A database for publications published by researchers and students at SimulaMet.
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Research area
Journal articles
Backsourcing of Information Technology - A Systematic Literature Review
Information Systems Research (2021).Status: Submitted
Backsourcing of Information Technology - A Systematic Literature Review
Context: Backsourcing is the process of insourcing previously outsourced activities. When companies experience environmental or strategic changes, or challenges with outsourcing, backsourcing can be a viable alternative. While outsourcing and related processes have been extensively studied in information technology, few studies report experiences with backsourcing.
Objectives: We intend to summarize the results of the research literature on the backsourcing of IT, with a focus on software development. By identifying practical relevance experience, we aim to present findings that may help companies considering backsourcing. In addition, we aim to identify gaps in the current research literature and point out areas for future work.
Method: Our systematic literature review (SLR) started with a search for empirical studies on the backsourcing of IT. From each study we identified the contexts in which backsourcing occurs, the factors leading to the decision to backsource, the backsourcing process itself, and the outcomes of backsourcing. We employed inductive coding to extract textual data from the papers identified and qualitative cross-case analysis to synthesize the evidence from backsourcing experiences.
Results: We identified 17 papers that reported 26 cases of backsourcing, six of which were related to software development. The cases came from a variety of contexts. The most common reasons for backsourcing were improving quality, reducing costs, and regaining control of outsourced activities. The backsourcing process can be described as containing five sub-processes: change management, vendor relationship management, competence building, organizational build-up, and transfer of ownership. Furthermore, we identified 14 positive outcomes and nine negative outcomes of backsourcing. Finally, we aggregated the evidence and detailed three relationships of potential use to companies considering backsourcing.
Conclusion: The backsourcing of IT is a complex process; its implementation depends on the prior outsourcing relationship and other contextual factors. Our systematic literature review may contribute to a better understanding of this process by identifying its components and their relationships based on the peer-reviewed literature. Our results may also serve as a motivation and baseline for further research on backsourcing and may provide guidelines and process fragments from which practitioners can benefit when they engage in backsourcing.
| Afilliation | Software Engineering |
| Project(s) | EDOS: Effective Digitalization of Public Sector, Department of IT Management |
| Publication Type | Journal Article |
| Year of Publication | 2021 |
| Journal | Information Systems Research |
| Publisher | Institute for Operations Research and the Management Sciences |
| Keywords | backshoring, Backsourcing, information technology, software development, Software Engineering, systematic literature review |
Exploring Dynamic Metabolomics Data With Multiway Data Analysis: a Simulation Study
Research Square (2021).Status: Submitted
Exploring Dynamic Metabolomics Data With Multiway Data Analysis: a Simulation Study
Background: Analysis of dynamic metabolomics data holds the promise to improve our understanding of underlying mechanisms in metabolism. For example, it may detect changes in metabolism due to the onset of a disease. Dynamic or time-resolved metabolomics data can be arranged as a three-way array with entries organized according to a subjects mode, a metabolites mode and a time mode. While such time-evolving multiway data sets are increasingly collected, revealing the underlying mechanisms and their dynamics from such data remains challenging. For such data, one of the complexities is the presence of a superposition of several sources of variation: induced variation (due to experimental conditions or inborn errors), individual variation, and measurement error. Multiway data analysis (also known as tensor factorizations) has been successfully used in data mining to find the underlying patterns in multiway data. In this paper, we study the use of multiway data analysis to reveal the underlying patterns and dynamics in time-resolved metabolomics data.
Results: We focus on simulated data arising from different dynamic models of increasing complexity, i.e., a simple linear system, a yeast glycolysis model, and a human cholesterol model. We generate data with induced variation as well as individual variation. Systematic experiments are performed to demonstrate the advantages and limitations of multiway data analysis in analyzing such dynamic metabolomics data and their capacity to disentangle the different sources of variations. We choose to use simulations since we want to understand the capability of multiway data analysis methods which is facilitated by knowing the ground truth.
Conclusion: Our numerical experiments demonstrate that despite the increasing complexity of the studied dynamic metabolic models, tensor factorization methods CANDECOMP/PARAFAC(CP) and Parallel Profiles with Linear Dependences (Paralind) can disentangle the sources of variations and thereby reveal the underlying mechanisms and their dynamics.
| Afilliation | Machine Learning |
| Project(s) | Department of Machine Intelligence, TrACEr: Time-Aware ConstrainEd Multimodal Data Fusion |
| Publication Type | Journal Article |
| Year of Publication | 2021 |
| Journal | Research Square |
| Publisher | Research Square |
| DOI | 10.21203/rs.3.rs-526282/v1 |
An AO-ADMM approach to constraining PARAFAC2 on all modes
arXiv (2021).Status: Submitted
An AO-ADMM approach to constraining PARAFAC2 on all modes
Analyzing multi-way measurements with variations across one mode of the dataset is a challenge in various fields including data mining, neuroscience and chemometrics. For example, measurements may evolve over time or have unaligned time profiles. The PARAFAC2 model has been successfully used to analyze such data by allowing the underlying factor matrices in one mode (i.e., the evolving mode) to change across slices. The traditional approach to fit a PARAFAC2 model is to use an alternating least squares-based algorithm, which handles the constant cross-product constraint of the PARAFAC2 model by implicitly estimating the evolving factor matrices. This approach makes imposing regularization on these factor matrices challenging. There is currently no algorithm to flexibly impose such regularization with general penalty functions and hard constraints. In order to address this challenge and to avoid the implicit estimation, in this paper, we propose an algorithm for fitting PARAFAC2 based on alternating optimization with the alternating direction method of multipliers (AO-ADMM). With numerical experiments on simulated data, we show that the proposed PARAFAC2 AO-ADMM approach allows for flexible constraints, recovers the underlying patterns accurately, and is computationally efficient compared to the state-of-the-art. We also apply our model to a real-world chromatography dataset, and show that constraining the evolving mode improves the interpretability of the extracted patterns.
| Afilliation | Machine Learning |
| Project(s) | Department of Machine Intelligence, TrACEr: Time-Aware ConstrainEd Multimodal Data Fusion |
| Publication Type | Journal Article |
| Year of Publication | 2021 |
| Journal | arXiv |
| Publisher | arXiv |
| URL | https://arxiv.org/abs/2110.01278 |
Journal articles
Revealing the State-of-the-Art in Large-Scale Agile Development: A Systematic Mapping Study
arXiv preprint arXiv:2007.05578 (2020).Status: Submitted
Revealing the State-of-the-Art in Large-Scale Agile Development: A Systematic Mapping Study
| Afilliation | Software Engineering |
| Project(s) | Department of IT Management |
| Publication Type | Journal Article |
| Year of Publication | 2020 |
| Journal | arXiv preprint arXiv:2007.05578 |
| Publisher | arXiv |
