe the operations the registry aliases name, and distinguish data processing from data analysis, data science, information processing broadly and data protection in law.", "characteristics": { "substance": "activity", "origin": "conceptual", "agency": "inert" }, "whatYouCanDoWithIt": [ "observed and measured" ], "distinguishingFeatures": [ "Names folded into this entry, which a task may need to split apart again: data roundtripping, serialization, ingest, data deduplication, denormalization, access operation, extract, transform, load, neural network training, OSM data processing, electronic data processing, text processing, JSON streaming.", "50 finer distinctions are held as aliases rather than separate entries, because telling them apart needs a task that asks for it.", "Described in 32 Wikipedia languages, which is a measure of how widely the thing is known, not of how important it is." ], "openQuestionsForResearch": [ "Which of the bundles below does a real task actually need, and which are ceremony?", "What does this thing have that the facets do not capture at all?", "Which neighbouring kind is most often confused with a data processing, and on what evidence are they told apart?" ], "whatItIsMadeOf": "something that happens over time", "physicalCharacter": [ "Does nothing on its own; everything it does, something else did to it.", "These come from the domain this entry sits in rather than from the entry itself, so treat them as a first guess about the whole domain applied to one thing." ], "whatCanBeDoneWithIt": [ "observe it, measure it, record its state" ], "howItIsRecognised": [ "Nothing to see. What is recognised is an instance of it, and which instances count is exactly what is argued about." ], "relatedModels": [ { "relation": "covers", "note": "Finer kinds folded into this entry because telling them apart needs a task that asks for it. Each is a model waiting to be split out when one does.", "targets": [ "data roundtripping", "serialization", "ingest", "data deduplication", "denormalization", "access operation", "extract, transform, load", "neural network training", "OSM data processing", "electronic data processing", "text processing", "JSON streaming" ] } ], "standing": "Described in 32 Wikipedia languages, which measures how widely it is written about rather than how important or how common it is. 50 finer distinctions are held inside this entry as names rather than as separate models.", "structure": { "bundles": [ { "id": "identity-and-classification", "name": "Identity, naming and classification", "description": "How an agent tells one data processing from another, and a data processing from things that resemble it.", "rationale": "Recognition comes before every other claim. Without stable identity nothing else in the model can be trusted to be about the same thing twice.", "layers": [ { "id": "naming-and-identifiers", "name": "Names and identifiers", "description": "The names this thing goes by and the identifiers that survive translation and time.", "findings": [ { "id": "preferred-name-and-aliases", "name": "Preferred name, aliases and local names", "description": "Which name to use, which names mean the same thing, and which merely sound similar.", "questions": [ { "id": "preferred-name-and-aliases-q01", "text": "What identifies and describes the name of a data processing, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "preferred-name-and-aliases-q02", "text": "Who or what asserted this about the name of a data processing, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "preferred-name-and-aliases-q03", "text": "What may an agent decide or do once the name of a data processing is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] }, { "id": "stable-identifiers", "name": "Stable identifiers and external keys", "description": "Identifiers that keep pointing at this kind of thing across systems and languages.", "questions": [ { "id": "stable-identifiers-q01", "text": "What identifies and describes an identifier for a data processing, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "stable-identifiers-q02", "text": "Who or what asserted this about an identifier for a data processing, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "stable-identifiers-q03", "text": "What may an agent decide or do once an identifier for a data processing is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] } ] }, { "id": "classification-and-granularity", "name": "Classification and granularity", "description": "Where a data processing sits among kinds, and how finely a task needs to cut it.", "findings": [ { "id": "kind-and-parents", "name": "Kind, parents and neighbouring kinds", "description": "The classes this thing belongs to and the ones it is next to.", "questions": [ { "id": "kind-and-parents-q01", "text": "What identifies and describes the kind of a data processing, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "kind-and-parents-q02", "text": "Who or what asserted this about the kind of a data processing, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "kind-and-parents-q03", "text": "What may an agent decide or do once the kind of a data processing is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] }, { "id": "distinguishing-features", "name": "Distinguishing features", "description": "What separates a data processing from the things most often confused with it.", "questions": [ { "id": "distinguishing-features-q01", "text": "What identifies and describes what distinguishes a data processing, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "distinguishing-features-q02", "text": "Who or what asserted this about what distinguishes a data processing, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "distinguishing-features-q03", "text": "What may an agent decide or do once what distinguishes a data processing is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] } ] } ] }, { "id": "state-and-lifecycle", "name": "State and lifecycle", "description": "The states a data processing passes through and the events that move it between them.", "rationale": "Most decisions about a thing depend on what state it is in now, which is a claim with a time on it, not a property.", "layers": [ { "id": "lifecycle-stages", "name": "Lifecycle stages", "description": "From coming into existence to ceasing to be one of these.", "findings": [ { "id": "stages-and-transitions", "name": "Stages and transitions", "description": "The stages worth naming and what moves a data processing between them.", "questions": [ { "id": "stages-and-transitions-q01", "text": "What identifies and describes the lifecycle of a data processing, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "stages-and-transitions-q02", "text": "Who or what asserted this about the lifecycle of a data processing, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "stages-and-transitions-q03", "text": "What may an agent decide or do once the lifecycle of a data processing is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] } ] }, { "id": "observations-and-status", "name": "Observations and current status", "description": "What is observed about a data processing, how often and by whom.", "findings": [ { "id": "observation-record", "name": "Observation record", "description": "How an observation of a data processing is recorded so that it can be superseded rather than overwritten.", "questions": [ { "id": "observation-record-q01", "text": "What identifies and describes an observation of a data processing, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "observation-record-q02", "text": "Who or what asserted this about an observation of a data processing, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "observation-record-q03", "text": "What may an agent decide or do once an observation of a data processing is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] } ] } ] }, { "id": "process-and-outcome", "name": "Process, inputs and outcome", "description": "How a data processing proceeds, what it needs and what it leaves behind.", "rationale": "An activity is known by its steps and its results, and both have to be recordable while it is still running.", "layers": [ { "id": "steps-and-sequence", "name": "Steps and sequence", "description": "The steps of a data processing, their order and what may run in parallel.", "findings": [ { "id": "steps-and-preconditions", "name": "Steps, preconditions and completion", "description": "What has to be true before each step of a data processing and what marks it done.", "questions": [ { "id": "steps-and-preconditions-q01", "text": "What identifies and describes a step of a data processing, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "steps-and-preconditions-q02", "text": "Who or what asserted this about a step of a data processing, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "steps-and-preconditions-q03", "text": "What may an agent decide or do once a step of a data processing is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] } ] }, { "id": "inputs-and-results", "name": "Inputs, resources and results", "description": "What a data processing consumes and what it produces.", "findings": [ { "id": "inputs-and-outputs", "name": "Inputs, outputs and side effects", "description": "The resources a data processing takes and the results it leaves, wanted or not.", "questions": [ { "id": "inputs-and-outputs-q01", "text": "What identifies and describes the inputs and results of a data processing, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "inputs-and-outputs-q02", "text": "Who or what asserted this about the inputs and results of a data processing, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "inputs-and-outputs-q03", "text": "What may an agent decide or do once the inputs and results of a data processing is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] } ] } ] }, { "id": "definitions-in-contest", "name": "Definitions and who holds them", "description": "What data processing is taken to mean, and by whom.", "rationale": "When a field disagrees about a concept, the disagreement is the content. A model that picks one definition silently destroys the information.", "layers": [ { "id": "competing-definitions", "name": "Competing definitions", "description": "The main readings and the traditions behind them.", "findings": [ { "id": "definition-map", "name": "Definitions and their holders", "description": "Each definition with the school or body that holds it.", "questions": [ { "id": "definition-map-q01", "text": "Which definitions of data processing are in use, and which tradition or body holds each?", "kind": "definition" }, { "id": "definition-map-q02", "text": "What turns on the difference between them in practice?", "kind": "boundary" } ] } ] }, { "id": "operationalisation", "name": "Operationalisation", "description": "How it is measured or applied when it has to be.", "findings": [ { "id": "operational-record", "name": "Measures and proxies", "description": "Instruments and indicators used to stand in for it.", "questions": [ { "id": "operational-record-q01", "text": "How is data processing operationalised or measured in practice, and by what instrument?", "kind": "measurement" }, { "id": "operational-record-q02", "text": "What does that operationalisation leave out, and when does that matter?", "kind": "boundary" } ] } ] } ] }, { "id": "instances-and-use", "name": "Instances, use and consequence", "description": "What counts as an instance of data processing and what follows from calling something that.", "rationale": "Applying a concept is an act with consequences, so a model must say what the label licenses and what it does not.", "layers": [ { "id": "instances", "name": "What counts as an instance", "description": "Clear cases, borderline cases and non-cases.", "findings": [ { "id": "instance-tests", "name": "Tests for an instance", "description": "What would settle whether something falls under it.", "questions": [ { "id": "instance-tests-q01", "text": "What would settle whether something is an instance of data processing?", "kind": "boundary" }, { "id": "instance-tests-q02", "text": "Which borderline cases are argued about, and on what grounds?", "kind": "definition" } ] } ] }, { "id": "consequence", "name": "Consequence of application", "description": "Rights, duties or decisions that follow from the label.", "findings": [ { "id": "consequence-record", "name": "What the label licenses", "description": "What an agent may do once something is classified this way.", "questions": [ { "id": "consequence-record-q01", "text": "What follows practically once something is treated as data processing?", "kind": "action" }, { "id": "consequence-record-q02", "text": "What must an agent not infer from the label alone?", "kind": "action" } ] } ] } ] }, { "id": "provenance-and-evidence", "name": "Provenance, evidence and time", "description": "Where every claim about a data processing came from and when it held.", "rationale": "A claim without a source and a time cannot be superseded, only overwritten, and an agent that overwrites loses the ability to explain itself.", "layers": [ { "id": "source-and-authority", "name": "Source and authority", "description": "Who said it, on what evidence, and how strongly.", "findings": [ { "id": "claim-provenance", "name": "Claim provenance and confidence", "description": "The authority behind each claim about a data processing and how confident it is.", "questions": [ { "id": "claim-provenance-q01", "text": "What identifies and describes a claim about a data processing, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "claim-provenance-q02", "text": "Who or what asserted this about a claim about a data processing, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "claim-provenance-q03", "text": "What may an agent decide or do once a claim about a data processing is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] } ] }, { "id": "time-and-versions", "name": "Time, versions and supersession", "description": "When a claim was true, when it was learnt, and what replaced it.", "findings": [ { "id": "validity-and-supersession", "name": "Validity period and supersession", "description": "How an old claim about a data processing is retired without being erased.", "questions": [ { "id": "validity-and-supersession-q01", "text": "What identifies and describes the validity of a claim about a data processing, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "validity-and-supersession-q02", "text": "Who or what asserted this about the validity of a claim about a data processing, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "validity-and-supersession-q03", "text": "What may an agent decide or do once the validity of a claim about a data processing is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] } ] } ] } ] }, "statistics": { "bundles": 6, "layers": 12, "findings": 14, "questions": 38 } } } }