PROV-DM, the PROV data model, is a data model for provenance that describes the entities, people and activities involved in producing a piece of data or thing. PROV-DM is structured in six components, dealing with: (1) entities and activities, and the time at which they were created, used, or ended; (2) agents bearing responsibility for entities that were generated and activities that happened; (3) derivations of entities from entities; (4) properties to link entities that refer to a same thing; (5) collections forming a logical structure for its members; (6) a simple annotation mechanism.

This document introduces a further set of concepts useful for understanding the PROV data model and defines inferences that are allowed on provenance descriptions and validity constraints that well-structured provenance descriptions should follow. These inferences and constraints are useful for readers who develop applications that generate provenance or reason over provenance.

PROV Family of Specifications

This document is part of the PROV family of specifications, a set of specifications defining various aspects that are necessary to achieve the vision of inter-operable interchange of provenance information in heterogeneous environments such as the Web. The specifications are:

How to read the PROV Family of Specifications

First Public Working Draft

This is the first public release of the PROV-CONSTRAINTS document. Following feedback, the Working Group has decided to reorganize the PROV-DM document substantially, separating the data model, from its constraints, and the notation used to illustrate it. The PROV-CONSTRAINTS release is synchronized with the release of the PROV-DM, PROV-O, PROV-PRIMER, and PROV-N documents.


Provenance is a record that describes the people, institutions, entities, and activities, involved in producing, influencing, or delivering a piece of data or a thing. This document complements the PROV-DM specification [[PROV-DM]] that defines a data model for provenance on the Web.


The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this document are to be interpreted as described in [[!RFC2119]].

Purpose of this document

PROV-DM is a conceptual data model for provenance (realizable using different serializations such as PROV-N, PROV-O, or PROV-XML). However, nothing in the PROV-DM specification [[PROV-DM]] forces PROV descriptions to be meaningful, that is, to correspond to a consistent history of objects and interactions. Furthermore, nothing in the PROV-DM specification enables applications to perform inferences over provenance descriptions.

This document specifies inferences over PROV descriptions that applications MAY employ, including definitions of some provenance descriptions in terms of others, and also defines a class of valid PROV descriptions by specifying constraints that valid PROV descriptions must satisfy. Applications SHOULD produce valid provenance and MAY reject provenance that is not valid in order to increase the usefulness of provenance and reliability of applications that process it.

This specification lists inferences and definitions together in one section (), defines the induced notion of equivalence (), and then considers two kinds of validity constraints (): structural constraints that prescribe properties of PROV descriptions that can be checked directly by inspecting the syntax, and event ordering constraints that require that the records in a PROV description are consistent with a sensible ordering of events relating the activities, entities and agents involved. In separate sections we consider additional constraints specific to collections and accounts ( and ).

Question to James: The term 'PROV Instance' seems to have a precise meaning. I read this as a PROV Description Set. Should we define it? Every where it occurs, there is a link to its first occurrence.

The specification also describes how the inferences, definitions, and constraints should be used (). Briefly, a PROV compliant application is allowed (but not required) to treat two PROV descriptions the same if they are equal after applying the inference rules and possibly reordering expressions, and we can define a canonical form for PROV instances obtained by applying all possible inference rules. In addition, a validating PROV application is required to check that the constraints are satisfied in (the normal form of) provenance data generated or consumed by the application.

Finally, the specification includes a section () describing the rationale for the inferences and constraints in greater detail, particularly background on events, attributes, the role of inference, and accounts. A formal mathematical model that further justifies the constraints and inferences is found in [[PROV-SEM]].


The audience for this document is the same as for [[PROV-DM]]: developers and users who wish to create, process, share or integrate provenance records on the (Semantic) Web. Not all PROV-compliant applications need to check validity when processing provenance, but many applications could benefit from the inference rules specified here. Conversely, applications that create or transform provenance should try to produce valid provenance, to make it more useful to other applications.

This document assumes familiarity with [[PROV-DM]].

Inferences and Definitions

In this section, we describe inferences and definitions that MAY be used on provenance data, and a notion of equivalence on PROV descriptions. An inference is a rule that can be applied to PROV descriptions to add new PROV expressions. A definition is a rule that states that a provenance expression is equivalent to some other expressions; thus, defined provenance expressions can be replaced by their definitions, and vice versa.

Inferences have the following general form:

IF hyp_1 and ... and hyp_k THEN there exists a_1 and ... and a_m such that conclusion_1 and ... and conclusion_n.

This means that if all of the provenance expressions matching hyp_1... hyp_k can be found in a PROV description, we can add all of the expressions concl_1 ... concl_n to the instance, possibly after generating fresh identifiers a_1,...,a_m for unknown objects. These fresh identifiers might later be found to be equal to known identifiers; these fresh identifiers play a similar role in PROV descriptions to existential variables in logic.

TODO: Is this re-inventing blank nodes in PROV-DM, and do we want to do this? A lot of the inferences have existentially quantified conclusions (and there is some theory that supports this). TODO: Make sure conjunctive reading of conclusion is clear.

Definitions have the following general form:

defined_exp holds IF AND ONLY IF there exists a_1,..., a_m such that defining_exp_1 and ... and defining_exp_n.

This means that a provenance expression defined_exp is defined in terms of other expressions. This can be viewed as a two-way inference: If defined_exp can be found in a PROV description, we can add all of the expressions defining_exp_1 ... defining_exp_n to the instance, possibly after generating fresh identifiers a_1,...,a_m for unknown objects. Conversely, if there exist identifiers a_1...a_m such that defining_exp_1 and ... and defining_exp_n hold in the instance, we can add the defined expression def_exp. When an expression is defined in terms of others, it is in a sense redundant; it is safe to replace it with its definition.

Component 1: Entities and Activities

Communication between activities is defined in terms as the existence of an underlying entity generated by one activity and used by the other.

Given two activities identified by a1 and a2, wasInformedBy(a2,a1) holds IF AND ONLY IF there is an entity with some identifier e and some sets of attribute-value pairs attrs1 and attrs2, such that wasGeneratedBy(-,e,a1,-,attrs1) and used(-,a2,e,-,attrs2) hold.

The relationship wasInformedBy is not transitive. Indeed, consider the following descriptions.


We cannot infer wasInformedBy(a3,a1) from these expressions. Indeed, from wasInformedBy(a2,a1), we know that there exists e1 such that e1 was generated by a1 and used by a2. Likewise, from wasInformedBy(a3,a2), we know that there exists e2 such that e2 was generated by a2 and used by a3. The following illustration shows a case for which transitivity cannot hold. The horizontal axis represents the event line. We see that e1 was generated after e2 was used. Furthermore, the illustration also shows that a3 completes before a1. So it is impossible for a3 to have used an entity generated by a1.

non transitivity of wasInformedBy
Counter-example for transitivity of wasInformedBy

Start of a2 by activity a1 is defined as follows.

Given two activities with identifiers a1 and a2, wasStartedByActivity(a2,a1) holds IF AND ONLY IF there exists an entity e such that wasGeneratedBy(-,e,a1,-,-) and wasStartedBy(-,a2,e,-,-) hold.

Component 2: Agents

Attribution identifies an agent as responsible for an entity. An agent can only be responsible for an entity if it was associated with an activity that generated the entity. If the activity, generation and association events are not explicit in the description, they can be inferred.
If wasAttributedTo(e,ag) holds for some identifiers e and ag, THEN there exists an activity with some identifier a such that the following statements hold:
activity(a, -, -,-)
wasGeneratedBy(-,e, a, -,_)
wasAssociatedWith(-,a, ag, -, -)

Responsibility relates agents where one agent acts on behalf of another, in the context of some activity. The supervising agent delegates some responsibility for part of the activity to the subordinate agent, while retaining some responsibility for the overall activity.

@@TODO: Could this be an inference? Does it imply that a1 is associated with all activities a2 is associated with?

Component 3: Derivations

Derivations with an explicit activity and no usage admit the following inference:

Given an activity a, entities denoted by e1 and e2, IF wasDerivedFrom(-,e2,e1, a, -) and wasGeneratedBy(-,e2,a,-,-) hold, THEN used(-,a,e1,-,-) also holds.

This inference is justified by the fact that the entity denoted by e2 is generated by at most one activity in a given account (see generation-uniqueness). Hence, this activity is also the one referred to by the usage of e1.

There is some redundancy in the following discussion.

The converse inference does not hold. From wasDerivedFrom(e2,e1) and used(a,e1,-), one cannot derive wasGeneratedBy(e2,a,-) because identifier e1 may occur in usages performed by many activities, which may have not generated the entity denoted by e2.

Note that derivation cannot in general be inferred from the existence of related usage and generation events. Indeed, when a generation wasGeneratedBy(g, e2, a, -, attrs2) precedes used(u, a, e1, -, attrs1), for some e1, e2, attrs1, attrs2, and a, one cannot infer derivation wasDerivedFrom(e2, e1, a, g, u) or wasDerivedFrom(e2,e1) since e2 cannot possibly be derived from e1, given the creation of e2 precedes the use of e1. That is, if e1 is generated by an activity before e2 is used, then obviously e2 cannot have been derived from e1. However, even if e2 happens used before e1 is generated, it is not safe to assume that e2 was derived from e1.

Derivation is not defined to be transitive. Domain-specific specializations of derivation may be defined in such a way that the transitivity property holds.

A revision admits the following inference, linking the two entities by a derivation, and stating them to be alternates.

Given two identifiers e1 and e2 identifying two entities, and an identifier ag identifying an agent, IF wasRevisionOf(-,e2,e1,ag) holds, THEN the following hold:
The following doesn't make sense because wasRevisionOf and wasDerivedFrom have different types.

wasRevisionOf is a strict sub-relation of wasDerivedFrom since two entities e2 and e1 may satisfy wasDerivedFrom(e2,e1) without being a variant of each other.

Motivation for quotation inference
IF wasQuotedFrom(e2,e1,ag2,ag1,attrs) holds for some identifiers e2, e1, ag2, ag1, THEN the following hold:

Traceability allows an entity to be transitively linked to another entity it is derived from, to an agent it is attributed to, or another agent having some responsibility, or a trigger of an activity that generated it.

Traceability can be inferred from existing descriptions, or can be asserted stating that a dependency path exists without its individual steps being expressed. This is captured by the following inferences:

Given two identifiers e2 and e1 for entities, the following statements hold:
  1. IF wasDerivedFrom(e2,e1,a,g2,u1) holds, for some a, g2, u1, THEN tracedTo(e2,e1) also holds.
  2. IF wasDerivedFrom(e2,e1) holds, THEN tracedTo(e2,e1) also holds.
  3. IF wasAttributedTo(e2,ag1,aAttr) holds, THEN tracedTo(e2,ag1) also holds.
  4. IF wasAttributedTo(e2,ag2,aAttr), wasGeneratedBy(-,e2,a,-,gAttr), and actedOnBehalfOf(ag2,ag1,a,rAttr) hold, for some a, ag2, ag1, aAttr, gAttr, and rAttr, THEN tracedTo(e2,ag1) also holds.
  5. IF wasGeneratedBy(e2,a,gAttr) and wasStartedBy(a,e1,sAttr) hold, for some a, gAttr , sAttr then tracedTo(e2,e1) holds.
  6. IF tracedTo(e2,e) and tracedTo(e,e1) hold for some e, THEN tracedTo(e2,e1) also holds.

We note that the inference rule traceability-inference does not allow us to infer anything about the attributes of the related entities, agents or events.

Component 4: Alternate Entities

TODO: There is currently no consensus what inferences on alternate or specialization should be assumed. The following section lists possible inferences that may or may not be adopted. Section is under review, pending ISSUE-29.

The relation alternateOf is an equivalence relation: reflexive, transitive and symmetric.

For any entity e, we have alternateOf(e,e).
For any entities e1, e2, e3, IF alternateOf(e1,e2) and alternateOf(e2,e3) THEN alternateOf(e1,e3).
For any entity e1, e2, IF alternateOf(e1,e2) THEN alternateOf(e2,e1).

Similarly, specialization is a partial order: it is reflexive, anti-symmetric and transitive.

For any entity e, we have specializationOf(e,e).
For any entities e1, e2, IF specializationOf(e1,e2) and specializationOf(e2,e1) THEN e1 = e2.
For any entities e1, e2, e3, IF specializationOf(e1,e2) and specializationOf(e2,e3) THEN specializationOf(e1,e3).

Finally, if one entity specializes another, then they are also alternates:

For any entities e1, e2, IF specializationOf(e1,e2) THEN alternateOf(e1,e2).
TODO: Possible inferences about attributes, generation, invalidation?
The following sections are retained from an older version, and are not consistent with the above constraints. This will be revised once the consensus on ISSUE-29 is clearer.


Specialization is neither symmetric nor anti-symmetric.

"Alice's toyota car on fifth Avenue" is a specialization of "Alice's toyota car", but the converse does not hold.
anti-symmetric counter-example???

Specialization is transitive. Indeed if specializationOf(e1,e2) holds, then there is some common thing, say T1-2 they both refer to, and e1 is a more specific aspect of this thing than e2. Likewise, if specializationOf(e2,e3) holds, then there is some common thing, say T2-3 they both refer to, and e2 is a more specific aspect of this thing than e3. The things T1-2 and T2-3 are the same since e2 is an aspect of both of them, so specializationOf(e1,e3) follows since e1 and e3 are aspects fo the same thing and e1 is more specific than e3.

A specialization of "this email message" would be, for example, the "printed version on my desk", which is a specialization of "my thoughts on this email thread". So, the "printed version on my desk" is also a specialization "my thoughts on this email thread".


Alternate not is reflexive. Indeed, alternate(e,e) does not hold for any arbitrary entity e since e may not be a specialization of another entity.

Alternate is symmetric. Indeed, if alternate(e1,e2) holds, then there exists an unspecified entity e, such that both e1 and e2 are specialization of e. Therefore, alternate(e2,e1) also holds.

Alternate is not transitive. Indeed, if alternate(e1,e2) holds, then there exists an unspecified entity e1-2, such that both e1 and e2 are specialization of e1-2. Likewise, if alternate(e2,e3) holds, then there exists an unspecified entity e2-3, such that both e2 and e3 are specialization of e2-3. It does not imply that there is a common entity e1-3 that both e1 and e3 specialize.

At 6pm, the customer in a chair is a woman in a red dress, who happens to be Alice. After she leaves, another customer arrives at 7pm, a man with glasses, who happens to be Bob. Transitivity does not hold since the womanInRedDress\ is not alternate of customerInChairAt7pm.



alternate(customerInChairAt6pm, customerInChairAt7pm)
specialization(customerInChairAt6pm, customerInChair)
specialization(customerInChairAt7pm, customerInChair)

The above example shows that customerInChairAt6pm and customerInChairAt7pm are two alternate entities that have no overlap, while womanInRedDress and customerInChairAt6pm do overlap. The following example illustrates another case of non-overlapping alternate entities.

Two copies of the same book, where copy A was destroyed before copy B was made.


For the purpose of checking inferences and constraints, we define a notion of equivalence of PROV descriptions. Equivalence is has the following characteristics:

Optional Attributes

TODO: Clarify how optional attributes are handled; clarify merging. The following is not very explicit about the difference between "not present" and "omitted but inferred".
PROV-DM allows for some attributes to be optionally expressed. Unless otherwise specified, when an optional attribute is not present in a description, some value SHOULD be assumed to exist for this attribute, though it is not known which. The only exceptions are:
  • Activities also allow for an optional start time attribute. If both are specified, they MUST be the same, as expressed by the following constraint.
  • Activities also allow for an optional end time attribute. If both are specified, they MUST be the same, as expressed by the following constraint.
  • In a quotation of the form wasQuotedFrom(e2,e1,-,-,attrs), the absence of an agent means: either no agent exists, or an agent exists but it is not identified.
  • In an association of the form wasAssociatedWith(a, ag, -, attr), the absence of a plan means: either no plan exists, or a plan exists but it is not identified.
  • In an association of the form wasAssociatedWith(a, -, pl, attr), an agent exists but it is not identified.
  • In a a delegation of the form actedOnBehalfOf(a, ag2, ag1, -, attr), the absence of an activity means that a2 acts on behalf of a1 for all activities with which a2 is associated.


We define the normal form of a PROV description as the set of provenance expressions resulting from merging all of the overlapping expressions in the instance and applying all possible inference rules to this set. Formally, we say that two PROV descriptions are equivalent if they have the same normal form (that is, after applying all possible inference rules, the two instances produce the same set of PROV-DM expressions.)

We should check that normal forms exist, i.e. that applying rules and definitions eventually terminates. More clarity is needed about enforcing uniqueness via merging vs. constraint checking.

An application that processes PROV-DM data SHOULD handle equivalent instances in the same way. (Common exceptions to this rule include, for example, pretty printers that seek to preserve the original order of statements in a file and avoid expanding inferences.)

Validity Constraints

This section defines a collection of constraints on PROV descriptions. An PROV description is valid if, after applying all possible inference and definition rules from Section 2, the resulting instance satisfies all of the constraints specified in this section. Applications that process PROV descriptions SHOULD check that the data they generate is valid and MAY reject input provenance data that is not valid.

There are two kinds of constraints:

The PROV data model is implicitly based on a notion of instantaneous events (or just events), that mark transitions in the world. Events include generation, usage, or invalidation of entities, as well as starting or ending of activities. This notion of event is not first-class in the data model, but it is useful for explaining its other concepts and its semantics [[PROV-SEM]]. Thus, events help justify inferences on provenance as well as validity constraints indicating when provenance is self-consistent. In we discuss the motivation for instantaneous events and their relationship to time in greater detail.

PROV-DM identifies five kinds of instantaneous events, namely entity generation event, entity usage event, entity invalidation event, activity start event and activity end event. PROV-DM adopts Lamport's clock assumptions [[CLOCK]] in the form of a reflexive, transitive partial order follows (and its inverse precedes) between instantaneous events. Furthermore, PROV-DM assumes the existence of a mapping from instantaneous events to time clocks, though the actual mapping is not in scope of this specification.

TODO: More about what it means for constraints to be satisfied; constraint template(s)

Uniqueness Constraints

Attribute uniqueness constraints?

We assume that the various identified objects of PROV-DM have unique expressions describing them within a PROV description.

Given an entity identifier e, there is at most one expression entity(e,attrs), where attrs is some set of attribute-values.

Given an activity identifier a, there is at most one expression activity(a,t1,t2,attrs), where attrs is some set of attribute-values.

TODO: Same goes for all other objects: agent, note, generation, usage, invalidation, start, end, communication, start by, attribution, association, responsibility, derivation, revision, quotation. We should find a way of saying this once concisely.

We assume that an entity has exactly one generation and invalidation event (either or both may, however, be left implicit). So, PROV-DM allows for two distinct generations g1 and g2 referencing the same entity provided they occur simultaneously. This implies that the two generation events are actually the same and caused by the same activity, though provenance may contain several descriptions for the same world activity.

Given an entity denoted by e, two activities denoted by a1 and a2, two time instants t1 and t2, and two sets of attribute-value pairs attrs1 and attrs2, IF wasGeneratedBy(id1, e, a1, t1, attrs1) and wasGeneratedBy(id2, e, a2, t2, attrs2) exist, THEN id1=id2, a1=a2, t1=t2 and attrs1=attrs2.
Wouldn't the above constraint violate uniqueness?
Invalidation uniqueness?

A generation can be used to indicate a generation time without having to specify the involved activity. A generation time is unique, as specified by the following constraint.

Seems redundant given generation-uniqueness
Given an entity denoted by e and two time instants t1 and t2, IF wasGeneratedBy(e, -, t1) and wasGeneratedBy(e, -, t2) hold, THEN t1=t2.

An activity start event is the instantaneous event that marks the instant an activity starts. It allows for an optional time attribute. Activities also allow for an optional start time attribute. If both are specified, they MUST be the same, as expressed by the following constraint.

IF activity(a,t1,t2,-) and wasStartedBy(id,a,e,t,-), THEN t=t1.

An activity end event is the instantaneous event that marks the instant an activity ends. It allows for an optional time attribute. Activities also allow for an optional end time attribute. If both are specified, they MUST be the same, as expressed by the following constraint.

IF activity(a,t1,t2,-) and wasEndedBy(id,a,e,t,-), THEN t = t2.

Event Ordering Constraints

Given that provenance consists of a description of past entities and activities, valid provenance descriptions MUST satisfy ordering constraints between instantaneous events, which we introduce in this section. For instance, an entity can only be used after it was generated; hence, we say that an entity's generation event precedes any of this entity's usage events. Should this ordering constraint be violated, the associated generation and usage could not be credible. The rest of this section defines the temporal interpretation of provenance descriptions as a set of instantaneous event ordering constraints.

To allow for minimalistic clock assumptions, like Lamport [[CLOCK]], PROV-DM relies on a notion of relative ordering of instantaneous events, without using physical clocks. This specification assumes that a partial order exists between instantaneous events.

Specifically, precedes is a partial order between instantaneous events. When we say e1 precedes e2, this means that either the two events are equal or e1 happened before e2. For symmetry, follows is defined as the inverse of precedes; that is, when we say e1 follows e2, this means that either the two events are equal or e1 happened after e2. Both relations are partial orders, meaning that they are reflexive, transitive, and antisymmetric.

Define reflexivity, transitivity and antisymmetry in glossary. Also, do we want to allow an event to "precede" itself?
The following discussion is unclear: what is being said here, and why?

PROV-DM also allows for time observations to be inserted in specific provenance descriptions, for each of the five kinds of instantaneous events introduced in this specification. The presence of a time observation for a given instantaneous event fixes the mapping of this instantaneous event to the timeline. The presence of time information in a provenance description instantiates the ordering constraint with that time information. It is expected that such instantiated constraints can help corroborate provenance information. We anticipate that verification algorithms could be developed, though this verification is outside the scope of this specification.

The following figure summarizes the ordering constraints in a graphical manner. For each subfigure, an event time line points to the right. Activities are represented by rectangles, whereas entities are represented by circles. Usage, generation and derivation are represented by the corresponding edges between entities and activities. The five kinds of instantaneous events are represented by vertical dotted lines (adjacent to the vertical sides of an activity's rectangle, or intersecting usage and generation edges). The ordering constraints are represented by triangles: an occurrence of a triangle between two instantaneous event vertical dotted lines represents that the event denoted by the left line precedes the event denoted by the right line.

Summary of instantaneous event ordering constraints for activities
constraints between events

Activity constraints

In this section we discuss constraints from the perspective of the lifetime of an activity. An activity starts, then during its lifetime uses, generates entities and communicates with or starts other activities, and finally ends. The following constraints amount to checking that all of the events associated with an activity take place within the activity's lifetime, and the start and end events mark the start and endpoints of its lifetime.

The existence of an activity implies that the activity start event always precedes the corresponding activity end event. This is illustrated by Subfigure ordering-activity-fig (a) and expressed by constraint start-precedes-end.

IF wasStartedBy(start,a,-,-) and wasEndedBy(end,a,-,-) THEN start precedes end.

A usage implies ordering of events, since the usage event had to occur during the associated activity. This is illustrated by Subfigure ordering-activity-fig (b) and expressed by constraint usage-within-activity.

  1. IF used(use,a,e,-,-) and wasStartedBy(start,a,-,-) THEN start precedes use.
  2. IF used(use,a,e,-,-) and wasEndedBy(end,a,-,-) THEN use precedes end.

A generation implies ordering of events, since the generation event had to occur during the associated activity. This is illustrated by Subfigure ordering-activity-fig (c) and expressed by constraint generation-within-activity.

  1. IF wasGeneratedBy(gen,a,e,-,-) and wasStartedBy(start,a,-,-) THEN start precedes gen.
  2. IF wasGeneratedBy(gen,a,e,-,-) and wasEndedBy(end,a,-,-) THEN gen precedes end.

Communication between two activities a1 and a2 also implies ordering of events, since some entity must have been generated by the former and used by the latter, which implies that the start event of a1 cannot follow the end event of a2. This is illustrated by Subfigure ordering-activity-fig (d) and expressed by constraint wasInformedBy-ordering.

IF wasInformedBy(a2,a1) and wasStartedBy(start,a1,-,-) and wasEndedBy(end,a2,-,-) THEN start precedes end.

Start of a2 by activity a1 also implies ordering of events, since a1 must have been active before a2 started. This is illustrated by Subfigure ordering-activity-fig (e) and expressed by constraint wasStartedByActivity-ordering.

IF wasStartedByActivity(a2,a1) and wasStartedBy(start1,a1,-,-) and wasStartedBy(start2,a2,-,-) THEN start1 precedes start2.

Entity constraints

As with activities, entities have lifetimes: they are generated, then can be used, revised, or other entities can be derived from them, and finally are invalidated.

Summary of instantaneous event ordering constraints for entities
ordering constraints for entities

Generation of an entity precedes its invalidation. (This follows from other constraints if the entity is used, but we state it explicitly to cover the case of an entity that is generated and invalidated without being used.)

IF wasGeneratedBy(gen,e,_,_) and wasInvalidatedBy(inv,e,-,-) THEN gen precedes inv.

A usage and a generation for a given entity implies ordering of events, since the generation event had to precede the usage event. This is illustrated by Subfigure ordering-entity-fig (a) and expressed by constraint generation-precedes-usage.

IF wasGeneratedBy(gen,e,_,_) and used(use,_,e,-) THEN gen precedes use.

All usages of an entity precede its invalidation, which is captured by constraint usage-precedes-invalidation (without any explicit graphical representation).

IF used(use,_,e,-) and wasInvalidatedBy(inv,e,_,_) THEN use precedes inv.

If there is a derivation between e2 and e1, then this means that the entity e1 had some form of influence on the entity e2; for this to be possible, some event ordering must be satisfied. First, we consider derivations, where the activity and usage are known. In that case, the usage of e1 has to precede the generation of e2. This is illustrated by Subfigure ordering-entity-fig (b) and expressed by constraint derivation-usage-generation-ordering.

IF wasDerivedFrom(d,e2,e1,a,g2,u1,-) THEN u1 precedes g2.

When the usage is unknown, a similar constraint exists, except that the constraint refers to its generation event, as illustrated by Subfigure ordering-entity-fig (c) and expressed by constraint derivation-generation-generation-ordering.

IF wasDerivedFrom(e2,e1,attrs) and wasGeneratedBy(gen1,e1,_,_) and wasGeneratedBy(gen2,e2,_,_) THEN gen1 precedes gen2.

Note that event ordering is between generations of e1 and e2, as opposed to derivation where usage is known, which implies ordering ordering between the usage of e1 and generation of e2.

The entity that triggered the start of an activity must exist before the activity starts. This is illustrated by Subfigure ordering-entity-trigger-fig (a) and expressed by constraint wasStartedBy-ordering.

  1. IF wasStartedBy(start,a,e,-) and wasGeneratedBy(gen,e,-,-) THEN gen precedes start.
  2. IF wasStartedBy(start,a,e,-) and wasInvalidatedBy(inv,e,-,-) THEN start precedes inv.

Similarly, the entity that triggered the end of an activity must exist before the activity ends, as illustrated by Subfigure ordering-entity-trigger-fig (b).

  1. IF wasEndedBy(end,a,e,-) and wasGeneratedBy(gen,e,-,-) THEN gen precedes end.
  2. IF wasEndedBy(end,a,e,-) and wasInvalidatedBy(inv,e,-,-) THEN end precedes inv.
Summary of instantaneous event ordering constraints for trigger entities
ordering constraints for trigger entities

Agent constraints

Like entities and activities, agents have lifetimes that follow a familiar pattern: an agent is generated, can participate in interactions such as starting, ending or association with an activity, attribution, or delegation, and finally the agent is invalidated.

Further constraints associated with agents appear in Figure ordering-agents and are discussed below.

Summary of instantaneous event ordering constraints (continued)
ordering constraints for agents

An activity that was associated with an agent must have some overlap with the agent. The agent may be generated, or may only become associated with the activity, after the activity start: so, the agent is required to exist before the activity end. Likewise, the agent may be destructed, or may terminate its association with the activity, before the activity end: hence, the agent invalidation is required to happen after the activity start. This is illustrated by Subfigure ordering-agents (a) and expressed by constraint wasAssociatedWith-ordering.

  1. IF wasAssociatedWith(a,ag) and wasStartedBy(start,a,-,-) and wasInvalidatedBy(inv,ag,-,-) THEN start precedes inv.
  2. IF wasAssociatedWith(a,ag) and wasGeneratedBy(gen,ag,-,-) and wasEndedBy(end,a,-,-) THEN gen precedes end.

An entity that was attributed to an agent must have some overlap with the agent. The agent is required to exist before the entity invalidation. Likewise, the entity generation must precede the agent destruction. This is illustrated by Subfigure ordering-agents (b) and expressed by constraint wasAttributedTo-ordering.

  1. IF wasAttributedTo(e,ag) and wasGeneratedBy(gen,e,-,-) and wasInvalidatedBy(inv,ag,-,-) THEN gen precedes inv.
  2. IF wasAttributedTo(e,ag) and wasGeneratedBy(gen,ag,-,-) and wasInvalidatedBy(inv,e,-,-) THEN gen precedes inv.

For responsibility, two agents need to have some overlap in their lifetime.

IF actedOnBehalfOf(ag2,ag1) and wasGeneratedBy(gen,ag1,-,-) and wasInvalidatedBy(inv,ag2,-,-) THEN gen precedes inv.

Collection Constraints

Work on collections and on these constraints is deferred until after the next working draft, so this section may not be stable.

Membership is a convenience notation, since it can be expressed in terms of an insertion into some collection. The membership definition is formalized by constraint membership-as-insertion.

memberOf(c, {(k1, v1), ...}) holds IF AND ONLY IF there exists a collection c0, such that derivedByInsertionFrom(c, c0, {(k1, v1), ...}).

A collection may be obtained by insertion or removal, or said to satisfy the membership relation. To provide an interpretation of collections, PROV-DM restricts one collection to be involved in a single derivation by insertion or removal, or to one membership relation. PROV-DM does not provide an interpretation for descriptions that consist of two (or more) insertion, removal, membership relations that result in the same collection.

The following constraint ensures unique derivation.

The following constraint is unclear.
A collection MUST NOT be derived through multiple insertions, removal, or membership relations.
Consider the following descriptions about three collections.
entity(c1, [prov:type="prov:Collection"  %% xsd:QName])
entity(c2, [prov:type="prov:Collection"  %% xsd:QName])
entity(c3, [prov:type="prov:Collection"  %% xsd:QName])

derivedByInsertionFrom(c3, c1, {("k1", e1), ("k2", e2)})
derivedByInsertionFrom(c3, c2, {("k3", e3)})

There is no interpretation for such descriptions since c3 is derived multiple times by insertion.

As a particular case, collection c is derived multiple times from the same c1.

derivedByInsertionFrom(id1, c, c1, {("k1", e1), ("k2", e2)})
derivedByInsertionFrom(id2, c, c1, {("k3", e3), ("k4", e4)})

The interpretation of such descriptions is also unspecified.

To describe the insertion of the 4 key-entity pairs, one would instead write:

derivedByInsertionFrom(id1, c, c1, {("k1", e1), ("k2", e2), ("k3", e3), ("k4", e4)})
The same is true for any combination of insertions, removals, and membership relations:

The following descriptions

derivedByInsertionFrom(c, c1, {("k1", e1)})
derivedByRemovalFrom(c, c2, {"k2"})
have no interpretation. Nor have the following:
derivedByInsertionFrom(c, c1, {("k1", e1)})
memberOf(c, {"k2"}).

Collection branching

It is allowed to have multiple derivations from a single root collection, as long as the resulting entities are distinct, as shown in the following example.
entity(c0, [prov:type="prov:EmptyCollection" %% xsd:QName])    // c0 is an empty collection
entity(c1, [prov:type="prov:Collection" %% xsd:QName])
entity(c2, [prov:type="prov:Collection" %% xsd:QName])
entity(c3, [prov:type="prov:Collection" %% xsd:QName])

derivedByInsertionFrom(c1, c0, {("k1", e1)})      
derivedByInsertionFrom(c2, c0, {("k2", e2)})       
derivedByInsertionFrom(c3, c1, {("k3", e3)})       
From this set of descriptions, we conclude:
  c1 = { ("k1", e1) }
  c2 = { ("k2", e2) }
  c3 = { ("k1", e1), ("k3", e3)}

Collections and Weaker Derivation Relation

The state of a collection is only known to the extent that a chain of derivations starting from an empty collection can be found. Since a set of descriptions regarding a collection's evolution may be incomplete, so is the reconstructed state obtained by querying those descriptions. In general, all descriptions reflect partial knowledge regarding a sequence of data transformation events. In the particular case of collection evolution, in which some of the state changes may have been missed, the more generic derivation relation should be used to signal that some updates may have occurred, which cannot be expressed as insertions or removals. The following example illustrates this.

In the example, the state of c2 is only partially known because the collection is constructed from partially known other collections.
entity(c0, [prov:type="prov:EmptyCollection" %% xsd:QName])    // c0 is an empty collection
entity(c1, [prov:type="prov:Collection" %% xsd:QName])    
entity(c2, [prov:type="prov:Collection" %% xsd:QName])    
entity(c3, [prov:type="prov:Collection" %% xsd:QName])    

derivedByInsertionFrom(c1, c0, {("k1", e1)})       
wasDerivedFrom(c2, c1)                       
derivedByInsertionFrom(c3, c2, {("k2", e2)})       
From this set of descriptions, we conclude:
  • c1 = { ("k1", e1) }
  • c2 is somehow derived from c1, but the precise sequence of updates is unknown
  • c3 includes ("k2", e2) but the earlier "gap" leaves uncertainty regarding ("k1", e1) (it may have been removed) or any other pair that may have been added as part of the derivation activities.
Do the insertion/removal derivation steps imply wasDerivedFrom, wasVersionOf, alternateOf?

Account Constraints

Work on accounts has been deferred until after the next working draft, so this section is very unstable

PROV-DM allows for multiple descriptions of entities (and in general any identifiable object) to be expressed.

Let us consider two descriptions of a same entity, which we have taken from two different contexts. A working draft published by the w3:Consortium:

entity(tr:WD-prov-dm-20111215, [ prov:type="pr:RecsWD" %% xsd:QName ])
The second version of a document edited by some authors:
entity(tr:WD-prov-dm-20111215, [ prov:type="document", ex:version="2" ])

Both descriptions are about the same entity identified by tr:WD-prov-dm-20111215, but they contain different attributes, describing the situation or partial state of the these entities according to the context in which they occur.

Two different descriptions of a same entity cannot co-exist in a same account as formalized in unique-description-in-account.

In some cases, there may be a requirement for two different descriptions of the same entity to be included in the same account. To satisfy the constraint unique-description-in-account, we can adopt a different identifier for one of them, and relate the two descriptions with the alternateOf relation.

We now reconsider the same two descriptions of a same entity, but we change the identifier for one of them:

entity(tr:WD-prov-dm-20111215, [ prov:type="pr:RecsWD" %% xsd:QName ])
entity(ex:alternate-20111215, [ prov:type="document", ex:version="2" ])
Since we are not specifying ways to take the union of two accounts, we may drop this discussion

Taking the union of two accounts is another account, formed by the union of the descriptions they respectively contain. We note that the resulting union may or may not invalidate some constraints:

How to reconcile such accounts is beyond the scope of this specification.

Material transplanted from old structural well-formedness constraints section. This example isn't very clear, since the sub-workflow-ness isn't represented in the data. According to what was written above, we should conclude that a0 and a2 are equal!

In the following descriptions, a workflow execution a0 consists of two sub-workflow executions a1 and a2. Sub-workflow execution a2 generates entity e, so does a0.

activity(a0, [prov:type="workflow execution"])
activity(a1, [prov:type="workflow execution"])
activity(a2, [prov:type="workflow execution"])


So, we have two different generations for entity e. Such an example is permitted in PROV-DM if the two activities denoted by a0 and a2 are a single thing happening in the world but described from different perspectives.

While this example is permitted in PROV-DM, it does not make the inter-relation between activities explicit, and it mixes descriptions expressed from different perspectives together. While this may acceptable in some specific applications, it becomes challenging for inter-operability. Indeed, PROV-DM does not offer any relation describing the structure of activities. Such descriptions are said not to be structurally well-formed.

Structurally well-formed provenance can be obtained by partitioning the generations into different accounts. This makes it clear that these generations provide alternative descriptions of the same real-world generation event, rather than describing two distinct generation events for the same entity. When accounts are used, the example can be encoded as follows.

The same example is now revisited, with the following descriptions that are structurally well-formed. Two accounts are introduced, and there is a single generation for entity e per account.

In a first account, entitled "summary", we find:

activity(a0,t1,t2,[prov:type="workflow execution"])

In a second account, entitled "detail", we find:

activity(a1,t1,t3,[prov:type="workflow execution"])
activity(a2,t3,t2,[prov:type="workflow execution"])

Structurally well-formed provenance satisfies some constraints, which force the structure of descriptions to be exposed by means of accounts. With these constraints satisfied, further inferences can be made about structurally well-formed descriptions. The uniqueness of generations in accounts is formulated as follows.

Compliance with this document

For the purpose of compliance, the normative sections of this document are (TODO). To be compliant:
Should we specify a way for PROV descriptions to say whether they are meant to be validated or not? Seems outside the scope of this document.

Rationale for inferences and constraints

This section collects all of the explanatory material that I was not certain how to interpret as an unambiguous inference or constraint. Some of these observations may need to be folded into the explanatory text in respective sections (for example for events, accounts or collections). Editing is also needed to decrease redundancy.

Entities and Attributes

When we talk about things in the world in natural language and even when we assign identifiers, we are often imprecise in ways that make it difficult to clearly and unambiguously report provenance: a resource with a URL may be understood as referring to a report available at that URL, the version of the report available there today, the report independent of where it is hosted over time, etc. However, to write precise descriptions of the provenance of things that change over time, we need ways of disambiguating which versions of things we are talking about.

To describe the provenance of things that can change over time, PROV-DM uses the concept of entities with fixed attributes. From a provenance viewpoint, it is important to identify a partial state of something, i.e. something with some aspects that have been fixed, so that it becomes possible to express its provenance (i.e. what caused the thing with these specific aspects). An entity encompasses a part of a thing's history during which some of the attributes are fixed. An entity can thus be thought of as a part of a thing with some associated partial state. Attributes in PROV-DM are used to fix certain aspects of entities.

An entity is a thing one wants to provide provenance for and whose situation in the world is described by some fixed attributes. An entity has a characterization interval, or lifetime, defined as the period between its generation event and its invalidation event. An entity's attributes are established when the entity is created and describe the entity's situation and (partial) state during an entity's lifetime.

A different entity (perhaps representing a different user or system perspective) may fix other aspects of the same thing, and its provenance may be different. Different entities that are aspects of the same thing are called alternate, and the PROV-DM relations of specialization and alternate can be used to link such entities.

Different users may take different perspectives on a resource with a URL. A provenance record might use one (or more) different entities to talk about different perspectives, such as:
  • a report available at a URL: fixes the nature of the thing, i.e. a document, and its location;
  • the version of the report available there today: fixes its version number, contents, and its date;
  • the report independent of where it is hosted and of its content over time: fixes the nature of the thing as a conceptual artifact.
The provenance of these three entities may differ, and may be along the following lines:
  • the provenance of a report available at a URL may include: the act of publishing it and making it available at a given location, possibly under some license and access control;
  • the provenance of the version of the report available there today may include: the authorship of the specific content, and reference to imported content;
  • the provenance of the report independent of where it is hosted over time may include: the motivation for writing the report, the overall methodology for producing it, and the broad team involved in it.

We do not assume that any entity is a better or worse description of reality than any other. That is, we do not assume an absolute ground truth with respect to which we can judge correctness or completeness of descriptions. In fact, it is possible to describe the processing that occurred for the report to be commissioned, for individual versions to be created, for those versions to be published at the given URL, etc., each via a different entity with attribute-value pairs that fix some aspects of the report appropriately.

Besides entities, a variety of other PROV-DM objects have attributes, including activity, generation, usage, start, end, communication, attribution, association, responsibility, and derivation. Each object has an associated duration interval (which may be a single time point), and attribute-value pairs for a given object are expected to be descriptions that hold for the object's duration.

However, the attributes of entities have special meaning because they are considered to be fixed aspects of underlying, changing things. This motivates constraints on alternateOf and specializationOf relating the attribute values of different entities.

TODO: Constraints on alternateOf/specializationOf for this?
TODO: Further discussion of entities moved from the old "Definitional constraints" section. Should merge with the surrounding discussion to avoid repetition.

An entity is a thing one wants to provide provenance for and whose situation in the world is described by some attribute-value pairs. An entity's attribute-value pairs are established as part of the entity description and their values remain unchanged for the lifetime of the entity. An entity's attribute-value pairs are expected to describe the entity's situation and (partial) state during an entity's characterization interval.

If an entity's situation or state changes, this may result in its description being invalid, because one or more attribute-value pairs no longer hold. In that case, from the PROV viewpoint, there exists a new entity, which needs to be given a distinct identifier, and associated with the attribute-value pairs that reflect its new situation or state.

Further considerations:


TODO: Further discussion of activities moved from old "Definitional constraints and inferences" section. Edit to avoid repeating information.

An activity is delimited by its start and its end events; hence, it occurs over an interval delimited by two instantaneous events. However, an activity record need not mention start or end time information, because they may not be known. An activity's attribute-value pairs are expected to describe the activity's situation during its interval, i.e. an interval between two instantaneous events, namely its start event and its end event.

Further considerations:

Description, Assertion, and Inference

PROV-DM is a provenance data model designed to express descriptions of the world.

A file at some point during its lifecycle, which includes multiple edits by multiple people, can be described by its type, its location in the file system, a creator, and content.

The data model is designed to capture activities that happened in the past, as opposed to activities that may or will happen. However, this distinction is not formally enforced. Therefore, PROV-DM descriptions are intended to be interpreted as what has happened, as opposed to what may or will happen.

This specification does not prescribe the means by which descriptions can be arrived at; for example, descriptions can be composed on the basis of observations, reasoning, or any other means.

Sometimes, inferences about the world can be made from descriptions conformant to the PROV-DM data model. This specification defines some such inferences, allowing new descriptions to be inferred from existing ones. Hence, descriptions of the world can result either from direct assertion or from inference by application of inference rules defined by this specification.

Events and Time

Time is critical in the context of provenance, since it can help corroborate provenance claims. For instance, if an entity is claimed to be obtained by transforming another, then the latter must have existed before the former. If it is not the case, then there is something wrong with such a provenance claim.

Although time is critical, we should also recognize that provenance can be used in many different contexts within individual systems and across the Web. Different systems may use different clocks which may not be precisely synchronized, so when provenance records are combined by different systems, we may not be able to align the times involved to a single global timeline. Hence, PROV-DM is designed to minimize assumptions about time.

Hence, to talk about the constraints on valid PROV-DM data, we refer to instantaneous events that correspond to interactions between activities and entities. The term "event" is commonly used in process algebra with a similar meaning. For instance, in CSP [[CSP]], events represent communications or interactions; they are assumed to be atomic and instantaneous.

Event Ordering

The following paragraphs are unclear and need to be revised, to address review concerns: if we aren't saying anything about how events and time relate, and time is the only concrete information about event ordering in PROV-DM, then how can implementations check that event ordering constraints are satisfied?

How the precedes partial order is implemented in practice is beyond the scope of this specification. This specification only assumes that each instantaneous event can be mapped to an instant in some form of timeline. The actual mapping is not in scope of this specification. Likewise, whether this timeline is formed of a single global timeline or whether it consists of multiple Lamport-style clocks is also beyond this specification. The follows and precedes orderings of events should be consistent with the ordering of their associated times over these timelines.

This specification defines event ordering constraints between instantaneous events associated with provenance descriptions. PROV-DM data MUST satisfy such constraints.

PROV-DM also allows for time observations to be inserted in specific descriptions, for each recognized instantaneous event introduced in this specification. The presence of a time observation for a given instantaneous event fixes the mapping of this instantaneous event to the timeline. It can also help with the verification of associated ordering constraints (though, again, this verification is outside the scope of this specification).

Types of Events

Five kinds of instantaneous events are used for the PROV-DM data model. The activity start and activity end events delimit the beginning and the end of activities, respectively. The entity usage, entity generation, and entity invalidation events apply to entities, and the generation and invalidation events delimit the characterization interval of an entity. More specifically:

An activity start event is the instantaneous event that marks the instant an activity starts.

An activity end event is the instantaneous event that marks the instant an activity ends.

An entity usage event is the instantaneous event that marks the first instant of an entity's consumption timespan by an activity. Before this instant the entity had not begun to be used by the activity.

An entity generation event is the instantaneous event that marks the final instant of an entity's creation timespan, after which it is available for use. The entity did not exist before this event.

An entity invalidation event is the instantaneous event that marks the initial instant of the destruction, invalidation, or cessation of an entity, after which the entity is no longer available for use. The entity no longer exists after this event.


Some of this discussion may belong in the account constraint section as motivation, or as formal constraints/inferences. In particular, the MUST, MAY, SHOULD statements should be clarified and put into the normative section.

It is common for multiple provenance records to co-exist. For instance, when emailing a file, there could be a provenance record kept by the mail client, and another by the mail server. Such provenance records may provide different explanations about something happening in the world, because they are created by different parties or observed by different witnesses. A given party could also create multiple provenance records about an execution, to capture different levels of details, targeted at different end-users: the programmer of an experiment may be interested in a detailed log of execution, while the scientists may focus more on the scientific-level description. Given that multiple provenance records can co-exist, it is important to have details about their origin, who they are attributed to, how they were generated, etc. In other words, an important requirement is to be able to express the provenance of provenance.

See ISSUE-343. Also, what is an account's set of descriptions?

An account is an entity that contains a bundle of provenance descriptions. PROV-DM does not provide an actual mechanism for creating accounts, i.e. for bundling up provenance descriptions and naming them. Accounts MUST satisfy some properties:

The last point is important. It indicates that within an account:
  • It is always possible to add new provenance descriptions, e.g. stating that a given entity was used by an activity, or derived from another. This is very much an open world assumption.
  • It is not permitted to add new attributes to a given entity (a form of closed world assumption from the attributes point of view), though it is always permitted to create a new description for an entity, which is a "copy" of the original description extended with novel attributes (cf Example merge-with-rename).

There is no construct in PROV-DM to create such bundles of descriptions. Instead, it is assumed that some mechanism, outside PROV-DM can create them. However, from a provenance viewpoint, such accounts are things whose provenance we may want to describe. In order to be able to do so, we need to see accounts as entities, whose origin can be described using PROV-DM vocabulary. Thus, PROV-DM introduces the reserved type Account.


WG membership to be listed here.