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检索条件"丛书名=Lecture Notes in Business Information Processing"

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Visually Representing History Dependencies in Event Logs

Visually Representing History Dependencies in Event Logs

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2nd International Conference on Process Mining (ICPM)

作者： Wetzel, Manuel Koschmider, Agnes Wilke, Thomas Univ Kiel Grp Proc Analyt Kiel Germany Univ Kiel Grp Theory Comp Kiel Germany

ISBN: (纸本)9783030726928;9783030726935

Many process mining tools produce directly-follows graphs (DFG) as visual representations of event logs. While the "directly follows" relation is a good starting point for visualizations, there are simple phenomena it does not capture, for instance, when whether or not an event directly follows another event depends on the event directly preceding it. We call this a history dependency. This paper presents an empirical study of preferences for visualizing history dependencies: plain DFGs and two enhanced variants of DFGs (with additional arcs or rectangles) are evaluated. Our empirical study provides strong support for making an effort (to discover and) to explicitly visualize history dependencies. A ProM plug-in generating such explicit visualization is described in this paper.

关键词： Directly-Follows Graph Process visualization Process discovery History dependency Empirical study

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Conceptual Modeling Versus User Story Mapping: Which is the Best Approach to Agile Requirements Engineering? 15th

Conceptual Modeling Versus User Story Mapping: Which is the ...

引用

15th International Conference on Research Challenges in information Science (RCIS)

作者： Tsilionis, Konstantinos Maene, Joris Heng, Samedi Wautelet, Yves Poelmans, Stephan Katholieke Univ Leuven Leuven Belgium Univ Liege HEC Liege Liege Belgium

ISBN: (纸本)9783030750176;9783030750183

User stories are primary requirements artifacts within agile methods. They are comprised of short sentences written in natural language expressing units of functionality for the to-be system. Despite their simple format, when modelers are faced with a set of user stories they might be having difficulty in sorting them, evaluating their redundancy, and assessing their relevancy in the effort to prioritize them. The present paper tests the ability of modelers to understand the requirements problem through a visual representation (named the Rationale Tree) which is a conceptual model and is built out of a user stories' set. The paper is built upon and extends previous work relating to the feasibility of generating such a representation out of a user stories' set by comparing the performance of the Rationale Tree with the User Story Mapping approach. This is achieved by performing a two-group quantitative comparative study. The identified comparative variables for each method were understandability, recognition of missing requirements/epics/themes, and adaptability. The Rational Tree was not easy to understand and did not perform as anticipated in assisting with the recognition of missing requirements/epics/themes. However, its employment allowed modelers to offer qualitative representations of a specific software problem. Overall, the present experiment evaluates whether a conceptual model could be a consistent solution towards the holistic comprehension of a software development problem within an agile setting, compared to more 'conventional' techniques used so far.

关键词： User stories User story User Story Mapping Rationale Tree Agile requirements engineering

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Spatial and Temporal Cross-Validation Approach for Misbehavior Detection in C-ITS 15th

Spatial and Temporal Cross-Validation Approach for Misbehavi...

引用

15th International Conference on Research Challenges in information Science (RCIS)

作者： Bouchouia, Mohammed Lamine Monteuuis, Jean-Philippe Jelassi, Ons Labiod, Houda Ben Jaballah, Wafa Petit, Jonathan Inst Polytech Paris Telecom Paris Paris France Qualcomm Technol Inc San Diego CA USA Thales Grp Courbevoie France

ISBN: (纸本)9783030750176;9783030750183

This paper proposes a novel approach to apply machine learning techniques to data collected from emerging cooperative intelligent transportation systems (C-ITS) using Vehicle-to-Vehicle (V2V) broadcast communications. Our approach considers temporal and spatial aspects of collected data to avoid correlation between the training set and the validation set. Connected vehicles broadcast messages containing safety-critical information at high frequency. Thus, detecting faulty messages induced by attacks is crucial for road-users safety. High frequency broadcast makes the temporal aspect decisive in building the cross-validation sets at the data preparation level of the data mining cycle. Therefore, we conduct a statistical study considering various fake position attacks. We statistically examine the difficulty of detecting the faulty messages, and generate useful features of the raw data. Then, we apply machine learning methods for misbehavior detection, and discuss the obtained results. We apply our data splitting approach to messagebased and communication-based data modeling and compare our approach to traditional splitting approaches. Our study shows that traditional splitting approaches performance is biased as it causes data leakage, and we observe a 10% drop in performance in the testing phase compared to our approach. This result implies that traditional approaches cannot be trusted to give equivalent performance once deployed and thus are not compatible with V2V broadcast communications.

关键词： Misbehavior detection V2X Machine learning Cross-validation sets Time series

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Dealing with Uncertain and Imprecise Time Intervals in OWL2: A Possibility Theory-Based Approach 15th

Dealing with Uncertain and Imprecise Time Intervals in OWL2:...

引用

15th International Conference on Research Challenges in information Science (RCIS)

作者： Achich, Nassira Ghorbel, Fatma Hamdi, Faycal Metais, Elisabeth Gargouri, Faiez Univ Sfax MIRACL Lab Sfax Tunisia CEDRIC Lab Conservatoire Natl Arts & Metiers CNAM Paris France

ISBN: (纸本)9783030750176;9783030750183

Dealing with temporal data imperfections in Semantic Web is still under focus. In this paper, we propose an approach based on the possibility theory to represent and reason about time intervals that are simultaneously uncertain and imprecise in OWL2. We start by calculating the possibility and necessity degrees related to the imprecision and uncertainty of the handled temporal data. Then, we propose an ontology-based representation for the handled data associated with the obtained measures and associative qualitative relations. For the reasoning, we extend Allen's interval algebra to treat both imprecision and uncertainty. All the proposed relations preserve the desirable properties of the original algebra and can be used for temporal reasoning by means of a transitivity table. We create a possibilistic temporal ontology based on the proposed semantic representation and the extension of Allen's relations. Inferences are based on a set of SWRL rules. Finally, we implement a prototype based on this ontology and we conduct a case study applied to temporal data entered by Alzheimer's patients in the context of a memory prosthesis.

关键词： Uncertain and imprecise temporal data Temporal representation Temporal reasoning Possibility theory Allen's interval algebra Possibilistic temporal ontology

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Concept Drift Detection on Streaming Data with Dynamic Outlier Aggregation

Concept Drift Detection on Streaming Data with Dynamic Outli...

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2nd International Conference on Process Mining (ICPM)

作者： Zellner, Ludwig Richter, Florian Sontheim, Janina Maldonado, Andrea Seidl, Thomas Ludwig Maximilians Univ Munchen Munich Germany

ISBN: (纸本)9783030726928;9783030726935

Many processes no matter what kind are regularly changing over time, adapting themselves to external and internal circumstances. Analyzing them in a streaming context is a very demanding task. Particularly the detection and classification of significant deviations is important to be able to re-integrate these possible micro-processes. Assuming a deviation of a certain process, the significance is implicitly given when a high number of instances contain this deviation similarly. To enhance a process the integration of or preventive measures against those anomalies is of high interest for all stakeholders as the actual process core gets discovered more and more in detail. Considering various areas of application, we focus on previously neglected but potentially significant anomalies like small changes in the disease process of a virus infection that has to be discovered to develop an appropriate reaction mechanism. We concentrate on non-conforming traces of a stream on which we compute a local outlier factor. This allows us to detect relations between traces based on changing outlier scores. Hence, hereby connected traces are clusters with which we achieve the detection of concept drift. We evaluate our approach on a synthetic event log and a real-world dataset corresponding to a process representing building permit applications which emphasizes the extensive applicability.

关键词： Concept drift detection Local outlier factor Micro-clusters

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Open Government Data for Non-expert Citizens: Understanding Content and Visualizations' Expectations 15th

Open Government Data for Non-expert Citizens: Understanding ...

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15th International Conference on Research Challenges in information Science (RCIS)

作者： Chokki, Abiola Paterne Simonofski, Anthony Frenay, Beno Vanderose, Beno Univ Namur Namur Belgium

ISBN: (纸本)9783030750176;9783030750183

Open government data (OGD) refers to data made available online by governments for anyone to freely reuse. Non-expert users, however, lack the necessary technical skills and therefore face challenges when trying to exploit it. Amongst these challenges, finding useful datasets for citizens is very difficult as their expectations are not always identified. Furthermore, findings the appropriate visualization that is more understandable by citizens is also a barrier. The goal of this paper is to decrease those two entry barriers by better understanding the expectations of nonexpert citizens. In order to reach that goal, we first seek to understand their content expectations through the usage statistics analysis of the OGD portal of Namur and through a complementary online survey of 43 participants. Second, we conduct interviews with 10 citizens to obtain their opinion on the appropriate and well-designed visualizations of the content they seek. The findings of this multi-method approach allow us to issue 5 recommendations for OGD portal publishers and developers to foster non-expert use of OGD.

关键词： Open Government Data Visualization Content Non-expert

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Novel Perspectives and Applications of Knowledge Graph Embeddings: From Link Prediction to Risk Assessment and Explainability 15th

Novel Perspectives and Applications of Knowledge Graph Embed...

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15th International Conference on Research Challenges in information Science (RCIS)

作者： Tissot, Hegler C. Univ Penn Philadelphia PA 19104 USA

ISBN: (纸本)9783030750176;9783030750183

Knowledge graph representation is an important embedding technology that supports a variety of machine learning related applications. By learning the distributed representation of multi-relational data, knowledge embedding models are supposed to efficiently deal with the semantic relatedness of their constituents. However, failing in the fundamental task of creating an appropriate form to represent knowledge harms any attempt of designing subsequent machine learning tasks. Several knowledge embedding methods have been proposed in the last decade. Although there is a consensus on the idea that enhanced approaches are more efficient, more complex projections in the hyperspace that indeed favor link prediction (or knowledge graph completion) can result in a loss of semantic similarity. We propose a new evaluation task that aims at performing risk assessment on domain-specific categorized multi-relational datasets, designed as a classification problem based on the resulting embeddings. We assess the quality of embedding representations based on the synergy of the resulting clusters of target subjects. We show that more sophisticated embedding approaches do not necessarily favor embedding quality, and the traditional link prediction validation protocol is a weak metric to measure the quality of embedding representation. Finally, we present insights about using the synergy analysis to provide risk assessment explainability based on the probability distribution of feature-value pairs within embedded clusters.

关键词： Knowledge graphs Link prediction Risk assessment

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Stochastic Process Discovery by Weight Estimation

Stochastic Process Discovery by Weight Estimation

引用

2nd International Conference on Process Mining (ICPM)

作者： Burke, Adam Leemans, Sander J. J. Wynn, Moe Thandar Queensland Univ Technol Brisbane Australia

ISBN: (纸本)9783030726928;9783030726935

Many algorithms now exist for discovering process models from event logs. These models usually describe a control flow and are intended for use by people in analysing and improving real-world organizational processes. The relative likelihood of choices made while following a process (i.e., its stochastic behaviour) is highly relevant information which few existing algorithms make available in their automatically discovered models. This can be addressed by automatically discovered stochastic process models. We introduce a framework for automatic discovery of stochastic process models, given a control-flow model and an event log. The framework introduces an estimator which takes a Petri net model and an event log as input, and outputs a Generalized Stochastic Petri net. We apply the framework, adding six new weight estimators, and a method for their evaluation. The algorithms have been implemented in the open-source process mining framework ProM. Using stochastic conformance measures, the resulting models have comparable conformance to existing approaches and are shown to be calculated more efficiently.

关键词： Stochastic Petri nets Process mining Stochastic process mining Stochastic process discovery

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A Language-Based Approach for Predicting Alzheimer Disease Severity 15th

A Language-Based Approach for Predicting Alzheimer Disease S...

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15th International Conference on Research Challenges in information Science (RCIS)

作者： Ben Ammar, Randa Ben Ayed, Yassine Sfax Univ Multimedia Informat Syst & Adv Comp Lab Sfax Tunisia

ISBN: (纸本)9783030750176;9783030750183

Alzheimer's disease (AD) is the most leading symptom of neurodegenerative dementia;AD is defined now as one of the most costly chronic diseases. For that automatic diagnosis and control of Alzheimer's disease may have a significant effect on society along with patient well-being. The Mini Mental State Examination (MMSE) is a prominent method for identifying whether a person might have dementia and about the dementia severity respectively. These methods are time-consuming and require well-educated personnel to administer. This study investigates another method for predicting MMSE score based on the language deterioration of people, using linguistic information from speech samples of picture description task. We use a regression model over a set of 169 patients with different degrees of dementia;we achieve a Mean Absolute Error (MAE) of 3.6 for MMSE. When focusing on selecting the best features, we improve the MAE to 0.55. Obtained results indicate that the proposed taxonomy of the linguistic features could operate as a cheap dementia test, probably also in non-clinical situations.

关键词： Alzheimer disease Mini Mental State Examination (MMSE) Machine learning Prediction of clinical scores

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Searching for Class Models 22nd

Searching for Class Models

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22nd International Working Conference on business Process Modeling, Development and Support (BPMDS) and the 26th International Working Conference on Exploring Modeling Methods for Systems Analysis and Development (EMMSAD)

作者： Bragilovski, Maxim Makias, Yifat Shamshila, Moran Stern, Roni Sturm, Arnon Ben Gurion Univ Negev Beer Sheva Israel

ISBN: (纸本)9783030791858;9783030791865

Models in model-based development play a major role and serve as the main design artifacts, in particular class models. As there are difficulties in developing high-quality models, different repositories of models are established to address that challenge, so developers would have a reference model. Following the existence of such repositories, there is a need for tools that can retrieve similar high-quality models. To search for models in these repositories, we propose a greedy algorithm that matches the developer's intention by considering semantic similarity, structure similarity, and type similarity. The initial evaluation indicates that the algorithm achieved high performance in finding the relevant class model fragments. Though additional examination is required, the sought algorithm can be easily adapted to other modeling languages for searching models and their encapsulated knowledge.

关键词： Model driven development Model repository Model search

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