Data Properties — Structural, Dynamic, Relational, Causal & Quantum Dimensions
Complete English Version
A. Structural Properties
(The DNA of data — its essence before interaction)
1. Consistency
Definition: Absence of internal contradictions.
Nature: Ontological.
Function: Maintain logical integrity.
Consequence: Reduces noise and critical errors.
Interactions: Coherence, Congruence, Stability.
Evaluation Methods:
- Rule and constraint validation
- Cross-comparison of dependent fields
- Semantic coherence checks
Applicable Models:
- Relational normalization (1NF–5NF)
- SAT solvers
- Consistency graphs
2. Quality
Definition: Accuracy, purity, and exactness.
Nature: Structural.
Function: Increase analysis reliability.
Consequence: Better predictions and inference.
Interactions: Reliability, Information Density.
Evaluation Methods:
- Data profiling
- Error rate analysis
- Completeness assessment
Applicable Models:
- ISO 8000 Data Quality
- Bayesian truth models
- RMSE / MAE
3. Equivalence
Definition: Different representations of the same reality.
Nature: Semantic.
Function: Enable integration without loss.
Consequence: Efficient dataset fusion.
Interactions: Semantic Alignment, Integrability.
Methods:
- Semantic matching
- Entity resolution
- Conceptual duplicate detection
Models:
- Ontologies (OWL)
- Knowledge Graphs
- Descriptive Logic
4. Semantic Elasticity
Definition: Ability to expand or compress without losing essence.
Nature: Structural.
Function: Adapt to variable granularity.
Consequence: Analytical versatility.
Interactions: Plasticity, Fluency.
Methods:
- Aggregation/disaggregation testing
- Semantic loss evaluation
- Granularity sensitivity
Models:
- Multidimensional OLAP
- Semantic hierarchies
- Semantic expansion trees
5. Integrability
Definition: Ability to merge with other systems without conflict.
Nature: Systemic.
Function: Ensure interoperability.
Consequence: Reduced friction.
Interactions: Equivalence, Connectivity.
Methods:
- Cross-system schema testing
- ETL validation
- Semantic conflict detection
Models:
- Semantic APIs
- ETL/ELT pipelines
- Integration ontologies
6. Informational Scarcity
Definition: Value derived from rarity or difficulty of acquisition.
Nature: Economic.
Function: Prioritize key information.
Consequence: Increased weight and impact.
Interactions: Weight, Impact.
Methods:
- Frequency analysis
- Uniqueness measurement
- Rarity scoring
Models:
- TF–IDF
- Zipf distributions
- Inverse entropy
7. Abstraction
Definition: Level of generality of data.
Nature: Epistemological.
Function: Enable generalization.
Consequence: Less detail, more universality.
Interactions: Contextual Compatibility, Trend Potential.
Methods:
- Generalization tests
- Conceptual mapping
- Context-loss evaluation
Models:
- Taxonomies
- Concept Lattices
- Hierarchical classification
8. Persistence
Definition: Time during which data remains valid or useful.
Nature: Temporal.
Function: Define window of use.
Consequence: Impacts predictions.
Interactions: Reliability, Stability.
Methods:
- Obsolescence tests
- Time-to-live (TTL)
- Semantic retention analysis
Models:
- Decay Models
- Time Series Stationarity
- Survival Analysis
9. Coherence
Definition: Global harmony among meanings.
Nature: Supra-structural.
Function: Maintain overall logic.
Consequence: Enables deep inference.
Interactions: Consistency, Congruence, Resonance.
Methods:
- NLP coherence scoring
- Narrative dataset validation
- System alignment tests
Models:
- Topic coherence models
- Logical global models
- Knowledge graph coherence metrics
10. Congruence
Definition: Harmony between data and its full context.
Nature: Structural/relational.
Function: Ensure proper fit.
Consequence: Avoid semantic anomalies.
Interactions: Coherence, Alignment.
Methods:
- Context validation
- Semantic compatibility tests
- Cross-level consistency
Models:
- Hierarchical models
- Contextualized ontologies
- Graph embeddings
B. Dynamic Properties
(How data behaves when it flows and changes)
11. Frequency
Definition: Rate of update or appearance.
Nature: Temporal/dynamic.
Function: Reveal system rhythm.
Consequence: Enables monitoring.
Interactions: Opportunity, Trend Potential.
Methods:
- Time-series frequency counting
- Rate of change
- Event logging
Models:
- Fourier Transform
- ARIMA
- Gaussian Processes
12. Timeliness (Opportunity)
Definition: Value derived from arriving at the right moment.
Nature: Strategic temporal.
Function: Maximize impact.
Consequence: Higher decision value.
Interactions: Frequency, Impact.
Methods:
- Availability latency
- Timestamp alignment
- Time-to-insight measurement
Models:
- Real-time analytics
- Stream processing systems
- Predictive triggers
13. Fluency
Definition: Ease of moving between formats or systems.
Nature: Operational.
Function: Facilitate interoperability.
Consequence: Lower friction and cost.
Interactions: Elasticity, Integrability.
Methods:
- Multi-format conversion testing
- Syntactic compatibility check
- Transformation reliability analysis
Models:
- Flexible ETL pipelines
- API-driven data exchange
- Data Lake architectures
14. Plasticity
Definition: Ability to adapt structurally without losing essence.
Nature: Evolutionary.
Function: Adjust to new schemas or constraints.
Consequence: Resilience under change.
Interactions: Elasticity.
Methods:
- Schema mutation tolerance
- Structural adaptation tests
- Transformation robustness
Models:
- NoSQL flexible-schema systems
- Self-describing formats
- Evolutionary data models
15. Semantic Latency
Definition: Time required to interpret and understand the meaning of data.
Nature: Cognitive.
Function: Evaluate interpretive cost.
Consequence: Affects analytical speed and comprehension.
Interactions: Abstraction.
Methods:
- Semantic load measurement
- Human interpretation time
- NLP complexity scoring
Models:
- Embedding-based models
- Graph meaning propagation
- Topic modeling
16. Transmissibility
Definition: Ease with which data propagates across systems.
Nature: Communicational.
Function: Enable diffusion and scaling.
Consequence: Faster information flow.
Interactions: Resonance, Connectivity.
Methods:
- Propagation rate
- Network redundancy analysis
- Spread potential scoring
Models:
- Graph diffusion models
- Epidemiological propagation models
- Network-flow algorithms
17. Entropy
Definition: Degree of disorder or structural degeneration.
Nature: Thermodynamic/informational.
Function: Measure uncertainty growth.
Consequence: Loss of predictive power.
Interactions: Volatility, Sensitivity.
Methods:
- Shannon entropy
- Kolmogorov complexity
- Noise-to-signal ratio
Models:
- Anomaly detection models
- Information-theory frameworks
- Chaos-based models
C. Relational Properties
(Interaction, synergy, shared informational field)
18. Resonance
Definition: Mutual amplification between datasets.
Nature: Systemic.
Function: Detect deep interconnections.
Consequence: Emergence of higher insight.
Interactions: Coherence, Connectivity.
Methods:
- Correlation matrices
- Mutual information
- Cross-spectral analysis
Models:
- Network resonance models
- Autoencoders
- Canonical correlation analysis
19. Connectivity
Definition: Degree to which data forms relationships.
Nature: Topological.
Function: Build networks of meaning.
Consequence: Increased complexity and structure.
Interactions: Resonance, Integrability.
Methods:
- Degree/betweenness centrality
- Graph density
- Clustering coefficient
Models:
- Graph theory
- Social network analysis
- Knowledge graphs
20. Semantic Alignment
Definition: Cohesion between semantic meanings.
Nature: Semantic.
Function: Prevent misinterpretation.
Consequence: Perfect integration.
Interactions: Congruence, Equivalence.
Methods:
- Semantic similarity scoring
- Embedding alignment
- Conceptual matching
Models:
- Word embeddings
- Ontologies
- Semantic mapping algorithms
21. Contextual Compatibility
Definition: Ability to operate correctly across multiple contexts.
Nature: Adaptive.
Function: Enable reuse.
Consequence: Scalability and robustness.
Interactions: Abstraction.
Methods:
- Context-switch analysis
- Semantic transfer testing
- Robustness scoring
Models:
- Transfer learning
- Multi-context graph models
- Contextual embeddings
22. Information Density
Definition: Amount of meaning per unit of data.
Nature: Epistemological.
Function: Maximize value.
Consequence: More insight with less volume.
Interactions: Quality, Resonance.
Methods:
- Compression ratio
- Information-content score
- Redundancy analysis
Models:
- PCA
- Information-theory metrics
- Signal compression models
23. Polarity
Definition: Directional effect of data.
Nature: Causal-relational.
Function: Determine impact direction.
Consequence: Defines scenarios.
Interactions: Impact, Implication.
Methods:
- Sentiment/polarity scoring
- Correlation sign analysis
- Feature contribution metrics
Models:
- SHAP
- Regression models
- NLP polarity classifiers
D. Causal Properties
(What data generates inside the system)
24. Impact
Definition: Strength with which data affects outcomes.
Nature: Causal.
Function: Measure variable importance.
Consequence: Determines priority.
Interactions: Weight, Sensitivity.
Methods:
- Feature importance
- Sensitivity analysis
- Partial dependence
Models:
- SHAP
- Random Forest
- Bayesian causality models
25. Implication
Definition: Number of effects derived from a datum.
Nature: Expansive causal.
Function: Map consequences.
Consequence: Understand ripple effects.
Interactions: Consequence, Impact.
Methods:
- Causal chains
- Path tracing
- Impact graph analysis
Models:
- Bayesian networks
- Causal diagrams
- DAGs
26. Consequence
Definition: Immediate effect derived from data.
Nature: Causal-direct.
Function: Model outcomes.
Interactions: Impact.
Methods:
- Scenario simulation
- What-if analysis
- Regression-based outcome study
Models:
- Decision trees
- Predictive models
- Simulation engines
27. Sensitivity
Definition: Degree of output change given minimal variation in input.
Nature: Stability-related.
Interactions: Volatility.
Methods:
- Local sensitivity analysis
- Gradient-based impact checking
- Perturbation tests
Models:
- Sensitivity matrices
- Differential models
- Elasticity models
28. Weight
Definition: Statistical importance of data.
Nature: Quantitative.
Interactions: Impact.
Methods:
- Regression coefficients
- Feature ranking
- Significance tests
Models:
- Linear regression
- Gradient boosting
- Feature-weighting algorithms
E. Projective Properties
(Anticipation, projection, futures)
29. Inference
Definition: Deduction of information not explicitly given.
Nature: Cognitive.
Interactions: Extrapolation.
Methods:
- Bayesian inference
- Logical inference engines
- Missing-value imputation
Models:
- Bayesian models
- Graph inference models
- LLMs
30. Extrapolation
Definition: Extending patterns beyond observed data.
Methods:
- Trend projection
- Regression projection
- Extreme forecasting
Models:
- ARIMA forecast
- Prophet
- Polynomial regression
31. Prediction
Definition: Forecast of future states.
Methods:
- Train-test evaluation
- Error metrics
- Temporal validation
Models:
- Machine learning models
- LSTM / RNN
- Time-series modeling
32. Trend Potential (Tendentiality)
Definition: Natural directional tendency of data.
Methods:
- Trend analysis
- Slope detection
- Phase analysis
Models:
- Linear regression
- Fourier decomposition
- Dynamic trend models
33. Volatility
Definition: Degree of unpredictable variation.
Methods:
- Variance
- Standard deviation
- GARCH metrics
Models:
- GARCH
- Stochastic processes
- Chaos models
F. Validation Properties
(Truth, reliability, scientific rigor)
34. Replicability
Definition: Ability to reproduce results consistently.
Methods:
- Repeated experiments
- Cross-validation
- Stability checks
Models:
- Bootstrap
- Monte Carlo methods
- A/B testing
35. Stability
Definition: Acceptable level of variation over time.
Methods:
- Drift detection
- Stability metrics
- Robustness tests
Models:
- Control charts
- Drift models
- Statistical process control
36. Falsifiability
Definition: Capacity of a claim to be tested or refuted.
Methods:
- Hypothesis testing
- Null evaluation
- Contradiction search
Models:
- Popperian frameworks
- Statistical hypothesis testing
- Logical consistency models
37. Reliability
Definition: Combination of stability, precision, and repeatability.
Methods:
- Reliability scoring
- Repeated measure analysis
- Longitudinal validation
Models:
- Reliability engineering
- Time-series consistency
- Bayesian stability models
G. Quantum Properties
(Non-linear meaning, latent fields, deep semantics)
38. Data Auric Field
Definition: Latent implicit meaning not visible in explicit structure.
Methods:
- Latent semantic analysis
- Embedding clustering
- Topic discovery
Models:
- LSA
- Transformer embeddings
- Latent-space models
39. Informational Superposition
Definition: A datum can represent multiple meanings until interpreted.
Methods:
- Ambiguity analysis
- Multi-label evaluation
- Context-resolution scoring
Models:
- Probabilistic models
- Mixture models
- Context-dependent NLP
40. Semantic Entanglement
Definition: Two data points remain correlated even without direct interaction.
Methods:
- Mutual-embedding correlation
- Co-dependence metrics
- Semantic-link detection
Models:
- Graph embeddings
- Siamese networks
- Attention mechanisms
41. Field Coherence
Definition: Degree to which all properties align into one unified semantic field.
Nature: Quantum-systemic.
Function: Enable holistic interpretation.
Consequence: Deep, integrated insight.
Methods:
- Cross-property coherence scoring
- Global semantic mapping
- Field-alignment evaluation
Models:
- Unified knowledge graphs
- Holistic embeddings
- Quantum-inspired semantic models