Provenance-Aware Mapping

• Provenance-aware mapping is a method for linking process-level records with infrastructure data to ensure reproducibility, traceability, and error detection.
• It leverages systematic architectures and mapping algorithms that join metadata from workflow engines and cloud resources using specific keys like host IPs.
• Its applications span scientific workflows, annotated database queries, and ontology mappings, enhancing auditability, performance, and interoperability.

Provenance-aware mapping is the systematic binding of provenance metadata—records of data origins, derivations, process steps, and governing resources—to the computational, data, and infrastructure artifacts arising in data-intensive scientific, database, or workflow systems. By constructing such mappings, systems support not only reproducibility and error tracing but also auditability and interoperability of complex computational processes across heterogeneous infrastructures.

1. Conceptual Foundations and Formal Definitions

A provenance-aware mapping connects two (or more) domains of metadata: typically, the process-level provenance (e.g., transformations, inputs/outputs, parameters) and resource-level provenance (e.g., physical or virtual infrastructure configurations). In cloud-based workflows, this is formalized as a function $M: P \to I$, where $P$ is the set of provenance records for process-level operations and $I$ is the set of infrastructure configuration records. For each recorded process $p \in P$, $M(p)$ gives the precise infrastructure instance (VM, host, container) under which it was executed (Hasham et al., 2015, Hasham et al., 2015, Hasham et al., 2018).

In database and data integration settings, provenance-aware mappings involve extending ordinary mappings—such as GLAV (Global-Local-As-View) rules or OBDA (Ontology-based Data Access) mappers—to propagate semiring or annotation tokens expressing provenance polynomials or graphs. This enables symbolic reconstruction of derivations, source contributions, and transformation lineage at the semantic level (Calvanese et al., 2019, Sen et al., 16 Apr 2025).

2. System Architectures and Data Models

Provenance-aware mapping systems are generally composed of these principal modules:

• Provenance Collectors: Plugins or wrappers that instrument workflow engines (e.g., Pegasus) or database planners (e.g., PostgreSQL extension as in ProvSQL), capturing process metadata such as job identifiers, parameters, logs, and host identifiers.
• Infrastructure Metadata Harvesters: Adapters that query the underlying resource management layer (cloud APIs, schedulers, IaaS providers) for runtime configurations—flavor (RAM, CPU, disk), image IDs, and network identifiers.
• Provenance Stores: Relational or semistructured stores with schemas that explicitly encode mappings between process-level jobs and infrastructure instances, indexed for efficient query and join operations (Hasham et al., 2018, Hasham et al., 2015, Hasham et al., 2015).
• Mapping Aggregators: Logic for joining or integrating process and resource provenance based on explicit join keys (e.g., host IPs, job IDs) and emitting “cloud-aware” or “resource-annotated” provenance records.

A typical relational schema in workflow settings includes tables mapping $(\text{workflow\_job\_id}, \text{hostIP})$ to $(\text{flavor}, \text{image})$ and, optionally, contextual metadata (timestamps, execution costs, software checksums).

Representation at the data-model level typically employs the Open Provenance Model (OPM) or the W3C PROV-O standard (Berriman et al., 2010, Shamdasani et al., 2015, Prudhomme et al., 2 Aug 2024). Entities (files, parameters), Activities (procedures, jobs), and Agents (users, compute sites) are linked by binary relationships such as wasGeneratedBy, used, and wasControlledBy, which are preserved or surfaced in the mapping process.

3. Mapping Algorithms and Instrumentation Strategies

The computation of provenance-aware mappings varies with system constraints and domain requirements but follows a common pattern:

1. Extraction: For each process, extract provenance metadata (job ID, inputs/outputs, host IP) and, asynchronously or synchronously, extract infrastructure metadata (VM IP, flavor, image).
2. Mapping/Join: Implement $M(p) = i$ if and only if $p.\text{hostIP} = i.\text{VM\_IP}$, yielding a set of augmented records mapping each process instance to its exact runtime configuration. In the cloud workflow context, this mapping supports exact reprovisioning (Hasham et al., 2015, Hasham et al., 2015, Hasham et al., 2018).
3. Annotation Propagation in Data Systems: For database queries, instrument the operator graph (selection, projection, join, aggregation, etc.) to propagate provenance annotations through semiring arithmetic (sum/product for alternate/conjoint derivations) and aggregate them per tuple in the output (Sen et al., 16 Apr 2025, Calvanese et al., 2019). Query rewriting injects annotation columns and pushes provenance computation through the relational plan, respecting the algebraic semantics.
4. Dynamic/On-demand Environments: Strategies such as direct (static pool), contextual (eager capture), and hybrid (lazy log parsing) address runtime environments with ephemeral resources or auto-scaling, using join on host IP and creation time to disambiguate mappings (Hasham et al., 2018).

In all settings, careful instrumentation and precise join keys are critical: failure to capture sufficient detail (image hash, kernel version, library checksums) can break reproducibility guarantees.

4. Applications: Scientific Workflows, Databases, and Ontologies

The primary application of provenance-aware mapping is in enabling reproducible, auditable, and queryable science:

• Cloud-Based and Grid Scientific Workflows: Systems such as ReCAP and affiliated prototypes in astronomy/neuroscience map each workflow job to the cloud instance on which it executed, capturing resource configurations. This allows for exact re-provisioning of infrastructure in repeat workflows, closing the provenance loop and restoring runtime accountability otherwise lost in elastic, non-persistent environments. For example, workflows lacking sufficient RAM consistently fail on "tiny" flavors, motivating the need for capturing hardware parameters as part of the mapping (Hasham et al., 2018, Hasham et al., 2015).
• Database Query Provenance: Systems like ProvSQL and GProM map data-intensive queries into provenance-annotated queries, propagating tuple identifiers and their algebraic combinations through each relational operator, and representing output provenance as shared circuit DAGs. This enables why- and where-provenance queries, provenance-driven debugging, and uncertainty quantification (Sen et al., 16 Apr 2025, Niu et al., 2018).
• Semantic Interoperability via Ontology Mapping: Cross-domain mapping is achieved by aligning provenance ontologies such as PROV-O to upper ontologies (BFO, CCO) using strong semantic relations (equivalentClass, subClassOf, SWRL rules). These alignments support interoperability, conservativity, and logical coherence, making provenance data interoperable across domains such as bioinformatics and earth sciences (Prudhomme et al., 2 Aug 2024).

5. System Requirements, Standards, and Evaluation

Provenance-aware mapping imposes rigorous requirements on system architecture:

• Reproducibility: Mapping must convey sufficient detail to enable workflow re-execution with bit-for-bit identical results, including infrastructure details, software versions, command-line arguments, and execution environment. Any omission (e.g., non-captured in-VM libraries) undermines exact repeatability (Berriman et al., 2010, Hasham et al., 2015, Hasham et al., 2018).
• Performance and Scalability: Indexing, aggregation, and join efficiency in the provenance store are critical for real-world workflows (tens of thousands of jobs per run). Mapping algorithms generally impose negligible overhead (<1–2%), and DAG-based provenance representations scale linearly with query and data size (Hasham et al., 2018, Sen et al., 16 Apr 2025).
• Standards Compliance: Use of W3C PROV, OPM, and semiring annotation frameworks ensures compatibility with external tools and analytics pipelines.
• Queryability: RESTful and/or SQL interfaces for provenance stores enable flexible lineage queries—tracing artifacts to algorithm versions, resource configurations, or original raw data.
• Extensibility: System architectures must admit new mapping targets (e.g., containers, orchestration systems) and evolve schemas as needed without system redesign.

Typical evaluation metrics include mapping overhead, reprovisioning success rate, and provenance query performance. Prototype evaluations demonstrate high mapping fidelity and negligible runtime costs, with reproducibility confirmed via MD5 or bitwise file comparisons (Hasham et al., 2015, Hasham et al., 2018).

6. Limitations, Open Challenges, and Future Directions

Despite significant progress, provenance-aware mapping faces several open challenges:

• Ephemeral Infrastructure and Dynamism: Real-world cloud deployments may involve auto-scaling, spot-instance usage, or preemptibles. Mapping strategies must accommodate highly dynamic resource pools, necessitating continual VM-catalogue maintenance and reconciliation (Hasham et al., 2018).
• Software Environment Drift: The mapping function generally does not capture dynamic in-VM changes (library installs, post-boot configuration) unless augmented with deeper instrumentation (e.g., container-level or OS-level tracepoints).
• Provenance Graph Summarization and Querying: As provenance graphs grow to terabyte scale, effective summarization, abstraction, and responsive querying become challenging open problems (Berriman et al., 2010).
• Cross-Domain Interoperability: Alignments between provenance models (e.g., PROV-O and BFO) remain semantically non-trivial, and fully synonymous alignments (i.e., mapping every concept by equivalence) are generally impossible without inventing intermediate classes, which may compromise conservativity. Consistency must be checked over canonical instance suites (Prudhomme et al., 2 Aug 2024).
• Privacy and Security in Provenance: Fine-grained mapping and disclosure may conflict with data sensitivity, motivating research into obfuscation, controlled disclosure, and provenance slicing (Acar et al., 2013).
• Integration with Streaming and Dynamic Data: The combination of static workflow provenance and continuously emerging streaming data remains an unresolved integration point (Berriman et al., 2010).

A plausible implication is that, as data science systems grow more complex and infrastructure more elastic, provenance-aware mapping will increasingly serve as a foundation for trustworthy, automated science. Continued adoption of open standards, modular architectures, and semantically rigorous mappings remains essential for scalable, interoperable, and auditable provenance systems.