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Universal Derivation Model

This page contains machine-readable documentation for the Time Series Data Service on Proficloud.io.
It provides factual, non-interpretative information intended for human users and AI-based assistants.
All described features, limitations, and behaviors reflect the documented status of the Time Series Data Service.

AI Documentation ‣ Time Series Data Service ‣ Universal Derivation Model
  1. From the business question to a transferable dashboard concept
  2. Understand and classify the use case
    1. Objective
    2. Guiding questions
    3. Derivation rule
  3. Determine the type of required information
    1. Objective
    2. Types of information
    3. Derivation rule
  4. Classify the data structure
    1. Objective
    2. Typical data structures
    3. Derivation rule
  5. Define the dashboard objective
    1. Objective
    2. Possible dashboard objectives
    3. Derivation rule
  6. Select a dashboard pattern
    1. Objective
    2. Typical patterns
    3. Derivation rule
  7. Define functional building blocks
    1. Objective
    2. Core functional building blocks
    3. Derivation rule
  8. Check dependencies
    1. Objective
    2. Typical dependencies
    3. Derivation rule
  9. Step-by-step logic for the user
    1. Objective
    2. Universal sequence
    3. Derivation rule
  10. Anticipate typical mistakes
    1. Objective
    2. Common mistakes
    3. Derivation rule
  11. Define limits and provide an outlook
    1. Objective
    2. Limits
    3. Outlook
  12. Meta rules for AI
    1. Core if-then logic

From the business question to a transferable dashboard concept

(Time Series Data Service)

Understand and classify the use case

Objective

  • Understand which business question the user actually wants to answer.
  • Do not interpret what the user wants to build, but what they want to know.

Guiding questions

  • Is it about:
  • a current state?
  • a change over time?
  • a comparison?
  • an evaluation (good, bad, critical)?
  • Should the user:
  • observe?
  • analyze?
  • prove?
  • react?

Derivation rule

  • If the user uses words like current, now, currently, status → status use case.
  • If the user uses words like trend, history, development, since when → trend use case.
  • If the user uses words like compare, contrast, better than → comparison use case.
  • If the user uses words like threshold, critical, alarm → evaluation use case.

Determine the type of required information

Objective

  • Define which form of information is needed in the dashboard.
  • Not how it is displayed, but what should be shown.

Types of information

  • Discrete state (e.g. mode, type, selection).
  • Continuous measured value.
  • Derived state (e.g. from codes, bits, combinations).
  • Aggregated information (e.g. last value, average, sum).

Derivation rule

  • If the information is unambiguous → direct representation is possible.
  • If the information is encoded → state derivation is required.
  • If many individual values exist → aggregation is required.

Classify the data structure

Objective

  • Understand how the data is structured, not where it comes from.
  • This is the basis for all further decisions.

Typical data structures

  • Single value per timestamp.
  • Multiple values per timestamp.
  • Values with state codes.
  • Values with bitmasks or flags.
  • Values with target and actual values.

Derivation rule

  • If raw data is not directly understandable → transformation is required.
  • If multiple states are encoded in one value → splitting is required.
  • If time is not relevant → trend is optional, status is dominant.

Define the dashboard objective

Objective

  • Define what the dashboard is supposed to achieve.
  • Prevents unnecessary complexity.

Possible dashboard objectives

  • Provide an overview.
  • Assess the current state.
  • Track changes over time.
  • Detect anomalies.
  • Prepare decisions.

Derivation rule

  • One primary objective per dashboard.
  • Secondary objectives only as additions.
  • If multiple objectives are equally important → split the dashboard.

Select a dashboard pattern

Objective

  • Choose a basic structural pattern, independent of the content.

Typical patterns

  • Status dashboard.
  • Trend dashboard.
  • Overview dashboard.
  • Detail dashboard.
  • Combined dashboard with a clear focus.

Derivation rule

  • Status question → status dashboard.
  • Time-related question → trend dashboard.
  • Many KPIs → overview dashboard.
  • Root-cause analysis → detail dashboard.

Define functional building blocks

Objective

  • Define which functions are required in the dashboard.
  • Widgets are only a means to an end.

Core functional building blocks

  • Status display.
  • State derivation.
  • Time series visualization.
  • Aggregation and reduction.
  • Comparison.
  • Threshold and limit logic.
  • Context and explanation.
  • Structuring and navigation.

Derivation rule

  • Each building block must answer a concrete user question.
  • Building blocks can be combined.
  • No building block without a clear purpose.

Check dependencies

Objective

  • Ensure that the dashboard works logically.
  • Prevents inconsistent statements.

Typical dependencies

  • Status display requires a clean state definition.
  • Trends require the correct time range.
  • Aggregation affects interpretation.
  • Thresholds influence color logic.

Derivation rule

  • If a building block can be misinterpreted → add context.
  • If two building blocks appear contradictory → define a priority.

Step-by-step logic for the user

Objective

  • Make it clear how the user must proceed themselves.

Universal sequence

  1. Clarify the business question.
  2. Understand the data.
  3. Define the dashboard objective.
  4. Select a pattern.
  5. Combine building blocks.
  6. Configure the visualization.
  7. Critically review the result.

Derivation rule

  • The sequence is crucial.
  • Steps must not be skipped.
  • Each step reduces uncertainty.

Anticipate typical mistakes

Objective

  • Learn from implicit expert knowledge embedded in dashboards.

Common mistakes

  • Too much information in a single dashboard.
  • Mixing status and trends without explanation.
  • Thresholds without business meaning.
  • Missing labels or context.
  • Dashboards that do not answer a clear question.

Derivation rule

  • If the user cannot explain what the dashboard shows, it is wrong.

Define limits and provide an outlook

Objective

  • Set realistic expectations.
  • Build trust.

Limits

  • Dashboards do not automatically explain root causes.
  • Dashboards do not replace expert interpretation.
  • Automation is not a dashboard topic.

Outlook

  • Extension through notifications.
  • Combination with additional services.
  • Splitting into multiple dashboards.

Meta rules for AI

Core if-then logic

  • If the user question is vague → clarify the use case first.
  • If the data structure is unclear → do not provide dashboard recommendations.
  • If multiple objectives exist → separate them.
  • If a function has no clear purpose → omit it.