Business Information Systems (BIS) are the proceedings of the same-named conference. BIS is a renowned international conference on business information systems. BIS 2021 was the 24th in a series of international conferences.
TIB Open Publishing (Technische Informationsbibliothek (TIB))
Execution of Multi-Perspective Declarative Process Models Using Complex Event Processing
The Internet of Things (IoT) enables continuous monitoring of phenomena based on sensing devices as well as analytics opportunities in smart environments. Complex Event Processing (CEP) comprises a set of techniques for making sense of the behavior of a monitored system by deriving higher level knowledge from lower level system events. Business Process Management (BPM) attempts to model processes and ensures that executed processes con-form with a predefined sequence. In IoT scenarios frequently a large number of events has to be analyzed in real-time to allow an instant response. While BPM reaches its limits in such situ-ations, CEP is able to analyze and process high volume streams of data in real-time. The evaluation and execution of rules and models of both paradigms are currently based on separate formalisms and are frequently implemented in heterogeneous systems. The presented paper integrates both domains by proposing an execution approach for multi-perspective declarative process process models completely based on CEP. The efficiency of the combined paradigms is validated in an implemented demonstration with simulated and real-life sensor data.
Copyright (c) 2021 Niklas Ruhkamp, Stefan Schönig https://creativecommons.org/licenses/by/4.0
Exploring Potential Impacts of Self-Sovereign Identity on Smart Service Systems
Self-sovereign identity (SSI) is a new paradigm, which puts users back in control of their own digital identity. This does not only strengthen the position of the users but implies new interaction schemes that may improve interoperability and usability. Smart services systems enable the integration of resources and activities and use smart products as boundary objects. As such systems typically involve digital interactions between multiple actors, it can be assumed that utilising SSI has a positive impact on them. To investigate how these potential improvements manifest themselves, we investigate electric vehicle charging as example of a smart service system. At the core of our conceptual analysis is the service process, which we extract from a reference model. Based on a SWOT analysis, we identify areas for transformation and derive an SSI-enabled interaction model for an electric vehicle charging service. The evaluation of the new process shows that SSI can reduce complexity of integration with partners and can provide a better customer experience through simplified registration and authentication. Moreover, SSI might even lead to the disintermediation of actors in the service system. Although SSI is still emerging, our findings underline its relevance as a mechanism to establish trust in smart service systems through the seamless and standardised integration of digital identities for humans, organisations, and things.
Copyright (c) 2021 Daniel Richter, Jürgen Anke https://creativecommons.org/licenses/by/4.0
Deep Learning for Customer Churn Prediction in E-Commerce Decision Support
Churn prediction is a Big Data domain, one of the most demanding use cases of recent time. It is also one of the most critical indicators of a healthy and growing business, irrespective of the size or channel of sales. This paper aims to develop a deep learning model for customers’ churn prediction in e-commerce, which is the main contribution of the article. The experiment was performed over real e-commerce data where 75% of buyers are one-off customers. The prediction based on this business specificity (many one-off customers and very few regular ones) is extremely challenging and, in a natural way, must be inaccurate to a certain ex-tent. Looking from another perspective, correct prediction and subsequent actions resulting in a higher customer retention are very attractive for overall business performance. In such a case, predictions with 74% accuracy, 78% precision, and 68% recall are very promising. Also, the paper fills a research gap and contrib-utes to the existing literature in the area of developing a customer churn prediction method for the retail sector by using deep learning tools based on customer churn and the full history of each customer’s transactions.
Copyright (c) 2021 Maciej Pondel, Maciej Wuczyński, Wiesława Gryncewicz, Łukasz Łysik, Marcin Hernes, Artur Rot, Agata Kozina https://creativecommons.org/licenses/by/4.0
Developing a Legal Form Classification and Extraction Approach for Company Entity Matching
This paper explores the data integration process step record linkage. Thereby we focus on the entity company. For the integration of company data, the company name is a crucial attribute, which often includes the legal form. This legal form is not concise and consistent represented among different data sources, which leads to considerable data quality problems for the further process steps in record linkage. To solve these problems, we classify and ex-tract the legal form from the attribute company name. For this purpose, we iteratively developed four different approaches and compared them in a benchmark. The best approach is a hybrid approach combining a rule set and a supervised machine learning model. With our developed hybrid approach, any company data sets from research or business can be processed. Thus, the data quality for subsequent data processing steps such as record linkage can be improved. Furthermore, our approach can be adapted to solve the same data quality problems in other attributes.
Copyright (c) 2021 Felix Kruse, Jan-Philipp Awick, Jorge Marx Gómez, Peter Loos https://creativecommons.org/licenses/by/4.0
Data continuously gathered monitoring the spreading of the COVID-19 pandemic form an unbounded flow of data. Accurately forecasting if the infections will increase or decrease has a high impact, but it is challenging because the pandemic spreads and contracts periodically. Technically, the flow of data is said to be imbalanced and subject to concept drifts because signs of decrements are the minority class during the spreading periods, while they become the majority class in the contraction periods and the other way round. In this paper, we propose a case study applying the Continuous Synthetic Minority Oversampling Technique (C-SMOTE), a novel meta-strategy to pipeline with Streaming Machine Learning (SML) classification algorithms, to forecast the COVID-19 pandemic trend. Benchmarking SML pipelines that use C-SMOTE against state-of-the-art methods on a COVID-19 dataset, we bring statistical evidence that models learned using C-SMOTE are better.
Copyright (c) 2021 Alessio Bernardo, Emanuele Della Valle https://creativecommons.org/licenses/by/4.0
Post-Brexit Power of European Union From the World Trade Network Analysis
We develop the Google matrix analysis of the multiproduct world trade network obtained from the UN COMTRADE database in recent years. The comparison is done between this new approach and the usual Import-Export description of this world trade network. The Google matrix analysis takes into account the multiplicity of trade transactions thus highlighting in a better way the world influence of specific countries and products. It shows that after Brexit, the European Union of 27 countries has the leading position in the world trade network ranking, being ahead of USA and China. Our approach determines also a sensitivity of trade country balance to specific products showing the dominant role of machinery and mineral fuels in multiproduct exchanges. It also underlines the growing influence of Asian countries.
Copyright (c) 2021 Justin Loye, Katia Jaffrès-Runser, Dima L. Shepelyansky https://creativecommons.org/licenses/by/4.0
Towards a Guideline Affording Overarching Knowledge Building in Data Analysis Projects
Tight and competitive market situations pose a serious challenge to enterprises in the manufacturing industry domain. Competing in the use of data analytics to enhance products and processes requires additional resources to deal with the complexity. On the contrary, the possibilities afforded by digitization and data analysis-based approaches make for a valuable asset. In this paper we suggest a guideline to a systematic course of action for the data-based creation of holistic insight. Building an overlaying corpus of knowledge accelerates the learning curve within specific projects as well as across projects by exceeding the project-specific view towards an integrated approach.
Copyright (c) 2021 Dorothea Schneider, Wibke Kusturica https://creativecommons.org/licenses/by/4.0
Optimization-Based Business Process Model Matching
The rapid increase in generation of business process models in the industry has raised the demand on the development of process model matching approaches. In this paper, we introduce a novel optimization-based business process model matching approach which can flexibly incorporate both the behavioral and label information of processes for the identification of correspondences between activities. Given two business process models, we achieve our goal by defining an integer linear program which maximizes the label similarities among process activities and the behavioral similarity between the process models. Our approach enables the user to determine the importance of the local label-based similarities and the global behavioral similarity of the models by offering the utilization of a predefined weighting parameter, allowing for flexibility. Moreover, extensive experimental evaluation performed on three real-world datasets points out the high accuracy of our proposal, outperforming the state of the art.
Copyright (c) 2021 Merih Seran Uysal, Dominik Hüser, Wil M.P van der Aalst https://creativecommons.org/licenses/by/4.0
Database-Less Extraction of Event Logs from Redo Logs
Process mining is widely adopted in organizations to gain deep insights about running business processes. This can be achieved by applying different process mining techniques like discovery, conformance checking, and performance analysis. These techniques are applied on event logs, which need to be extracted from the organization’s databases beforehand. This not only implies access to databases, but also detailed knowledge about the database schema, which is often not available. In many real-world scenarios, however, process execution data is available as redo logs. Such logs are used to bring a database into a consistent state in case of a system failure. This paper proposes a semi-automatic approach to extract an event log from redo logs alone. It does not require access to the database or knowledge of the databaseschema. The feasibility of the proposed approach is evaluated on two synthetic redo logs.
Copyright (c) 2021 Dorina Bano, Tom Lichtenstein, Finn Klessascheck, Mathias Weske https://creativecommons.org/licenses/by/4.0
Towards a Concept for Building a Big Data Architecture with Microservices
Microservices and Big Data are renowned hot topics in computer science that have gained a lot of hype. While the use of microservices is an approach that is used in modern software development to increase flexibility, Big Data allows organizations to turn today’s information deluge into valuable insights. Many of those Big Data architectures have rather monolithic elements. However, a new trend arises in which monolithic architectures are replaced with more modularized ones, such as microservices. This transformation provides the benefits from microservices such as modularity, evolutionary design and extensibility while maintaining the old monolithic product’s functionality. This is also valid for Big Data architectures. To facilitate the success of this transformation, there are certain beneficial factors. In this paper, those aspects will be presented and the transformation of an exemplary Big Data architecture with somewhat monolithic elements into a microservice favoured one is outlined.
Copyright (c) 2021 Aamir Shakir, Daniel Staegemann, Matthias Volk, Naoum Jamous, Klaus Turowski https://creativecommons.org/licenses/by/4.0
Contextual Personality-Aware Recommender System Versus Big Data Recommender System
Many personality theories suggest that personality influences customer shopping preference. Thus, this research analyses the potential ability to improve the accuracy of the collaborative filtering recommender system by incorporating the Five-Factor Model personality traits data obtained from customer text reviews. The study uses a large Amazon dataset with customer reviews and information about verified customer product purchases. However, evaluation results show that the model leveraging big data by using the whole Amazon dataset provides better recommendations than the recommender systems trained in the contexts of the customer personality traits.
Copyright (c) 2021 Marcin Szmydt https://creativecommons.org/licenses/by/4.0
Generating a Condensed Representation for Positive and Negative Association Rules
Given a large collection of transactions containing items, a basic common association rules problem is the huge size of the extracted rule set. Pruning uninteresting and redundant association rules is a promising approach to solve this problem. In this paper, we propose a Condensed Representation for Positive and Negative Association Rules representing non-redundant rules for both exact and approximate association rules based on the sets of frequent generator itemsets, frequent closed itemsets, maximal frequent itemsets, and minimal infrequent itemsets in database B. Experiments on dense (highly-correlated) databases show a significant reduction of the size of extracted association rule set in database B.
Copyright (c) 2021 Bemarisika Parfait, André Totohasina https://creativecommons.org/licenses/by/4.0
Supporting an Expert-centric Process of New Product Introduction With Statistical Machine Learning
Industries that sell products with short-term or seasonal life cycles must regularly introduce new products. Forecasting the demand for New Product Introduction (NPI) can be challenging due to the fluctuations of many factors such as trend, seasonality, or other external and unpredictable phenomena (e.g., COVID-19 pandemic). Traditionally, NPI is an expertcentric process. This paper presents a study on automating the forecast of NPI demands using statistical Machine Learning (namely, Gradient Boosting and XGBoost). We show how to overcome shortcomings of the traditional data preparation that underpins the manual process. Moreover, we illustrate the role of cross-validation techniques for the hyper-parameter tuning and the validation of the models. Finally, we provide empirical evidence that statistical Machine Learning can forecast NPI demand better than experts.
Copyright (c) 2021 Shima Zahmatkesh, Alessio Bernardo, Emanuele Falzone, Edgardo Di Nicola Carena, Emanuele Della Valle https://creativecommons.org/licenses/by/4.0
Evaluating the New AI and Data Driven Insurance Business Models for Incumbents and Disruptors: Is there Convergence?
AI and data technologies are a catalyst for fundamental changes to insurance business models. The current upheaval is seeing some incumbent insurers trying to do the same more effectively, while others evolve to fully utilize the new capabilities and users these new technologies bring. At the same time, technologically advanced organizations from outside the sector are entering and disrupting it. Within this upheaval however, there are signs of a convergence towards an ideal and prevailing business model. This research identifies one exemplar incumbent and one disruptor and evaluates whether their models are converging and will become similar eventually. The findings support a high degree of convergence, but some differences are likely to remain even after this transitionary period. The differences identified are firstly in the evaluation of risk and secondly that traditional insurers prioritize revenue generation from what is their primary activity, while new entrants prioritize expanding their user base.
Copyright (c) 2021 Alex Zarifis, Xusen Cheng https://creativecommons.org/licenses/by/4.0
Evaluation of Deep Learning Instance Segmentation Models for Pig Precision Livestock Farming
In this paper, the deep learning instance segmentation architectures DetectoRS, SOLOv2, DETR and Mask R-CNN were applied to data from the field of Pig Precision Livestock Farming to investigate whether these models can address the specific challenges of this domain. For this purpose, we created a custom dataset consisting of 731 images with high heterogeneity and high-quality segmentation masks. For evaluation, the standard metric for benchmarking instance segmentation models in computer vision, the mean average precision, was used. The results show that all tested models can be applied to the considered domain in terms of prediction accuracy. With a mAP of 0.848, DetectoRS achieves the best results on the test set, but is also the largest model with the greatest hardware requirements. It turns out that increasing model complexity and size does not have a large impact on prediction accuracy for instance segmentation of pigs. DETR, SOLOv2, and Mask R-CNN achieve similar results to DetectoRS with a parameter count almost three times smaller. Visual evaluation of predictions shows quality differences in terms of accuracy of segmentation masks. DetectoRS generates the best masks overall, while DETR has advantages in correctly segmenting the tail region. However, it can be observed that each of the tested models has problems in assigning segmentation masks correctly once a pig is overlapped. The results demonstrate the potential of deep learning instance segmentation models in Pig Precision Livestock Farming and lay the foundation for future research in this area.
Copyright (c) 2021 Jan-Hendrik Witte, Johann Gerberding, Christian Melching, Jorge Marx Gómez https://creativecommons.org/licenses/by/4.0
Text-Aware Predictive Monitoring of Business Processes
The real-time prediction of business processes using historical event data is an important capability of modern business process monitoring systems. Existing process prediction methods are able to also exploit the data perspective of recorded events, in addition to the control-flow perspective. However, while well-structured numerical or categorical attributes are considered in many prediction techniques, almost no technique is able to utilize text documents written in natural language, which can hold information critical to the prediction task. In this paper, we illustrate the design, implementation, and evaluation of a novel text-aware process prediction model based on Long Short-Term Memory (LSTM) neural networks and natural language models. The proposed model can take categorical, numerical and textual attributes in event data into account to predict the activity and timestamp of the next event, the outcome, and the cycle time of a running process instance. Experiments show that the text-aware model is able to outperform state-of-the-art process prediction methods on simulated and real-world event logs containing textual data.
Copyright (c) 2021 Marco Pegoraro, Merih Seran Uysal, David Benedikt Georgi, Wil M.P van der Aalst https://creativecommons.org/licenses/by/4.0
Development of the Information Security Management System Standard for Public Sector Organisations in Estonia
Standardisation gives us a common understanding or processes to do something in a commonly accepted way. In information security management, it means to achieve the appropriate security level in the context of known and unknown risks. Each government’s goal should be to provide digital services to its citizens with the acceptable level of confidentiality, integrity and availability. This study elicits the EU countries’ requirements for information security management system (ISMS) standards and provides the standards’ comparison requirements. The Estonian case is an example to illustrate the method when choosing or developing the appropriate ISMS standard to public sector organisations.
Copyright (c) 2021 Mari Seeba, Raimundas Matulevičius, Ilmar Toom https://creativecommons.org/licenses/by/4.0
Potentials and Barriers of Agility in Small and Medium Sized Enterprises
Our explorative, qualitative study uncovers the dynamic processes of agility unleashing or inhibiting potentials within German small and medium sized enterprises through the lens of digitization. Based on an analysis of 22 interviews, we propose a conceptual model, which illuminates antecedents and external determinants of agility and their impact on potentials and performance in organizational settings. In this process we determine digitization both as an external driver and as an internal provider of agility. Resource constraints and traditional leadership styles are SME-specific barriers to agility. How extensively the potentials of agility can be utilized dependents on the moderating factors firm size and department.
Copyright (c) 2021 Joerg Bueechl, Ralf Haerting, Michael Pressl, Raphael Kaim https://creativecommons.org/licenses/by/4.0
Stream Processing Tools for Analyzing Objects in Motion Sending High-Volume Location Data
Recently we observe a significant increase in the amount of easily accessible data on transport and mobility. This data is mostly massive streams of high velocity, magnitude, and heterogeneity, which represent a flow of goods, shipments and the movements of fleet. It is therefore necessary to develop a scalable framework and apply tools capable of handling these streams. In the paper we propose an approach for the selection of software for stream processing solutions that may be used in the transportation domain. We provide an overview of potential stream processing technologies, followed by the method for choosing the selected software for real-time analysis of data streams coming from objects in motion. We have selected two solutions: Apache Spark Streaming and Apache Flink, and benchmarked them on a real-world task. We identified the caveats and challenges when it comes to implementation of the solution in practice.
Copyright (c) 2021 Krzysztof Wecel, Marcin Szmydt, Milena Stróżyna https://creativecommons.org/licenses/by/4.0
Metadata management is a crucial success factor for companies today, as for example, it enables exploiting data value fully or enables legal compliance. With the emergence of new concepts, such as the data lake, and new objectives, such as the enterprise-wide sharing of data, metadata management has evolved and now poses a renewed challenge for companies. In this context, we interviewed a globally active manufacturer to reveal how metadata management is implemented in practice today and what challenges companies are faced with and whether these constitute research gaps. As an outcome, we present the company’s metadata management goals and their corresponding solution approaches and challenges. An evaluation of the challenges through a literature and tool review yields three research gaps, which are concerned with the topics: (1) metadata management for data lakes, (2) categorizations and compositions of metadata management tools for comprehensive metadata management, and (3) the use of data marketplaces as metadata-driven exchange platforms within an enterprise. The gaps lay the groundwork for further research activities in the field of metadata management and the industry case represents a starting point for research to realign with real-world industry needs.
Copyright (c) 2021 Rebecca Eichler, Corinna Giebler, Christoph Gröger, Eva Hoos, Holger Schwarz, Bernhard Mitschang https://creativecommons.org/licenses/by/4.0
A Scoping Review of the Digital Transformation Literature Using Scientometric Analysis
Digital transformation is the rapidly expanding research field dealing with the increased impact of digital technologies on both business and society. Due to the large number of papers and the semantic ambiguity surrounding the terminology, covering such a broad topic is difficult. To help researchers gain a better understanding of the knowledge structure of the research field, we conduct a scoping review using scientometrics. We searched for publications dealing with digital transformation on both Scopus and Web of Science. We downloaded their bibliometric data and thoroughly merged and cleaned it using lemmatization and stemmatization. This dataset was analyzed using VOSviewer to create co-author networks and co-word occurrence graphs of the titles, abstracts, and keywords. We also visualized the growth of the research field and retrieved the top conferences and journals based on the number of papers and the number of citations. K-means clustering was performed on the abstracts and keywords to find similar research focuses. These findings highlight the broad scope of the research field, the ambiguity of the terminology, the lack of collaboration, and the absence of research into the impact of digital transformation on society. Moving forward, more research needs to be done to establish the boundaries of digital transformation and to investigate the importance of society in this phenomenon.
Copyright (c) 2021 Ziboud Van Veldhoven, Vedavya Etikala, Alexandre Goossens, Jan Vanthienen https://creativecommons.org/licenses/by/4.0
Innovations do not emerge in isolation but are at least to some extent recombinations of previously existing building blocks. In this paper, we will build on the recombination processes feature set broadening and deepening to show how individuals innovate with IT. In our understanding, the out-comes of innovative use can be performative (improving existing task performance) or creative (leading to new deliverables). We build on a longitudinal case of stresstracking initially designed to improve meditation, but ultimately increasing work productivity by using the meditation tool in an innovative way. Using a theoretically grounded analysis framework, we were able to derive eight propositions on the attainment of performative and creative outcome of innovative IT use. We postulate that innovation only occurs through repeating cycles of recombination processes. Particularly, we propose that it is instrumental to run through a phase that does not benefit any task-related outcomes to trigger true creative outcomes.
Copyright (c) 2021 Katharina Ebner, Genevieve Bassellier, Stefan Smolnik https://creativecommons.org/licenses/by/4.0
An Integrated Group Decision-Making Approach Considering Uncertainty Conditions
The management of business information processes needs effective decision-making models. That means to involve different methods, techniques, and principles to improve competitiveness and to achieve the planned business results. In this context, the article deals with the problem of group decision-making under uncertain conditions. To cope with such problems some well-known optimization strategies of Wald, Laplace, Hurwitz, and Savage are modified to take into account the experts’ opinions with different importance when forming the final group decision. Numerical testing is based on a case study for CRM software selection. The results are discussed based on the proposed models under two different cases derived from the case study. The conducted numerical testing of the proposed models demonstrates their applicability to cope simultaneously with multiple experts’ evaluations and uncertainty conditions.
Copyright (c) 2021 Daniela Borissova, Zornitsa Dimitrova https://creativecommons.org/licenses/by/4.0
Interoperability of Health Digitalization
: Introduction : Maternal and child health (MCH) is a global priority as health care innovation continues to evolve, including the use of information and communication technology. Studies showed that interoperable information systems can improve the quality of health services and at the same time facilitate the integration of data for the purpose of monitoring and evaluating the performance of health services, especially MCH. Aims : This study aims to identify various maternal and child health information systems used in Indonesia and opportunity of interoperability between systems to support continuum of care services. Methodology: Qualitative descriptive research was conducted in Yogyakarta Province from November to December 2020. This study assessed MCH applications that have been used in public and private primary health care, hospitals, health offices and in the community by identifying their functions and mapped data elements used by each application to assess potential interoperability between systems. The online focus group discussions with various application providers was conducted to explore the challenges of interoperability between digital systems. Results and Discussion : There were 18 maternal and child health information systems have been developed by the government (central and local), health facilities and private sector. The initiation of interoperability between systems has not yet occurred, except to support regular reporting at the health office and Ministry of Health level. Interoperability between information systems required efforts to improve information technology facilities and infrastructure, development of health data standards, strengthening governance and regulation and utilization of data as an effort to monitor, evaluation and continuity of interoperability between systems to support the digitalization of services and routine reporting. Conclusions and Recommendations : Digitalization of MCH services in Indonesia has the opportunity to support the continuum of care through an interoperable system. However, several enabler factors need to be prepared to support interoperability between information systems.
Copyright (c) 2021 Lutfan Lazuardi, Guardian Yoki Sanjaya , Pungkas Bahjuri Ali, Renova Glorya Montesori Siahaan, Lia Achmad, Hanifah Wulandari https://creativecommons.org/licenses/by/4.0
The Digitalization of Local Owner-Operated Retail Outlets: How Environmental and Organizational Factors Drive the Use of Digital Tools and Applications
The digitalization of the retail industry is a disruptive innovation process which endangers the very existence of Local Owner Operated Retail Outlets (LOOROs). Despite the manifold digital options to regain competitive power, LOOROs struggle in their digital transformation and persist often in their traditional business behaviour. As their customers get more and more used to buying via digital channels, they more and more expect the provision of digital services. This paper and the presented survey among 223 LOORO owners from 26 cities in Germany aim to understand why the LOOROs are so hesitant. Our findings show high insecurity among LOOROs about what to do and where to begin the digitalization route. The owners of LOOROs are often decoupled from their near and far business environment. This leads to a wrong self-assessment and implies the risk that the services provided do neither match the competitive environment nor customer expectations.
Copyright (c) 2021 Lars Bollweg, Soeren Baersch, Richard Lackes, Markus Siepermann, Peter Weber https://creativecommons.org/licenses/by/4.0
A Novel Example-Dependent Cost-Sensitive Stacking Classifier to Identify Tax Return Defaulters
Tax evasion refers to an entity indulging in illegal activities to avoid paying their actual tax liability. A tax return statement is a periodic report comprising information about income, expenditure, etc. One of the most basic tax evasion methods is failing to file tax returns or delay filing tax return statements. The taxpayers who do not file their returns, or fail to do so within the stipulated period are called tax return defaulters. As a result, the Government has to bear the financial losses due to a taxpayer defaulting, which varies for each taxpayer. Therefore, while designing any statistical model to predict potential return defaulters, we have to consider the real financial loss associated with the misclassification of each individual. This paper proposes a framework for an example-dependent cost-sensitive stacking classifier that uses cost-insensitive classifiers as base generalizers to make predictions on the input space. These predictions are used to train an example-dependent cost-sensitive meta generalizer. Based on the meta-generalizer choice, we propose four variant models used to predict potential return defaulters for the upcoming tax-filing period. These models have been developed for the Commercial Taxes Department, Government of Telangana, India. Applying our proposed variant models to GST data, we observe a significant increase in savings compared to conventional classifiers. Additionally, we develop an empirical study showing that our approach is more adept at identifying potential tax return defaulters than existing example-dependent cost-sensitive classification algorithms.
Copyright (c) 2021 Sanat Bhargava, M. Ravi Kumar , Priya Mehta, Jithin Mathews, Sandeep Kumar, Ch Sobhan Babu https://creativecommons.org/licenses/by/4.0
Process mining aims at deriving process knowledge from event logs, which contain data recorded during process executions. Typically, event logs need to be generated from process execution data, stored in different kinds of information systems. In complex domains like healthcare, data is available only at different levels of granularity. Event abstraction techniques allow the transformation of events to a common level of granularity, which enables effective process mining. Existing event abstraction techniques do not sufficiently take into account domain knowledge and, as a result, fail to deliver suitable event logs in complex application domains. This paper presents an event abstraction method based on domain ontologies. We show that the method introduced generates semantically meaningful high-level events, suitable for process mining; it is evaluated on real-world patient treatment data of a large U.S. health system.
Copyright (c) 2021 Finn Klessascheck, Tom Lichtenstein, Martin Meier, Simon Remy, Jan Philipp Sachs, Luise Pufahl, Riccardo Miotto, Erwin Boettinger, Mathias Weske https://creativecommons.org/licenses/by/4.0
Ontological Modeling of the State Economic Development Policy for Cultural Industries
The article discusses an ontological approach to solving the problem of forming state policy of economic development of cultural and creative industries and the corresponding intellectual-information management systems. The purpose of this article is to develop an effective toolkit (based on ontologies) for making optimal decisions in the field of state regulation of the cultural and creative industries, taking into account the dynamic factors of the external environment. The ontological approach considered in the article assumes the presence of three levels of models: meta-ontology, models of subject areas of cultural and creative industries and models of making appropriate management decisions on the formation of economic development policy of cultural and creative industries. The novelty of the proposed approach lies in the purposeful nature of ontological modeling of such complex system as the state policy of economic development of cultural and creative industries. The system under consideration has certain goals, tasks, resources, processes, factors of influence, risks and other characteristics. These characteristics include, in particular, the structure of the model, the ability to highlight the essential objects of real relations of the considered subject areas, the ability to represent knowledge for the joint work of specialists in computer modeling, the processing of expert knowledge and the generation of management decisions within the framework of the corresponding intellectual-information systems.
Copyright (c) 2021 Kostiantyn Tkachenko, Olha Tkachenko, Oleksandr Tkachenko, Mariia Proskurina, Iryna Parkhomenko https://creativecommons.org/licenses/by/4.0
Semantic Representation of Domain Knowledge for Professional VR Training
Domain-specific knowledge representation is an essential element of efficient management of professional training. Formal and powerful knowledge representation for training systems can be built upon the semantic web standards, which enable reasoning and complex queries against the content. Virtual reality training is currently used in multiple domains, in particular, if the activities are potentially dangerous for the trainees or require advanced skills or expensive equipment. However, the available methods and tools for creating VR training systems do not use knowledge representation. Therefore, creation, modification and management of training scenarios is problematic for domain experts without expertise in programming and computer graphics. In this paper, we propose an approach to creating semantic virtual training scenarios, in which users’ activities, mistakes as well as equipment and its possible errors are represented using domain knowledge understandable to domain experts. We have verified the approach by developing a user-friendly editor of VR training scenarios for electrical operators of high-voltage installations.
Copyright (c) 2021 Jakub Flotyński, Paweł Sobociński, Sergiusz Strykowski, Dominik Strugała, Paweł Buń, Filip Górski, Krzysztof Walczak https://creativecommons.org/licenses/by/4.0
Mapping of ImageNet and Wikidata for Knowledge Graphs Enabled Computer Vision
Knowledge graphs are used as a source of prior knowledge in numerous computer vision tasks. However, such an approach requires to have a mapping between ground truth data labels and the target knowledge graph. We linked the ILSVRC 2012 dataset (often simply referred to as ImageNet) labels to Wikidata entities. This enables using rich knowledge graph structure and contextual information for several computer vision tasks, traditionally benchmarked with ImageNet and its variations. For instance, in few-shot learning classification scenarios with neural networks, this mapping can be leveraged for weight initialisation, which can improve the final performance metrics value. We mapped all 1000 ImageNet labels – 461 were already directly linked with the exact match property (P2888), 467 have exact match candidates, and 72 cannot be matched directly. For these 72 labels, we discuss different problem categories stemming from the inability of finding an exact match. Semantically close non-exact match candidates are presented as well. The mapping is publicly available athttps://github.com/DominikFilipiak/imagenet-to-wikidata-mapping.
Copyright (c) 2021 Dominik Filipiak, Anna Fensel, Agata Filipowska https://creativecommons.org/licenses/by/4.0
Predicting E-commerce Item Sales With Web Environment Temporal Background
In this paper, we study the effect of Web environment temporal background in pre-dicting e-commerce item sales, especially those in temporary sales. Temporary sales nowadaysare a popular strategy for quickly clearing inventories. For traditional recommender systems,predicting the sales of an item is done based on its past purchase records. For temporarysales items, however, such records are not available. In order to make recommendation forsuch items, contextual information, such as product descriptions, is usually used. We investi-gate whether temporal background in the Web environment can be additional useful contextualinformation in recommender systems. It is assumed that items consistent with the temporalbackground would have higher demands. We propose a method for representing the temporalbackground using word embeddings of e-commerce activities and social media data, and eval-uate their effect on sales prediction. Through empirical analysis with real-world data, we foundthat temporal background does have positive effects for sales prediction. The findings in thispaper can be conveniently incorporated into future recommender system designs.
Copyright (c) 2021 Yihong Zhang, Takahiro Hara https://creativecommons.org/licenses/by/4.0
Social Media Crisis Communication Model for Building Public Resilience: A Preliminary Study
There is an ongoing discussion about the effectiveness of social media usage on the ability of people to recover from the crisis. However, the existing social media crisis communication models could not address the dynamic feature of social media users and the crisis, respectively. Therefore, the objective of this study is to conduct a preliminary investigation of the social media crisis communication model for building public resilience. Thus, 34 items were generated from the literature concerning the crisis, crisis response, social interaction, and resilience. The items were validated by three experts via content validity index and modified kappa statistics. After passing the validation test, the instruments were pre-tested by 32 participants. The reliability of the items was analyzed using Cronbach’s alpha. Also, the model fits and mediation were examined by the regression model, and the hypotheses were independently assessed in process macro models. Based on the result obtained, each of the constructs satisfied the internal consistency requirement; crisis (0.743), crisis response (0.724), social media interaction (0.716), and resilience (0.827). Furthermore, the result also indicates that the regression model is a good fit for the data. The independent variables statistically significantly predict the dependent variable, p < 0.05. Also, the result of the process macro models indicates that all the hypotheses are independently supported.
Copyright (c) 2021 Umar Ali Bukar, Marzanah A Jabar, Fatimah Sidi, Rozi Nor Haizan Nor, Salfarina Abdullah https://creativecommons.org/licenses/by/4.0
24th International Conference on Business Information Systems
Copyright (c) 2021 Witold Abramowicz, Sören Auer https://creativecommons.org/licenses/by/4.0