---
title: AdProv Method for Adaptation Provenance
url: https://www.emergentmind.com/topics/adprov-method
type: topic
---

# AdProv Method for Adaptation Provenance

Searching arXiv for the cited AdProv paper and closely related provenance/workflow context.
AdProv is a method for collecting, storing, retrieving, and visualizing the provenance of process and workflow adaptations, with a particular emphasis on runtime modifications to running instances. It is introduced as an adaptation-centric provenance framework that treats changes to a workflow as first-class provenance subjects rather than as incidental by-products of ordinary execution logging. In this formulation, provenance does not end with recording what activities executed and what data was produced; it also encompasses what was changed in the process, where the change occurred, who initiated it, and when it was made [2510.05936]. The method is positioned for both business processes and scientific workflows, where adaptation provenance supports, respectively, correctness, compliance, security, privacy, process mining, reproducibility, and understanding exploratory workflow evolution.

## 1. Scope and problem addressed

AdProv addresses the lack of systematic provenance support for workflow and process adaptations, especially runtime modifications applied during execution. The paper distinguishes between evolutionary changes, which affect future instances of a process model, and ad-hoc or runtime changes, which affect running instances. Its primary concern is the second category: instance-level adaptations made while execution is already underway [2510.05936].

The motivation is that ordinary provenance approaches typically focus on workflow models, execution traces, inputs and outputs, or data lineage. Those representations are insufficient when the process itself is changed at runtime. In adaptive settings, provenance must include not only what activities executed and what data flowed, but also what was changed in the workflow, where in the control flow it was changed, who made the change, when the change was made, and optionally what impact the change had. AdProv is therefore not merely an execution logging method; it is a method for preserving the provenance of how execution was altered.

The paper situates this gap across both BPM and scientific workflow research. In BPM, prior work is described as concentrating more on change detection, change logs, and process mining for improvement than on provenance of adaptations as a primary concern. In scientific workflows, provenance is more mature, but adaptation provenance is still not treated as a first-class provenance object. The method is presented as a response to this absence of systematic support for runtime workflow adaptation provenance [2510.05936].

## 2. Method perspective and conceptual foundations

AdProv is explicitly defined as a method, developed using method engineering ideas. Its stated goal is to ensure the provenance of adaptations of running processes and workflows. Concretely, it prescribes steps to capture provenance of adaptations, store provenance information, retrieve provenance information, and visualize provenance information [2510.05936].

The perspective is adaptation-centric. The cooperating participants are primarily software agents and components rather than human actors: workflow engines or information systems producing execution events, software that detects changes and creates change events, the Provenance Holder, retrieval components, and visualization tools. This framing matters because AdProv is not only a conceptual model but also a systems-oriented method with data structures, exchange formats, and service architecture.

The paper identifies the following method components: a process model, process execution events, change events, process logs, process instance state or process trace, a data model for complete provenance information, a data model for adaptation provenance information, a Provenance Holder service architecture, and a visualization tool and mapping model. This suggests that AdProv is intended as an end-to-end provenance management framework rather than a single schema or logging convention [2510.05936].

A central conceptual contribution is the notion of a change event. A change event enriches ordinary execution events with adaptation-specific information. The paper specifies that change events must contain the type of adaptation, the location where the adaptation occurred, the adaptation itself, the initiator, and the timestamp of making the change, explicitly distinguished from the time of execution. Optionally, change events may include the impact of the change and other supplementary information. This change-event structure is the operational core of the method [2510.05936].

## 3. Adaptation, change events, and supported adaptation types

An adaptation in AdProv is a modification applied to a workflow or process, especially during execution. The method is primarily concerned with runtime and ad-hoc changes affecting one or some running instances. For implementation purposes, the paper records two control-flow adaptation types: insert and delete. The rationale given is that, according to process adaptation patterns, all process changes can be decomposed into combinations of insert and delete [2510.05936].

The change event is the principal provenance-bearing construct. It captures the adaptation as an event with explicit metadata. The required fields are concise enough to support interoperability while still making the adaptation semantically meaningful:

| Change-event element | Role |
|---|---|
| adaptation type | e.g. `insert`, `delete` |
| location | where the adaptation occurred |
| adaptation itself | what was changed |
| initiator | who made the change |
| timestamp of making the change | when the change was made |

The paper also discusses actors, artifacts, activities, timestamps, dependencies, and execution context largely through PROV-style concepts. Actors or initiators are the resources or persons responsible for execution or change. Activities include both ordinary process activities and adaptation actions. Artifacts are represented through logs and provenance entities. Dependencies are expressed semantically through relations among activities, entities, and agents. Versioning, however, is not formalized as a dedicated version model in AdProv; it remains implicit [2510.05936].

The restriction to insert and delete should not be mistaken for a claim that only two real-world changes exist. Rather, the paper states that these two control-flow adaptation types are sufficient for implementation because broader process changes can be decomposed into them. This suggests a normalized representation of change rather than an exhaustive ontology of adaptation categories.

## 4. Method flow and operational phases

AdProv is organized into five high-level phases: produce process execution events, collect provenance information, store provenance information, retrieve provenance information, and visualize provenance information. These phases define the method’s lifecycle [2510.05936].

The first phase produces process execution events. These events may originate from workflow engines, general-purpose information systems, or other software systems that capture execution state. For AdProv to work, adaptations must either be explicitly represented in the execution events or be inferable from them.

The second phase, collection, admits two paths. If the environment already emits explicit adaptation events, those can be consumed directly and translated into the internal provenance model. If not, change events must first be identified. The paper gives two options for this implicit path: model-versus-instance comparison in PAIS-style systems, and mining-based identification from logs using process mining, anomaly detection, process drift detection, or change mining methods. In either case, identified changes are translated into the format expected by the provenance infrastructure [2510.05936].

The store phase records all execution events and change events as provenance information. The storage model is left as a design decision, but the paper recommends a structure close to PROV-O or PROV-DM, and notes that the realization stores events in a structure resembling PROV-DM.

The retrieve phase is user-triggered and may target one process instance or multiple instances. Retrieval can return provenance close to the storage model or transform it into another representation; the paper recommends PROV-O-compatible output for semantic interoperability.

The final phase visualizes either adaptation provenance alone or full execution provenance including adaptations. Because retrieval is aligned with PROV-O, standard provenance visualization tools can be used [2510.05936].

A plausible implication is that AdProv deliberately separates provenance acquisition from provenance interpretation. The method does not require a single logging substrate or a single visualization backend, provided that the intermediate provenance representation remains semantically interoperable.

## 5. XES extension and Provenance Holder architecture

The method assumes process execution events are available in XES and extends XES with adaptation-specific semantics. Ordinary XES is described as insufficient for runtime adaptation provenance, so the paper defines an Adaptation XES extension. The extension supports recording adaptation type, location, adaptation content, initiator, and timestamp of making the change, with optional impact and related metadata [2510.05936].

This extension is not presented merely as a serialization convenience. It is the logging mechanism that makes runtime adaptations representable as provenance-bearing records within event-centric process infrastructures. In the running example of an online shopping workflow, the original process contains `Add item to cart` and `Checkout`, and a runtime adaptation inserts `Go to cart`. The adapted XES log therefore contains ordinary execution events, an execution event for the inserted activity, and additional information about type of change, initiator, and time of making the change [2510.05936].

The architectural realization of AdProv is the Provenance Holder. The paper identifies three main component types: Controller, Adapter, and one or more Provenance Providers. The Adapter is the service interface and exposes two external operations: collecting provenance data and retrieving provenance information. It is split into a standardized service interface side and a customizable integration side. The Controller orchestrates four internal methods—Record, Retrieve, Validate, and Migrate. Provenance Providers are backend components that must implement record, retrieve, and migrate operations [2510.05936].

The event flow is described as follows: workflow or process environments emit execution events; if available, change events are emitted directly; otherwise, a change-identification step derives them; the Adapter receives these events; the Controller validates and routes them; the Provider records them; later, retrieval requests return provenance in PROV-compatible form for visualization. The paper does not prescribe a specific DBMS or query language, which indicates an intentionally pluggable storage layer.

This architecture is significant because it turns AdProv from a purely descriptive method into an implementable service pattern. The method is not confined to theoretical modeling of provenance semantics; it includes an operational boundary for integrating with workflow systems and retrieving adaptation provenance on demand.

## 6. Mapping to PROV-O and interoperability

To ensure semantic consistency and interoperability, AdProv defines a mapping to PROV-O and PROV-DM. The paper gives explicit conceptual alignments: process activities and changes map to PROV-O Activities, resources in process instances map to PROV-O Agents, and event log elements map to PROV-O Entities. It further states that PROV-O Activities are actions that write into event log elements, which are modeled as Entities [2510.05936].

In the online shopping example, the provenance visualization contains process activities `AddItemToCart`, `GoToCart`, and `Checkout`, as well as the adaptation action `InsertActivity`. These are modeled as PROV-O activities. Agents include `ResourceA`, associated with execution of process activities, and `PersonA`, associated with the insertion adaptation. The inserted activity `GoToCart` is related to `InsertActivity`, expressing that it was inserted at runtime [2510.05936].

The paper explicitly exemplifies `prov:wasAssociatedWith` semantics for activities carried out by resources or persons. It does not, however, provide a full mapping table, RDF snippets, OWL axioms, or SPARQL examples. This is an important boundary on what can be claimed. The mapping is semantically clear at the level of class alignment and example relations, but not fully formalized as a complete ontology-to-log translation specification.

This suggests that AdProv prioritizes interoperability at the representational level rather than complete ontology engineering. The use of PROV-O allows adaptation provenance to be visualized and exchanged with existing provenance tools, while the XES extension preserves compatibility with process mining ecosystems.

## 7. Example, significance, and limitations

The running example is a simple online shopping workflow in which `Go to cart` is inserted at runtime between `Add item to cart` and `Checkout`. AdProv captures the original execution events, the execution event for the inserted activity, and adaptation metadata such as change type, initiator, and timestamp of making the change. After storage and retrieval, the provenance graph shows the execution activities, the insertion activity, the relation between inserted activity and insertion action, and the responsible agents [2510.05936].

The significance of this example is methodological rather than empirical. It demonstrates that AdProv can represent not only what happened in a process instance but also that the instance itself was changed during execution, who changed it, and when. In BPM settings, this supports checking correctness of changes, compliance verification, and learning from past adaptations. In scientific workflows, it supports understanding workflow evolution, reproducibility, trust, and FAIR and RARE research practices [2510.05936].

The evaluation in the paper is a feasibility discussion rather than a benchmark study. The paper does not provide performance measurements, storage overhead, precision or recall of change detection, or comparative user studies. It also notes that suitable visualization may differ by domain and remains an open issue. Further limitations include the restriction to insert and delete control-flow adaptations, the absence of a full formal model of adaptation provenance, the lack of a complete PROV-O mapping table, and the absence of a formal versioning model [2510.05936].

The novelty of AdProv lies in the combination of method, change-event concept, XES extension, PROV-O mapping, and Provenance Holder architecture, all oriented specifically toward provenance of process adaptations rather than ordinary workflow execution provenance. Existing approaches may support workflow execution provenance, change detection, or evolution provenance, but AdProv is presented as a dedicated method and framework for runtime adaptation provenance [2510.05936]. This suggests that its principal contribution is not a new mining algorithm or ontology in isolation, but an integrated provenance-management method for adaptive workflows across business and scientific domains.

Source: https://www.emergentmind.com/topics/adprov-method