Apache Ossie Roadmap (Community-Informed)

This roadmap synthesizes community discussions and voting signals from the Ossie GitHub Discussions board. It groups work into three categories:

  • Current Efforts / Working Groups — strategic initiatives with active working groups driving spec evolution now
  • Future Efforts — strategic initiatives planned for future working groups
  • Enhancements & Additions — incremental improvements that extend the current model

Current Efforts / Working Groups

These are the strategic initiatives where working groups are actively driving spec evolution.

Metric Semantics & Core Semantic Model

Goal: Enable expressive, composable, and well-defined semantic models with clear entity, relationship, and grain semantics.

Motivation: The current model lacks sufficient support for metrics at different grains, filters, aggregation semantics, and relationships between metrics. Ambiguity in how entities, joins, and grain are represented limits interoperability.

Key Discussions:

Roadmap Deliverables:

  • Standard metrics specification language
  • First-class aggregation, relationship, and grain semantics, including a specification that documents the expected behavior that the community has aligned on
  • Support for derived and cumulative metrics
  • Explicit entity modeling
  • Enhanced relationship definitions & capabilities
  • Cross-domain modeling support
  • Reusable semantic filter definitions

Catalog Integration & Semantic Services

Goal: Integrate Ossie with data catalogs and enable centralized semantic services.

Motivation: Semantic models need to be discoverable, governable, and shareable across systems.

Roadmap Deliverables:

  • Integration patterns with catalogs (e.g., Polaris)
  • Standalone semantic service / registry
  • Discovery, versioning, and access control for Ossie models

Related Issues:

Ontology & Semantic Interoperability

Goal: Enable Ossie to describe business concepts independently of physical data layout, supporting ontology-based semantic models and cross-model conceptual alignment.

Motivation: Many semantic representations (e.g., Palantir, Goldman Sachs Legend) use ontologies to define meaning, and dimensional semantic models naturally layer on top of these. Ossie currently solves structural interoperability — any tool can read and write semantic models in a common format — but it does not yet solve conceptual interoperability, where different models may describe the same business concept using different names or structures. An ontology layer would let organizations define canonical business concepts (Customer, Order, Product, etc.) independently of where the data lives and map physical semantic models back to shared definitions.

Key Discussions:

Roadmap Deliverables:

  • Ontology layer describing business concepts above the physical/logical semantic model
  • Schema mappings between ontology concepts and Ossie datasets/fields
  • Support for relational ontologies and non-tabular data models
  • Shared semantic definitions enabling conceptual interoperability across models

Related Issues:

Future Efforts

These strategic initiatives are planned for future working groups as the spec matures.

Dataset Abstraction & Logical Modeling

Goal: Decouple semantic definitions from physical storage.

Motivation: Users want reusable semantic models independent of underlying tables or views.

Key Discussions:

Roadmap Deliverables:

  • Mapping layer between logical and physical datasets
  • Reusable semantic definitions across environments
  • Reusable datasets and relationships shared across semantic models

Related Issues:

Semantic Query Language & Reference Engine

Goal: Define a standard query interface for interacting with Ossie models and provide a canonical implementation for interpreting and executing them.

Motivation: Consumers (BI tools, AI systems, APIs) need a consistent way to query semantic models independent of underlying SQL dialects. A reference engine ensures consistent interpretation of the spec and accelerates ecosystem adoption.

Roadmap Deliverables:

  • Standard semantic query language (Ossie-native or SQL-extended)
  • Mapping from semantic queries → execution plans
  • Support for metrics, dimensions, filters, and relationships
  • Reference compiler from Ossie → SQL
  • Canonical handling of joins, aggregations, and filters
  • Test suite to validate conformance across implementations

Related Issues:

SQL Dialect, Expressions, and Execution Boundaries

Goal: Clarify the role of SQL and execution within Ossie.

Motivation: There is tension between portability and practical execution requirements.

Key Discussions:

Roadmap Deliverables:

  • Explicit dialect handling strategy
  • Clear boundaries between semantic definition and execution
  • Optional templating support

Related Issues:

Dimensions, Hierarchies, and Time Semantics

Goal: Standardize how dimensions and time are modeled.

Motivation: Inconsistent handling of hierarchies and time impacts usability and interoperability.

Key Discussions:

Roadmap Deliverables:

  • Hierarchical dimension modeling
  • Standardized time semantics
  • Calendar abstractions

Related Issues:

AI-Native Semantic Layer

Goal: Enable Ossie as a reliable foundation for AI-driven analytics.

Motivation: There is growing demand for structured semantic context and grounded query generation.

Key Discussions:

Roadmap Deliverables:

  • Standardized AI context metadata
  • Verified or curated query definitions
  • Mechanisms for controlling AI exposure to semantic elements

Governance, Identity, and Validation

Goal: Ensure trust, stability, and long-term interoperability.

Motivation: Enterprise adoption requires consistent identifiers, validation, and governance hooks.

Key Discussions:

Roadmap Deliverables:

  • Stable identifiers across environments
  • Validation and conformance standards
  • Governance and certification frameworks

Related Issues:

Industry / Domain-Specific Semantic Models

Goal: Accelerate adoption through reusable, standardized domain models.

Motivation: Organizations repeatedly recreate similar semantic models (e.g., SaaS, finance, retail). Standardized models can drive faster adoption and consistency.

Roadmap Deliverables:

  • Curated domain-specific semantic model templates
  • Best-practice metric and dimension definitions by industry
  • Interoperable model packages aligned with Ossie

Enhancements & Additions (Incremental Improvements)

These items improve usability, clarity, and completeness without fundamentally changing the spec.

Naming, Terminology, and UX Improvements

Goal: Align Ossie vocabulary with how practitioners think about semantic models, and improve the authoring experience.

Motivation: Several naming conventions in the current spec create confusion or clash with established industry terminology. Clearer naming reduces onboarding friction and improves readability of Ossie definitions.

Roadmap Deliverables:

  • Revised terminology that reflects community consensus (e.g., “Dimension” over “Field”)
  • Consistent naming conventions for source references, descriptions, and display labels

Key Discussions:

Data Types and Field Semantics

Goal: Provide native support for rich data typing so downstream tools can interpret fields without guesswork.

Motivation: Consuming systems (BI tools, AI agents, dashboards) frequently need to know whether a field represents a currency, a physical unit, or sensitive data — but this context is lost in the current spec and must be re-inferred or hard-coded per tool.

Roadmap Deliverables:

  • First-class unit and currency annotations on measures and dimensions
  • Standardized semantic field type taxonomy (dimension type, data type, PII classification)

Key Discussions:

Related Issues:

Extended Metadata for Apache Ossie

Goal: Introduce a lightweight, optional metadata layer that improves how data is interpreted, presented, and consumed — without affecting execution semantics.

Motivation: Ossie standardizes structural and logical semantics well, but there is limited support for conveying interpretability context such as display conventions, default aggregation behavior, KPI polarity, sorting preferences, and alignment to external semantic concepts. These details are often redefined or inferred inconsistently across developers, BI tools, and AI systems.

Roadmap Deliverables:

  • Extended Metadata Proposal for Ossie — optional, backward-compatible metadata fields (e.g., measurement, display_format, semantic_type, default_aggregation, desired_direction, default_sort, semantic_mappings)
  • Richer application-specific extension points beyond custom_extensions
  • Sample value annotations for documentation and AI grounding

Key Discussions:

Developer Experience & Documentation

Goal: Lower the barrier to adopting and correctly using Ossie through better guidance, examples, and tooling-friendly formatting.

Motivation: New adopters and tool authors need clearer documentation, real-world samples, and support for rich-text descriptions to effectively author and consume Ossie models.

Roadmap Deliverables:

  • Comprehensive usage guides with annotated examples, especially for AI context fields
  • Data modeling best-practice documentation
  • Markdown support in description fields for richer inline documentation

Key Discussions:

Existing Artifacts:

Specialized Capabilities

Goal: Extend Ossie to support domain-specific data types, audience definitions, and patterns that go beyond traditional tabular analytics.

Motivation: Geospatial analytics, time-series modeling, and audience segmentation have unique requirements that benefit from first-class spec support rather than ad-hoc workarounds.

Roadmap Deliverables:

  • Spatial field types, spatial relationships, and geographic hierarchies
  • Date spine model support for time-series alignment and gap-filling
  • Audience / segment definitions as first-class constructs

Key Discussions:

Tooling & Ecosystem Support

Goal: Provide reference tooling that makes it easy to validate, convert, and adopt Ossie models.

Motivation: Broad ecosystem adoption depends on practical tools that let teams validate their models against the spec and convert between Ossie and existing vendor formats without manual effort.

Roadmap Deliverables:

  • Validator code (schema validation, linting, conformance checks)
  • Participant ↔ Ossie converter code (read/write interoperability with existing tools)

Existing Artifacts:

Related Issues: