Towards a domain-driven distributed reference architecture for big data systems

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Ataei, Pouya; Litchfield, Alan

Abstract: The proliferation of digital devices, rapid development of software and the inf ... Expand

Abstract: The proliferation of digital devices, rapid development of software and the infrastructure of today, have augmented user's capability to produce data at an unprecedented rate. The accelerated growth of data could be called the era of big data and forced a paradigm shift in data engineering because the variety, velocity and volume of data overwhelmed existing systems. While companies attempt to extract benefit from big data, success rates are still low. Challenges such as rapid changes in technology, organizational culture, complexity in data engineering, impediments to system development, and a lack of effective big data architectures mean that only an estimated 20% of companies achieved their goals. To this end, this study explores a domain-driven distributed big data reference architecture that addresses issues in data architecture, data engineering, and system development. This reference architecture is empirically grounded and evaluated through deployment in a real-world scenario as an instantiated prototype, solving a problem in practice. The results of the evaluation demonstrate utility and applicability but with architectural trade-offs and challenges. Collapse

Topics: software as a service cloud computing data lake systems development big data

Methods: design artifact simulation data modeling design science structured literature research

Creating Intelligent Computational Edge through Semantic Mediation

2023 | HICSS | Citations: 0

Authors: Juric, Radmila; Ronchieri, Elisabetta; Suh, Sang

Abstract: This research proposes semantic mediation based on reasoning and the first order ... Expand

Abstract: This research proposes semantic mediation based on reasoning and the first order logic for mediating the best possible configuration of Computational Edge, relevant for software applications which may benefit for running computations with proximity to their data sources. The mediation considers the context in which these applications exist and exploits the semantic of that context for decision making on where computational elements should reside and which data they should use. The application of semantic mediation could address the initiative to accommodate algorithms from predictive and learning technologies, push AI towards computational edges and potentially contribute towards creating a computing continuum. Collapse

Topics: cloud computing mobile system internet of things edge computing artificial intelligence

Methods: ontological modelling computational algorithm literature study literature sample

Application Massive Data Processing Platform for Smart Manufacturing Based on Optimization of Data Storage

2023 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Ren, Bin; Chen, Yuquiang; Wang, Fujie

Abstract: The aim of smart manufacturing is to reduce manpower requirements of the product ... Expand

Abstract: The aim of smart manufacturing is to reduce manpower requirements of the production line by applying technology of huge amounts of data to the manufacturing industry. Smart manufacturing is also called Industry 4.0, and the platform for processing huge amounts of data has an indispensable role. The massive data processing platform is like the brain of the entire factory, receiving all data from production line sensors via edge computing, processing, and analyzing, and finally making feedback decisions. With the innovation of production technology, the data that the platform needs to process has become diverse and complex, and the amount has become increasingly large. As well, many precision manufacturing industries have begun to enter the field of Industry 4.0. In addition to the accuracy and availability of data processing, there is emphasis on the real-time nature of data processing. After the sensor receives the data, the platform must provide feedback within a short period of time. This article proposes a massive data processing platform based on the Lambda architecture, which has the coexistence of stream processing and batch processing to meet real-time feedback needs of high-precision manufacturing. To verify the effectiveness of the optimization, it is based on real data from the manufacturing industry. To generate a large amount of test data to confirm the optimization of the storage of pictures. The results show that it optimizes the storage and optimization of the image data generated by the Automated Optical Inspection technology used in manufacturing today and optimizes the query for data storage. It also reduces the consumption of a large amount of memory as expected, and the query for Hive reduced the time spent. Collapse

Topics: Apache HBase Apache Hadoop Hadoop Distributed File System stream processing Python

Methods: experiment

A Prototypical Dashboard for Knowledge-Based Expert Systems used for Real-Time Anomaly Handling in Smart Manufacturing

2023 | HICSS | Citations: 0

Authors: Stahmann, Philip

Abstract: The use of machine learning in digitized production increases potentials for pro ... Expand

Abstract: The use of machine learning in digitized production increases potentials for production automation. A milestone on the path to autonomous production is real-time anomaly detection. However, increasing complexity of production makes autonomous decisions difficult to understand for humans as central stakeholders. In this paper, we create a dashboard that incorporates elements from knowledge-based systems, requirements for real-time anomaly detection, and design guidelines for dashboards. Using design science research, the dashboard is designed, implemented and comprehensively evaluated with 98 participants. After the second design science iteration, the dashboard is approved in terms of usefulness and ease of use. Our research primarily contributes to practice, as our implementation constitutes a starting point for designing the interface between humans and autonomous production. We also contribute to academia as the dashboard is an instantiation in the research field of interface design for knowledge-based systems, which can be further developed in future research. Collapse

Topics: digital dashboard expert system production management industry 4.0 internet technology

Methods: survey anomaly detection qualitative interview survey design design guideline

A GPU-Accelerated Approach to Static Stability Assessments for Pallet Loading in Air Cargo

2022 | HICSS | Citations: 0

Authors: Mazur, Philipp Gabriel; Lee, No-San; Schoder, Detlef

Abstract: The static stability constraint is one of the most important constraints in pall ... Expand

Abstract: The static stability constraint is one of the most important constraints in pallet loading and plays a substantial role when assembling safe and loadable palletizing layouts. Current approaches reach their limits as soon as additional complexity is added, which is a given in the practice of air cargo logistics, or when performance becomes important. As our central objective, we explore a new approach to calculate static stability more performantly and to cover more complexity by relaxing several simplifying assumptions. The approach is implemented in a prototype and builds on the emerging technology of graphical processing unit acceleration in combination with physics engines. We propose a new artifact design and summarize the howto knowledge in the form of abstracted design principles. Our results demonstrate an improvement in terms of performance depending on the underlying hardware. We develop a conceptual model to assist future research in choosing a solution technology. Collapse

Topics: computer hardware application programming interface artificial intelligence agile software development stream processing

Methods: simulation computational algorithm design principle conceptual modelling Student's t-test

Demystifying analytical information processing capability: The case of cybersecurity incident response

2021 | Decision Support Systems | Citations: 7

Authors: Naseer, Humza; Maynard, Sean B.; Desouza, Kevin C.

Abstract: Little is known about how organizations leverage business analytics (BA) to deve ... Expand

Abstract: Little is known about how organizations leverage business analytics (BA) to develop, process, and exploit analytical information in cybersecurity incident response (CSIR). Drawing on information processing theory (IPT), we conducted a field study using a multiple case study design to answer the following research question: How do organizations exploit analytical information in the process of cybersecurity incident response by using business analytics? We developed a theoretical framework that explains how organizations respond to the dy­ namic cyber threat environment by exploiting analytical information processing capability in the CSIR process. This, in turn, leads to positive outcomes in enterprise security performance, delivering both strategic and financial benefits. Our findings contribute to the BA and cybersecurity literature by providing useful insights into BA applications and the facilitation of analytics-driven decision making in CSIR. Further, they contribute to IPT by providing new insights about analytical information needs, mechanisms to seek analytical information, and analytical information use in the process of CSIR. Collapse

Topics: IT security IT security threat analytical information system decision making malware

Methods: case study qualitative interview theory development multiple case study anomaly detection

Theories: information processing theory

Cloud and edge based data analytics for privacy-preserving multi-modal engagement monitoring in the classroom

2021 | Information Systems Frontiers | Citations: 0

Authors: Preuveneers, Davy; Garofalo, Giuseppe; Joosen, Wouter

Abstract: Learning management systems are service platforms that support the administratio ... Expand

Abstract: Learning management systems are service platforms that support the administration and delivery of training programs and educational courses. Prerecorded, real-time or interactive lectures can be offered in blended, flipped or fully online classrooms. A key challenge with such service platforms is the adequate monitoring of engagement, as it is an early indicator for a student’s learning achievements. Indeed, observing the behavior of the audience and keeping the participants engaged is not only a challenge in a face-to-face setting where students and teachers share the same physical learning environment, but definitely when students participate remotely. In this work, we present a hybrid cloud and edge-based service orchestration framework for multi-modal engagement analysis. We implemented and evaluated an edge-based browser solution for the analysis of different behavior modalities with cross-user aggregation through secure multiparty computation. Compared to contemporary online learning systems, the advantages of our hybrid cloud-edge based solution are twofold. It scales up with a growing number of students, and also mitigates privacy concerns in an era where the rise of analytics in online learning raises questions about the responsible use of data. Collapse

Topics: privacy cloud computing online learning system website JavaScript

Methods: deep learning literature study biometric measurement TensorFlow oculometry

DBL SmartCity: An Open-Source IoT Platform for Managing Large BIM and 3D Geo-Referenced Datasets

2019 | HICSS | Citations: 0

Authors: Kolaric, Sinisˇa; Shelden, Dennis

Abstract: The ‘smart city’ approach has been promoted as an effective way to manage urban ... Expand

Abstract: The ‘smart city’ approach has been promoted as an effective way to manage urban environments. Information and communication technology in general, as well as ‘Internet of Things’ systems in particular, constitute an essential component of all smart city initiatives. However, many past and current smart city implementations place only an insufficient emphasis on the geo-spatial and 3D nature of data. In order to fill this gap, we present DBL SmartCity, an open-source smart city IoT platform that is based on open standards and designed from the ground-up to effectively store, manage, and present large sets of BIM and 3D geo-referenced data. Collapse

Topics: mobile system internet of things open source smart city stream processing

Methods: data modeling design requirement literature study survey design principle

Privacy-Preserving Process Mining: Differential Privacy for Event Logs

2019 | Business & Information Systems Engineering | Citations: 0

Authors: Mannhardt, Felix; Koschmider, Agnes; Baracaldo, Nathalie; Weidlich, Matthias; Michael, Judith

Abstract: Privacy regulations for data can be regarded as a major driver for data soverei ... Expand

Abstract: Privacy regulations for data can be regarded as a major driver for data sovereignty measures. A specific example for this is the case of event data that is recorded by information systems during the processing of entities in domains such as e-commerce or health care. Since such data, typically available in the form of event log files, contains personalized information on the specific processed entities, it can expose sensitive information that may be traced back to individuals. In recent years, a plethora of methods have been developed to analyse event logs under the umbrella of process mining. However, the impact of privacy regulations on the technical design as well as the organizational application of process mining has been largely neglected. This paper set out to develop a protection model for event data privacy which applies the wellestablished notion of differential privacy. Starting from common assumptions about the event logs used in process mining, this paper presents potential privacy leakages and means to protect against them. The paper also shows at which stages of privacy leakages a protection model for event logs should be used. Relying on this understanding, the notion of differential privacy for process discovery methods is instantiated, i.e., algorithms that aim at the construction of a process model from an event log. The general feasibility of our approach is demonstrated by its application to two publicly available real-life events logs. Collapse

Topics: privacy process mining differential privacy database system systems design

Methods: event log business process modeling computational algorithm machine learning design artifact

Leveraging Big Data and Analytics for Auditing: Towards a Taxonomy

2018 | International Conference on Information Systems | Citations: 1

Authors: Krieger, Felix; Drews, Paul

Abstract: The application of big data and analytics to auditing is sparking a lot of inter ... Expand

Abstract: The application of big data and analytics to auditing is sparking a lot of interest in both research and practice. Along with other technological developments, big data and analytics are expected to drive the digitization of auditing and to improving its effectivity and efficiency. While several use cases and first literature reviews on this topic have already been published, the categories for classifying the use cases are still fragmented. By employing a systematic taxonomy development process, we developed a taxonomy that draws upon conceptual work and use cases from the academic literature. This taxonomy provides dimensions and characteristics that help to classify use cases for big data in analytics in auditing in a structured manner. Collapse

Topics: big data data management stream processing process mining decision making

Methods: optimization model business process modeling social network analysis computational algorithm machine learning