arXiv subjects

Matthias Weidlich

Publications and source records attributed to Matthias Weidlich.

At least 37 records · Page 2Linked to original sources

arXiv2023

PMDG: Privacy for Multi-Perspective Process Mining through Data Generalization

Anonymization of event logs facilitates process mining while protecting sensitive information of process stakeholders. Existing techniques, however, focus on the privatization of the control-flow. Other process perspectives, such as roles, resources, and objects are neglected or subject to randomization, which breaks the dependencies between the perspectives. Hence, existing techniques are not suited for advanced process mining tasks, e.g., social network mining or predictive monitoring. To address this gap, we propose PMDG, a framework to ensure privacy for multi-perspective process mining through data generalization. It provides group-based privacy guarantees for an event log, while preserving the characteristic dependencies between the control-flow and further process perspectives. Unlike existin privatization techniques that rely on data suppression or noise insertion, PMDG adopts data generalization: a technique where the activities and attribute values referenced in events are generalized into more abstract ones, to obtain equivalence classes that are sufficiently large from a privacy point of view. We demonstrate empirically that PMDG outperforms state-of-the-art anonymization techniques, when mining handovers and predicting outcomes.

cs.DB↗

arXiv2022

Efficient Integration of Multi-Order Dynamics and Internal Dynamics in Stock Movement Prediction

Advances in deep neural network (DNN) architectures have enabled new prediction techniques for stock market data. Unlike other multivariate time-series data, stock markets show two unique characteristics: (i) \emph{multi-order dynamics}, as stock prices are affected by strong non-pairwise correlations (e.g., within the same industry); and (ii) \emph{internal dynamics}, as each individual stock shows some particular behaviour. Recent DNN-based methods capture multi-order dynamics using hypergraphs, but rely on the Fourier basis in the convolution, which is both inefficient and ineffective. In addition, they largely ignore internal dynamics by adopting the same model for each stock, which implies a severe information loss. In this paper, we propose a framework for stock movement prediction to overcome the above issues. Specifically, the framework includes temporal generative filters that implement a memory-based mechanism onto an LSTM network in an attempt to learn individual patterns per stock. Moreover, we employ hypergraph attentions to capture the non-pairwise correlations. Here, using the wavelet basis instead of the Fourier basis, enables us to simplify the message passing and focus on the localized convolution. Experiments with US market data over six years show that our framework outperforms state-of-the-art methods in terms of profit and stability. Our source code and data are available at \url{https://github.com/thanhtrunghuynh93/estimate}.

q-fin.ST↗

arXiv2022

Model-Agnostic and Diverse Explanations for Streaming Rumour Graphs

The propagation of rumours on social media poses an important threat to societies, so that various techniques for rumour detection have been proposed recently. Yet, existing work focuses on \emph{what} entities constitute a rumour, but provides little support to understand \emph{why} the entities have been classified as such. This prevents an effective evaluation of the detected rumours as well as the design of countermeasures. In this work, we argue that explanations for detected rumours may be given in terms of examples of related rumours detected in the past. A diverse set of similar rumours helps users to generalize, i.e., to understand the properties that govern the detection of rumours. Since the spread of rumours in social media is commonly modelled using feature-annotated graphs, we propose a query-by-example approach that, given a rumour graph, extracts the $k$ most similar and diverse subgraphs from past rumours. The challenge is that all of the computations require fast assessment of similarities between graphs. To achieve an efficient and adaptive realization of the approach in a streaming setting, we present a novel graph representation learning technique and report on implementation considerations. Our evaluation experiments show that our approach outperforms baseline techniques in delivering meaningful explanations for various rumour propagation behaviours.

cs.SI↗

arXiv2022

Detecting Rumours with Latency Guarantees using Massive Streaming Data

Today's social networks continuously generate massive streams of data, which provide a valuable starting point for the detection of rumours as soon as they start to propagate. However, rumour detection faces tight latency bounds, which cannot be met by contemporary algorithms, given the sheer volume of high-velocity streaming data emitted by social networks. Hence, in this paper, we argue for best-effort rumour detection that detects most rumours quickly rather than all rumours with a high delay. To this end, we combine techniques for efficient, graph-based matching of rumour patterns with effective load shedding that discards some of the input data while minimising the loss in accuracy. Experiments with large-scale real-world datasets illustrate the robustness of our approach in terms of runtime performance and detection accuracy under diverse streaming conditions.

cs.SI↗

arXiv2021

GECCO: Constraint-driven Abstraction of Low-level Event Logs

Process mining enables the analysis of complex systems using event data recorded during the execution of processes. Specifically, models of these processes can be discovered from event logs, i.e., sequences of events. However, the recorded events are often too fine-granular and result in unstructured models that are not meaningful for analysis. Log abstraction therefore aims to group together events to obtain a higher-level representation of the event sequences. While such a transformation shall be driven by the analysis goal, existing techniques force users to define how the abstraction is done, rather than what the result shall be. In this paper, we propose GECCO, an approach for log abstraction that enables users to impose requirements on the resulting log in terms of constraints. GECCO then groups events so that the constraints are satisfied and the distance to the original log is minimized. Since exhaustive log abstraction suffers from an exponential runtime complexity, GECCO also offers a heuristic approach guided by behavioral dependencies found in the log. We show that the abstraction quality of GECCO is superior to baseline solutions and demonstrate the relevance of considering constraints during log abstraction in real-life settings.

cs.DB↗

arXiv2021

SaCoFa: Semantics-aware Control-flow Anonymization for Process Mining

Privacy-preserving process mining enables the analysis of business processes using event logs, while giving guarantees on the protection of sensitive information on process stakeholders. To this end, existing approaches add noise to the results of queries that extract properties of an event log, such as the frequency distribution of trace variants, for analysis.Noise insertion neglects the semantics of the process, though, and may generate traces not present in the original log. This is problematic. It lowers the utility of the published data and makes noise easily identifiable, as some traces will violate well-known semantic constraints.In this paper, we therefore argue for privacy preservation that incorporates a process semantics. For common trace-variant queries, we show how, based on the exponential mechanism, semantic constraints are incorporated to ensure differential privacy of the query result. Experiments demonstrate that our semantics-aware anonymization yields event logs of significantly higher utility than existing approaches.

cs.DB↗

arXiv2021

A Distance Measure for Privacy-preserving Process Mining based on Feature Learning

To enable process analysis based on an event log without compromising the privacy of individuals involved in process execution, a log may be anonymized. Such anonymization strives to transform a log so that it satisfies provable privacy guarantees, while largely maintaining its utility for process analysis. Existing techniques perform anonymization using simple, syntactic measures to identify suitable transformation operations. This way, the semantics of the activities referenced by the events in a trace are neglected, potentially leading to transformations in which events of unrelated activities are merged. To avoid this and incorporate the semantics of activities during anonymization, we propose to instead incorporate a distance measure based on feature learning. Specifically, we show how embeddings of events enable the definition of a distance measure for traces to guide event log anonymization. Our experiments with real-world data indicate that anonymization using this measure, compared to a syntactic one, yields logs that are closer to the original log in various dimensions and, hence, have higher utility for process analysis.

cs.CR↗

arXiv2021

Privacy-aware Process Performance Indicators: Framework and Release Mechanisms

Process performance indicators (PPIs) are metrics to quantify the degree with which organizational goals defined based on business processes are fulfilled. They exploit the event logs recorded by information systems during the execution of business processes, thereby providing a basis for process monitoring and subsequent optimization. However, PPIs are often evaluated on processes that involve individuals, which implies an inevitable risk of privacy intrusion. In this paper, we address the demand for privacy protection in the computation of PPIs. We first present a framework that enforces control over the data exploited for process monitoring. We then show how PPIs defined based on the established PPINOT meta-model are instantiated in this framework through a set of data release mechanisms. These mechanisms are designed to provide provable guarantees in terms of differential privacy. We evaluate our framework and the release mechanisms in a series of controlled experiments. We further use a public event log to compare our framework with approaches based on privatization of event logs. The results demonstrate feasibility and shed light on the trade-offs between data utility and privacy guarantees in the computation of PPIs.

cs.CR↗

arXiv2020

The Internet-of-Things Meets Business Process Management: A Manifesto

The Internet of Things (IoT) refers to a network of connected devices collecting and exchanging data over the Internet. These things can be artificial or natural, and interact as autonomous agents forming a complex system. In turn, Business Process Management (BPM) was established to analyze, discover, design, implement, execute, monitor and evolve collaborative business processes within and across organizations. While the IoT and BPM have been regarded as separate topics in research and practice, we strongly believe that the management of IoT applications will strongly benefit from BPM concepts, methods and technologies on the one hand; on the other one, the IoT poses challenges that will require enhancements and extensions of the current state-of-the-art in the BPM field. In this paper, we question to what extent these two paradigms can be combined and we discuss the emerging challenges.

cs.CY↗

arXiv2020

Fire Now, Fire Later: Alarm-Based Systems for Prescriptive Process Monitoring

Predictive process monitoring is a family of techniques to analyze events produced during the execution of a business process in order to predict the future state or the final outcome of running process instances. Existing techniques in this field are able to predict, at each step of a process instance, the likelihood that it will lead to an undesired outcome.These techniques, however, focus on generating predictions and do not prescribe when and how process workers should intervene to decrease the cost of undesired outcomes. This paper proposes a framework for prescriptive process monitoring, which extends predictive monitoring with the ability to generate alarms that trigger interventions to prevent an undesired outcome or mitigate its effect. The framework incorporates a parameterized cost model to assess the cost-benefit trade-off of generating alarms. We show how to optimize the generation of alarms given an event log of past process executions and a set of cost model parameters. The proposed approaches are empirically evaluated using a range of real-life event logs. The experimental results show that the net cost of undesired outcomes can be minimized by changing the threshold for generating alarms, as the process instance progresses. Moreover, introducing delays for triggering alarms, instead of triggering them as soon as the probability of an undesired outcome exceeds a threshold, leads to lower net costs.

cs.LG↗

arXiv2020

Entropia: A Family of Entropy-Based Conformance Checking Measures for Process Mining

This paper presents a command-line tool, called Entropia, that implements a family of conformance checking measures for process mining founded on the notion of entropy from information theory. The measures allow quantifying classical non-deterministic and stochastic precision and recall quality criteria for process models automatically discovered from traces executed by IT-systems and recorded in their event logs. A process model has "good" precision with respect to the log it was discovered from if it does not encode many traces that are not part of the log, and has "good" recall if it encodes most of the traces from the log. By definition, the measures possess useful properties and can often be computed quickly.

cs.AI↗

arXiv2020

Partial Order Resolution of Event Logs for Process Conformance Checking

While supporting the execution of business processes, information systems record event logs. Conformance checking relies on these logs to analyze whether the recorded behavior of a process conforms to the behavior of a normative specification. A key assumption of existing conformance checking techniques, however, is that all events are associated with timestamps that allow to infer a total order of events per process instance. Unfortunately, this assumption is often violated in practice. Due to synchronization issues, manual event recordings, or data corruption, events are only partially ordered. In this paper, we put forward the problem of partial order resolution of event logs to close this gap. It refers to the construction of a probability distribution over all possible total orders of events of an instance. To cope with the order uncertainty in real-world data, we present several estimators for this task, incorporating different notions of behavioral abstraction. Moreover, to reduce the runtime of conformance checking based on partial order resolution, we introduce an approximation method that comes with a bounded error in terms of accuracy. Our experiments with real-world and synthetic data reveal that our approach improves accuracy over the state-of-the-art considerably.

cs.AI↗

arXiv2020

PRIPEL: Privacy-Preserving Event Log Publishing Including Contextual Information

Event logs capture the execution of business processes in terms of executed activities and their execution context. Since logs contain potentially sensitive information about the individuals involved in the process, they should be pre-processed before being published to preserve the individuals' privacy. However, existing techniques for such pre-processing are limited to a process' control-flow and neglect contextual information, such as attribute values and durations. This thus precludes any form of process analysis that involves contextual factors. To bridge this gap, we introduce PRIPEL, a framework for privacy-aware event log publishing. Compared to existing work, PRIPEL takes a fundamentally different angle and ensures privacy on the level of individual cases instead of the complete log. This way, contextual information as well as the long tail process behaviour are preserved, which enables the application of a rich set of process analysis techniques. We demonstrate the feasibility of our framework in a case study with a real-world event log.

cs.DB↗

arXiv2020

Monotone Precision and Recall Measures for Comparing Executions and Specifications of Dynamic Systems

The behavioural comparison of systems is an important concern of software engineering research. For example, the areas of specification discovery and specification mining are concerned with measuring the consistency between a collection of execution traces and a program specification. This problem is also tackled in process mining with the help of measures that describe the quality of a process specification automatically discovered from execution logs. Though various measures have been proposed, it was recently demonstrated that they neither fulfil essential properties, such as monotonicity, nor can they handle infinite behaviour. In this paper, we address this research problem by introducing a new framework for the definition of behavioural quotients. We proof that corresponding quotients guarantee desired properties that existing measures have failed to support. We demonstrate the application of the quotients for capturing precision and recall measures between a collection of recorded executions and a system specification. We use a prototypical implementation of these measures to contrast their monotonic assessment with measures that have been defined in prior research.

cs.FL↗

arXiv2019

Parallel Computation of Graph Embeddings

Graph embedding aims at learning a vector-based representation of vertices that incorporates the structure of the graph. This representation then enables inference of graph properties. Existing graph embedding techniques, however, do not scale well to large graphs. We therefore propose a framework for parallel computation of a graph embedding using a cluster of compute nodes with resource constraints. We show how to distribute any existing embedding technique by first splitting a graph for any given set of constrained compute nodes and then reconciling the embedding spaces derived for these subgraphs. We also propose a new way to evaluate the quality of graph embeddings that is independent of a specific inference task. Based thereon, we give a formal bound on the difference between the embeddings derived by centralised and parallel computation. Experimental results illustrate that our approach for parallel computation scales well, while largely maintaining the embedding quality.

cs.LG↗

arXiv2019

CROSSBOW: Scaling Deep Learning with Small Batch Sizes on Multi-GPU Servers

Deep learning models are trained on servers with many GPUs, and training must scale with the number of GPUs. Systems such as TensorFlow and Caffe2 train models with parallel synchronous stochastic gradient descent: they process a batch of training data at a time, partitioned across GPUs, and average the resulting partial gradients to obtain an updated global model. To fully utilise all GPUs, systems must increase the batch size, which hinders statistical efficiency. Users tune hyper-parameters such as the learning rate to compensate for this, which is complex and model-specific. We describe CROSSBOW, a new single-server multi-GPU system for training deep learning models that enables users to freely choose their preferred batch size - however small - while scaling to multiple GPUs. CROSSBOW uses many parallel model replicas and avoids reduced statistical efficiency through a new synchronous training method. We introduce SMA, a synchronous variant of model averaging in which replicas independently explore the solution space with gradient descent, but adjust their search synchronously based on the trajectory of a globally-consistent average model. CROSSBOW achieves high hardware efficiency with small batch sizes by potentially training multiple model replicas per GPU, automatically tuning the number of replicas to maximise throughput. Our experiments show that CROSSBOW improves the training time of deep learning models on an 8-GPU server by 1.3-4x compared to TensorFlow.

cs.DC↗

arXiv2018

Blockchains for Business Process Management - Challenges and Opportunities

Blockchain technology promises a sizable potential for executing inter-organizational business processes without requiring a central party serving as a single point of trust (and failure). This paper analyzes its impact on business process management (BPM). We structure the discussion using two BPM frameworks, namely the six BPM core capabilities and the BPM lifecycle. This paper provides research directions for investigating the application of blockchain technology to BPM.

cs.SE↗

arXiv2015

Modeling Styles in Business Process Modeling

Research on quality issues of business process models has recently begun to explore the process of creating process models. As a consequence, the question arises whether different ways of creating process models exist. In this vein, we observed 115 students engaged in the act of modeling, recording all their interactions with the modeling environment using a specialized tool. The recordings of process modeling were subsequently clustered. Results presented in this paper suggest the existence of three distinct modeling styles, exhibiting significantly different characteristics. We believe that this finding constitutes another building block toward a more comprehensive understanding of the process of process modeling that will ultimately enable us to support modelers in creating better business process models.

cs.SE↗