Abstract Detecting and reacting to non-technical losses (NTL) is a fundamental activity that energy providers need to face in their daily routines. This is known to be challenging since the phenomenon of NTL is multi-factored, dynamic and extremely contextual, which makes artificial intelligence (AI) and, in particular, machine learning, natural areas to bring effective and tailored solutions. If the human factor is disregarded in the process of detecting NTL, there is a high risk of performance degradation since typical problems like dataset shift and biases cannot be easily identified by an algorithm. This paper presents a case study on incorporating explainable AI (XAI) in a mature NTL detection system that has been in production in the last years both in electricity and gas. The experience shows that incorporating this capability brings interesting improvements to the initial system and especially serves as a common ground where domain experts, data scientists, and business analysts can meet.

Process mining techniques can be used to analyse business processes using the data logged during their execution. These techniques are leveraged in a wide range of domains, including healthcare, where it focuses mainly on the analysis of diagnostic, treatment, and organisational processes. Despite the huge amount of data generated in hospitals by staff and machinery involved in healthcare processes, there is no evidence of a systematic uptake of process mining beyond targeted case studies in a research context. When developing and using process mining in healthcare, distinguishing characteristics of healthcare processes such as their variability and patient-centred focus require targeted attention. Against this background, the Process-Oriented Data Science in Healthcare Alliance has been established to propagate the research and application of techniques targeting the data-driven improvement of healthcare processes. This paper, an initiative of the alliance, presents the distinguishing characteristics of the healthcare domain that need to be considered to successfully use process mining, as well as open challenges that need to be addressed by the community in the future.

Abstract Non-technical losses (NTL) is a problem that many utility companies try to solve, often using black-box supervised classification algorithms. In general, this approach achieves good results. However, in practice, NTL detection faces technical, economic, and transparency challenges that cannot be easily solved and which compromise the quality and fairness of the predictions. In this work, we contextualise these problems in an NTL detection system built for an international utility company. We explain how we have mitigated them by moving from classification into a regression system and introducing explanatory techniques to improve its accuracy and understanding. As we show in this work, the regression approach can be a good option to mitigate these technical problems, and can be adjusted in order to capture the most striking NTL cases. Moreover, explainable AI (through Shapley Values) allows us to both validate the correctness of the regression approach in this context beyond benchmarking, and improve the transparency of our system drastically.

The detection of Non-Technical Losses using black-box supervised classification algorithms faces technical, economic, and transparency challenges that compromise the quality and fairness of predictions. In this work, we explain how we have mitigated them in a deployed NTL detection system by moving from classification into a regression system and introducing explanatory techniques to improve its accuracy and understanding.

Process mining techniques aim at analyzing and monitoring processes through event data. Formal models like Petri nets serve as an effective representation of the processes. A central question in the field is to assess the conformance of a process model with respect to the real process executions. The notion of anti-alignment, which represents a model run that is as distant as possible to the process executions, has been demonstrated to be crucial to measure precision of models. However, the only known algorithm for computing anti-alignments has a high complexity, which prevents it from being applied on real-life problem instances. In this paper we propose a novel algorithm for computing anti-alignments, based on the well-known graph-based $A^{*}$ scheme. By introducing a discount factor in the edit distance used for the search of anti-alignments, we obtain the first efficient algorithm to approximate them. We show how this approximation is quite accurate in practice, by comparing it with the optimal results for small instances where the optimal algorithm can also compute anti-alignments. Finally, we compare the obtained precision metric with respect to the state-of-the-art metrics in the literature for real-life examples.

State-of-the-art process discovery methods construct free-choice process models from event logs. Consequently, the constructed models do not take into account indirect dependencies between events. Whenever the input behaviour is not free-choice, these methods fail to provide a precise model. In this paper, we propose a novel approach for enhancing free-choice process models by adding non-free-choice constructs discovered a-posteriori via region-based techniques. This allows us to benefit from the performance of existing process discovery methods and the accuracy of the employed fundamental synthesis techniques. We prove that the proposed approach preserves fitness with respect to the event log while improving the precision when indirect dependencies exist. The approach has been implemented and tested on both synthetic and real-life datasets. The results show its effectiveness in repairing models discovered from event logs.

Predictive Business Process Monitoring is becoming an essential aid for organizations, providing online operational support of their processes. This paper tackles the fundamental problem of equipping predictive business process monitoring with explanation capabilities, so that not only the what but also the why is reported when predicting generic KPIs like remaining time, or activity execution. We use the game theory of Shapley Values to obtain robust explanations of the predictions. The approach has been implemented and tested on real-life benchmarks, showing for the first time how explanations can be given in the field of predictive business process monitoring.

Computing a mapping between two process models is a crucial technique, since it enables reasoning and operating across processes, like providing a similarity score between two processes, or merging different process variants to generate a consolidated process model. In this paper we present a new flexible technique for process model mapping, based on the relaxation labeling constraint satisfaction algorithm. The technique can be instantiated so that different modes are devised, depending on the context. For instance, it can be adapted to the case where one of the mapped process models is incomplete, or it can be used to ground an adaptable similarity measure between process models. The approach has been implemented inside the open platform NLP4BPM, providing a visualization of the performed mappings and computed similarity scores. The experimental results witness the flexibility and usefulness of the technique proposed.

The alignment of observed and modeled behavior is an essential element for organizations, since it opens the door for conformance checking 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. In this article, a novel approach is presented to tackle the challenge of computing an alignment for large-problem instances that correspond to well-formed process models. Given an observed trace, first it uses a novel replay technique to find an initial candidate trace in the model. Then a local search framework is applied to try to improve the alignment until no further improvement is possible. The implementation of the presented technique reveals a magnificent reduction both in computation time and in memory usage. Moreover, although the proposed technique does not guarantee the derivation of an alignment with minimal cost, the experiments show that in practice the quality of the obtained solutions is close to optimal.

The Business Process Management (BPM) field focuses in the coordination of labor so that organizational processes are smoothly executed in a way that products and services are properly delivered. At the same time, NLP has reached a maturity level that enables its widespread application in many contexts, thanks to publicly available frameworks. In this position paper, we show how NLP has potential in raising the benefits of BPM practices at different levels. Instead of being exhaustive, we show selected key challenges were a successful application of NLP techniques would facilitate the automation of particular tasks that nowadays require a significant effort to accomplish. Finally, we report on applications that consider both the process perspective and its enhancement through NLP.

Conformance checking techniques asses the suitability of a process model in representing an underlying process, observed through a collection of real executions. These techniques suffer from the well-known state space explosion problem, hence handling process models exhibiting large or even infinite state spaces remains a challenge. One important metric in conformance checking is to asses the precision of the model with respect to the observed executions, i.e., characterize the ability of the model to produce behavior unrelated to the one observed. By avoiding the computation of the full state space of a model, current techniques only provide estimations of the precision metric, which in some situations tend to be very optimistic, thus hiding real problems a process model may have. In this paper we present the notion of anti-alignment as a concept to help unveiling traces in the model that may deviate significantly from the observed behavior. Using anti-alignments, current estimations can be improved, e.g., in precision checking. We show how to express the problem of finding anti-alignments as the satisfiability of a Boolean formula, and provide a tool which can deal with large models efficiently. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Data from utility meters (gas, electricity, water) is a rich source of information for distribution companies, beyond billing. In this paper we present a supervised technique, which primarily but not only feeds on meter information, to detect meter anomalies and customer fraudulent behavior (meter tampering). Our system detects anomalous meter readings on the basis of models built using machine learning techniques on past data. Unlike most previous work, it can incrementally incorporate the result of field checks to grow the database of fraud and non-fraud patterns, therefore increasing model precision over time and potentially adapting to emerging fraud patterns. The full system has been developed with a company providing electricity and gas and already used to carry out several field checks, with large improvements in fraud detection over the previous checks which used simpler techniques.

No abstract available.

The discovery of process models from event logs has emerged as one of the crucial problems for enabling the continuous support in the life-cycle of an information system. However, in a decade of process discovery research, the algorithms and tools that have appeared are known to have strong limitations in several dimensions. The size of the logs and the formal properties of the model discovered are the two main challenges nowadays. In this paper we propose the use of numerical abstract domains for tackling these two problems, for the particular case of the discovery of Petri nets. First, numerical abstract domains enable the discovery of general process models, requiring no knowledge (e.g., the bound of the Petri net to derive) for the discovery algorithm. Second, by using divide and conquer techniques we are able to control the size of the process discovery problems. The methods proposed in this paper have been implemented in a prototype tool and experiments are reported illustrating the significance of this fresh view of the process discovery problem.

Random fluctuations will be a major factor interfering with the operation of nanometer scale electronic devices. This article presents circuit architectures that can exploit such fluctuations, if signals have a particle-like (discrete, token-based) character. We define an abstract circuit primitive that, though lacking functionality when used with fluctuation-free signals, becomes universal when fluctuations are allowed. Key to the power of a signal’s fluctuations is the ability to explore the state space of a circuit. This ability is used to resolve deadlock situations, which could otherwise only be averted by increased design complexity. The results in this article suggest that in the design of future computers, signal fluctuations, rather than being an impediment to be avoided at any cost, may be an important ingredient to achieve efficient operation.

A central problem in the area of Process Mining is to obtain a formal model that represents the processes that are conducted in a system. If realized, this simple motivation allows for powerful techniques that can be used to formally analyze and optimize a system, without the need to resort to its semiformal and sometimes inaccurate specification. The problem addressed in this paper is known as Process Discovery: to obtain a formal model from a set of system executions. The theory of regions is a valuable tool in process discovery: it aims at learning a formal model (Petri nets) from a set of traces. On its genuine form, the theory is applied on an automaton and therefore one should convert the traces into an acyclic automaton in order to apply these techniques. Given that the complexity of the region-based techniques depends on the size of the input automata, revealing the underlying cycles and folding the initial automaton can incur in a significant complexity alleviation of the region-based techniques. In this paper, we follow this idea by incorporating region information in the cycle detection algorithm, enabling the identification of complex cycles that cannot be obtained efficiently with state-of-the-art techniques. The experimental results obtained by the devised tool suggest that the techniques presented in this paper are a big step into widening the application of the theory of regions in Process Mining for industrial scenarios.

The continuous scaling of nanoelectronics is increasing the complexity of chip multiprocessors (CMPs) and exacerbating the memory wall problem. As CMPs become more complex, the memory subsystem is organized into more hierarchical structures to better exploit locality. During the exploration and design of CMP architectures, it is essential to efficiently analyze their performance. However, performance is highly determined by the latency of the memory subsystem, which in turn has a cyclic dependency with the memory traffic generated by the cores. This paper proposes a scalable analytical method to estimate the performance of highly parallel CMPs (hundreds of cores) with hierarchical interconnect fabrics. The method can use customizable probabilistic models and solves the cyclic dependencies by using a fixed-point strategy. The technique is shown to be a very accurate and efficient strategy when compared to the results obtained by simulation.

This paper investigates the use of formal verification techniques to create online judges that can assist in teaching logic circuit design. Formal verification not only contributes to give an exact assessment about correctness, but also saves the instructor the tedious task of designing test cases. The paper explains how formal verification has been integrated in an online judge. It also describes the courseware created for a course on logic circuits and the successful experience of using it in a one-week summer course with students from secondary and high school.

Process Conformance is becoming a crucial area due to the changing nature of processes within an Information System. By confronting specifications against system executions (the main problem tackled in process conformance), both system bugs and obsolete/incorrect specifications can be revealed. This paper presents novel techniques to enrich the process conformance analysis for the precision dimension. The new features of the metric proposed in this paper provides a complete view of the precision between a log and a model. The techniques have been implemented as a plug-in in an open-source Process Mining platform and experimental results witnessing both the theory and the goals of this work are presented.

With the scaling of process technologies, communication delays represent a bottleneck for the performance of circuits. One of the main issues that has to be handled is the variability of such delays. Latency-insensitive circuits offer a form of elast

A central problem in the area of Process Mining is to obtain a formal model that represents selected behavior of a system. The theory of regions has been applied to address this problem, enabling the derivation of a Petri net whose language includes

The theory of regions was introduced in the early nineties as a method to bridge state and event-based models. This paper tackles the problem of deriving a Petri net from a state-based model, using the theory of regions. Some of the restrictions required in the traditional approach are dropped in this paper, together with significant extensions that make the approach applicable in new scenarios. One of these scenarios is Process Mining, where accepting (discovering) additional behavior in the synthesized Petri net is sometimes valued. The algorithmic emphasis used in this paper contributes to the demystification of the theory of regions as been only a good theoretical exercise, opening the door for its application in the industrial domain.

Elasticity in circuits and systems provides tolerance to variations in computation and communication delays. This paper presents a comprehensive overview of elastic circuits for those designers who are mainly familiar with synchronous design. Elasticity can be implemented both synchronously and asynchronously, although it was traditionally more often associated with asynchronous circuits. This paper shows that synchronous and asynchronous elastic circuits can be designed, analyzed, and optimized using similar techniques. Thus, choices between synchronous and asynchronous implementations are localized and deferred until late in the design process.

Nanometer-scale structures suitable for computing have been investigated by several research groups in recent years. A common feature of these structures is their dynamic evolution through cascaded local interactions embedded on a discrete grid. Finding configurations capable of conducting computations is a task that often requires tedious experiments in laboratories. Formal methods—though used extensively for the specification and verification of software and hardware computing systems—are virtually unexplored with respect to computational structures at atomic scales. This paper presents a systematic approach toward the application of formal methods in this context, using techniques like abstraction, model-checking, and symbolic representations of states to explore and discover computational structures. The proposed techniques are applied to a system of CO molecules on a grid of Copper atoms, resulting in the design of a complete library of combinational logic gates based on this molecular system. The techniques are also applied on (more general) systems of cellular automata that employ an asynchronous mode of timing. The use of formal methods may narrow the gap between Physical Chemistry and Computer Science, allowing the description of interactions of nanometer scale systems on a level of abstraction suitable to devise computing devices.

State encoding is one of the most difficult problems in the synthesis of asynchronous controllers. This paper presents a method that can solve the problem of large controllers specified with signal transition graphs. The method is based on the structural theory of Petri nets and uses integer-linear programming to insert state signals in locations that guarantee the consistency and absence of critical races. The structural nature of the proposed method makes it conservative, i.e., a solution cannot be guaranteed, even if it exists. Nevertheless, the experiments show that this limitation did not preclude finding a solution for all the examples presented in this paper. The method can be customized for area or delay optimization. The experimental results confirm the quality of the circuits, as compared with state-based methods. They also show the significant benefits that could be obtained if logic synthesis would be incorporated in synthesis frameworks that generate controllers by syntax-directed translation.

A novel method to solve the state encoding problem in Signal Transition Graphs is presented. It is based on the structural theory of Petri nets and can be applied to large specifications with hundreds of signals. This new method opens the door to incorporate logic synthesis in the design flow of large control circuits obtained from high-level specifications. The experimental results validate the quality of the encoded circuits and show the significant improvements that can be obtained by the synthesis of large controllers.

The continuous minituarization of integrated circuits may reach atomic scales in a couple of decades. Some researchers have already built simple computation engines by manipulating individual atoms on metal surfaces. This paper presents a systematic approach to automate the design of logic gates using molecule cascades. Temporal logic is used to characterize molecular interactions and specify the behavior of logic gates. Model-checking techniques are used for the exploration of structures behaviorally equivalent to the logic gates. As an example, a complete library of combinational logic gates has been designed using a particular molecular system. This new approach provides a methodology to bridge the gap between physical chemists and computer scientists in seeking computational structures at atomic scales.

A novel strategy for the logic synthesis of asynchronous control circuits is presented. It is based on the structural theory of Petri nets and integer linear programming. Techniques that are capable of checking implementability conditions, such as complete state coding, and deriving a gate netlist to implement the specified behavior are presented. These techniques can handle Petri net specifications consisting of several thousands of transitions and provide a significant speed-up compared with techniques that have previously been proposed