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Anomaly detection in process mining aims to recognize outlying or unexpected behavior in event logs for purposes such as the removal of noise and identification of conformance violations. Existing techniques for this task are primarily frequency-based, arguing that behavior is anomalous because it is uncommon. However, such techniques ignore the semantics of recorded events and, therefore, do not take the meaning of potential anomalies into consideration. In this work, we overcome this caveat and focus on the detection of anomalies from a semantic perspective, arguing that anomalies can be recognized when process behavior does not make sense. To achieve this, we propose an approach that exploits the natural language associated with events. Our key idea is to detect anomalous process behavior by identifying semantically inconsistent execution patterns. To detect such patterns, we first automatically extract business objects and actions from the textual labels of events. We then compare these against a process-independent knowledge base. By populating this knowledge base with patterns from various kinds of resources, our approach can be used in a range of contexts and domains. We demonstrate the capability of our approach to successfully detect semantic execution anomalies through an evaluation based on a set of real-world and synthetic event logs and show the complementary nature of semantics-based anomaly detection to existing frequency-based techniques.
Innovat MOOC
(2023)
The COVID-19 pandemic has revealed the importance for university teachers to have adequate pedagogical and technological competences to cope with the various possible educational scenarios (face-to-face, online, hybrid, etc.), making use of appropriate active learning methodologies and supporting technologies to foster a more effective learning environment. In this context, the InnovaT project has been an important initiative to support the development of pedagogical and technological competences of university teachers in Latin America through several trainings aiming to promote teacher innovation. These trainings combined synchronous online training through webinars and workshops with asynchronous online training through the MOOC “Innovative Teaching in Higher Education.” This MOOC was released twice. The first run took place right during the lockdown of 2020, when Latin American teachers needed urgent training to move to emergency remote teaching overnight. The second run took place in 2022 with the return to face-to-face teaching and the implementation of hybrid educational models. This article shares the results of the design of the MOOC considering the constraints derived from the lockdowns applied in each country, the lessons learned from the delivery of such a MOOC to Latin American university teachers, and the results of the two runs of the MOOC.
Gait analysis is an important tool for the early detection of neurological diseases and for the assessment of risk of falling in elderly people. The availability of low-cost camera hardware on the market today and recent advances in Machine Learning enable a wide range of clinical and health-related applications, such as patient monitoring or exercise recognition at home. In this study, we evaluated the motion tracking performance of the latest generation of the Microsoft Kinect camera, Azure Kinect, compared to its predecessor Kinect v2 in terms of treadmill walking using a gold standard Vicon multi-camera motion capturing system and the 39 marker Plug-in Gait model. Five young and healthy subjects walked on a treadmill at three different velocities while data were recorded simultaneously with all three camera systems. An easy-to-administer camera calibration method developed here was used to spatially align the 3D skeleton data from both Kinect cameras and the Vicon system. With this calibration, the spatial agreement of joint positions between the two Kinect cameras and the reference system was evaluated. In addition, we compared the accuracy of certain spatio-temporal gait parameters, i.e., step length, step time, step width, and stride time calculated from the Kinect data, with the gold standard system. Our results showed that the improved hardware and the motion tracking algorithm of the Azure Kinect camera led to a significantly higher accuracy of the spatial gait parameters than the predecessor Kinect v2, while no significant differences were found between the temporal parameters. Furthermore, we explain in detail how this experimental setup could be used to continuously monitor the progress during gait rehabilitation in older people.
In rural/remote areas, resource constrained smart micro-grid (RCSMG) architectures can provide a cost-effective power supply alternative in cases when connectivity to the national power grid is impeded by factors such as load shedding. RCSMG architectures can be designed to handle communications over a distributed lossy network in order to minimise operation costs. However, due to the unreliable nature of lossy networks communication data can be distorted by noise additions that alter the veracity of the data. In this chapter, we consider cases in which an adversary who is internal to the RCSMG, deliberately distorts communicated data to gain an unfair advantage over the RCSMG’s users. The adversary’s goal is to mask malicious data manipulations as distortions due to additive noise due to communication channel unreliability. Distinguishing malicious data distortions from benign distortions is important in ensuring trustworthiness of the RCSMG. Perturbation data anonymisation algorithms can be used to alter transmitted data to ensure that adversarial manipulation of the data reveals no information that the adversary can take advantage of. However, because existing data perturbation anonymisation algorithms operate by using additive noise to anonymise data, using these algorithms in the RCSMG context is challenging. This is due to the fact that distinguishing benign noise additions from malicious noise additions is a difficult problem. In this chapter, we present a brief survey of cases of privacy violations due to inferences drawn from observed power consumption patterns in RCSMGs centred on inference, and propose a method of mitigating these risks. The lesson here is that while RCSMGs give users more control over power management and distribution, good anonymisation is essential to protecting personal information on RCSMGs.
The interplay between process and decision models plays a crucial role in business process management, as decisions may be based on running processes and affect process outcomes. Often process models include decisions that are encoded through process control flow structures and data flow elements, thus reducing process model maintainability. The Decision Model and Notation (DMN) was proposed to achieve separation of concerns and to possibly complement the Business Process Model and Notation (BPMN) for designing decisions related to process models. Nevertheless, deriving decision models from process models remains challenging, especially when the same data underlie both process and decision models. In this paper, we explore how and to which extent the data modeled in BPMN processes and used for decision-making may be represented in the corresponding DMN decision models. To this end, we identify a set of patterns that capture possible representations of data in BPMN processes and that can be used to guide the derivation of decision models related to existing process models. Throughout the paper we refer to real-world healthcare processes to show the applicability of the proposed approach. (C) 2019 Elsevier Ltd. All rights reserved.
Confidence Counts
(2021)
The increasing reliance on online learning in higher education has been further expedited by the on-going Covid-19 pandemic. Students need to be supported as they adapt to this new learning environment. Research has established that learners with positive online learning self-efficacy beliefs are more likely to persevere and achieve their higher education goals when learning online. In this paper, we explore how MOOC design can contribute to the four sources of self-efficacy beliefs posited by Bandura [4]. Specifically, we will explore, drawing on learner reflections, whether design elements of the MOOC, The Digital Edge: Essentials for the Online Learner, provided participants with the necessary mastery experiences, vicarious experiences, verbal persuasion, and affective regulation opportunities, to evaluate and develop their online learning self-efficacy beliefs. Findings from a content analysis of discussion forum posts show that learners referenced three of the four information sources when reflecting on their experience of the MOOC. This paper illustrates the potential of MOOCs as a pedagogical tool for enhancing online learning self-efficacy among students.
CrashNet
(2021)
Destructive car crash tests are an elaborate, time-consuming, and expensive necessity of the automotive development process. Today, finite element method (FEM) simulations are used to reduce costs by simulating car crashes computationally. We propose CrashNet, an encoder-decoder deep neural network architecture that reduces costs further and models specific outcomes of car crashes very accurately. We achieve this by formulating car crash events as time series prediction enriched with a set of scalar features. Traditional sequence-to-sequence models are usually composed of convolutional neural network (CNN) and CNN transpose layers. We propose to concatenate those with an MLP capable of learning how to inject the given scalars into the output time series. In addition, we replace the CNN transpose with 2D CNN transpose layers in order to force the model to process the hidden state of the set of scalars as one time series. The proposed CrashNet model can be trained efficiently and is able to process scalars and time series as input in order to infer the results of crash tests. CrashNet produces results faster and at a lower cost compared to destructive tests and FEM simulations. Moreover, it represents a novel approach in the car safety management domain.
Viper
(2021)
Key-value stores (KVSs) have found wide application in modern software systems. For persistence, their data resides in slow secondary storage, which requires KVSs to employ various techniques to increase their read and write performance from and to the underlying medium. Emerging persistent memory (PMem) technologies offer data persistence at close-to-DRAM speed, making them a promising alternative to classical disk-based storage. However, simply drop-in replacing existing storage with PMem does not yield good results, as block-based access behaves differently in PMem than on disk and ignores PMem's byte addressability, layout, and unique performance characteristics. In this paper, we propose three PMem-specific access patterns and implement them in a hybrid PMem-DRAM KVS called Viper. We employ a DRAM-based hash index and a PMem-aware storage layout to utilize the random-write speed of DRAM and efficient sequential-write performance PMem. Our evaluation shows that Viper significantly outperforms existing KVSs for core KVS operations while providing full data persistence. Moreover, Viper outperforms existing PMem-only, hybrid, and disk-based KVSs by 4-18x for write workloads, while matching or surpassing their get performance.
Functional dependencies (FDs) play an important role in maintaining data quality. They can be used to enforce data consistency and to guide repairs over a database. In this work, we investigate the problem of missing values and its impact on FD discovery. When using existing FD discovery algorithms, some genuine FDs could not be detected precisely due to missing values or some non-genuine FDs can be discovered even though they are caused by missing values with a certain NULL semantics. We define a notion of genuineness and propose algorithms to compute the genuineness score of a discovered FD. This can be used to identify the genuine FDs among the set of all valid dependencies that hold on the data. We evaluate the quality of our method over various real-world and semi-synthetic datasets with extensive experiments. The results show that our method performs well for relatively large FD sets and is able to accurately capture genuine FDs.
TransPipe
(2021)
Online learning environments, such as Massive Open Online Courses (MOOCs), often rely on videos as a major component to convey knowledge. However, these videos exclude potential participants who do not understand the lecturer’s language, regardless of whether that is due to language unfamiliarity or aural handicaps. Subtitles and/or interactive transcripts solve this issue, ease navigation based on the content, and enable indexing and retrieval by search engines. Although there are several automated speech-to-text converters and translation tools, their quality varies and the process of integrating them can be quite tedious. Thus, in practice, many videos on MOOC platforms only receive subtitles after the course is already finished (if at all) due to a lack of resources. This work describes an approach to tackle this issue by providing a dedicated tool, which is closing this gap between MOOC platforms and transcription and translation tools and offering a simple workflow that can easily be handled by users with a less technical background. The proposed method is designed and evaluated by qualitative interviews with three major MOOC providers.
Social networking sites (SNS) are a rich source of latent information about individual characteristics. Crawling and analyzing this content provides a new approach for enterprises to personalize services and put forward product recommendations. In the past few years, commercial brands made a gradual appearance on social media platforms for advertisement, customers support and public relation purposes and by now it became a necessity throughout all branches. This online identity can be represented as a brand personality that reflects how a brand is perceived by its customers. We exploited recent research in text analysis and personality detection to build an automatic brand personality prediction model on top of the (Five-Factor Model) and (Linguistic Inquiry and Word Count) features extracted from publicly available benchmarks. Predictive evaluation on brands' accounts reveals that Facebook platform provides a slight advantage over Twitter platform in offering more self-disclosure for users' to express their emotions especially their demographic and psychological traits. Results also confirm the wider perspective that the same social media account carry a quite similar and comparable personality scores over different social media platforms. For evaluating our prediction results on actual brands' accounts, we crawled the Facebook API and Twitter API respectively for 100k posts from the most valuable brands' pages in the USA and we visualize exemplars of comparison results and present suggestions for future directions.
Learning During COVID-19
(2021)
During the COVID-19 pandemic, learning in higher education and beyond shifted en masse to online formats, with the short- and long-term consequences for Massive Open Online Course (MOOC) platforms, learners, and creators still under evaluation. In this paper, we sought to determine whether the COVID-19 pandemic and this shift to online learning led to increased learner engagement and attainment in a single introductory biology MOOC through evaluating enrollment, proportional and individual engagement, and verification and performance data. As this MOOC regularly operates each year, we compared these data collected from two course runs during the pandemic to three pre-pandemic runs. During the first pandemic run, the number and rate of learners enrolling in the course doubled when compared to prior runs, while the second pandemic run indicated a gradual return to pre-pandemic enrollment. Due to higher enrollment, more learners viewed videos, attempted problems, and posted to the discussion forums during the pandemic. Participants engaged with forums in higher proportions in both pandemic runs, but the proportion of participants who viewed videos decreased in the second pandemic run relative to the prior runs. A higher percentage of learners chose to pursue a certificate via the verified track in each pandemic run, though a smaller proportion earned certification in the second pandemic run. During the pandemic, more enrolled learners did not necessarily correlate to greater engagement by all metrics. While verified-track learner performance varied widely during each run, the effects of the pandemic were not uniform for learners, much like in other aspects of life. As such, individual engagement trends in the first pandemic run largely resemble pre-pandemic metrics but with more learners overall, while engagement trends in the second pandemic run are less like pre-pandemic metrics, hinting at learner “fatigue”. This study serves to highlight the life-long learning opportunity that MOOCs offer is even more critical when traditional education modes are disrupted and more people are at home or unemployed. This work indicates that this boom in MOOC participation may not remain at a high level for the longer term in any one course, but overall, the number of MOOCs, programs, and learners continues to grow.
Exploring Change
(2018)
Data and metadata in datasets experience many different kinds of change. Values axe inserted, deleted or updated; rows appear and disappear; columns are added or repurposed, etc. In such a dynamic situation, users might have many questions related to changes in the dataset, for instance which parts of the data are trustworthy and which are not? Users will wonder: How many changes have there been in the recent minutes, days or years? What kind of changes were made at which points of time? How dirty is the data? Is data cleansing required? The fact that data changed can hint at different hidden processes or agendas: a frequently crowd-updated city name may be controversial; a person whose name has been recently changed may be the target of vandalism; and so on. We show various use cases that benefit from recognizing and exploring such change. We envision a system and methods to interactively explore such change, addressing the variability dimension of big data challenges. To this end, we propose a model to capture change and the process of exploring dynamic data to identify salient changes. We provide exploration primitives along with motivational examples and measures for the volatility of data. We identify technical challenges that need to be addressed to make our vision a reality, and propose directions of future work for the data management community.
Schelling's classical segregation model gives a coherent explanation for the wide-spread phenomenon of residential segregation. We introduce an agent-based saturated open-city variant, the Flip Schelling Process (FSP), in which agents, placed on a graph, have one out of two types and, based on the predominant type in their neighborhood, decide whether to change their types; similar to a new agent arriving as soon as another agent leaves the vertex. We investigate the probability that an edge {u,v} is monochrome, i.e., that both vertices u and v have the same type in the FSP, and we provide a general framework for analyzing the influence of the underlying graph topology on residential segregation. In particular, for two adjacent vertices, we show that a highly decisive common neighborhood, i.e., a common neighborhood where the absolute value of the difference between the number of vertices with different types is high, supports segregation and, moreover, that large common neighborhoods are more decisive. As an application, we study the expected behavior of the FSP on two common random graph models with and without geometry: (1) For random geometric graphs, we show that the existence of an edge {u,v} makes a highly decisive common neighborhood for u and v more likely. Based on this, we prove the existence of a constant c>0 such that the expected fraction of monochrome edges after the FSP is at least 1/2+c. (2) For Erdős–Rényi graphs we show that large common neighborhoods are unlikely and that the expected fraction of monochrome edges after the FSP is at most 1/2+o(1). Our results indicate that the cluster structure of the underlying graph has a significant impact on the obtained segregation strength.
The transversal hypergraph problem asks to enumerate the minimal hitting sets of a hypergraph. If the solutions have bounded size, Eiter and Gottlob [SICOMP'95] gave an algorithm running in output-polynomial time, but whose space requirement also scales with the output. We improve this to polynomial delay and space. Central to our approach is the extension problem, deciding for a set X of vertices whether it is contained in any minimal hitting set. We show that this is one of the first natural problems to be W[3]-complete. We give an algorithm for the extension problem running in time O(m(vertical bar X vertical bar+1) n) and prove a SETH-lower bound showing that this is close to optimal. We apply our enumeration method to the discovery problem of minimal unique column combinations from data profiling. Our empirical evaluation suggests that the algorithm outperforms its worst-case guarantees on hypergraphs stemming from real-world databases.
We present a system-level synthesis approach for heterogeneous multi-processor on chip, based on Answer Set Programming(ASP). Starting with a high-level description of an application, its timing constraints and the physical constraints of the target device, our goal is to produce the optimal computing infrastructure made of heterogeneous processors, peripherals, memories and communication components. Optimization aims at maximizing speed, while minimizing chip area. Also, a scheduler must be produced that fulfills the real-time requirements of the application. Even though our approach will work for application specific integrated circuits, we have chosen FPGA as target device in this work because of their reconfiguration capabilities which makes it possible to explore several design alternatives. This paper addresses the bottleneck of problem representation size by providing a direct and compact ASP encoding for automatic synthesis that is semantically equivalent to previously established ILP and ASP models. We describe a use-case in which designers specify their applications in C/C++ from which optimum systems can be derived. We demonstrate the superiority of our approach toward existing heuristics and exact methods with synthesis results on a set of realistic case studies. (C) 2018 Elsevier Inc. All rights reserved.
VLDB 2021
(2021)
The 47th International Conference on Very Large Databases (VLDB'21) was held on August 16-20, 2021 as a hybrid conference. It attracted 180 in-person attendees in Copenhagen and 840 remote attendees. In this paper, we describe our key decisions as general chairs and program committee chairs and share the lessons we learned.
MOOCs have been produced using a variety of instructional design approaches and frameworks. This paper presents experiences from the instructional approach based on the ADDIE model applied to designing and producing MOOCs in the Erasmus+ strategic partnership on Open Badge Ecosystem for Research Data Management (OBERRED). Specifically, this paper describes the case study of the production of the MOOC “Open Badges for Open Science”, delivered on the European MOOC platform EMMA. The key goal of this MOOC is to help learners develop a capacity to use Open Badges in the field of Research Data Management (RDM). To produce the MOOC, the ADDIE model was applied as a generic instructional design model and a systematic approach to the design and development following the five design phases: Analysis, Design, Development, Implementation, Evaluation. This paper outlines the MOOC production including methods, templates and tools used in this process including the interactive micro-content created with H5P in form of Open Educational Resources and digital credentials created with Open Badges and issued to MOOC participants upon successful completion of MOOC levels. The paper also outlines the results from qualitative evaluation, which applied the cognitive walkthrough methodology to elicit user requirements. The paper ends with conclusions about pros and cons of using the ADDIE model in MOOC production and formulates recommendations for further work in this area.
With increasing numbers of flights worldwide and a continuing rise in airport traffic, air-traffic management is faced with a number of challenges. These include monitoring, reporting, planning, and problem analysis of past and current air traffic, e.g., to identify hotspots, minimize delays, or to optimize sector assignments to air-traffic controllers. To cope with these challenges, cyber worlds can be used for interactive visual analysis and analytical reasoning based on aircraft trajectory data. However, with growing data size and complexity, visualization requires high computational efficiency to process that data within real-time constraints. This paper presents a technique for real-time animated visualization of massive trajectory data. It enables (1) interactive spatio-temporal filtering, (2) generic mapping of trajectory attributes to geometric representations and appearance, and (3) real-time rendering within 3D virtual environments such as virtual 3D airport or 3D city models. Different visualization metaphors can be efficiently built upon this technique such as temporal focus+context, density maps, or overview+detail methods. As a general-purpose visualization technique, it can be applied to general 3D and 3+1D trajectory data, e.g., traffic movement data, geo-referenced networks, or spatio-temporal data, and it supports related visual analytics and data mining tasks within cyber worlds.
An independency (cliquy) tree of an n-vertex graph G is a spanning tree of G in which the set of leaves induces an independent set (clique). We study the problems of minimizing or maximizing the number of leaves of such trees, and fully characterize their parameterized complexity. We show that all four variants of deciding if an independency/cliquy tree with at least/most l leaves exists parameterized by l are either Para-NP- or W[1]-hard. We prove that minimizing the number of leaves of a cliquy tree parameterized by the number of internal vertices is Para-NP-hard too. However, we show that minimizing the number of leaves of an independency tree parameterized by the number k of internal vertices has an O*(4(k))-time algorithm and a 2k vertex kernel. Moreover, we prove that maximizing the number of leaves of an independency/cliquy tree parameterized by the number k of internal vertices both have an O*(18(k))-time algorithm and an O(k 2(k)) vertex kernel, but no polynomial kernel unless the polynomial hierarchy collapses to the third level. Finally, we present an O(3(n) . f(n))-time algorithm to find a spanning tree where the leaf set has a property that can be decided in f (n) time and has minimum or maximum size.