Structural computation of alignments of business processes over partial orders

Relating event data and process models is becoming an important element for organizations. This paper presents a novel approach for aligning traces and process models. The approach is based on the structural theory of Petri nets (the marking equation), applied over an unfolding of the initial process model. Given an observed trace, the approach adopts an iterative optimization mechanism on top of the unfolding, computing at each iteration part of the resulting alignment. In contrast to the previous work that is primarily grounded in the marking equation, this approach is guaranteed to provide real solutions, and tries to mimic as much as possible the events observed in the trace. Experiments witness the significance of this approach both in quality and execution time perspectives.Peer ReviewedPostprint (author's final draft

Aligning Modeled and Observed Behavior: A Compromise Between Complexity and Quality

International audienceCertifying that a process model is aligned with the real process executions is perhaps the most desired feature a process model may have: aligned process models are crucial for organizations, since strategic decisions can be made easier on models instead of on plain data. In spite of its importance, the current algorithmic support for computing alignments is limited: either techniques that explicitly explore the model behavior (which may be worst-case exponential with respect to the model size), or heuristic approaches that cannot guarantee a solution, are the only alternatives. In this paper we propose a solution that sits right in the middle in the complexity spectrum of alignment techniques; it can always guarantee a solution, whose quality depends on the exploration depth used and local decisions taken at each step. We use linear algebraic techniques in combination with an iterative search which focuses on progressing towards a solution. The experiments show a clear reduction in the time required for reaching a solution, without sacrificing significantly the quality of the alignment obtained

Light methods for conformance checking of business processes

Conformance Checking is a new research discipline devoted to identify deviations between business process models and their real executions. Identifying deviations boils down to the notion of alignment conceptually. An alignment quantifies to what degree a process model can imitate what happened in its observed behavior, i.e., an event log. Accordingly, an optimal alignment is the best combination by which the process model can imitate the corresponding observed behavior. The state of the art technique for alignment computation has exponential time and space complexity, hindering its applicability for medium and large instances. The main aim of this thesis is to propose light and efficient methods for alignment computation. By finding a suitable trade-off between computation time, memory consumption and optimality, a familly of techniques is proposed such that depending on the input assumptions and required guarantees, a user can select the right technique for her particular problem. Generally speaking, the methods presented in this thesis can be categorized as: - Classical approaches: These techniques exploit Integer Linear Programming (ILP), as well as structural theory of Petri nets, to formulate alignment computation as an optimization of a set of linear equations. A modification to this strategy which trades-off between complexity and quality is to integrate it with state of the art approach. - Heuristic approaches: These techniques take advantages of heuristic functions to explore the search space of alignments, to find the optimal one(s). This can be done by obtaining an initial solution, and iteratively improving it until saturation or reaching a certain criterion. Another contribution is by adopting a Genetic Algorithm with well specific designed operators, by which exploration of the corresponding search space can be speed up toward the best solution(s). - Model reduction: An alternative way to boost the effectiveness of alignment computation is by reducing model and observed behavior without loosing alignment information. This structure reduction not only boosts the alignment computation, but also provides a big picture of detected deviations. Above that, a divide-and-conquer strategy will be provided for the ILP approach, such that it breaks the original problem into a set of smaller independent problems that can be solved independently. Experiments witness the merit of proposed approaches with respect to state of the art technique in different perspectives, such as resource consumption, execution time, quality and accuracy of the solutions found. All methods have been implemented as a stand-alone tool box called ALI.Conformance checking és una nova disciplina dedicada a identificar desviacions entre els models de processos de negoci i les seves execucions reals. Identificar les desviacions porta directament al concepte d'alineament. Un alineament quantifica el grau en que un model de procés pot imitar el que va passar en el seu comportament observat, és a dir, un registre d'esdeveniments. En conseqüència, una alineament òptim és la millor combinació per la qual el model de procés pot imitar el comportament observat. La tècnica de referència per a la computació d'alineaments té una complexitat exponencial, el que dificulta la seva aplicabilitat per a casos mitjans o grans. L'objectiu principal d'aquesta tesi és proposar mètodes eficients per a la computació d'alineaments. En trobar un punt raonable entre el temps d'execució, el consum de memòria i la optimalitat, es proposa una família de tècniques de manera que, segons els supòsits d'entrada i les garanties requerides, un usuari pot seleccionar la tècnica adequada per al seu problema. En termes generals, els mètodes presentats en aquesta tesi es poden classificar com: Enfocaments clàssics: aquestes tècniques utilitzen la Programació Lineal Entera (anglès, ILP), així com la teoria estructural de les xarxes Petri, per realitzar la computació d'alineaments com una optimització d'un conjunt d'equacions lineals. Una modificació d'aquesta estratègia, que pondera la complexitat i la qualitat, és la d'integrar-la amb l'enfocament de referència. Aproximacions heurístiques: aquestes tècniques aprofiten funcions heurístiques per explorar l'espai de cerca d'alineaments, per trobar les solucions properes a l'òptim. Això es pot fer obtenint una solució inicial, que es millorarà iterativament fins a la saturació, o bé assolint un criteri determinat de convergència. Una altra contribució és l'adopció d'un algoritme evolutiu amb operadors específics, que permeten guiar l'exploració de l'espai de cerca corresponent cap a les millors solucions. Reducció de models: una forma alternativa de potenciar l'efectivitat de la computació d'alineaments és reduint el comportament modelat i observat sense perdre la informació d'alineaments. Aquesta reducció d'estructura no només alleugereix el problema, sinó que també proporciona una visió abstracta de les desviacions detectades. Addicionalment, es proposa una estratègia de divideix i vèncer per a l'enfocament de l'ILP, que trenca el problema original en un conjunt de problemes independents més petits que es poden resoldre de forma independent. Els experiments realitzats per cada tècnica demostren la capacitat dels algorismes proposats, en diferents perspectives, com ara el consum de recursos, el temps d'execució, la qualitat i la precisió de les solucions trobades. Tots els mètodes s'han implementat en el software open-source ALI.Postprint (published version

Model and Event Log Reductions to Boost the Computation of Alignments

International audienceThe alignment of observed and modeled behavior is a pivotal issue in process mining because it opens the door for assessing the quality of a process model, as well as the usage of the model as a precise predictor for the execution of a process. This paper presents a novel technique for reduction of a process model based on the notion of indication, by which, the occurrence of an event in the model reveals the occurrence of some other events, hence relegating the later set as less important information when model and log alignment is computed. Once indications relations are computed in the model, both model and log can be reduced accordingly, and then fed to the state of the art approaches for computing alignments. Finally, the (macro)-alignment derived is expanded in these parts containing high-level events that represent a set of indicated events, by using an efficient algorithm taken from bioinformatics that guarantees optimality in the local parts of the alignment. The implementation of the presented techniques shows a significant reduction both in computation time and in memory usage, the latter being a significant barrier to apply the alignment technology on large instances

An evolutionary technique to approximate multiple optimal alignments

The alignment of observed and modeled behavior is an essential aid for organizations, since it opens the door for root-cause analysis and enhancement of processes. The state-of-the-art technique for computing alignments has exponential time and space complexity, hindering its applicability for medium and large instances. Moreover, the fact that there may be multiple optimal alignments is perceived as a negative situation, while in reality it may provide a more comprehensive picture of the model’s explanation of observed behavior, from which other techniques may benefit. This paper presents a novel evolutionary technique for approximating multiple optimal alignments. Remarkably, the memory footprint of the proposed technique is bounded, representing an unprecedented guarantee with respect to the state-of-the-art methods for the same task. The technique is implemented into a tool, and experiments on several benchmarks are provided.Peer Reviewe

A recursive paradigm for aligning observed behavior of large structured process models

The alignment of observed and modeled behavior is a crucial problem in process mining, since it opens the door for conformance checking and enhancement of process models. The state of the art techniques for the computation of alignments rely on a full exploration of the combination of the model state space and the observed behavior (an event log), which hampers their applicability for large instances. This paper presents a fresh view to the alignment problem: the computation of alignments is casted as the resolution of Integer Linear Programming models, where the user can decide the granularity of the alignment steps. Moreover, a novel recursive strategy is used to split the problem into small pieces, exponentially reducing the complexity of the ILP models to be solved. The contributions of this paper represent a promising alternative to fight the inherent complexity of computing alignments for large instances.Peer Reviewe

Measuring Connectivity Tolerance in Wireless Sensor Networks using Graph Theory Applications: A Fast Algorithm

Abstract — Wireless sensor networks today has been attracted many diverse areas in both academic and business domains because of their facility and applicability, low deployment cost and other factors. These networks aside from challenges that traditional networks have, face new challenges. These challenges can be tackled from other fields and by many tools. Since these networks can be seen from graph theory perspective as an abstract graph where sensors become nodes and links become edges in the graph, graph theory applications can be used to analyze and tackled some challenges in these networks. In this paper we first propose an exhaustive algorithm for measuring connectivity tolerance in WSNs then, since WSNs have a dynamic structure, we proposed a fast algorithm that in worst case has time complexity O(nlogn) and O(n) in normal case for measuring connectivity tolerance. Beside this parameters these algorithms can produce special data that is called meta-data, this meta-data can be used for other routing protocols or mechanisms in networks such that they do not need to run graph algorithm again, just need to operate on meta-data to obtain desire parameters. The organization of this paper is as follow, first we review the works that have been done common in both graph theory and wireless sensor networks, and in last we propose an fast algorithm for calculating connectivity tolerance for two arbitrary sensors in wireless sensor network due sensor corruption or link loss with the use of graph theory and graph mining techniques. This algorithm will test on most used sensors deployments, three sensors deployments namely, Uniform, Normal and Random distributions, then the results and conclusion will present according to these distributions. IJSE

Aligning modeled and observed behavior:a compromise between computation complexity and quality

\u3cp\u3eCertifying that a process model is aligned with the real process executions is perhaps the most desired feature a process model may have: aligned process models are crucial for organizations, since strategic decisions can be made easier on models instead of on plain data. In spite of its importance, the current algorithmic support for computing alignments is limited: either techniques that explicitly explore the model behavior (which may be worst-case exponential with respect to the model size), or heuristic approaches that cannot guarantee a solution, are the only alternatives. In this paper we propose a solution that sits right in the middle in the complexity spectrum of alignment techniques; it can always guarantee a solution, whose quality depends on the exploration depth used and local decisions taken at each step. We use linear algebraic techniques in combination with an iterative search which focuses on progressing towards a solution. The experiments show a clear reduction in the time required for reaching a solution, without sacrificing significantly the quality of the alignment obtained.\u3c/p\u3