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Sökning: hsv:(NATURVETENSKAP) hsv:(Data och informationsvetenskap) > Malmö universitet

  • Resultat 1-10 av 592
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1.
  • John, Meenu Mary, et al. (författare)
  • Towards an AI-driven business development framework: A multi-case study
  • 2023
  • Ingår i: Journal of Software: Evolution and Process. - : Wiley. - 2047-7481 .- 2047-7473. ; 35:6
  • Tidskriftsartikel (refereegranskat)abstract
    • Artificial intelligence (AI) and the use of machine learning (ML) and deep learning (DL) technologies are becoming increasingly popular in companies. These technologies enable companies to leverage big quantities of data to improve system performance and accelerate business development. However, despite the appeal of ML/DL, there is a lack of systematic and structured methods and processes to help data scientists and other company roles and functions to develop, deploy and evolve models. In this paper, based on multi-case study research in six companies, we explore practices and challenges practitioners experience in developing ML/DL models as part of large software-intensive embedded systems. Based on our empirical findings, we derive a conceptual framework in which we identify three high-level activities that companies perform in parallel with the development, deployment and evolution of models. Within this framework, we outline activities, iterations and triggers that optimize model design as well as roles and company functions. In this way, we provide practitioners with a blueprint for effectively integrating ML/DL model development into the business to achieve better results than other (algorithmic) approaches. In addition, we show how this framework helps companies solve the challenges we have identified and discuss checkpoints for terminating the business case.
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2.
  • Munappy, Aiswarya Raj, 1990, et al. (författare)
  • On the Trade-off Between Robustness and Complexity in Data Pipelines
  • 2021
  • Ingår i: Quality of Information and Communications Technology. - Cham : Springer. - 9783030853464 - 9783030853471 ; 1439, s. 401-415
  • Konferensbidrag (refereegranskat)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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3.
  • Spalazzese, Romina, et al. (författare)
  • INTERO: An Interoperability Model for Large Systems
  • 2020
  • Ingår i: IEEE Software. - : IEEE. - 1937-4194 .- 0740-7459. ; 37:3, s. 38-45
  • Tidskriftsartikel (refereegranskat)abstract
    • The INTERO (interoperability) model helps organizations manage and improve interoperability among their large, evolving software systems. They can analyze a specific interoperability problem, conceive strategies to enhance interoperability, and reevaluate the problem to determine whether interoperability has improved.
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4.
  • Munappy, Aiswarya Raj, 1990, et al. (författare)
  • Data Management Challenges for Deep Learning
  • 2019
  • Ingår i: Proceedings - 45th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2019. - : IEEE. ; , s. 140-147
  • Konferensbidrag (refereegranskat)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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5.
  • Vogel, Bahtijar, et al. (författare)
  • What is an Open IoT Platform? : Insights from a Systematic Mapping Study
  • 2020
  • Ingår i: Future Internet. - Basel, Switzerland : MDPI. - 1999-5903. ; 12:4
  • Tidskriftsartikel (refereegranskat)abstract
    • Today, the Internet of Things (IoT) is mainly associated with vertically integrated systems that often are closed and fragmented in their applicability. To build a better IoT ecosystem, the open IoT platform has become a popular term in the recent years. However, this term is usually used in an intuitive way without clarifying the openness aspects of the platforms. The goal of this paper is to characterize the openness types of IoT platforms and investigate what makes them open. We conducted a systematic mapping study by retrieving data from 718 papers. As a result of applying the inclusion and exclusion criteria, 221 papers were selected for review. We discovered 46 IoT platforms that have been characterized as open, whereas 25 platforms are referred as open by some studies rather than the platforms themselves. We found that the most widely accepted and used open IoT platforms are NodeMCU and ThingSpeak that together hold a share of more than 70% of the declared open IoT platforms in the selected papers. The openness of an IoT platform is interpreted into different openness types. Our study results show that the most common openness type encountered in open IoT platforms is open-source, but also open standards, open APIs, open data and open layers are used in the literature. Finally, we propose a new perspective on how to define openness in the context of IoT platforms by providing several insights from the different stakeholder viewpoints.
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6.
  • Bosch, Jan, 1967, et al. (författare)
  • Engineering AI Systems: A Research Agenda
  • 2020
  • Ingår i: Artificial Intelligence Paradigms for Smart Cyber-Physical Systems. - : IGI Global. - 9781799851011 - 9781799851028 ; , s. 1-19
  • Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
    • Artificial intelligence (AI) and machine learning (ML) are increasingly broadly adopted in industry. However, based on well over a dozen case studies, we have learned that deploying industry-strength, production quality ML models in systems proves to be challenging. Companies experience challenges related to data quality, design methods and processes, performance of models as well as deployment and compliance. We learned that a new, structured engineering approach is required to construct and evolve systems that contain ML/DL components. In this chapter, the authors provide a conceptualization of the typical evolution patterns that companies experience when employing ML as well as an overview of the key problems experienced by the companies that they have studied. The main contribution of the chapter is a research agenda for AI engineering that provides an overview of the key engineering challenges surrounding ML solutions and an overview of open items that need to be addressed by the research community at large.
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7.
  • Dakkak, Anas, et al. (författare)
  • Customer Support In The Era of Continuous Deployment: A Software-Intensive Embedded Systems Case Study
  • 2022
  • Ingår i: Proceedings - 2022 IEEE 46th Annual Computers, Software, and Applications Conference, COMPSAC 2022. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 914-923
  • Konferensbidrag (refereegranskat)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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8.
  • Lewenhagen, Kenneth, et al. (författare)
  • An Interdisciplinary Web-based Framework for Data-driven Placement Analysis of CCTV Cameras
  • 2021
  • Ingår i: Proceedings of the 2021 Swedish Workshop on Data Science, SweDS 2021. - : Institute of Electrical and Electronics Engineers Inc.. - 9781665418300
  • Konferensbidrag (refereegranskat)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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9.
  • Figalist, Iris, et al. (författare)
  • An End-to-End Framework for Productive Use of Machine Learning in Software Analytics and Business Intelligence Solutions
  • 2020
  • Ingår i: 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
  • Konferensbidrag (refereegranskat)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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10.
  • Fredriksson, Teodor, 1992, et al. (författare)
  • Classification of Complex-Valued Radar Data using Semi-Supervised Learning: a Case Study
  • 2023
  • Ingår i: Proceedings - 2023 49th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2023. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 102-107
  • Konferensbidrag (refereegranskat)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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