Platform-Independent Data Model

• Platform-Independent Data Model is a formal abstraction that enables data interoperability and portability by decoupling from specific databases, hardware, and middleware.
• It integrates canonical models, serialization formats, algebraic unification, and automata-based methods to achieve consistent cross-platform data operations.
• Practical applications include unified databases, industrial informatics, and flexible automation systems, demonstrating significant improvements in performance and scalability.

A platform-independent data model is a formal abstraction that enables data interoperability, manipulation, and reasoning across diverse concrete storage and execution environments, without coupling to specific database, hardware, API, or middleware technologies. Its role is critical in unified databases, knowledge integration, industrial informatics, in-network processing, collaborative data spaces, model-driven engineering, and flexible automation systems. The following sections synthesize canonical models, serialization strategies, translation mechanisms, algebraic unification, automata-based formalisms, and application architectures from leading research.

1. Canonical Platform-Independent Model Definitions

Different research lines propose rigorously specified, technology-neutral data models. Notable examples include:

• Property Graph Exchange Model: A platform-independent property graph $PG$ is a tuple

$$PG = (V,\;E_d,\;E_u,\;L_V,\;L_E,\;P_V,\;P_E,\;\mathit{end})$$

where $V$ is a finite vertex set, $E_d$/$E_u$ are directed/undirected edges, $L_V$/$L_E$ assign sets of labels, $P_V$/$P_E$ assign multi-valued properties, and $\mathit{end}$ specifies edge endpoints—all agnostic to backend graph engine (Chiba et al., 2019).

• Unified Database Model (UDM): The UDBMS architecture defines a global universe $\mathcal{U}$ unifying atomic scalars, tuples, maps, lists, trees, and graphs. Its schema $S = (E, R, F, G)$ covers entity collections of heterogeneous type, flexible path and graph constraints, supporting simultaneous relational, key-value, document, and graph models (Lu et al., 2016).
• Ontology-Based Multi-Model Layer: The ArchiGraph system models

$M = (C, P, I, \sigma)$

with $C$ ontology classes, $P$ properties (typed as data/object), $I$ individuals, and a mapping function $\sigma$ that virtualizes data placement in relational, NoSQL, or triple-store backends (Gorshkov et al., 2021).

• Data Space High-Level Architecture Model (DS-HLAM): DS-HLAM abstracts a data collaboration platform as five sets—Organizations $O$, Data Provision Mechanisms $M$, Data Units $D$, Social Mechanisms $S$, and Rules $R$—with formal operations and transaction automata ensuring cross-implementation consistency and workflow success (Dobashi et al., 28 Aug 2025).
• Automata-Based Platform-Independent Model (PHSA): The AMDA method encodes software designs in Parallel Hierarchical Sequential Automata, with states, events, conditions, memory, and actions, suitable for translating UML/OCL into portable execution logic (Dayan et al., 2020).
• Layered Assembly Data Model: LightRocks uses a four-tier hierarchy—Assembly Plan, Process, Task/Skill, and Action—to factor all robot-domain specificity to the bottom layer, keeping upper-model reuse and abstraction (Butting et al., 2016).
• eXtended Finite State Machine (XFSM): OPP’s XFSM tuple $(I, O, S, D, F, U, T)$ describes platform-independent stateful packet processing, mapping abstract states, inputs, conditions, and updates to hardware primitives without implementation entanglement (Bianchi et al., 2016).

2. Serialization Formats and Mapping Strategies

Platform independence in practice requires data serialization formats that (a) fully represent the abstract model and (b) avoid engine-specific features.

• Flat and JSON Property Graph: The "PG" line-oriented text and "JSON-PG" array-based format map graph vertices/edges, multi-labels, and multi-valued properties in a backend-neutral fashion. These formats admit lossless bidirectional conversion with Neo4j, PGX, Neptune, and other graph DBs (Chiba et al., 2019).
• XML PHSA Representation: AMDA stores the PIM automaton as XML per DTD, representing states, transitions, conditions, memory, and I/O. Transformation engines (e.g., via XSLT) convert this to PSMs and eventually platform code, preserving semantics (Dayan et al., 2020).
• Ontology and Multi-Model Mapping: RDF triples are abstracted by ArchiGraph as mapping $\sigma: T \rightarrow$ Table, Join Table, or Collection, depending on property and class, with adapters and mapping tables facilitating SPARQL queries and SHACL validation regardless of physical storage (Gorshkov et al., 2021).
• DS-HLAM Data Unit Formalization: Each data unit $d$ is a header-payload pair, with headers as attribute-type/value sets and payloads supporting any underlying representation (Dobashi et al., 28 Aug 2025).

3. Algebraic Unification and Query Processing

Unified query processing, optimization, and indexing are essential for platform-independent models spanning multiple paradigms.

• Unified Algebra (UDBMS): Operators generalized from relational algebra extend to JSON/tree (path-expressions, subtree extraction), and graph (pattern-match, edge-join). Query languages embed SQL, JSON-PATH, and GRAPH-MATCH, translating through the unified algebra (Lu et al., 2016).
• SPARQL–Storage Rewriting: SPARQL queries are parsed into basic graph patterns, then each triple pattern is lifted into subplans by the multi-model abstraction layer, dispatching to relational/NoSQL adapters and joining results in-memory (Gorshkov et al., 2021).
• Index Structures: UDBMS maintains global inverted indexes (term→entity/path/position), multidimensional tree indexes (value, path, model code, structure code), and builds ad-hoc projections for relational-JSON-graph cross-joins (Lu et al., 2016).

4. Automata, State Machines, and Platform Independence

Formal automata are widely adopted to encode platform-independent behavior and transaction logic.

• DS-HLAM Transaction Automaton: Collaboration workflows are formalized by $M = (Q,\Sigma,\delta,q_0,\{q_f\})$, with defined states, success conditions, and transition rules ensuring interoperability and digital sovereignty (Dobashi et al., 28 Aug 2025).
• AMDA Hierarchical Automata: Parallel automata blocks (SSA_k) encapsulate states, events, outputs, memory, and conditional transitions, hierarchically composed to reflect UML object and statechart structure. Automated translation into PSM code preserves event-state-output semantics (Dayan et al., 2020).
• OPP's XFSM Decoupling: The separation of abstract state-machine logic from hardware primitives (flow-context tables, TCAM, logic blocks, ALUs, action engine) enables the same XFSM program to run on any compatible programmable switch (Bianchi et al., 2016).

5. Mechanisms for Achieving Platform Independence

Technical strategies for platform-neutrality span multiple layers:

• Abstract Data Types and Sets: Models define entities, labels, properties, attributes, and relationships as sets or mappings, independent of physical schema, datatype restrictions, or API details (Chiba et al., 2019, Dobashi et al., 28 Aug 2025).
• Layered Abstraction and Adapters: Translation layers and adapters route data access, inserts, and queries to the optimal backend (relational, NoSQL, triple store), preserving ontology, shape, and integrity constraints (Gorshkov et al., 2021).
• Hierarchical Decomposition: DS-HLAM formalizes vertical consistency, treating endpoints, tables, or topics as refinements of data provision mechanisms, and enforces constraint-preserving decomposition (Dobashi et al., 28 Aug 2025).
• Symbol Table and Code Generators: LightRocks employs MontiCore symbol table checking to ensure Tasks/Skills reference only abstract Actions, and code generators bind these Actions to platform-specific robot APIs as required (Butting et al., 2016).
• Automata-Based Bisimulation: AMDA proves bisimulation between PIM automata and PSM code, with transformation steps affecting only types and I/O but not event-state logic, thereby guaranteeing portability (Dayan et al., 2020).

6. Representative Applications and Evaluation

Platform-independent models have been demonstrated in diverse domains:

Application	Core Data Model	Platform-Independent Mechanism
Property Graph Exchange	Property Graph	PG/JSON-PG, toolkits for bidirectional DB conversion (Chiba et al., 2019)
Industrial Data Integration	Ontology/RDF	Multi-model abstraction layer, SPARQL over adapters (Gorshkov et al., 2021)
Data Collaboration Platform	DS-HLAM	Automata-based transaction, abstract components (Dobashi et al., 28 Aug 2025)
Unified DBMS	$\mathcal{U}$	Multi-model schema, algebra, index, ACID/BASE (Lu et al., 2016)
Robot Assembly	Process/Task/Skill	Abstraction hierarchy, code generator binding (Butting et al., 2016)
Wire-Speed Packet Processing	XFSM	Abstract state machine, hardware primitive mapping (Bianchi et al., 2016)

Empirical benchmarks in ArchiGraph show its ontology-backed abstraction layer achieves up to 15$\times$ speedup over pure RDF engines in scan/filter queries on large-scale synthetic company/person/project graphs, while maintaining consistent SPARQL and SHACL support (Gorshkov et al., 2021). OPP’s XFSM abstraction enables the same bytecode to run on FPGA, ASIC, or commodity programmable switches, sustaining hundreds of Gbps throughput at fixed packet-processing latency (Bianchi et al., 2016).

7. Limitations and Prospective Directions

While current models deliver substantial platform independence, several limitations persist:

• Domain-Specific Instantiations: Full realizations in healthcare, manufacturing, and other verticals are ongoing—formal decompositions and concrete subtype libraries for entities and rules are still required (Dobashi et al., 28 Aug 2025).
• Toolchain Automation: Standardized mechanisms for automata, schema, index, and ontology translation across implementations and for verifying vertical/horizontal consistency are areas for further development (Dobashi et al., 28 Aug 2025, Dayan et al., 2020).
• Performance and Scalability: Scalability for very large datasets, multi-model joins, and extremely complex automata has constraints at the physical layer and may require architectural refinements (multi-stage XFSM, scalable adapters, hierarchical indexes) (Bianchi et al., 2016, Lu et al., 2016).
• Expressiveness vs. Integration: Achieving full semantic and transactional expressiveness sometimes necessitates trade-offs in integration latency, query complexity, and preservation of shape constraints under schema evolution (Lu et al., 2016).

Platform-independent data models, grounded in rigorous abstractions, layered mappings, algebraic and automata-theoretic formalisms, and extensible serialization formats, are foundational to cross-technology data interoperability, reusable software engineering, scalable knowledge integration, and flexible automation (Chiba et al., 2019, Lu et al., 2016, Gorshkov et al., 2021, Dobashi et al., 28 Aug 2025, Dayan et al., 2020, Butting et al., 2016, Bianchi et al., 2016).