Top PDF A Constraint-Based Approach for Managing Declarative Temporal Business Process Models

A Constraint-Based Approach for Managing Declarative Temporal Business Process Models

A Constraint-Based Approach for Managing Declarative Temporal Business Process Models

There is an increasing interest in aligning information systems in a process-oriented way. As an alternative of the traditional imperative models which tend to be too rigid, processes may be specified in a declarative (e.g., constraint-based) way. Nonetheless, in general, offering operational support (e.g., generating possible execution traces) to declarative business process models entails more complexity when compared to imperative modeling alternatives. Such support becomes even more complex in many real scenarios where the management of complex temporal relations between the process activities is crucial (i.e., the temporal perspective should be managed). Despite the needs for enabling process flexibility and dealing with temporal constraints, most existing tools are unable to manage both. In a previous work, we then proposed TConDec-R, which is a constraint-based process modeling language which allows for the specification of temporal constraints. However, TConDec-R revealed a number of limitations that are overcome with the present work. More specifically, this paper significantly extends and improves our previous work by (1) defining TConDec-R process models based on high-level elements from the constraint programming paradigm, (2) introducing a constraint-based tool with a client/server architecture for providing operational support to TConDec-R process models, and (3) performing an empirical evaluation of the approach.
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The Temporal Perspective: Expressing Temporal Constraints and Dependencies in Process Models

The Temporal Perspective: Expressing Temporal Constraints and Dependencies in Process Models

We characterize the temporal perspective of process modeling by providing a se- ries of generic recurring temporal constructs. This characterization is independ- ent of any specific modeling formalism or approach. We precisely define each temporal construct and when possible provide a formal temporal account of these constructs based on Allen’s interval algebra [12]. Allen’s interval algebra is the most popular in Artificial Intelligence. Although the management of time in the context of business processes has been studied (e.g. [13, 14, 15, 16, 17, 18, 19, 20, 21, 22]), previous work does not address all the temporal constructs studied in this paper. Furthermore, although Allen’s interval algebra has been applied to many application areas, it has not received much attention in the business proc- ess community. To the best of our knowledge, only Lu et. al. [22] use Allen’s alge- bra as the basis for flexible business process execution via constraint satisfaction. Our characterization of the temporal perspective for workflow specifications pro- vides a basis and objective means to evaluate the temporal expressiveness of vari- ous formalisms and tools. Also it opens the way to the integration of formal vali- dation tools, such as constraint satisfaction or theorem proving systems, to verify the temporal satisfiability of the workflow specification.
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Data-aware Synthetic Log Generation for Declarative Process Models

Data-aware Synthetic Log Generation for Declarative Process Models

2 Data-aware Synthetic Log Generation for Declarative Process Models Abstract: In Business Process Management, process mining is a class of techniques for learning pro- cess structure from an execution log. This structure is represented as a process model: either procedural or declarative. Examples of declarative languages are Declare, DPIL and DCR Graphs. In order to test and improve process mining algorithms a lot of logs with different parameters are required, and it is not always possible to get enough real logs. And this is where artificial logs are useful. There exist techniques for log generation from DPIL and declare-based models. But there are no tools for generating logs from MP-Declare – multi- perspective version of Declare with data support. This thesis introduces an approach to log generation from MP-Declare models using two different model checkers: Alloy and NuSMV. In order to improve performance, we applied optimization to baseline approaches available in the literature. All of the discussed techniques are implemented and tested using existing conformance checking tools and our tests. To evaluate performance of our genera- tors and compare them with existing ones, we measured time required for generating log and how it changes with different parameters and models. We also designed several metrics for computing log variability, and applied them to reviewed generators.
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Efficient Discovery of Understandable Declarative Process Models from Event Logs

Efficient Discovery of Understandable Declarative Process Models from Event Logs

– Second, of the millions of potential constraints, many may be trivially true. For example, the response constraint in Fig. 2 holds for any event log that does not contain events relating to activity a. Moreover, one constraint may dominate another constraint. If the stronger constraint holds (e.g., (a → ♦b)), then automatically the weaker constraint (e.g., ♦a → ♦b) also holds. Showing all constraints that hold typically results in unreadable models. This paper addresses these two problems using a two-phase approach. In the first phase, we generate the list of candidate constraints by using an Apriori al- gorithm. This algorithm is inspired by the seminal Apriori algorithm developed by Agrawal and Srikant for mining association rules [7]. The Apriori algorithm uses the monotonicity property that all subsets of a frequent item-set are also frequent. In the context of this paper, this means that sets of activities can only be frequent if all of their subsets are frequent. This observation can be used to dramatically reduce the number of interesting candidate constraints. In the sec- ond phase, we further prune the list of candidate constraints by considering only the ones that are relevant (based on the event log) according to (the combination of) simple metrics, such as Confidence and Support, and more sophisticated met- rics, such as Interest Factor (IF) and Conditional-Probability Increment Ratio (CPIR), as explained in Section 4. Moreover, discovered constraints with high CPIR values are emphasized like highways on a roadmap whereas constraints with low CPIR values are greyed out. This further improves the readability of discovered Declare models.
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Ontology Mapping of Business Process Modeling Based on Formal Temporal Logic

Ontology Mapping of Business Process Modeling Based on Formal Temporal Logic

H. Temporal logic based models/systems The essential role of time in the modeling of natural processes has given rise in recent years to a body of artificial intelligence research into temporal theory. This research has led to a variety of temporal systems, attempting to capture the primary elements of time. However, as time goes on, the world may change its state from one into another, triggered by some certain events or processes that take place over time. Although different temporal systems show considerable commonality in structure, they also show considerable differences in formalization. In the literature, there are three choices regarding the primitive for the ontology of time: instantaneous points, durative intervals and both points and intervals and problems may arise when one conflates different views of temporal structure. A natural approach to representing and reasoning about the actions, events, processes is to associate them with time elements (i.e., instantaneous points and/or durative intervals) [18].
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Aligning Data-Aware Declarative Process Models and Event Logs

Aligning Data-Aware Declarative Process Models and Event Logs

final state from the current state then it is labeled as permanently successful. If the transition is the last activity and the current state is non-final or if is not the last activity, the current state is non-final and there exists no transition to a final state from the current state, then the trace is labeled as permanently violated. If the trace has not reached the last activity, the current state is final and there exist a transition from the current state to a non-final state then the trace is labeled as temporarily successful. If the trace has not reached the last activity, the current state is non-final and there exist a transition to a final state then the trace is labeled as temporarily violated. This approach was not used in the context of aligning data-aware declare models. At attempt at using finite state machines in the alignment of declarative models is found in [8]. Just like in the above- mentioned approaches for aligning procedural languages, [8] proposed an alignment-based conformance checking approach for declarative models. The approach also tries to replay activities in an event log against a Declare model by labeling moves in log where the move is only recognized by the log or move in model where a move is only recognized by the model and move in both. To determine the transitions, the Declare model is represented as final state automata for each constraint. Also, legal moves are associated with costs and the A* algorithm is employed to find the optimal alignment. However, this approach only recognizes the control flow perspective ignoring data, time and resources.
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Probabilistic Declarative Process Mining

Probabilistic Declarative Process Mining

Abstract. The management of business processes is receiving much at- tention, since it can support significant efficiency improvements in orga- nizations. One of the most interesting problems is the representation of process models in a language that allows to perform reasoning on it. Various knowledge-based languages have been lately developed for such a task and showed to have a high potential due to the advantages of these languages with respect to traditional graph-based notations. In this work we present an approach for the automatic discovery of knolwedge-based process models expressed by means of a probabilistic logic, starting from a set of process execution traces. The approach first uses the DPML algorithm [16] to extract a set of integrity constraints from a collection of traces. Then, the learned constraints are translated into Markov Logic formulas and the weights of each formula are tuned using the Alchemy system. The resulting theory allows to perform proba- bilistic classification of traces. We tested the proposed approach on a real database of university students’ careers. The experiments show that the combination of DPML and Alchemy achieves better results than DPML alone.
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Managing Large Collections of Business Process Models - Current Techniques and Challenges

Managing Large Collections of Business Process Models - Current Techniques and Challenges

There exist various approaches that can be used to ex- press and execute queries over a collection of business pro- cess models [6, 7, 8, 9]. These approaches vary with respect to their expressive power. Most notably, there is a distinc- tion between approaches that enable the formulation of a query as a business process model fragment [7, 8] and those that provide specific constructs for expressing pro- cess model queries [6, 9]. The first class of approaches aim to identify all models in a repository that contain the query fragment modulo similar activity labels. The second class uses a declarative approach whereby one can specify the existence or absence of specific (transitive) paths between process model activities. Queries can be formulated over control-flow aspects of process models [6, 7, 8], although some approaches also embrace other aspects, including the resources that are used to perform the process, the process trigger, the process goal, the location(s) at which the pro- cess is executed, or the categories in which the process is classified [7].
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Declarative Process Mining on the Cloud

Declarative Process Mining on the Cloud

Producing clear and understandable process models is one of the key goals of process dis- covery. Procedural approaches are dependent on describing the control-flow by providing all the possible options on process execution, and this results in extremely complex models especially for processes characterized by high variability. Alternative to procedural ap- proach is declarative approach. Declarative process models specify behavioral constraints meaning that if the constraint does not prohibit something, it is allowed. As a result, this produces more compact and flexible models. One of the languages used in the declarative approaches is Declare. Declare is based on Linear Temporal Logic, which is introduced in Section 2.2, Section 2.3 afterwards will describe Declare language [3].
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Using a Temporal Constraint Network for Business Process Execution 1

Using a Temporal Constraint Network for Business Process Execution 1

Brisbane, Australia {ruopeng, shazia, vnair, guido}@itee.uq.edu.au Abstract Business process management (BPM) has emerged as a dominant technology in current enterprise systems and business solutions. However, the technology continues to face challenges in coping with dynamic business environments where requirements and goals are constantly changing. In this paper, we present a modelling framework for business processes that is conducive to dynamic change and the need for flexibility in execution. This framework is based on the notion of process constraints. Process constraints may be specified for any aspect of the process, such as task selection, control flow, resource allocation, etc. Our focus in this paper is on a set of scheduling constraints that are specified through a temporal constraint network. We will demonstrate how this specification can lead to increased flexibility in process execution, while maintaining a desired level of control. A key feature and strength of the approach is to use the power of constraints, while still preserving the intuition and visual appeal of graphical languages for process modelling. .
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Capturing Variability in Business Process Models: The Provop Approach

Capturing Variability in Business Process Models: The Provop Approach

Different work exists on how specialization can be applied to deal with process model variability taking advantage of the generative power of a specialization hier- archy [30, 41]. In the context of the MIT Process Handbook [30], for example, it is shown how specialization can be enabled for simple state diagrams and dataflow dia- grams respectively. For both kinds of diagrams a corresponding set of transformation rules is provided that results in process specializations when being applied to a partic- ular model. Similarly, [41] discusses transformation rules to define specialization for process models based on Petri Nets. Finally, [30] shows how specialization can be used to generate a taxonomy of processes to facilitate the exploration of design alternatives and the reuse of existing designs. Obviously, specialization and process taxonomies also allow to capture process variants to some degree. As opposed to the discussed ap- proaches, Provop follows an operational approach, which is independent of the under- lying process meta model. In addition, we provide comprehensive support for context- and constraint-based configuration of process variants.
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Enhancing Business Process Management with a Constraint-Based Approach

Enhancing Business Process Management with a Constraint-Based Approach

5.4. Complexity The above differences between rules and constraints result in a higher complexity of the constraint technology. Because of this constraint-based systems are often criticized to be inefficient managing large constraint nets. Despite there exist a couple of efficient (i. a. heuristic) algorithms handling different kinds of constraint problems the fact is less relevant in this case at all. In the BPM approach de- scribed in this paper the focus is on the control flow in process models managed by a process engine. Constraints are used additional to BPM techniques. So there is less the problem of complexity of constraints as in the useful enhancement of BPM with constraints. In contrast to constraint-based systems, where constraints are the sole knowledge representation technique, supporting BPM we have only simple constraints. Also the complexity of the constraint net is about a manage- able size, because the control flow is mostly determined by the process model. The constraint technology is used to provide a flexible way to react on viola- tions of know limitations and restrictions and thus supporting the process engine executing the process model.
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Process Mining and Verification of Properties: An Approach based on Temporal Logic

Process Mining and Verification of Properties: An Approach based on Temporal Logic

Today’s information systems need to compromise between two requirements: (1) being adaptive and self-managing and (2) being able to be audited. Within the context of this struggle, we have developed a tool called LTL Checker. This tool has been developed in the context of the ProM framework 1 . The ProM framework offers a wide range of tools related to process mining, i.e., extracting information from event logs [6]. Process mining is motivated by the fact that many business processes leave their “footprints” in transactional information systems (cf. WFM, ERP, CRM, SCM, and B2B systems), i.e., business events are recorded in so-called event logs. Until recently, the information in these logs was rarely used to analyze the underlying processes. Process mining aims at improving this by providing techniques and tools for discovering process, control, data, organizational, and social structures from event logs, i.e., the basic idea of process mining is to diagnose business processes by mining event logs for knowledge.
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Improving Business Process Models with Agent-based Simulation and Process Mining

Improving Business Process Models with Agent-based Simulation and Process Mining

Due to space restrictions, here we focused on the analysis of the control-flow alone, but the same approach can be applied to the analysis of the organiza- tional perspective, which includes the handover of work between agents and the collaboration of agents within each case. This can be done by selecting other columns for analysis, namely the sender column or the receiver column in the event log of Table 2. In future work, we are planning to improve several aspects of the proposed approach, namely establishing guidelines for the conversion of BPMN models to Markov models, supporting the automatic generation of an AOR simulation scenario from a given hierarchical model, and expanding the set of metrics used in the analysis and evaluation phase.
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Constraint Based Models of Lexical Borrowing

Constraint Based Models of Lexical Borrowing

high level, this is an instance of the well-known prob- lem of modeling string transductions, our interest is being able to identify correspondences across lan- guages with minimal supervision, so as to make the technique applicable in low-resource settings. To re- duce the supervision burden, we propose a model that includes awareness of the morpho-phonological re- pair strategies that native speakers of a language sub- consciously employ to adapt a loanword to phonolog- ical constraints of the recipient language (§3). To this end, we use constraint-based theories of phonology, as exemplified by Optimality Theory (OT) (Prince and Smolensky, 2008; McCarthy, 2009), which non- computational linguistic work has demonstrated to be particularly well suited to account for phonolog- ically complex borrowing processes (Kang, 2011). We operationalize OT constraints as features in our borrowing model (§4).
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A framework for simulation-based optimization of business process models

A framework for simulation-based optimization of business process models

The Assignment Problem is a classical problem in the field of combinatorial optimization, having a wide range of appli- cations in a variety of contexts. In general terms, the Assignment Problem consists of determining the best assignment of tasks to agents according to a predefined objective function. Different variants of the Assignment Problem have been extensively investigated in the literature in the last 50 years. In this work, we introduce and analyze the problem of optimizing a business process model with the objective of finding the most beneficial assignment of tasks to agents. Despite similarities, this problem is distinguished from the traditional Assignment Problem in that we consider tasks to be part of a business process model, being interconnected according to defined rules and constraints. In other words, assigning a business process to agents is a more complex form of the Assignment Problem. Two main categories of business processes, assignment-independent and assignment-dependent, are distinguished. In the first category, different assignments of tasks to agents do not affect the flow of the business process, while processes in the second category contain critical tasks that may change the workflow, depending on who performs them. In each category several types of processes are studied. Algorithms for finding optimal and near-optimal solutions to these categories are presented. For the first category, depending on the type of process, the Hungarian algorithm is combined with either the analytical method or simulation to provide an optimal solution. For the second category, we introduce two algorithms. The first one finds an optimal solution, but is feasible only when the number of critical tasks is small. The second algorithm is applicable to large number of critical tasks, but provides a near-optimal solution. In the second algorithm a hill-climbing heuristic method is combined with the Hungarian algorithm and simulation to find an overall near-optimal solution. A series of tests is conducted which demonstrates that the proposed algorithms efficiently find optimal solutions for assignment-independent and near-optimal solutions for assignment-dependent processes.
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A Goal-based approach for business process learning

A Goal-based approach for business process learning

Case-based reasoning (CBR) is an approach to problem solving and learning [ 2], that has been applied to process modeling by different authors, and hence has some relation to our research objectives. New problems are dealt with by drawing on past experiences, described in cases stored in case-bases, and by adapting their solutions to the new problem situation. In [ 5][ 12], a modified CBR approach – a conversational CBR (CCBR) aims to allow run-time changes to process models by the end user. LPM can improve the performance of CBR-based approaches by providing a formal definition of similarity criteria as proposed earlier based on Context and Goal similarities combined with the normally used occurrence statistics in CBR variants researches. Still, the proposed approach does not establish a methodology to analyze the relevance of past experience to specific process contexts, nor does it use instance outcomes (soft-goal levels) in its analysis.
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MODELS FOR THE INTERNATIONALIZATION OF THE BUSINESS: A DIVERSITY- BASED APPROACH

MODELS FOR THE INTERNATIONALIZATION OF THE BUSINESS: A DIVERSITY- BASED APPROACH

The model of business network emphasizes the value of commercial, personal and cognitive relationships between its members. This model assumes that the organizational network of the company is a major incentive for internationalization and the companies produce their resources by interacting with other partners. The companies of the network can be both individually independent and dependent on the resources controlled by other companies. The degree of dependence gradually increases and that means the resources of one company become more dependent on the ones of other companies for the benefit of all parties (Hollensen, 2008; Rubaeva, 2010; Căescu and Dumitru, 2011). The business networks work throughout exchange relationships and their needs and capacities are mediated by the interactions during those relationships.
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An approach to business process simulation using mined probabilistic models

An approach to business process simulation using mined probabilistic models

Once generated, the model can be customised based on the needs of the simulation performers. The customisations can be such as the addition of other activities, modification of activation rules, and/or modification of activity behaviour. For example, if there is a need to customize the model and add a rule, that warehouse accepts incoming transport only after 9:00am and only until 6:00pm, the activity “accept transport” could be modified that its activation rule in pseudo code is “$time > (9:00am) and $time < (6:00pm) and $started = true and most_probable_next_event(accept transport) = true” . Another example could be that the performers want to test only a single decision path. In that case, generated activities could be modified to generate a specific set of data attributes that force a specific process path as opposed to pseudo- random data. This way, the approach allows simulation performers to automatically generate the initial simulation model from an event log and then modify it for their needs to test what-if scenarios or analyse general process behaviour.
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Unsupervised Declarative Knowledge Induction for Constraint Based Learning of Information Structure in Scientific Documents

Unsupervised Declarative Knowledge Induction for Constraint Based Learning of Information Structure in Scientific Documents

Little prior work exists on automatic constraint learning. Recently, (McClosky and Manning, 2012) investigated the approach for timeline extraction. They used a set of gold relations and their temporal spans and applied distant learning to find approxi- mate instances for classifier training. A set of con- straint templates specific to temporal learning were also specified. In contrast, we do not use manually specified guidance in constraint learning. Particu- larly, we construct constraints from latent variables (topics in topic modeling) estimated from raw text rather than applying maximum likelihood estimation over observed variables (fluents and temporal ex- pressions) in labeled data. Our method is therefore less dependent on human supervision. Even more recently, (Anzaroot et al., 2014) presented a super- vised dual-decomposition based method, in the con- text of citation field extraction, which automatically generates large families of constraints and learn their costs with a convex optimization objective during training. Our work is unsupervised, as opposed to their model which requires a manually annotated training corpus for constraint learning.
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