# A Provenance Maturity Model

Kerry Taylor, Robert Woodcock, Susan Cuddy, Peter Thew, David Lemon

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Kerry Taylor, Robert Woodcock, Susan Cuddy, Peter Thew, David Lemon. A Provenance Maturity Model. 11th International Symposium on Environmental Software Systems (ISESS), Mar 2015, Melbourne, Australia. pp.1-18, 10.1007/978-3-319-15994-2_1 . hal-01328521

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<figure><img src="image_1.png" /></figure>
A Provenance Maturity Model

Kerry Taylor¹², Robert Woodcock³, Susan Cuddy⁴, Peter Thew¹ and David Lemon⁴

¹ CSIRO Digital Productivity, Canberra, Australia

² Australian National University, Canberra, Australia

³ CSIRO Mineral Resources, Canberra, Australia

⁴ CSIRO Land and Water, Canberra, Australia

firstname.lastname@csiro.au

**Abstract.** The history of a piece of information is known as "provenance". From extensive interactions with hydro-and geo-scientists in Australian science agencies we found both widespread demand for provenance and widespread confusion about how to manage it and how to develop requirements for managing it.

We take inspiration from the well-known software development Capability Maturity Model to design a Maturity Model for provenance management that we call the PMM. The PMM can be used to assess the state of existing practices within an organisation or project, to benchmark practices and existing tools, to develop requirements for new provenance projects, and to track improvements in provenance management across an organisational unit.

We present the PMM and evaluate it through application in a workshop of scientists across three data-intensive science projects. We find that scientists recognise the value of a structured approach to requirements elicitation that ensures that aspects are not overlooked.

**Keywords:** provenance · reproducibility · lineage · pedigree · requirements

# 1 Introduction

As the trend towards data-intensive cyberscience picks up pace, scientists are becoming increasingly concerned about data provenance. As consumers of data, scientists need to know: Where did this data come from? Is it good enough for me to use? Can I trust it? As producers of data and expert opinion, scientists need to ensure their results are scientifically credible, repeatable, and justified by the methods used and the reasoned interpretations made.

Knowledge of the history of a piece of information (where it came from, what it was generated for, and the workflow that generated it) is known as "provenance", although the terms "audit trail", "lineage" and "pedigree" are common synonyms. Understanding the provenance of a piece of information can be as important as the information itself. Using provenance, it should be possible to understand whether or not a piece of information is fit for the intended purpose or whether the information should be trusted.

While these general concerns are quite widespread, implementing software systems for long-term provenance management needs much more thought about requirements for the business context, capture, representation and storage, retrieval, and usability. From extensive interactions with hydro-and geo-scientists in Australian science agencies we found widespread confusion around how to move from high level descriptions of outcomes towards statements of requirements for selection of tools and methods for implementation. This problem is magnified in multidisciplinary science projects.
In this paper a novel tool for developing requirements for provenance is presented, the Provenance Maturity Model (PMM). The PMM has been developed together with a method for provenance requirements elicitation that is not further discussed here. It has been specifically developed for a context of multiple, disconnected stakeholders who are generally unaware of the drivers, challenges and tradeoffs of provenance management, but it can be useful in any scenario where an implementation of provenance management is required, such as hydrological modelling [15], agricultural research [3], emergency management [13] and chemistry lab notebooks [2]. The PMM can also be used to classify existing or aspirational tools and approaches to provenance to aid in tool selection after requirements are established.

Our development of the PMM was driven by our experience in the Bioregional Assessment program of the Australian Government; a program to understand the potential impacts of coal seam gas and large coal mining on water resources and water-related assets. It is a complex inter-governmental resource development decision-making process, with expectations of significant long term commercial and public interest in the decisions to be made. While there was very strong awareness of the necessity for provenance amongst the stakeholders, we struggled to discuss the breadth and depth of the impact of simple requirements statements. Elements of *maturity* have a major impact on the cost, distance from current practice, the ability to capture, and the ability to interoperate over organisational and disciplinary boundaries.

The content of the PMM was assembled by the authors' analysis of remarks and expectations of stakeholders in that and previous resource-exploitation scenarios. A survey of the research literature was also used to insert capabilities and maturity points that we may have missed. The PMM then provided a way to hold the conversations that were necessary to understand these issues where previously we had role, process and scope confusion. We needed the additional dimension of maturity to facilitate the conversation along with a context of cost-benefit and risk.

## 2 The PMM

Like the Capability Maturity Model (CMM) [12], the PMM contains a matrix of capabilities described at five levels of maturity. The capabilities are grouped into *Provenance Business*, *Data Management*, *Provenance Capture*, *Provenance Representation and Storage*, *Provenance Retrieval*, and *Usability*.

<table><caption>Table 1: PMM Capabilities(Rows)</caption><tr><td>PROVENANCE BUSINESS</td><td>This category relates to the adoption and commitment to provenance management. It also contains a grab bag of issues that need to be addressed for any particular provenance venture</td></tr><tr><td>Longevity</td><td>How long are provenance records designed to be kept?</td></tr><tr><td>Software maturity</td><td>What is the maturity of the software being used; for example does it have a known history of usage elsewhere or is it unknown to the organisation?</td></tr><tr><td>Organizational awareness: culture and behaviour</td><td>Is there a culture that understands and appreciates provenance?</td></tr><tr><td>Value recognition</td><td>Does the organisation care about provenance?</td></tr><tr><td>Governance</td><td>How formalised is the approach to provenance?</td></tr><tr><td>Perspective</td><td>The social extent of provenance sharing and interpretability</td></tr></table>
<table><tr><td>Transaction costs: Production vs. retrieval cost</td><td>How to balance the cost of detailed record keeping versus the cost of collection recording to provide an answer to a query</td></tr><tr><td>DATA MANAGEMENT</td><td>This category of capabilities relates not so much to provenance as to the management of the underlying data over which provenance operates. It is included here because certain features are necessary preconditions to provenance maturity</td></tr><tr><td>Data safety</td><td>The resilience of data (that is the subject of provenance) to failures in hardware and/or processing errors</td></tr><tr><td>Data versioning</td><td>Is there a scheme to identify data versions?</td></tr><tr><td>Data lineage</td><td>Is information kept to identify the source of data?</td></tr><tr><td>Identifier Management</td><td>Is the underlying data unambiguously identified?</td></tr><tr><td>Digital Preservation</td><td>How well preserved are the digital artefacts that are the subject of provenance?</td></tr><tr><td>PROVENANCE CAPTURE</td><td>This category refers to the processes, methods and tools for initial capture, or record-keeping, of provenance</td></tr><tr><td>Decision making and consultation</td><td>What records are kept on decisions made by people and groups and how integrated are these records to a provenance database?</td></tr><tr><td>Temporal scope (Start and end points)</td><td>In the scope of a process, when do we start capturing provenance and when does it end?</td></tr><tr><td>Granularity of capture</td><td>The smallest unit of a process that generates a provenance record - from a complete workflow down to individual executables and commands</td></tr><tr><td>Temporality/Currency</td><td>When was the provenance record made with respect to the execution of the process: during the process step-by-step, at specific stages or after the fact?</td></tr><tr><td>Software tools used in process</td><td>What information is captured on software used within a workflow & the workflow system itself; for example version number?</td></tr><tr><td>Hardware/platform</td><td>Information on the hardware environment when a workflow is executed</td></tr><tr><td>Provenance capture integration</td><td>How integrated is the collection of provenance records in the process execution? For example does the carrying out of the process generate provenance transparently or are the provenance records obtained by post-processing log files or independent data entry?</td></tr><tr><td>Provenance quality</td><td>How trustworthy is the provenance record that is kept?</td></tr><tr><td>Sophistication of automation</td><td>How automated is the capture of provenance record; for example manually or embedded into workflow processes</td></tr><tr><td>PROVENANCE REPRESENTATION & STORAGE</td><td>These capabilities discuss the static aspect of provenance information; in between creating it and using it</td></tr><tr><td>Provenance format</td><td>Do the provenance records adhere to a standard, and is that standard an international one or bespoke to the organisation?</td></tr></table>
<table><tr><td>Provenance language</td><td>How formalised is the content of the provenance records; for example free text or a strictly controlled language (with specific meanings)</td></tr><tr><td>Provenance security</td><td>Can the provenance records be validated or can they be corrupted or altered after collection; is there a way to determine if corruption has occurred?</td></tr><tr><td>PROVENANCE RETRIEVAL</td><td>These capabilities address the methods and support for obtaining provenance information</td></tr><tr><td>Provenance availability</td><td>How easy it is for people (or systems) to access the provenance records?</td></tr><tr><td>Provenance discovery</td><td>How easy is it to find the particular provenance record they need?</td></tr><tr><td>Licence to use provenance</td><td>Are the conditions of use of the provenance itself well understood?</td></tr><tr><td>PROVENANCE USABILITY</td><td>These capabilities refer to how provenance can be, or is, used: what is it all for?</td></tr><tr><td>Human readable</td><td>What attention is given to supporting the human interpretation of the provenance record?</td></tr><tr><td>Repeatable by automation</td><td>Can a workflow be repeated using the provenance records to identify all components and parameters that were used in the original workflow?</td></tr><tr><td>Reusable, that is, repeatable with improvement</td><td>Can a workflow be repeated using the provenance records but also allow deliberate substitutions; for example, an updated model or new dataset?</td></tr><tr><td>Transparent</td><td>Can you see what judgement decisions were made, and why?</td></tr><tr><td>Answerable (variation in results can be explained)</td><td>Can the provenance records identify the component that is the source of error or difference within a workflow with respect to an alternative?</td></tr><tr><td>Cross-disciplinary application</td><td>Is everyone talking the same language? Are they being forced inappropriately to talk the same language?</td></tr></table>

The five levels of maturity (columns) are labelled and described as follows where the original CMM title *Repeatable* has been replaced with *Tactical* to avoid conflicts with the use of that term in the study of provenance. The descriptions of each level have been modified to be more appropriate for provenance.

**Initial (Chaotic)** It is characteristic of provenance treatment at this level that it is (typically) undocumented and in a state of dynamic change, tending to be driven in an ad hoc, uncontrolled and reactive manner by users or events. This provides a chaotic or unstable approach to provenance management and provenance services and certainly implies that any services developed will be of very low functionality and scope.

**Tactical** It is characteristic of provenance treatment at this level that many aspects of data production and management are carried out with record-keeping in mind. Some attention is being made to ensure that identified processes are repeatable in some circumstances, possibly with consistent results. Discipline is unlikely to be rigorous, but where it exists it may help to ensure that following an audit trail is possible under stress.

**Defined** It is characteristic of provenance treatment at this level that there are sets of defined and documented standard processes established and routinely followed. These standard pro-
cesses both require and enable standard tools to be developed and used. Such tools both assist in the implementation of the processes and offer provenance services to derive value from provenance across the organization or community.

**Managed** It is characteristic of provenance treatment at this level that it has become uncontroversial: moved into the background as well-managed practices that are embedded in the fabric of business, travelling smoothly from project to project. It is consistently and effectively controlled and widely used.

**Optimising** It is a characteristic of provenance management at this level that the focus is on continually improving performance of provenance management itself as a lever for continuous improvement of performance of the underlying scientific or administrative processes, through both incremental and innovative technological improvements. Provenance is a highly valued component of business delivering transparency, accountability and knowledge management.

The full PMM matrix of 33 capabilities by 5 maturity levels is included at the end of this paper.

# 3 Evaluation

The PMM was used for requirements elicitation for three projects of two government science agencies in August 2013, over a two-day workshop. Background material was provided, including the PMM evaluation of some existing tools, many of which were known to the participants, such as ISO-19115-LE [6] and Prov-O [7]. The workshop included a half-day of presentations on the nature of provenance and some known tools and methods; brief presentations on the selected projects' goals; an introduction on the PMM and how to use it; a hands-on application of PMM to the three projects in project groups; a subsequent requirements documentation exercise; a plenary analysis of consequent requirements for the agency as a whole; and the joint development of an architecture sketch for the agency-wide provenance management. Those who were present for the full two days excluding the PMM developers and facilitators, that is 13 people, were surveyed at the beginning and again at the end of the workshop.

The survey participants were invited to respond to 21 questions of which 18 were phrased on a 5-level balanced Likert scale and all 21 included requests for free-text comments. In some cases participants indicated responses either in between or spanning consecutive levels; in all cases these have been treated as if the lowest (i.e. least positive) level was selected.

The lowest-scoring question overall was the pre-workshop question "Please rate your familiarity in dealing with issues or tools relating to provenance (1=not all aware, 5=extremely aware)", for which the minimal response was 1, the maximum 5, and the average 3.2. The highest-scoring question overall was the post-workshop "The approach to provenance management for your project will benefit by the application of the PMM as you have used it in the workshop (1=strongly disagree, 5=strongly agree)". The minimum response was 3 and the maximum was 5, with average 4.8. Other high-scoring questions referred to the contribution of the PMM towards developing user requirements. The lowest-scoring post-workshop question (average 3.7) referred to the ease of determining evaluation criteria, an element of the PMM application methodology that is out of scope for this paper. We can conclude that the participants found the PMM worthwhile.

The participants were also invited to suggest needs for clarification or other improvement to the PMM version that was used in the workshop. The PMM presented here has had some wording and sequencing adjustments since the workshop to take account of those suggestions.
# 4 Related Work

We have evaluated some existing tools and methods with respect to the PMM in order to provide some benchmarks to assist in interpretation of the PMM, and on the other hand, to assist project designers to locate tools that might help them achieve desired maturity levels. In this context, Prov-O [7] features due to its potential contribution towards high-maturity provenance Representation and Storage, therefore also contributing to high maturity Retrieval and Usability. Because of both its underlying flexible graph representation, and the ontology inference coupled with domain-specific ontology extensions, it is possible to use it to traverse widely differing domain-oriented provenance records through semantic, executable mappings to Prov-O, such as described in [3]. This can be done even when the primary record-keeping may be entirely ontology-unaware, such as is enabled by the mapping from ISO19115 Lineage to Prov-O [14]. An alternative ISO19115-driven extension to Prov-O has been developed [9] that would be useful when an early commitment to Prov-O is made. We can envisage the particular utility of the Prov-O graph representation to support dynamic provenance assembly for federated information systems like [16], [1] and [17] that support retrieval of data products or query-answering over compositions of resources. In unpublished work, we are also exploiting the inference capability to support arbitrary provenance comparison, building on the graph matching of [8].

Recent work on provenance for an integrated ecosystem approach to management of large marine ecosystems [5] demonstrates a high level of maturity for Capture, whereby a Web application for the development of data products and charts, tables, and map visualisations also keeps track of steps taken then embeds the provenance in the final PDF report. The related Global Change Information system will demonstrate a high level of maturity for Data Management, particularly for identifier management over a heterogenous contributor community [10].

The notion of *Research Objects* [11], especially computational research objects [4], contributes to a very high level of maturity in Usability, with provenance records very closely tied to executable components for repeatability and reusability and also to the scientific practice.

# 5 Conclusion

We present the Provenance Maturity Model as a part of a structured approach to developing requirements for provenance management in data-intensive science. The PMM lays out many characteristics of provenance management in a matrix where preferred options may be considered and selected in the context of some evaluation criteria. We found that scientists recognise the value of a structured approach to requirements elicitation that ensures the depth and breadth of the issues are considered and that aspects are not overlooked. The value of the framework in clarifying the language in an “industry standard” approach is also appreciated.

We recommend using the PMM in a workshop environment once the scientific content of a project is well enough understood to commence. We recommend developing an evaluation criteria then proceeding to check cells in the PMM by consensus, adding additional rows if necessary. Later, system and software requirements can be developed in a conventional way, with frequent reference to the instantiated PMM. A record of tools and business processes that have previously been benchmarked by the PMM can help to fill in a solution architecture. Much later, the PMM can help to review provenance goals and to consider advancing the maturity.

In future work we would like to re-evaluate the PMM in an alternative cultural, organisational and problem context, and also to track the influence on project results through project life-cycle case studies.

*Acknowledgement* The authors thank the anonymous reviewers and the many collaborators for their questions and insight, especially Neal Evans and Brian Hanisch of Geoscience Australia.
References

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2. Nico Adams, Armin Haller, Alexander Krumpholz, and Kerry Taylor. A semantic lab notebook—report on a use case modelling an experiment of a microwave-based quarantine method. In *Linked Science (LISC2013)*, volume 1116. CEUR proceedings, October 2013.

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4. David De Roure. Towards computational research objects. In *Proceedings of the 1st International Workshop on Digital Preservation of Research Methods and Artefacts*, DPRMA '13, pages 16–19, New York, NY, USA, 2013. ACM.

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<caption>Table 2: The PMM capabilities × Maturity levels</caption>

**PROVENANCE BUSINESS** This category relates to the adoption and commitment to provenance management. It also contains a grab bag of issues that need to be addressed for any particular provenance venture.

<table><thead><tr><th>Capability</th><th>Initial (Chaotic)</th><th>Tactical</th><th>Defined</th><th>Managed</th><th>Optimising</th></tr></thead><tbody><tr><td>Longevity</td><td>No provenance</td><td>Lifetime of a process instance execution</td><td>Lifetime of an identified problem or project to which knowledge is being applied</td><td>Lifetime of a consequent action plan or agreement</td><td>Deliberately unbounded without prescription in advance; i.e. evolutionary</td></tr><tr><td>Software maturity</td><td>Tools are not aware of provenance</td><td>Experimentally developed</td><td>Extensively trialled</td><td>Several alternative implementations available, at least some are robust</td><td>Appropriate products are available and are also supported and maintained</td></tr><tr><td>Organisational awareness: culture and behaviour</td><td>No support for provenance</td><td>Individual or small team initiative</td><td>Major organisational support for trial or development but lacking maintenance commitment</td><td>Policy; Long term commitment supported by explicit funding stream</td><td>Legislation or Regulation</td></tr><tr><td>Value recognition</td><td>No support for provenance</td><td>Value of provenance is recognised in the custodian (asset)</td><td>Value of provenance is exploited opportunistically</td><td>Provenance value exploitation is intrinsic part of business model</td><td>Provenance is recognised as a knowledge base of evolving scientific method and used for continuous improvement processes</td></tr><tr><td>Governance</td><td>No support for provenance</td><td>Implicit (everybody knows it, but it is not formally captured)</td><td>Explicit. Written, formal, contractual management</td><td>Provenance record management plans are comprehensive and followed</td><td>Formal accountability and governance throughout lifecycle established with &amp; between parties</td></tr><tr><td>Perspective</td><td>No support for provenance</td><td>Individual: I know what I did</td><td>Team: We know what we did</td><td>Organisation: we know what our teams did</td><td>Community: we all know what everyone did</td></tr></tbody></table>
<table><thead><tr><th>Capability</th><th>Initial (Chaotic)</th><th>Tactical</th><th>Defined</th><th>Managed</th><th>Optimising</th></tr></thead><tbody><tr><td>Transaction costs: Production vs. retrieval cost</td><td>No support for provenance</td><td>Low cost of production but retrieval is very expensive and slow and not always possible</td><td>Trade-off recognised and accommodated in many cases</td><td>Incremental cost of retrieval close to zero where justified</td><td>Identified cost / benefit habitually used in design decisions</td></tr></tbody></table>

**DATA MANAGEMENT** This category of capabilities relates not so much to provenance as to the management of the underlying data over which provenance operates. It is included here because certain features are necessary preconditions to provenance maturity

<table><thead><tr><th>Capability</th><th>Initial (Chaotic)</th><th>Tactical</th><th>Defined</th><th>Managed</th><th>Optimising</th></tr></thead><tbody><tr><td>Data safety</td><td>No backups</td><td>Data is manually backed up</td><td>Data backup is part of an automated backup process</td><td>Data backups are stored off-site</td><td>Data backups undergo regular restore tests</td></tr><tr><td>Data versioning</td><td>Unknown versions</td><td>Old data replaced by new data. Version identification associated with new data</td><td>Old data is archived and associated with version identification.</td><td>Version control of collections</td><td>Data elements (e.g database tuples) dated and annotated with provenance</td></tr><tr><td>Data licensing</td><td>Unknown</td><td>Licence conditions are recorded, or a system wide standard licence applies</td><td>Licence need not be system-wide and conditions are retrievable from point of access to data</td><td>Licence need not be system-wide and conditions are retrievable from point of access to data</td><td>Flexible licencing policies can be expressed and computationally validated in the context of the intended use of the data</td></tr><tr><td>Identifier Management</td><td>None</td><td>Locally-scoped identifiers are assigned, possibly through a file-naming convention</td><td>Systematic, unique identifier assignment, crossing datatypes and technology platforms, supporting retrieval of identified objects</td><td>Systematic, globally unique and resolvable identifier management</td><td>International standards followed; resolvable identifiers maintained over time</td></tr></tbody></table>
<table><thead><tr><th>Capability</th><th>Initial (Chaotic)</th><th>Tactical</th><th>Defined</th><th>Managed</th><th>Optimising</th></tr></thead><tbody><tr><td>Digital preservation</td><td>None</td><td>Some project materials and output data are archived</td><td>Systematic data preservation mechanisms are in place for identified strategic data</td><td>Digital preservation strategies extend to digital artefacts such as software and minuted decisions</td><td>Digital preservation strategies follow international standards and are regularly reviewed for scope, best practice, and longevity</td></tr></tbody></table>

**PROVENANCE CAPTURE** This category refers to the processes, methods and tools for initial capture, or record-keeping, of provenance

<table><thead><tr><th>Capability</th><th>Initial (Chaotic)</th><th>Tactical</th><th>Defined</th><th>Managed</th><th>Optimising</th></tr></thead><tbody><tr><td>Decision making and consultation</td><td>No records</td><td>Decision-taking meetings and individual choices in process are documented and justified</td><td>Documentation retrievable from multiple access points</td><td>Judgement choices are entirely transparent and fully integrated into provenance record and services</td><td>Reasoning services incorporated e.g. to identify decisions that follow policy (or not)</td></tr><tr><td>Temporal scope (Start and end points)</td><td>No provenance</td><td>Case-by-case; driven by other procedural concerns</td><td>Defined and applied at project level with end purposes in mind</td><td>System-wide principles for provenance established that determine scope</td><td>Generally lifecycle-complete, but entirely adaptable to cases without loss of verity</td></tr><tr><td>Granularity of capture</td><td>Undetermined</td><td>Coarse grained. Resources and methods are loosely described</td><td>High-level components captured but not all well described in terms of role or properties</td><td>Low-level components captured from the point of view of decisions made, steps taken, tools and data used</td><td>Granularity of capture is driven by understanding of future requirements</td></tr><tr><td>Temporality / Currency</td><td>Provenance not captured</td><td>Reconstructed on demand, after the fact</td><td>Constructed on demand from contemporary notes</td><td>Real time production but decaying record</td><td>Designed for long term storage and interpretation</td></tr></tbody></table>
<table><thead><tr><th>Capability</th><th>Initial (Chaotic)</th><th>Tactical</th><th>Defined</th><th>Managed</th><th>Optimising</th></tr></thead><tbody><tr><td>Software tools used in process</td><td>Untraceable</td><td>Identified by commonly understood monikers</td><td>Versions, dates and providers rigorously identified</td><td>Originals archived with descriptions, including metadata</td><td>Fully integrated into provenance services</td></tr><tr><td>Hardware / platform</td><td>Untraceable</td><td>Identified by commonly understood monikers</td><td>Versions, dates and providers rigorously identified</td><td>Originals archived with descriptions, including metadata</td><td>Fully integrated into provenance services</td></tr><tr><td>Provenance capture integration</td><td>No integration</td><td>Provenance is created by separate processes, usually running in parallel to workflows</td><td>Integration into tools and workflows</td><td>Standards based capture from tools and workflows</td><td>Plug and play with whatever is needed, tracking provenance</td></tr><tr><td>Provenance quality</td><td>Unknown</td><td>Unreliable or partial; user feedback collected and published</td><td>Measured occasionally; quality may be inferred from other attributes such as author, date</td><td>Measured routinely, quality limits and impact are understood</td><td>Effective methods to detect and improve bad provenance in place. All provenance is trustworthy; or trustworthiness is well documented</td></tr><tr><td>Sophistication of automation</td><td>None / manual</td><td>Policies for collection are established and followed but interpretations are localised</td><td>Habitual recording in a systematic (e.g. tabular) way in identifiable documents that are systematically archived and validated</td><td>Capture is embedded in data- and decision-processing software; some aspects demanding operator input</td><td>Integrated into the culture and toolsets</td></tr></tbody></table>
**PROVENANCE REPRESENTATION & STORAGE** These capabilities discuss the static aspect of provenance information; in between creating it and using it

<table><thead><tr><th>Capability</th><th>Initial (Chaotic)</th><th>Tactical</th><th>Defined</th><th>Managed</th><th>Optimising</th></tr></thead><tbody><tr><td>Provenance format</td><td>No provenance</td><td>Provenance may be mentioned in key papers; simple schemes like filename conventions and time stamps may be used</td><td>Formal standard for provenance format adopted and practised at key places</td><td>Flexible standard or tool-dependent formats prescribed and followed according to minimal capture granularity</td><td>Interoperable mappings over multiple formats implemented; may rely on overarching standard</td></tr><tr><td>Provenance language</td><td>None</td><td>Interpretation relies on natural language methods</td><td>Information is captured by link to controlled vocabulary with glossary</td><td>Semantics is captured by link to a formal ontology</td><td>Representations are interpreted for interoperability and adaptation to context of use</td></tr><tr><td>Provenance security</td><td>None</td><td>Original provider of provenance identifiable</td><td>Formal processes for authentication and audit trail</td><td>Provenance is signed and tamper-proof, within an organisation on selected transactions</td><td>Non-repudiation</td></tr></tbody></table>

**PROVENANCE RETRIEVAL** These capabilities address the methods and support for obtaining provenance information.

<table><thead><tr><th>Capability</th><th>Initial (Chaotic)</th><th>Tactical</th><th>Defined</th><th>Managed</th><th>Optimising</th></tr></thead><tbody><tr><td>Provenance availability</td><td>Clueless</td><td>Non-automated; requires judgement</td><td>Database or web page</td><td>Web Service</td><td>Direct availability to analysis and reporting tools; API supporting structured queries</td></tr></tbody></table>
<table><thead><tr><th>Capability</th><th>Initial (Chaotic)</th><th>Tactical</th><th>Defined</th><th>Managed</th><th>Optimising</th></tr></thead><tbody><tr><td>Provenance discovery</td><td>"Phone a friend"</td><td>Retrievable from point of data product identification</td><td>Full text search over provenance records-can retrieve corresponding data through provenance search</td><td>Search by provenance structure and components: can retrieve corresponding data</td><td>Search for provenance patterns: can analyse provenance itself as subject of enquiry</td></tr><tr><td>Licence to use provenance</td><td>Unknown</td><td>Licence conditions are recorded or a system-wide standard licence applies</td><td>Licence need not be system-wide and special conditions are retrievable from point of access to provenance</td><td>Privacy or confidentiality conditions on access to provenance are enforced by tools</td><td>Flexible licensing policies can be expressed and validated computationally at the point of access to provenance</td></tr></tbody></table>

**PROVENANCE USABILITY** These capabilities refer to how provenance can be, or is, used: what is it all for?

<table><thead><tr><th>Capability</th><th>Initial (Chaotic)</th><th>Tactical</th><th>Defined</th><th>Managed</th><th>Optimising</th></tr></thead><tbody><tr><td>Human readable</td><td>No</td><td>Only</td><td>Predominantly, with some machine-processed structure</td><td>Detailed, but detail can obscure meaning</td><td>Presentation tools take account of user perspective and purpose</td></tr><tr><td>Repeatable by automation</td><td>No</td><td>There is some chance that automated sub-processes are repeatable with considerable investment of effort</td><td>Partially automated to the extent that the general method can be reapplied for (typically) different results</td><td>Fully automated, repeatable results is possible in some cases</td><td>Processes are automatically repeatable</td></tr><tr><td>Reusable, that is, repeatable with improvement</td><td>Opaque</td><td>There is some chance (decreasing over time) that sub-processes are repeatable with considerable investment of effort</td><td>Editing of parameters or selected data for rerun is supported</td><td>Processes may be arbitrarily edited or built upon or varied for improved results</td><td>Patterns in provenance are discernible and used for process improvement</td></tr></tbody></table>
<table><thead><tr><th>Capability</th><th>Initial (Chaotic)</th><th>Tactical</th><th>Defined</th><th>Managed</th><th>Optimising</th></tr></thead><tbody><tr><td>Transparent</td><td>Opaque</td><td>Only within small project teams</td><td>Formalised approach to transparency ensures that some decision points are noted and justified</td><td>Methods and tools are unambiguously identified but may not be interpretable by interested parties</td><td>Open access to justified methods and tools for all nominated parties; explanatory capability</td></tr><tr><td>Answerable (variation in results can be explained)</td><td>Impossible to say</td><td>Some sub-processes may be examined to explain variation but confidence is low</td><td>Variation can be attributed to plausible differences based on managed time-stamps or versions</td><td>Failure to reproduce can be diagnosed to identifiably different components</td><td>Automated; diagnosis limited only by original provenance collection granularity; user feedback quality included</td></tr><tr><td>Cross-disciplinary application</td><td>Unsuitable</td><td>Relies on serendipity</td><td>Provenance is available in a widely-used format that may be partly accessible to multiple discipline areas; generally relies on a lowest-common-denominator approach</td><td>Provenance is made available through multiple portals or in multiple formats to suit different discipline areas</td><td>Provenance management works for complex and intractable cross-disciplinary problems; Inter- or trans-disciplinary working is deliberately supported through management of multiple viewpoints</td></tr></tbody></table>