Patent Application
20150032499
1. Technical Field
The present invention relates to business process models, and more particularly to mapping of items in business process models.
2. Description of the Related Art
Business process models generally are designed to have a certain level of detail to permit users to make qualitative assessments about the state of a business enterprise. However, business process models are not necessarily complete or correct and are often simplified. There are several reasons for these characteristics of business process models. For example, business processes are typically not understood sufficiently to design a complete model. Gaining the knowledge needed to model a detailed and accurate view is a very resource and time consuming task. Further, a simplified version of the process model is oftentimes sufficient for purposes of providing an overview. In addition, because the activities can be complex (e.g. exceptions) and entail a substantial amount of individual knowledge, it is very difficult to model them without sacrificing a certain degree of freedom.
Processes that are heavily human-driven entail a significant amount of knowledge, as they have a large number of exceptions. Thus, they are modeled in a simplified way to preserve a high degree of freedom. Information about how a process is conducted is in the minds of individuals, groups or, if activities are (semi) automated, are buried in application logic. Extracting this type of knowledge is a time consuming and resource intensive task. In addition business processes and systems supporting them change over time, which in turn would entail re-discovering the process models from the corresponding entities.
One exemplary embodiment of the present invention is directed to a method for mapping an event type to an activity in a business process model. In accordance with the method, the event type and the activity are tokenized by determining event tokens for event type labels in the event type and determining activity tokens for activity labels in the activity. In addition, a score matrix is generated for pairs of the event tokens and the activity tokens indicating a degree of similarity between the event token and the activity token in each of the pairs. The method also includes determining whether the event type and the activity are correlated by determining scores of the pairs of event tokens and activity tokens that are ranked highest in the score matrix. Further, a mapping report indicating whether the event type and the activity are correlated in the business process model is output.
Another exemplary embodiment is directed to a computer readable storage medium comprising a computer readable program for mapping an event type to an activity in a business process model, where the computer readable program when executed on a computer causes the computer to perform the steps of: tokenizing the event type and the activity by determining event tokens for event type labels in the event type and determining activity tokens for activity labels in the activity; generating a score matrix for pairs of the event tokens and the activity tokens indicating a degree of similarity between the event token and the activity token in each of the pairs; determining whether the event type and the activity are correlated by determining scores of the pairs of event tokens and activity tokens that are ranked highest in the score matrix; and outputting a mapping report indicating whether the event type and the activity are correlated in the business process model.
Another exemplary embodiment is directed to a system for mapping an event type to an activity in a business process model. The system includes a tokenizer, a token mapper and a similarity calculator. The tokenizer is configured to tokenize the event type and the activity by determining event tokens for event type labels in the event type and determining activity tokens for activity labels in the activity. In addition, the token mapper is configured to generate a score matrix for pairs of the event tokens and the activity tokens indicating a degree of similarity between the event token and the activity token in each of the pairs and is configured to perform at least one of an assessment of whether any of the event tokens and the activity tokens are natural language synonyms or a determination of a string edit distance between the event token and the activity token in each pair of at least a subset of the pairs. Further, the similarity calculator is configured to determine whether the event type and the activity are correlated by determining scores of the pairs of event tokens and activity tokens that are ranked highest in the score matrix and to output a mapping report indicating whether the event type and the activity are correlated in the business process model.
These and other features and advantages will become apparent from the following detailed description of illustrative embodiments thereof, which is to be read in connection with the accompanying drawings.
The disclosure will provide details in the following description of preferred embodiments with reference to the following figures wherein:
Business process activities can be represented through multiple events. In addition, workflow engines can emit events to mark the start and end of an activity. At times, the modeled granularity of activities does not represent each step that is taken in practice, in the actual business process the model represents. As indicated above, mapping event types to activities entails the accumulation of a substantial amount of knowledge. If that knowledge were present or known, then the process model would have been designed to incorporate the knowledge and would be more detailed. Mapping event types to activities would preserve the connection from actual runtime events to business processes modeled at a coarse grain, thereby enabling several application use-cases. Automatically mapping between event types and activities in a modeled process presents a very difficult challenge.
Process models can be modeled using various tools following various modeling standards, such as, for example, Business Process Model and Notation (BPMN) and Event-driven Process Chain (EPC). Most of the systems that support business processes emit events ranging from record entries in databases to ESB messages. Each of those events can be collected, stored and uniquely named. Depending on the abstraction level of business process models, individual activities may be represented by several events. This is often the case in workflow engines that trigger, for example, start and end events for each activity. Another scenario is that the activities in the process model are not represented to a granularity that is sufficiently fine to reflect each step that is taken in in practice due to the several reasons noted above. The challenge of mapping event types to activities is that it entails acquiring knowledge across abstraction layers, e.g. of the intended business logic, and a determination of the link between the higher level activity and the particular system that generates events that correspond to this activity. Modeled processes in practice contain only an activity name and, in some mature cases, additional resource information such as departments or group identifiers (ids).
The preferred systems and methods described herein recieves a list of event types and process models, such as, for example BPMN models, as an input. The mapping component employs several text analytic techniques to suggest a mapping between event types and activities of a given process model. One activity might contain a mapping to multiple event types. The mapping component is capable of incorporating external semantic knowledge. For example, the mapping component can resolve that the number 051 is the identifier of the marketing department. The suggested mapping can be adjusted by a user.
The mapping described herein can be used in many different scenarios and can offer several benefits. For example, the mapping can improve explorative process analytics. Here, events can be correlated to isolate a specific process (i.e. create traces) that correspond to business process models. As event types are mapped to activities in that process model, a user would be able to use the process model as a reference point to analyze process executions. Queries could be used to constrain the resultant set, and by clicking on activities, the corresponding events can be retrieved or could be highlighted in the resultant set.
The mapping can improve process mining. For example, assume a large end-to-end process includes coarse grained activities and several event types are mapped on certain activities. Then, process mining techniques can be applied to permit understanding of how specific activities are conducted. This knowledge can then be used to detail the process model. Queries can be employed to segment data and improve understanding using a process model as a reference point.
Further, the mapping can improve process deviation detection. A process model can be viewed as the current reference point for purposes of understanding how a business process should be executed. Events mapped to activities deliver a model that can be monitored for deviations or groups of deviations. For example, new patterns of behavior might evolve and could be mined and analyzed. The mapping might reveal that some groups of new behavior could be new best practices that then can be incorporated into the process model. Otherwise, if the behavior is negative or detrimental to the business process in some way, then this can prompt the user to change the process or introduce policies that avoid this kind of behavior.
As will be appreciated by those skilled in the art, aspects of the present invention may be embodied as a system, method or computer program product. Accordingly, aspects of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module” or “system.” Furthermore, aspects of the present invention may take the form of a computer program product embodied in one or more computer readable medium(s) having computer readable program code embodied thereon.
Any combination of one or more computer readable medium(s) may be utilized. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing. Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).
Aspects of the present invention are described below with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks. The computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Reference in the specification to “one embodiment” or “an embodiment” of the present principles, as well as other variations thereof, means that a particular feature, structure, characteristic, and so forth described in connection with the embodiment is included in at least one embodiment of the present principles. Thus, the appearances of the phrase “in one embodiment” or “in an embodiment”, as well any other variations, appearing in various places throughout the specification are not necessarily all referring to the same embodiment.
It is to be appreciated that the use of any of the following “/”, “and/or”, and “at least one of”, for example, in the cases of “A/B”, “A and/or B” and “at least one of A and B”, is intended to encompass the selection of the first listed option (A) only, or the selection of the second listed option (B) only, or the selection of both options (A and B). As a further example, in the cases of “A, B, and/or C” and “at least one of A, B, and C”, such phrasing is intended to encompass the selection of the first listed option (A) only, or the selection of the second listed option (B) only, or the selection of the third listed option (C) only, or the selection of the first and the second listed options (A and B) only, or the selection of the first and third listed options (A and C) only, or the selection of the second and third listed options (B and C) only, or the selection of all three options (A and B and C). This may be extended, as readily apparent by one of ordinary skill in this and related arts, for as many items listed.
Referring now to the drawings in which like numerals represent the same or similar elements and initially to
As illustrated in
For example, Activity 1 and Event Type 3 are discussed here for expository purposes. The process described herein can be applied to any (Activity n, Event Type m) pair and, preferably is applied to each (Activity n, Event Type m) pair. In one example, the activities can be mapped through text labels. For example, as illustrated in
With reference now to
As illustrated in
Optionally, the system/method 400 can employ a token stemmer 412. For example, as illustrated in
The token mapper 414 can receive tokens (mail, package) 602 and (send, pkg) 604, or, alternatively, tokens (mailed, package) 502 and (send, pkg) 504 and output a score matrix 702. Here, the token mapper can generate a score matrix for pairs of the event tokens and the activity tokens indicating a degree of similarity between the event token and the activity token in each of the pairs. For example, the score matrix can provide a score, score (x,y), for each entry of the score matrix, where each entry corresponds to a different or unique combination of (activity type token, event type token) pairs, as illustrated in
score(x,y)=(string_edit_dist(x,y)/max(len(x),len(y)))
where string_edit_dist(x,y) denotes the string edit distance of activity token, event token pair (x,y), len(x) denotes the length of (i.e., the number of characters in) the Activity token x, len(y) denotes the length of (i.e., the number of characters in) the Event token y, and max(len(x),len(y)) denotes the maximum or largest value between len(x) and len(y). Here, the string edit distance can be calculated as a levenshtein distance, which measures the similarity between two sequences. At step 810, the Token Mapper 414 can store the score, score(x,y), in the score matrix 702 at position x,y. At step 812, the Token Mapper 414 can determine whether the Event Type Y has more tokens. If the Event Type Y includes additional tokens, then the method can proceed to step 806 and can be repeated. Otherwise, the method proceeds to step 814. At step 814, the Token Mapper 414 determines whether Activity X has more tokens. If so, then the method proceeds to step 802 and is repeated. Otherwise, the method can end and the system/method 400 can evaluate another (Activity, Event Type) pair. Returning to step 804, if the Activity token x is a natural langue term, then the method can proceed to step 816, at which the Token Mapper 414 can obtain the next token y in Event Type Y. At step 818, the Token Mapper 414 can determine whether y is a natural language term and x and y are natural language synonyms. If so, then the method can proceed to step 820, at which the Token Mapper 414 assigns a score, score(x,y), of 1 in the score matrix 702 at entry x,y, which is the maximum score that can be assigned in the score matrix 702. Thereafter, the method can proceed to step 822, at which the Token Mapper 414 can determine whether the Event Type Y has more tokens. If the Event Type Y includes additional tokens, then the method can proceed to step 816 and can be repeated. Otherwise, the method proceeds to step 814, which can be performed, as discussed above. If the Token Mapper 414 determines that y is not a natural language term and/or x and y are not natural language synonyms, then the method can proceed to step 824, at which the Token Mapper 414 can compute the score, score (x,y), using a string edit distance, as discussed above with respect to step 808. The method can then proceed to step 828, at which the Token Mapper 414 can store the score, score(x,y), in the score matrix 702 at position x,y. Thereafter, the method can proceed to step 816 and can be repeated.
Turning now to the similarity calculator 416, as illustrated in
Referring again to the score matrix 702, the Similarity Calculator 416 can determine scores of the pairs of event tokens and activity tokens that are ranked highest in the score matrix. For example, the Similarity Calculator 416 can determine first scores denoting a highest ranked pair of the pairs of the event tokens and the activity tokens from each row of the score matrix and second scores denoting a highest ranked pair of the pairs of the event tokens and the activity tokens from each column of the score matrix. For example, in the score matrix 702, the (mail, send) score of 1 is the highest score of the row denoted by “mail” and the (package, pkg) score of 0.428 is the highest score of the row denoted by “package.” Thus, the token pairs (mail, send) and (package, pkg) denote the highest ranked pair of the pairs of the event tokens and the activity tokens from each row of the score matrix. Similarly, the (mail, send) score of 1 is the highest score of the column denoted by “send” and the (package, pkg) score of 0.428 is the highest score of the column denoted by “pkg.” Thus, the token pairs (mail, send) and (package, pkg) denote the highest ranked pair of the pairs of the event tokens and the activity tokens from each column of the score matrix.
In addition, the Similarity Calculator 416 can determine each of the first scores by subtracting a from a maximum score a matrix score in the score matrix of the highest ranked pair from each row of the score matrix, and can determine each of the second scores by subtracting from the maximum score a matrix score in the score matrix of the highest ranked pair from each column of the score matrix. For example, as noted above, the maximum score of the score matrix is 1. As illustrated in lines 1-2 of Table 2, the Similarity Calculator 416 can subtract from 1 the matrix score of (mail, send) and can subtract from 1 the matrix score of (package, pkg) to obtain the first scores of 0 and 0.572. Similarly, as illustrated in lines 1-2 of Table 2, the Similarity Calculator 416 can subtract from 1 the matrix score of (mail, send) and can subtract from 1 the matrix score of (package, pkg) to obtain the second scores of 0 and 0.572. The Similarity Calculator 416 can add the first scores to obtain M10=0.572 and can add the second scores to obtain M01=0.572, as illustrated in lines 1,3 and 4,6 of Table 2. Thereafter, the Similarity Calculator 416 can add the first and second scores M10 and M01, respectively, to obtain M11, as illustrated in lines 7-8 of Table 2. Further, as illustrated in line 9 of Table 2, the Similarity Calculator 416 can compute the similarity score, similarity(Activity 1, Event Type 3), as follows: similarity(Activity 1, Event Type 3)=(M11/(M11+M10+M01))=0.4447.
Thus, after obtaining the similarity score for the Activity 1, Event Type 3 pair, the Similarity Calculator 416 can update the cell or entry of the Activity 1, Event Type 3 pair in the suggestion matrix 206 with the similarity score of 0.4447 if the similarity score is above some user-defined threshold. Accordingly, the Similarity Calculator 416 can indicate in the mapping report 902 that the event type and the activity are correlated in response to determining that the similarity score is above a threshold score. Thresholds here are application-specific. Thus, a reasonable threshold level should be adapted to the particular setting in which the business processes exists. For example, if the nomenclature of the domain is fairly well established, such as, for example, in medicine and patient-care, then the threshold can be set fairly high, i.e. above 0.80. However, if the domain is new and there are many different terms for similar ideas and there exists many initializations, and abbreviations, relatively low thresholds should be set for matching and perhaps a manual inspection of the results should be instituted to ensure that any inconsistent or incorrect matching based on low scores are pruned.
The Similarity Calculator 416 can calculate the similarity score and can populate/update the suggestion matrix 206 for other Activity and Event Type pairs in the same manner. Thereafter, the Similarity Calculator 416 can output the suggestion matrix 206 at block 418 in
Referring now to
Having described preferred embodiments of a system and method for business process event mapping (which are intended to be illustrative and not limiting), it is noted that modifications and variations can be made by persons skilled in the art in light of the above teachings. It is therefore to be understood that changes may be made in the particular embodiments disclosed which are within the scope of the invention as outlined by the appended claims. Having thus described aspects of the invention, with the details and particularity required by the patent laws, what is claimed and desired protected by Letters Patent is set forth in the appended claims.
