tekobterm — Structured analysis of business activity
tekobterm is an independent analytical reference that documents methods and structures used to observe and classify organizational activities. The site focuses on systematic description of processes, ordered sequences, and activity groupings using formal datasets and temporal constructs. Content is technical and descriptive, intended for internal review, research, and documentation of analytical approaches rather than advisory or promotional communications.
The material covers categorical taxonomies, observational protocols, event sequencing, and data summarization techniques. It emphasizes reproducible recording of operational steps, consistent labeling of activity elements, and clear rules for grouping and aggregation within structured timelines.
Methodology: observation and categorization
Observation protocols are documented to ensure consistency when recording activity sequences. The methodology uses defined observation windows and event markers to capture transitions between tasks and handoffs. Each observed action is assigned a categorical label drawn from an explicit taxonomy. Labels are applied at the level of atomic actions and can be aggregated into hierarchical groupings to reflect nested operational contexts.
Data capture formats include timestamped event lists, structured transcripts of process traces, and relational tables describing actor-role mappings. The approach distinguishes between observable state changes and inferred intent; annotations explicitly indicate whether an entry derives from direct observation or from internal interpretation to preserve auditability.
Categorical schema
Schemas define permitted labels, hierarchical relations, and metadata fields for each observed unit. Schemas are versioned and include change logs for traceability.
- Atomic action labels with controlled vocabulary
- Temporal markers and windowing rules
- Annotation provenance and confidence markers
Data model: structured datasets and timelines
The data model uses normalized tables to represent events, entities, and relationships. Event records include a canonical timestamp, an actor identifier, a location marker when relevant, and a reference to the applied categorical label. Timelines are derived views that order events and compute interval groupings according to explicit adjacency and concurrency rules. Structured datasets include schema definitions, index recommendations, and transformation recipes for reproducible aggregation.
Temporal constructs include discrete events, bounded intervals, and rolling sessions. Aggregations are defined by grouping keys and window functions; all grouping rules are documented to support internal review and reproducibility rather than to assert impact or outcomes.
Taxonomy and logical grouping
Taxonomies provide deterministic rules for grouping observed actions into logical classes and composite structures. Grouping rules include adjacency thresholds, role continuity checks, and contextual qualifiers. The taxonomy supports multiple orthogonal classification axes to permit analyses along operational function, resource type, and temporal behavior. Each axis includes a controlled vocabulary and examples to reduce ambiguity during annotation.
Rules for grouping are expressed as explicit statements and where applicable as canonical SQL or pseudocode for reproducible application. Taxonomy changes are recorded with rationale and mapping rules to prior versions to preserve longitudinal coherence during internal review or research replication.
Visual constructs for documentation
Visual representations favor abstract process-oriented graphics rather than conventional dashboards. Recommended constructs include flow paths, layered system maps, and sequence diagrams that emphasize relationships and ordering. Visual artifacts are annotated with legend entries that explain label semantics and aggregation rules. Graphics are created to support internal inspection and should include clear axis, scale, and provenance metadata when relevant.
Review and versioning
Documentation practices require versioned schemas, annotated change logs, and review checklists. Each dataset carries metadata fields describing capture method, observer identity, and schema version. Versioning supports rollbacks and mapping functions to earlier schema releases to maintain continuity for internal analysis.