| 1. |
- Figalist, Iris, et al.
(författare)
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Mining customer satisfaction on b2b online platforms using service quality and web usage metrics
- 2020
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Ingår i: Proceedings - Asia-Pacific Software Engineering Conference, APSEC. - : IEEE. - 1530-1362. ; 2020-December, s. 435-444, s. 435-444
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Konferensbidrag (refereegranskat)abstract
- In order to distinguish themselves from their competitors, software service providers constantly try to assess and improve customer satisfaction. However, measuring customer satisfaction in a continuous way is often time and cost intensive, or requires effort on the customer side. Especially in B2B contexts, a continuous assessment of customer satisfaction is difficult to achieve due to potential restrictions and complex provider-customer-end user setups. While concepts such as web usage mining enable software providers to get a deep understanding of how their products are used, its application to quantitatively measure customer satisfaction has not yet been studied in greater detail. For that reason, our study aims at combining existing knowledge on customer satisfaction, web usage mining, and B2B service characteristics to derive a model that enables an automated calculation of quantitative customer satisfaction scores. We apply web usage mining to validate these scores and to compare the usage behavior of satisfied and dissatisfied customers. This approach is based on domain-specific service quality and web usage metrics and is, therefore, suitable for continuous measurements without requiring active customer participation. The applicability of the model is validated by instantiating it in a real-world B2B online platform.
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| 2. |
- Kunnappilly, Ashalatha, et al.
(författare)
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UML-based modeling and analysis of 5G service orchestration
- 2020
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Ingår i: Proceedings - Asia-Pacific Software Engineering Conference, APSEC. - : IEEE Computer Society. - 9781728195537 ; , s. 129-138
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Konferensbidrag (refereegranskat)abstract
- The fifth generation of cellular wireless technol- ogy, 5G, bears the promise to transform the future network connectivity by providing seamless, low-latency and reliable interconnections between devices. In this paper, we focus on modeling and analyzing 5G service orchestration that deals with virtual network function placement, resource assignment and traffic routing, which are the building blocks of generating network slices catering to various application requirements. In order to ensure that a particular network slice works as stated by the application's service level agreement, it is essential that the constituent virtual network functions are placed in proper hosts, allocated adequate resources in terms of processing power, memory, bandwidth, and routed such that the constraints of the hosts and the network are met. This is a complex problem to solve if one considers the diverse set of requirements of 5G services. We tackle this problem by proposing a UML-based modeling and analysis framework, called UML5G Service Orchestration Profile, which allows one to describe 5G network slices and service orchestration via a specialized profile, and analyze as-sociated quality-of-service requirements by checking constraints expressed in Object Constraint Language. Our framework allows a designer to model any candidate orchestration scheme for 5G networks and verify if the network function placement, resource assignment, and routing guarantee the application's quality-of-service requirements, at design time. We evaluate the framework on a prototype implementation of an orchestration algorithm that generates a multitude of allocation configurations that we automatically check against requirements formalized in Object Constraint Language. Our contribution facilitates modeling and design-time evaluation of network slicing and service orchestration schemes in 5G-based solutions.
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| 3. |
- Munappy, Aiswarya Raj, 1990, et al.
(författare)
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Towards automated detection of data pipeline faults
- 2020
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Ingår i: Proceedings - Asia-Pacific Software Engineering Conference, APSEC. - : IEEE. - 1530-1362. ; 2020-December, s. 346-355, s. 346-355
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Konferensbidrag (refereegranskat)abstract
- Data pipelines play an important role throughout the data management process. It automates the steps ranging from data generation to data reception thereby reducing the human intervention. A failure or fault in a single step of a data pipeline has cascading effects that might result in hours of manual intervention and clean-up. Data pipeline failure due to faults at different stages of data pipelines is a common challenge that eventually leads to significant performance degradation of data-intensive systems. To ensure early detection of these faults and to increase the quality of the data products, continuous monitoring and fault detection mechanism should be included in the data pipeline. In this study, we have explored the need for incorporating automated fault detection mechanisms and mitigation strategies at different stages of the data pipeline. Further, we identified faults at different stages of the data pipeline and possible mitigation strategies that can be adopted for reducing the impact of data pipeline faults thereby improving the quality of data products. The idea of incorporating fault detection and mitigation strategies is validated by realizing a small part of the data pipeline using action research in the analytics team at a large software-intensive organization within the telecommunication domain.
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| 4. |
- Zhang, Hongyi, 1996, et al.
(författare)
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Federated learning systems: Architecture alternatives
- 2020
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Ingår i: Proceedings - Asia-Pacific Software Engineering Conference, APSEC. - : IEEE. - 1530-1362. ; 2020-December, s. 385-394, s. 385-394
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Konferensbidrag (refereegranskat)abstract
- Machine Learning (ML) and Artificial Intelligence (AI) have increasingly gained attention in research and industry. Federated Learning, as an approach to distributed learning, shows its potential with the increasing number of devices on the edge and the development of computing power. However, most of the current Federated Learning systems apply a single-server centralized architecture, which may cause several critical problems, such as the single-point of failure as well as scaling and performance problems. In this paper, we propose and compare four architecture alternatives for a Federated Learning system, i.e. centralized, hierarchical, regional and decentralized architectures. We conduct the study by using two well-known data sets and measuring several system performance metrics for all four alternatives. Our results suggest scenarios and use cases which are suitable for each alternative. In addition, we investigate the trade-off between communication latency, model evolution time and the model classification performance, which is crucial to applying the results into real-world industrial systems.
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