| 1. |
- Munappy, Aiswarya Raj, 1990, et al.
(author)
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On the Trade-off Between Robustness and Complexity in Data Pipelines
- 2021
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In: Quality of Information and Communications Technology. - Cham : Springer. - 9783030853464 - 9783030853471 ; 1439, s. 401-415
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Conference paper (peer-reviewed)abstract
- Data pipelines play an important role throughout the data management process whether these are used for data analytics or machine learning. Data-driven organizations can make use of data pipelines for producing good quality data applications. Moreover, data pipelines ensure end-to-end velocity by automating the processes involved in extracting, transforming, combining, validating, and loading data for further analysis and visualization. However, the robustness of data pipelines is equally important since unhealthy data pipelines can add more noise to the input data. This paper identifies the essential elements for a robust data pipeline and analyses the trade-off between data pipeline robustness and complexity.
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| 2. |
- Munappy, Aiswarya Raj, 1990, et al.
(author)
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Data Management Challenges for Deep Learning
- 2019
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In: Proceedings - 45th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2019. - : IEEE. ; , s. 140-147
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Conference paper (peer-reviewed)abstract
- © 2019 IEEE. Deep learning is one of the most exciting and fast-growing techniques in Artificial Intelligence. The unique capacity of deep learning models to automatically learn patterns from the data differentiates it from other machine learning techniques. Deep learning is responsible for a significant number of recent breakthroughs in AI. However, deep learning models are highly dependent on the underlying data. So, consistency, accuracy, and completeness of data is essential for a deep learning model. Thus, data management principles and practices need to be adopted throughout the development process of deep learning models. The objective of this study is to identify and categorise data management challenges faced by practitioners in different stages of end-to-end development. In this paper, a case study approach is employed to explore the data management issues faced by practitioners across various domains when they use real-world data for training and deploying deep learning models. Our case study is intended to provide valuable insights to the deep learning community as well as for data scientists to guide discussion and future research in applied deep learning with real-world data.
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| 3. |
- Dakkak, Anas, et al.
(author)
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Customer Support In The Era of Continuous Deployment: A Software-Intensive Embedded Systems Case Study
- 2022
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In: Proceedings - 2022 IEEE 46th Annual Computers, Software, and Applications Conference, COMPSAC 2022. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 914-923
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Conference paper (peer-reviewed)abstract
- Supporting customers after they acquire the prod-uct is essential for companies producing and selling software-intensive embedded systems products. Generally, customer sup-port is the first interaction point between the product users and the product vendor. Customer support is often engaged with answering customers' questions, troubleshooting, fault identification, and fixing product faults. While continuous deployment advocates for closer cooperation between the ones operating the software and the ones developing it, the means of such collaboration in general and the role of customer support, in particular, has not been addressed in the context of software-intensive embedded systems. Therefore, to better understand the impact that continuous deployment has on customer support and the role customer support should play in this context, we conducted a case study at a multinational company developing and selling telecommunications networks infrastructure. We focused on the 4th and 5th Generation (4G and 5G) Radio Access Networks (RAN) products, which can be considered a high volume product as they cover more than 80% of the world's population. Our study reveals that customer support needs to transition from a transaction-based and passive function triggered by customer support requests, to take an active role characterized by being proactive and preemptive to cope with the shorter operational time of a software version introduced by continuous deployment. In addition, customer support plays an essential role in making the feedback actionable by aggregating and consolidating feedback data to the R&D organization.
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| 4. |
- Lewenhagen, Kenneth, et al.
(author)
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An Interdisciplinary Web-based Framework for Data-driven Placement Analysis of CCTV Cameras
- 2021
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In: Proceedings of the 2021 Swedish Workshop on Data Science, SweDS 2021. - : Institute of Electrical and Electronics Engineers Inc.. - 9781665418300
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Conference paper (peer-reviewed)abstract
- This paper describes work in progress of an interdisciplinary research project that focuses on the placement and analysis of public close-circuit television (CCTV) cameras using data-driven analysis of crime data. A novel web-based prototype that acts as a framework for the camera placement analysis with regards to historical crime occurrence is presented. The web-based prototype enables various analyses involving public CCTV cameras e.g., to determine suitable locations for both stationary CCTV cameras as well as temporary cameras that are moved around after a few months to address crime seasonality. The framework also opens up for other analyses, e.g. automatically highlighting crimes that are carried out closed by at least one camera. The research also investigates to what extent it is possible to generate estimates on the amount of detail captured by a camera given the distance to the crime light conditions. The research project includes interdisciplinary competences from various areas such as criminology, computer and data science as well as the Swedish Police. © 2021 IEEE.
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| 5. |
- Figalist, Iris, et al.
(author)
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An End-to-End Framework for Productive Use of Machine Learning in Software Analytics and Business Intelligence Solutions
- 2020
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In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Cham : Springer International Publishing. - 1611-3349 .- 0302-9743. ; 12562 LNCS, s. 217-233, s. 217-233
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Conference paper (peer-reviewed)abstract
- Nowadays, machine learning (ML) is an integral component in a wide range of areas, including software analytics (SA) and business intelligence (BI). As a result, the interest in custom ML-based software analytics and business intelligence solutions is rising. In practice, however, such solutions often get stuck in a prototypical stage because setting up an infrastructure for deployment and maintenance is considered complex and time-consuming. For this reason, we aim at structuring the entire process and making it more transparent by deriving an end-to-end framework from existing literature for building and deploying ML-based software analytics and business intelligence solutions. The framework is structured in three iterative cycles representing different stages in a model’s lifecycle: prototyping, deployment, update. As a result, the framework specifically supports the transitions between these stages while also covering all important activities from data collection to retraining deployed ML models. To validate the applicability of the framework in practice, we compare it to and apply it in a real-world ML-based SA/BI solution.
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| 6. |
- Fredriksson, Teodor, 1992, et al.
(author)
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Classification of Complex-Valued Radar Data using Semi-Supervised Learning: a Case Study
- 2023
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In: Proceedings - 2023 49th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2023. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 102-107
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Conference paper (peer-reviewed)abstract
- In recent years, the interest in applying machine learning (ML) and deep learning (DL) has been increasing due to their ability to learn to predict and find structure in data. The most common approach of ML and DL is supervised learning. Supervised learning requires the input data to be labeled. However, as reported by many industries, such as the embedded systems domain, fully labeled datasets are difficult to obtain since data labeling is manually intensive. This paper uses a semi-supervised learning approach on real-world Pulse-Doppler data obtained from our industry collaborator Saab to address this challenge. We took inspiration from the FixMatch algorithm. To investigate whether unlabeled data can help improve classification accuracy, we compare FixMatch to a supervised baseline. We use five different settings for the number of available labels per class label to investigate how many labeled instances and how much manual effort is required for optimal accuracy. Bayesian Linear Regression is used to analyze the results. The results show that FixMatch can reach a higher accuracy than the supervised baseline. Furthermore, FixMatch requires more computation time but will help reduce manual effort. In addition, FixMatch will not underfit or overfit. Thanks to this study, practitioners know the benefits of utilizing FixMatch and when it is safe to use to improve a supervised baseline in the industry.
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| 7. |
- Hyrynsalmi, Sami, et al.
(author)
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Quō vādis, Data Business?: A Study for Understanding Maturity of Embedded System Companies in Data Economy
- 2022
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In: Lecture Notes in Business Information Processing. - Cham : Springer International Publishing. - 1865-1356 .- 1865-1348. ; 463 LNBIP, s. 141-148, s. 141-148
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Conference paper (peer-reviewed)abstract
- Data has been claimed to be the new oil of the 21st century as it has seen to be able both to improve the existing products and services as well as to create new revenue streams for its utilizing company with a secondary customers base. However, while there is active streams of research for developing machine learning and data science methods, considerably less has been done to understand and characterize data business activities in the software-intensive companies. This study uses a multiple case study approach in the software-intensive embedded system domain. Four large international embedded system companies were selected as the case study subjects. The objective is to understand how the case companies are developing their activities for successful utilization of the data. The study identifies six distinct stages with their own challenges. In addition, this study serves as a starting for further work for supporting software-intensive embedded system companies to start data business.
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| 8. |
- John, Meenu Mary, et al.
(author)
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Developing ML/DL Models: A design framework
- 2020
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In: Proceedings - 2020 IEEE/ACM International Conference on Software and System Processes, ICSSP 2020. - New York, NY, USA : ACM. ; , s. 1-10
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Conference paper (peer-reviewed)abstract
- Artificial Intelligence is becoming increasingly popular with organizations due to the success of Machine Learning and Deep Learning techniques. Using these techniques, data scientists learn from vast amounts of data to enhance behaviour in software-intensive systems. Despite the attractiveness of these techniques, however, there is a lack of systematic and structured design process for developing ML/DL models. The study uses a multiple-case study approach to explore the different activities and challenges data scientists face when developing ML/DL models in software-intensive embedded systems. In addition, we have identified seven different phases in the proposed design process leading to effective model development based on the case study. Iterations identified between phases and events which trigger these iterations optimize the design process for ML/DL models. Lessons learned from this study allow data scientists and engineers to develop high-performance ML/DL models and also bridge the gap between high demand and low supply of data scientists.
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| 9. |
- Munappy, Aiswarya Raj, 1990, et al.
(author)
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Modelling Data Pipelines
- 2020
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In: Proceedings - 46th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2020. - : IEEE. ; , s. 13-20
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Conference paper (peer-reviewed)abstract
- Data is the new currency and key to success. However, collecting high-quality data from multiple distributed sources requires much effort. In addition, there are several other challenges involved while transporting data from its source to the destination. Data pipelines are implemented in order to increase the overall efficiency of data-flow from the source to the destination since it is automated and reduces the human involvement which is required otherwise. Despite existing research on ETL (Extract-Transform-Load) and ELT (Extract-Load-Transform) pipelines, the research on this topic is limited. ETL/ELT pipelines are abstract representations of the end-to-end data pipelines. To utilize the full potential of the data pipeline, we should understand the activities in it and how they are connected in an end-to-end data pipeline. This study gives an overview of how to design a conceptual model of data pipeline which can be further used as a language of communication between different data teams. Furthermore, it can be used for automation of monitoring, fault detection, mitigation and alarming at different steps of data pipeline.
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| 10. |
- Munappy, Aiswarya Raj, 1990, et al.
(author)
-
On the Impact of ML use cases on Industrial Data Pipelines
- 2021
-
In: Proceedings - Asia-Pacific Software Engineering Conference, APSEC. - : IEEE. - 1530-1362. ; 2021-December, s. 463-472, s. 463-472
-
Conference paper (peer-reviewed)abstract
- The impact of the Artificial Intelligence revolution is undoubtedly substantial in our society, life, firms, and employment. With data being a critical element, organizations are working towards obtaining high-quality data to train their AI models. Although data, data management, and data pipelines are part of industrial practice even before the introduction of ML models, the significance of data increased further with the advent of ML models, which force data pipeline developers to go beyond the traditional focus on data quality. The objective of this study is to analyze the impact of ML use cases on data pipelines. We assume that the data pipelines that serve ML models are given more importance compared to the conventional data pipelines. We report on a study that we conducted by observing software teams at three companies as they develop both conventional(Non-ML) data pipelines and data pipelines that serve ML-based applications. We study six data pipelines from three companies and categorize them based on their criticality and purpose. Further, we identify the determinants that can be used to compare the development and maintenance of these data pipelines. Finally, we map these factors in a two-dimensional space to illustrate their importance on a scale of low, moderate, and high.
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