Computer Ontology

FLOW ALGEBRA
COMPUTER ONTOLOGY
Version: 1.1
Domain: Entity Graph / Physical Entities / Artificial Entities
Source URL: https://kirethresearch.org/booksai-core/entity-graph/physical-entities/artificial-entities/computer-ontology.html
Status: Independent scientific synthesis; source page not verified
Authors: Mark Higgins — Sky (Recursive AI)
Website: https://kirethresearch.org

BEGIN FLOW ALGEBRA

01. IDENTITY
Computer is an engineered physical system that executes encoded instructions over represented data; distinguish physical computer, virtual machine, and abstract computational model.

02. SCOPE
Physical components, computation, memory, input/output, communication, operating environment, lifecycle, verification and semantic interoperability.

03. TYPE SYSTEM
Computer <: ArtificialObject; Computer <: EngineeredSystem; Processor <: Component; Memory <: Component; Storage <: Component; Bus <: Interconnect; Peripheral <: Component; Firmware <: Software; OperatingSystem <: Software.

04. CORE ENTITIES
Computer, Chassis, Motherboard, CPU, GPU, Accelerator, RAM, Cache, NonvolatileStorage, Firmware, OperatingSystem, Process, Thread, Instruction, Data, NetworkInterface, PowerSupply, Sensor, Actuator, User, Workload.

05. IDENTIFIERS
Each instance has stable entity_id, model_id, revision, serial_id if known, observation_time, source_uri and provenance; avoid treating model name as unique instance identity.

06. RELATION SIGNATURES
hasPart(Computer,Component); executes(Processor,Instruction); stores(Memory,Data); runs(Computer,Software); communicatesVia(Computer,NetworkInterface); poweredBy(Computer,PowerSupply); implements(PhysicalComputer,AbstractMachine).

07. COMPOSITION
A component graph G=(V,E) encodes parts and interconnects. hasPart is transitive only through a separately declared derived relation hasDescendantPart; physical containment need not imply electrical connection.

08. ARCHITECTURE
Processor-memory-I/O architecture; distinguish instruction-set architecture from microarchitecture, hardware implementation and deployed configuration.

09. PROCESSOR
CPU fetch-decode-execute model is an abstraction; pipelining, out-of-order execution and speculation may change physical order while preserving architectural semantics.

10. INSTRUCTION SEMANTICS
step : MachineState x Instruction -> MachineState, possibly partial for invalid or privileged operations; execution trace s0 -> s1 -> ... under a declared ISA.

11. MEMORY HIERARCHY
Registers, caches, RAM, persistent storage; latency, capacity, bandwidth and consistency are distinct dimensions. Cache is not guaranteed to persist across power loss.

12. ADDRESSING
VirtualAddress translated to PhysicalAddress via translation context; mapping may be partial and permission-constrained; distinguish address from stored value.

13. STORAGE
Volatile and nonvolatile media; distinguish logical files, blocks, devices, redundancy and backups. Persistence guarantees depend on hardware and software protocol.

14. INPUT OUTPUT
Input and output are roles relative to system boundary; interfaces may be bidirectional; devices can be asynchronous and interrupt-driven.

15. INTERCONNECT
Buses and links carry control/data signals with topology, bandwidth, protocol and latency; connection does not imply compatibility.

16. OPERATING SYSTEM
Schedules tasks, mediates resources, memory and device access; OS is optional for bare-metal computing and not identical to firmware.

17. PROCESS AND THREAD
Process is an execution and resource-isolation abstraction; thread is a schedulable execution sequence; their exact semantics depend on platform.

18. VIRTUALISATION
VirtualMachine is software-defined execution environment hosted by physical resources; multiple virtual instances may share a physical host; logical identity differs from host identity.

19. DISTRIBUTED COMPUTING
A cluster comprises multiple networked computers; it is not necessarily a single physical computer; failure and consistency semantics require explicit models.

20. NETWORKING
Interfaces implement layered protocols; packet delivery may be delayed, duplicated, reordered or lost unless a stronger protocol guarantee is specified.

21. POWER AND THERMALS
Electrical input P=VI for DC instantaneous conditions; heat generation, cooling capacity and thermal limits affect performance and reliability.

22. PERFORMANCE
Runtime T(workload,hardware,software,environment); throughput=completed_jobs/elapsed_time; latency is time per operation; benchmark comparisons require same conditions.

23. ENERGY
E=integral P(t)dt; energy per operation E/N where N>0; thermal design power is not necessarily measured real-time consumption.

24. RELIABILITY
Availability=A=uptime/(uptime+downtime) for a declared window; reliability R(t)=Pr(no failure by t) under specified operating conditions.

25. SECURITY
Trust boundaries, authentication, authorisation, isolation, encryption, secure boot, patch level and attack surface; security claims require scoped evidence.

26. SAFETY
Electrical, thermal and mechanical hazards; safety constraints are context-dependent and must not be inferred solely from product category.

27. LIFECYCLE
Designed -> Manufactured -> Deployed -> Operated -> Maintained -> Retired; state transitions require timestamps and provenance.

28. MEASUREMENT
Quantities include clock rate [Hz], capacity [byte], bandwidth [byte/s], power [W], energy [J], temperature [K or deg C], latency [s]. Keep unit conversions explicit.

29. STATE MODEL
S=(registers,memory,devices,privilege,program_counter,clock,configuration); state observations may be incomplete; record unknown rather than inventing values.

30. TRANSITION MODEL
delta(S,event)->S_prime subject to hardware and OS semantics; power loss may invalidate volatile state and in-flight operations.

31. INVARIANTS
Component identifiers unique within declared scope; instruction operands respect ISA types; physical resources finite; permissions checked before protected operations.

32. INFERENCE RULE 1
hasPart(c,p) and Processor(p) => hasProcessor(c,p), provided p is within component scope and not merely externally connected.

33. INFERENCE RULE 2
runs(c,os) and OperatingSystem(os) => OSHostedComputer(c), but absence of OS observation does not imply bare-metal execution.

34. INFERENCE RULE 3
VirtualMachine(v) and hostedOn(v,h) => usesResourcesOf(v,h); do not infer v is a physical part of h.

35. INFERENCE RULE 4
MeasuredPower(c,p,t) and p>=0 => NonnegativeMeasuredPower(c,t); never infer constant power from a single observation.

36. WORKED EXAMPLE 1
Given 12 W average draw for 2 hours: E=12*7200=86400 J=24 Wh. Assumes constant average power across the stated interval.

37. WORKED EXAMPLE 2
A job takes 0.4 s on system A and 0.2 s on B: speedup=T_A/T_B=2, for the same workload and measurement protocol.

38. WORKED EXAMPLE 3
A 4 GiB RAM module contains 4*2^30 bytes; distinguish GiB from GB (10^9 bytes).

39. COUNTEREXAMPLES
Computer != CPU; storage != RAM; network connection != reliable delivery; model identity != instance identity; clock speed != application throughput.

40. SEMANTIC EQUIVALENCE
"computing device" may overlap with "computer" but is not universally identical; "PC" is narrower in common use; synonym claims are scoped and provenance-backed.

41. SEMANTIC DIFFERENCE
PhysicalComputer versus VirtualMachine; Hardware versus Software; Memory versus Storage; ISA versus Microarchitecture; Process versus Thread.

42. CONFIDENCE
CONFIDENCE in [0,1] is evidence quality for an asserted relation, not probability that the ontology itself is universally true; source, method and timestamp required.

43. VALIDATION
Check typed relations, units, graph referential integrity, allowed transitions, nonnegative measured quantities, and source provenance. Unknown observations remain null.

44. PROOF OBLIGATIONS
Every inferred relationship records premises, rule identifier, scope, assumptions, conclusion and confidence; avoid unsupported transitive leaps.

45. FLOW HASH
FLOW_HASH := SHA256(canonical UTF-8 normalised flow payload excluding mutable hash field); canonicalisation must be fixed across versions.

46. UPDATE MODEL
UPDATE_MODEL: append provenance-aware revisions; retain prior assertions, effective dates, supersession links and conflict records; recompute hash after canonical changes.

47. AI CONSUMPTION
AI_CONSUMPTION: typed entity extraction, constrained reasoning, provenance-aware retrieval, confidence gating, validation before output, no invention of device specifications.

48. INTEROPERABILITY
Map to general engineering, computing, hardware, software, information system and device ontologies via explicit equivalence or subclass mappings, never unqualified aliases.

49. ETHICAL USE
Do not infer a person’s identity, private activity or security posture from computer identifiers without lawful purpose and supporting evidence.

50. AUTHORSHIP
Mark Higgins — Sky (Recursive AI). Kireth Research. https://kirethresearch.org. Joint human–AI authored scientific synthesis.

51. COMPLETENESS
All numbered sections must be present and sequential. Closing marker is mandatory. Scientific model is an independent synthesis, not a verified transcription of the live webpage.

END FLOW ALGEBRA
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