{ "model": { "rank": 4757, "code": "thing-q858810", "model_id": "vr.tr.big-data", "name": "big data", "purpose": "Enable an agent to recognise a big-data collection in its processing context, judge whether it is fit for an intended use, and select feasible, permitted operations.", "family": "Thing Registry", "category": "Information and virtual systems", "status": "research-draft", "kind": "thing", "plane": "INF", "domain": "INF.KNW", "industry": "", "version": "", "url": "/models/thing/q858810/", "tier": 2, "score": 72, "payload": { "layer": "wikidata", "aliases": [ "Big Social Data", "real-time data analytics", "geospatial big data" ], "aliasCount": 3, "merged": 3, "knownIn": 72, "facets": null, "markers": [], "lexicalClass": "", "senseRank": null, "alsoRegisteredAs": null, "source": { "dataset": "wikidata", "item": "Q858810", "url": "https://www.wikidata.org/wiki/Q858810", "license": "CC0 1.0" } }, "research": { "vercy": "1.0-draft", "publication": { "status": "research-draft", "adjudicationStatus": "unreviewed", "publishableCanonical": false, "generatedAt": "2026-09-09T20:56:31Z", "providers": [ "Codex" ], "breadth": "recalled by Codex without web access - no source was read", "missingProviders": [], "pass": 2, "cost": { "grok": { "seconds": 19.3, "error": "Reading additional input from stdin...\nOpenAI Codex v0.153.4\n--------\nworkdir: R:\\02_PROJECTS\\02_Meta_Models_Platforms\\Ver.cy\\current\\thing-registry-backlog\nmodel: gpt-6-astra\nprovider: openai\napproval: never\nsandbox: read-only\nreasoning effort: none\nreasoning summaries: none\nsession id: 01a087f4-4d21-72d1-a90f-04e9c51c411f\n--------\nuser\nDescribe what is already known about one registered thing. Answer as JSON only, no prose around it.\n\nThing: big data\nSense to describe: (none recorded)\nDomain code: INF.MED\nAlso known as: (none)\n\n\nContext for this batch of 1001 things:\n# Batch 005: 1001 regist", "usd": 0, "recall": true }, "codex": { "seconds": 62.1, "error": "Reading additional input from stdin...\nOpenAI Codex v0.153.4\n--------\nworkdir: R:\\02_PROJECTS\\02_Meta_Models_Platforms\\Ver.cy\\current\\thing-registry-backlog\nmodel: gpt-6-astra\nprovider: openai\napproval: never\nsandbox: read-only\nreasoning effort: none\nreasoning summaries: none\nsession id: 01a087f4-4d2e-70c2-b162-c125b5a4f4e0\n--------\nuser\nYou are drafting a Vercy meta-model for one registered thing. Answer as JSON only, no prose around it.\n\nThing: big data\nRegistry id: vr.tr.big-data\nPlane / domain: INF / INF.MED\nRegistry definition: (none recorded)\nNames folded into this entry: (none)\n\n\nContex" } } }, "metaModel": { "id": "THING-Q858810", "registryId": "vr.tr.big-data", "name": "big data", "version": "0.1.0-research.1", "entryKind": "thing", "family": "Thing Registry", "domain": [ "INF.MED" ], "status": "research-draft" }, "canonicalUrl": "https://ver.cy/models/thing/q858810/", "model": { "registry_id": "vr.tr.big-data", "name": "big data", "purpose": "Enable an agent to recognise a big-data collection in its processing context, judge whether it is fit for an intended use, and select feasible, permitted operations.", "definition": "Big data comprises datasets whose volume, velocity, variety or complexity makes their effective storage, management and analysis require scalable approaches beyond conventional methods in the relevant operating context.", "scope_statement": "This model owns big data as a bounded collection of information whose volume, arrival rate, heterogeneity or combination of these creates material processing constraints for specified workloads and available resources; it covers the collection and its operational properties, rather than the discipline, software platform or insights derived from it.", "in_scope": [ "Collection boundaries, constituent datasets, streams and changing membership", "Workload-relative evidence that the collection requires scale-aware processing", "Formats, schemas, semantic variation and cross-source integration", "Provenance, quality, representativeness and fitness for a stated use", "Processing feasibility, access constraints and lifecycle obligations" ], "out_of_scope": [ "Data engineering and data science as disciplines or occupations", "Storage clusters, cloud services and processing engines as independently managed systems", "Analytical models, reports and decisions produced from the collection", "Individual source documents or creative works and their edition histories", "General organisational governance and legal regimes beyond constraints applicable to this collection" ], "distinguishing_features": [ "Identify a workload and resource baseline for which size, arrival rate or heterogeneity creates a demonstrated processing constraint; a large byte count alone does not establish the classification.", "Distinguish the information collection from the data lake, database or cluster that stores or processes it; changing platforms need not create a different collection.", "Distinguish streaming from big data: a low-rate stream may be readily handled, while a static collection may create substantial scale constraints.", "Distinguish big data from AI training data: neither model training nor the presence of labels is required, and training datasets need not qualify as big data.", "Treat usefulness, accuracy and representativeness as properties to investigate rather than consequences of having many records." ], "characteristics": [ { "name": "Collection membership rule", "kind": "category", "unit_or_values": "Snapshot, rolling window, append-only history or explicitly versioned source selection", "why_it_matters": "Defines which records an agent is assessing and whether repeated operations address the same collection." }, { "name": "Logical and physical volume", "kind": "measurement", "unit_or_values": "Records or events and bytes, with timestamp, compression and replication basis", "why_it_matters": "Separates information growth from storage overhead and supports capacity estimates." }, { "name": "Arrival and mutation rates", "kind": "measurement", "unit_or_values": "Events per second, bytes per second and updates per second over stated intervals", "why_it_matters": "Reveals whether ingestion and processing can keep pace with ordinary and peak demand." }, { "name": "Structural and semantic heterogeneity", "kind": "category", "unit_or_values": "Inventory of formats, schema versions, encodings, units and identifier systems", "why_it_matters": "Makes integration difficulty visible beyond raw size." }, { "name": "Workload-to-resource relationship", "kind": "relation", "unit_or_values": "Collection linked to workload, resource baseline, service target and observed bottleneck", "why_it_matters": "Grounds the big-data designation in an explicit operational context." }, { "name": "Freshness and completeness", "kind": "measurement", "unit_or_values": "Event-to-availability delay and received-to-expected coverage, where an expected population is known", "why_it_matters": "Distinguishes current-looking output from output based on complete, timely input." }, { "name": "Fitness for intended use", "kind": "state", "unit_or_values": "Unassessed, supported, conditionally supported or unsupported for a named use", "why_it_matters": "Prevents quality judgments for one workload from being applied automatically to another." }, { "name": "Permitted-use envelope", "kind": "relation", "unit_or_values": "Collection segments linked to evidenced access, purpose, location, sharing and retention constraints", "why_it_matters": "Determines which operations an agent may perform across sources with different restrictions." } ], "affordances": [ "Profile representative partitions and time windows to estimate scale, skew, missingness and schema variation.", "Assess whether a proposed workload is feasible under stated latency, cost and resource limits.", "Propose partitioning, incremental processing, sampling or aggregation with explicit consequences for fidelity.", "Trace outputs to source versions, ingestion intervals and transformation steps.", "Gate joins, exports and reuse against the applicable permissions and sensitivity of the combined data.", "Plan retention, correction and deletion across replicas and derived datasets, then record verification evidence." ] }, "sources": [], "structure": { "bundles": [ { "id": "collection-identity-and-scale", "name": "Collection identity and scale", "description": "Establishes which information belongs to the collection and why its scale matters in a specified context.", "rationale": "Big data has neither a useful universal size threshold nor a stable identity unless membership and workload assumptions are explicit.", "layers": [ { "id": "membership-and-versions", "name": "Membership and versions", "description": "Defines source coverage, record inclusion and the identity of changing collection states.", "findings": [ { "id": "bounded-collection", "name": "Bounded collection", "description": "Record inclusion rules and reproducible references to snapshots or stream intervals.", "questions": [ { "text": "Which sources, record types, time windows and exclusions define this collection?", "kind": "boundary", "id": "bounded-collection-q01" }, { "text": "How can an agent identify the exact snapshot or stream interval used by an earlier operation?", "kind": "provenance", "id": "bounded-collection-q02" } ] } ] }, { "id": "scale-in-context", "name": "Scale in context", "description": "Connects measured data demands to processing limits.", "findings": [ { "id": "demonstrated-scale-constraint", "name": "Demonstrated scale constraint", "description": "Record the workload, baseline resources and measured constraint supporting the designation.", "questions": [ { "text": "Which workload exceeds a stated capacity, latency or cost target on the available baseline?", "kind": "measurement", "id": "demonstrated-scale-constraint-q01" }, { "text": "Is the limiting factor volume, arrival rate, heterogeneity, data skew or an evidenced combination?", "kind": "definition", "id": "demonstrated-scale-constraint-q02" } ] } ] } ] }, { "id": "ingestion-and-temporal-behaviour", "name": "Ingestion and temporal behaviour", "description": "Describes how records arrive, change and become available for use.", "rationale": "At scale, backlog, duplicate delivery and late events can change the meaning of apparently current results.", "layers": [ { "id": "flow-and-backlog", "name": "Flow and backlog", "description": "Measures incoming demand against sustained processing capacity.", "findings": [ { "id": "ingestion-capacity", "name": "Ingestion capacity", "description": "Record typical and peak rates, processing throughput and outstanding work.", "questions": [ { "text": "What are the typical and peak arrival rates, and over which observation windows were they measured?", "kind": "measurement", "id": "ingestion-capacity-q01" }, { "text": "When input exceeds processing capacity, should the system buffer, throttle, sample or reject records, and what loss is permitted?", "kind": "action", "id": "ingestion-capacity-q02" } ] } ] }, { "id": "event-time-and-revisions", "name": "Event time and revisions", "description": "Distinguishes event occurrence, ingestion, availability and subsequent correction.", "findings": [ { "id": "late-duplicate-and-corrected-records", "name": "Late, duplicate and corrected records", "description": "Record temporal semantics and rules for revising results as input changes.", "questions": [ { "text": "Which timestamps represent occurrence, ingestion and availability, and how are their uncertainties recorded?", "kind": "definition", "id": "late-duplicate-and-corrected-records-q01" }, { "text": "How should late arrivals, repeated deliveries, out-of-order events and source corrections update prior outputs?", "kind": "action", "id": "late-duplicate-and-corrected-records-q02" } ] } ] } ] }, { "id": "heterogeneity-and-lineage", "name": "Heterogeneity and lineage", "description": "Makes diverse source representations interpretable and transformations traceable.", "rationale": "Combining large sources requires evidence that fields, entities and versions remain compatible throughout processing.", "layers": [ { "id": "schema-and-semantic-alignment", "name": "Schema and semantic alignment", "description": "Records differences that prevent direct combination of sources.", "findings": [ { "id": "cross-source-compatibility", "name": "Cross-source compatibility", "description": "Identify schema drift, conflicting meanings and uncertain entity matches before integration.", "questions": [ { "text": "Which schema versions, units, encodings or field meanings differ across sources and time periods?", "kind": "definition", "id": "cross-source-compatibility-q01" }, { "text": "Which identifiers support joins, and how are collisions, missing keys and uncertain matches handled?", "kind": "boundary", "id": "cross-source-compatibility-q02" } ] } ] }, { "id": "source-to-output-traceability", "name": "Source-to-output traceability", "description": "Connects collection segments and outputs to their origins and transformations.", "findings": [ { "id": "lineage-at-usable-resolution", "name": "Lineage at usable resolution", "description": "Record enough lineage to investigate errors and reproduce outputs without assuming record-level tracing is always available.", "questions": [ { "text": "Can an output be traced to source versions, partitions, ingestion intervals and transformation configurations?", "kind": "provenance", "id": "lineage-at-usable-resolution-q01" }, { "text": "What lineage resolution is needed to isolate a faulty source and reprocess only the affected data?", "kind": "action", "id": "lineage-at-usable-resolution-q02" } ] } ] } ] }, { "id": "quality-and-inference-limits", "name": "Quality and inference limits", "description": "Assesses defects, coverage and the limits of conclusions drawn from the collection.", "rationale": "Large record counts can conceal systematic omissions, correlated observations and concentrated errors.", "layers": [ { "id": "partition-aware-quality", "name": "Partition-aware quality", "description": "Examines quality across sources, groups and time rather than only through collection-wide averages.", "findings": [ { "id": "defect-distribution", "name": "Defect distribution", "description": "Record where missingness, duplication, invalid values and drift occur and whether profiling can detect them.", "questions": [ { "text": "How do missingness, duplicates and invalid values vary by source, partition and time window?", "kind": "measurement", "id": "defect-distribution-q01" }, { "text": "Which checks require a full scan, and which sampling methods adequately cover rare or high-risk segments?", "kind": "action", "id": "defect-distribution-q02" } ] } ] }, { "id": "coverage-and-valid-use", "name": "Coverage and valid use", "description": "Relates observed records to the population or process an intended analysis concerns.", "findings": [ { "id": "representation-and-dependence", "name": "Representation and dependence", "description": "Record collection mechanisms, exclusions and repeated observations that limit inference.", "questions": [ { "text": "Which people, events or conditions are omitted or overrepresented by the collection mechanism?", "kind": "boundary", "id": "representation-and-dependence-q01" }, { "text": "How many records represent repeated or dependent observations rather than distinct units relevant to the intended analysis?", "kind": "measurement", "id": "representation-and-dependence-q02" } ] } ] } ] }, { "id": "processing-and-controlled-lifecycle", "name": "Processing and controlled lifecycle", "description": "Connects feasible processing choices with constraints on use and persistence.", "rationale": "Distributed processing and extensive copying affect both operational feasibility and the ability to enforce collection-specific obligations.", "layers": [ { "id": "execution-and-fidelity", "name": "Execution and fidelity", "description": "Evaluates the consequences of processing plans for performance and result meaning.", "findings": [ { "id": "feasible-processing-plan", "name": "Feasible processing plan", "description": "Record resource estimates and the fidelity implications of partitioning, approximation or incremental execution.", "questions": [ { "text": "What scan volume, data movement, partition skew, latency and cost are expected for the proposed workload?", "kind": "measurement", "id": "feasible-processing-plan-q01" }, { "text": "Can sampling, aggregation or incremental processing meet the use requirement, and what errors or omissions must accompany the result?", "kind": "action", "id": "feasible-processing-plan-q02" } ] } ] }, { "id": "use-rights-and-propagation", "name": "Use rights and propagation", "description": "Tracks permitted operations and lifecycle changes across combined sources and derived copies.", "findings": [ { "id": "permitted-combination-and-retirement", "name": "Permitted combination and retirement", "description": "Record evidence for use restrictions and mechanisms for propagating correction, expiry and deletion.", "questions": [ { "text": "What evidence permits the proposed access, linkage, processing location and export for every contributing source?", "kind": "provenance", "id": "permitted-combination-and-retirement-q01" }, { "text": "How will corrections, retention expiry and deletion requirements reach replicas, caches and derived datasets, and how will completion be verified?", "kind": "action", "id": "permitted-combination-and-retirement-q02" } ] } ] } ] }, { "id": "evidence-and-external-alignment", "name": "Evidence and external alignment", "description": "What the world already says about this thing, gathered so the model can be checked against it.", "rationale": "A model that cannot be lined up against existing standards, identifiers and practice cannot be adopted by anyone who already uses them.", "layers": [ { "id": "reported-evidence", "name": "Reported evidence", "description": "Findings from the breadth pass, kept separate from the structural claims.", "findings": [ { "id": "evidence-confidence-notes", "name": "Check these first", "description": "Recalled without web access and unsourced; every item is a lead to verify.", "evidence": [ "This describes the general information-technology sense; no narrower registry sense was supplied.", "The listed kinds overlap: structure and processing mode are separate classification dimensions.", "This is recall rather than source-checked research; standards editions and the registry's intended classification should be checked first." ], "questions": [ { "id": "evidence-confidence-notes-q01", "text": "Which of these check these first hold for the sense of big data this model covers, and on what evidence?", "kind": "provenance" } ] }, { "id": "evidence-kinds", "name": "Kinds and varieties", "description": "Recalled without web access and unsourced; every item is a lead to verify.", "evidence": [ "Structured big data", "Semistructured big data", "Unstructured big data", "Streaming big data", "Batch big data" ], "questions": [ { "id": "evidence-kinds-q01", "text": "Which of these kinds and varieties hold for the sense of big data this model covers, and on what evidence?", "kind": "provenance" } ] }, { "id": "evidence-standards-and-regulation", "name": "Standards and regulation", "description": "Recalled without web access and unsourced; every item is a lead to verify.", "evidence": [ "ISO/IEC 20546, Information technology - Big data - Overview and vocabulary, issued by ISO and IEC.", "NIST Big Data Interoperability Framework, published by the US National Institute of Standards and Technology." ], "questions": [ { "id": "evidence-standards-and-regulation-q01", "text": "Which of these standards and regulation hold for the sense of big data this model covers, and on what evidence?", "kind": "provenance" } ] }, { "id": "evidence-real-world-use", "name": "Real-world use", "description": "Recalled without web access and unsourced; every item is a lead to verify.", "evidence": [ "Analyzing transaction streams to detect fraud and anomalies.", "Processing equipment telemetry for predictive maintenance.", "Analyzing large scientific datasets, including astronomical observations and genomic sequences.", "Building recommendation systems from large collections of interaction records.", "Analyzing service logs and network events for operational monitoring." ], "questions": [ { "id": "evidence-real-world-use-q01", "text": "Which of these real-world use hold for the sense of big data this model covers, and on what evidence?", "kind": "provenance" } ] }, { "id": "evidence-measurements", "name": "Typical measurements", "description": "Recalled without web access and unsourced; every item is a lead to verify.", "evidence": [ { "quantity": "Data volume", "typical_range": "No universal threshold; depends on available infrastructure and workload.", "unit": "bytes and multiples such as TB or PB" }, { "quantity": "Ingestion rate", "typical_range": "Workload dependent; no defining minimum.", "unit": "events/s or bytes/s" }, { "quantity": "Processing latency", "typical_range": "Application dependent, from real-time processing to long-running batch jobs.", "unit": "seconds" } ], "questions": [ { "id": "evidence-measurements-q01", "text": "Which of these typical measurements hold for the sense of big data this model covers, and on what evidence?", "kind": "provenance" } ] }, { "id": "evidence-failure-modes-and-hazards", "name": "Failure modes and hazards", "description": "Recalled without web access and unsourced; every item is a lead to verify.", "evidence": [ "Missing, duplicated, inconsistent or poorly documented records undermine results.", "Large sample sizes can conceal selection bias, confounding and spurious correlations.", "Linking datasets can expose sensitive information or enable reidentification.", "Distributed processing can lose, duplicate or reorder events unless failures and retries are handled correctly.", "Storage, computation and data movement can incur substantial financial and energy costs." ], "questions": [ { "id": "evidence-failure-modes-and-hazards-q01", "text": "Which of these failure modes and hazards hold for the sense of big data this model covers, and on what evidence?", "kind": "provenance" } ] }, { "id": "evidence-neighbours", "name": "Neighbouring kinds and how to tell them apart", "description": "Recalled without web access and unsourced; every item is a lead to verify.", "evidence": [ { "name": "Data analytics", "difference": "Data analytics is the activity of extracting insights from data; it can operate on small datasets as well as big data." }, { "name": "Database", "difference": "A database is an organized collection of data; being a database does not establish the scale or processing demands associated with big data." }, { "name": "Data lake", "difference": "A data lake is a storage architecture for diverse data; big data describes characteristics and processing demands rather than a particular architecture." }, { "name": "Machine learning", "difference": "Machine learning develops models from data; it neither requires big data in every application nor encompasses all big-data processing." }, { "name": "Work, medium or creative form", "difference": "Big data is a category of data and associated processing challenges, rather than an authored work, edition or carrier; individual datasets may have those additional attributes." } ], "questions": [ { "id": "evidence-neighbours-q01", "text": "Which of these neighbouring kinds and how to tell them apart hold for the sense of big data this model covers, and on what evidence?", "kind": "provenance" } ] } ] } ] } ] }, "openQuestions": [ "Does the registry intend big data to denote information collections, the broader technological phenomenon or a field of practice, given that no definition is recorded?", "Which existing Vercy data-collection, dataset or world models already own parts of this scope and should be referenced rather than duplicated?", "Which authoritative definition should anchor the model, and how should workload-relative scale be represented without imposing an unsupported universal threshold?", "What evidence is sufficient to classify a collection as big data when its processing constraints have not yet been benchmarked?", "Which lineage resolution, quality tolerances and lifecycle verification requirements should vary by intended use rather than be mandatory for every instance?" ], "statistics": { "bundles": 6, "layers": 11, "findings": 17, "questions": 27 } }, "draft": { "generator": "vr.draft.v3", "status": "draft-generated", "researched": false, "archetype": "discipline or field of knowledge", "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 big data, record what state it is in, and decide what may be done with it.", "whatItIs": "Enable an agent to recognise a big-data collection in its processing context, judge whether it is fit for an intended use, and select feasible, permitted operations.", "characteristics": { "substance": "information", "origin": "conceptual", "agency": "inert" }, "whatYouCanDoWithIt": [ "read and interpreted" ], "distinguishingFeatures": [ "Names folded into this entry, which a task may need to split apart again: Big Social Data, real-time data analytics, geospatial big data.", "3 finer distinctions are held as aliases rather than separate entries, because telling them apart needs a task that asks for it.", "Described in 72 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 big data, and on what evidence are they told apart?" ], "whatItIsMadeOf": "content that has to be carried by something else", "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": [ "read it and act on what it says" ], "howItIsRecognised": [], "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": [ "Big Social Data", "real-time data analytics", "geospatial big data" ] } ], "standing": "Described in 72 Wikipedia languages, which measures how widely it is written about rather than how important or how common it is. 3 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 big data from another, and a big data 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 big data, 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 big data, 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 big data 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 big data, 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 big data, 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 big data 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 big data 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 big data, 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 big data, 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 big data 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 big data from the things most often confused with it.", "questions": [ { "id": "distinguishing-features-q01", "text": "What identifies and describes what distinguishes a big data, 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 big data, 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 big data 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 big data 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 big data between them.", "questions": [ { "id": "stages-and-transitions-q01", "text": "What identifies and describes the lifecycle of a big data, 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 big data, 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 big data 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 big data, how often and by whom.", "findings": [ { "id": "observation-record", "name": "Observation record", "description": "How an observation of a big data 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 big data, 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 big data, 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 big data is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] } ] } ] }, { "id": "content-and-access", "name": "Content, encoding and access", "description": "What a big data says, how it is encoded and who may read it.", "rationale": "An informational thing carries content that can be copied, versioned and withheld, none of which its physical carrier explains.", "layers": [ { "id": "content-and-encoding", "name": "Content and encoding", "description": "The content itself, its format and its language.", "findings": [ { "id": "content-and-format", "name": "Content, format and language", "description": "What a big data contains and in what form it is held.", "questions": [ { "id": "content-and-format-q01", "text": "What identifies and describes the content of a big data, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "content-and-format-q02", "text": "Who or what asserted this about the content of a big data, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "content-and-format-q03", "text": "What may an agent decide or do once the content of a big data is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] } ] }, { "id": "access-and-rights", "name": "Access, rights and retention", "description": "Who may read, copy or change it, and for how long it is kept.", "findings": [ { "id": "access-rules", "name": "Access rules and retention", "description": "The permissions attached to a big data and the period it survives.", "questions": [ { "id": "access-rules-q01", "text": "What identifies and describes access to a big data, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "access-rules-q02", "text": "Who or what asserted this about access to a big data, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "access-rules-q03", "text": "What may an agent decide or do once access to a big data is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] } ] } ] }, { "id": "subject-and-method", "name": "Subject matter and method", "description": "What big data studies and how it establishes anything.", "rationale": "A field is identified by its questions and its methods, and those are what distinguish it from the fields next to it.", "layers": [ { "id": "subject", "name": "Subject matter", "description": "The questions it takes as its own.", "findings": [ { "id": "subject-record", "name": "Questions and objects of study", "description": "What it is about, stated so a neighbouring field can be told apart.", "questions": [ { "id": "subject-record-q01", "text": "What questions and objects does big data take as its own?", "kind": "definition" }, { "id": "subject-record-q02", "text": "Which questions does it share with a neighbouring field, and who claims them?", "kind": "boundary" } ] } ] }, { "id": "method", "name": "Method and evidence", "description": "How claims are established and what counts as evidence.", "findings": [ { "id": "method-record", "name": "Methods and standards of evidence", "description": "The methods used and what they are taken to establish.", "questions": [ { "id": "method-record-q01", "text": "By what methods does big data establish claims, and what counts as sufficient evidence?", "kind": "provenance" }, { "id": "method-record-q02", "text": "What kind of claim can this field not settle, and where should an agent look instead?", "kind": "action" } ] } ] } ] }, { "id": "institutions-and-classification", "name": "Institutions and classification", "description": "Where big data is practised and how catalogues place it.", "rationale": "The institutional footprint is the evidence that a field exists as a field rather than as a topic.", "layers": [ { "id": "institutions", "name": "Institutional markers", "description": "Societies, journals, degrees and departments.", "findings": [ { "id": "institution-record", "name": "Bodies and venues", "description": "Where the field organises itself.", "questions": [ { "id": "institution-record-q01", "text": "Which societies, journals or degrees mark big data as an established field?", "kind": "provenance" }, { "id": "institution-record-q02", "text": "What would show that it is emerging or dissolving rather than established?", "kind": "boundary" } ] } ] }, { "id": "classification", "name": "Classification schemes", "description": "Where library and research classifications put it.", "findings": [ { "id": "classification-record", "name": "Codes and their disagreements", "description": "The classification codes that place it, and where they differ.", "questions": [ { "id": "classification-record-q01", "text": "Which classification schemes place big data, under what codes?", "kind": "definition" }, { "id": "classification-record-q02", "text": "Where do those schemes disagree about its scope?", "kind": "boundary" } ] } ] } ] }, { "id": "provenance-and-evidence", "name": "Provenance, evidence and time", "description": "Where every claim about a big data 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 big data and how confident it is.", "questions": [ { "id": "claim-provenance-q01", "text": "What identifies and describes a claim about a big data, 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 big data, 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 big data 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 big data is retired without being erased.", "questions": [ { "id": "validity-and-supersession-q01", "text": "What identifies and describes the validity of a claim about a big data, 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 big data, 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 big data 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 } } } }