| 1. |
- Abghari, Shahrooz, et al.
(författare)
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Higher order mining for monitoring district heating substations
- 2019
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Ingår i: Proceedings - 2019 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2019. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781728144931 ; , s. 382-391
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Konferensbidrag (refereegranskat)abstract
- We propose a higher order mining (HOM) approach for modelling, monitoring and analyzing district heating (DH) substations' operational behaviour and performance. HOM is concerned with mining over patterns rather than primary or raw data. The proposed approach uses a combination of different data analysis techniques such as sequential pattern mining, clustering analysis, consensus clustering and minimum spanning tree (MST). Initially, a substation's operational behaviour is modeled by extracting weekly patterns and performing clustering analysis. The substation's performance is monitored by assessing its modeled behaviour for every two consecutive weeks. In case some significant difference is observed, further analysis is performed by integrating the built models into a consensus clustering and applying an MST for identifying deviating behaviours. The results of the study show that our method is robust for detecting deviating and sub-optimal behaviours of DH substations. In addition, the proposed method can facilitate domain experts in the interpretation and understanding of the substations' behaviour and performance by providing different data analysis and visualization techniques.
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| 2. |
- Devagiri, Vishnu Manasa
(författare)
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Clustering Techniques for Mining and Analysis of Evolving Data
- 2021
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The amount of data generated is on rise due to increased demand for fields like IoT, smart monitoring applications, etc. Data generated through such systems have many distinct characteristics like continuous data generation, evolutionary, multi-source nature, and heterogeneity. In addition, the real-world data generated in these fields is largely unlabelled. Clustering is an unsupervised learning technique used to group, analyze and interpret unlabelled data. Conventional clustering algorithms are not suitable for dealing with data having previously mentioned characteristics due to memory and computational constraints, their inability to handle concept drift, distributed location of data. Therefore novel clustering approaches capable of analyzing and interpreting evolving and/or multi-source streaming data are needed. The thesis is focused on building evolutionary clustering algorithms for data that evolves over time. We have initially proposed an evolutionary clustering approach, entitled Split-Merge Clustering (Paper I), capable of continuously updating the generated clustering solution in the presence of new data. Through the progression of the work, new challenges have been studied and addressed. Namely, the Split-Merge Clustering algorithm has been enhanced in Paper II with new capabilities to deal with the challenges of multi-view data applications. A multi-view or multi-source data presents the studied phenomenon/system from different perspectives (views), and can reveal interesting knowledge that is not visible when only one view is considered and analyzed. This has motivated us to continue in this direction by designing two other novel multi-view data stream clustering algorithms. The algorithm proposed in Paper III improves the performance and interpretability of the algorithm proposed in Paper II. Paper IV introduces a minimum spanning tree based multi-view clustering algorithm capable of transferring knowledge between consecutive data chunks, and it is also enriched with a post-clustering pattern-labeling procedure. The proposed and studied evolutionary clustering algorithms are evaluated on various data sets. The obtained results have demonstrated the robustness of the algorithms for modeling, analyzing, and mining evolving data streams. They are able to adequately adapt single and multi-view clustering models by continuously integrating newly arriving data.
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| 3. |
- Devagiri, Vishnu Manasa
(författare)
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Mining Evolving and Heterogeneous Data : Cluster-based Analysis Techniques
- 2024
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- A large amount of data is generated from fields like IoT, smart monitoring applications, etc., raising demand for suitable data analysis and mining techniques. Data produced through such systems have many distinct characteristics, like continuous generation, evolving nature, multi-source origin, and heterogeneity, and in addition are usually not annotated. Clustering is an unsupervised learning technique used to group and analyze unlabeled data. Conventional clustering algorithms are unsuitable for dealing with data with the mentioned characteristics due to memory, computational constraints, and their inability to handle the heterogeneous and evolving nature of the data. Therefore, novel clustering approaches are needed to analyze and interpret such challenging data. This thesis focuses on building and studying advanced clustering algorithms that can address the main challenges of today's real-world data: evolving and heterogeneous nature. An evolving clustering approach capable of continuously updating the generated clustering solution in the presence of new data is initially proposed, which is later extended to address the challenges of multi-view data applications. Multi-view or multi-source data presents the studied phenomenon or system from different perspectives (views) and can reveal interesting knowledge that is invisible when only one view is considered and analyzed. This has motivated us to continue exploring data from different perspectives in several other studies of this thesis. Domain shift is another common problem when data is obtained from various devices or locations, leading to a drop in the performance of machine learning models if they are not adapted to the current domain (device, location, etc.). The thesis explores the domain adaptation problem in a resource-constraint way using cluster integration techniques. A new hybrid clustering technique for analyzing the heterogeneous data is also proposed. It produces homogeneous groups, facilitating continuous monitoring and fault detection.The algorithms and techniques proposed in this thesis are evaluated on various data sets, including real-world data from industrial partners in domains like smart building systems, smart logistics, and performance monitoring of industrial assets. The obtained results demonstrated the robustness of the algorithms for modeling, analyzing, and mining evolving data streams and/or heterogeneous data. They can adequately adapt single and multi-view clustering models by continuously integrating newly arriving data.
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| 4. |
- Devagiri, Vishnu Manasa, et al.
(författare)
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Multi-view data analysis techniques for monitoring smart building systems
- 2021
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Ingår i: Sensors. - : MDPI. - 1424-8220. ; 21:20
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Tidskriftsartikel (refereegranskat)abstract
- In smart buildings, many different systems work in coordination to accomplish their tasks. In this process, the sensors associated with these systems collect large amounts of data generated in a streaming fashion, which is prone to concept drift. Such data are heterogeneous due to the wide range of sensors collecting information about different characteristics of the monitored systems. All these make the monitoring task very challenging. Traditional clustering algorithms are not well equipped to address the mentioned challenges. In this work, we study the use of MV Multi-Instance Clustering algorithm for multi-view analysis and mining of smart building systems’ sensor data. It is demonstrated how this algorithm can be used to perform contextual as well as integrated analysis of the systems. Various scenarios in which the algorithm can be used to analyze the data generated by the systems of a smart building are examined and discussed in this study. In addition, it is also shown how the extracted knowledge can be visualized to detect trends in the systems’ behavior and how it can aid domain experts in the systems’ maintenance. In the experiments conducted, the proposed approach was able to successfully detect the deviating behaviors known to have previously occurred and was also able to identify some new deviations during the monitored period. Based on the results obtained from the experiments, it can be concluded that the proposed algorithm has the ability to be used for monitoring, analysis, and detecting deviating behaviors of the systems in a smart building domain. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
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| 5. |
- García Martín, Eva, et al.
(författare)
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Energy-aware very fast decision tree
- 2021
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Ingår i: International Journal of Data Science and Analytics. - : Springer. - 2364-415X .- 2364-4168. ; 11:2, s. 105-126
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Tidskriftsartikel (refereegranskat)abstract
- Recently machine learning researchers are designing algorithms that can run in embedded and mobile devices, which introduces additional constraints compared to traditional algorithm design approaches. One of these constraints is energy consumption, which directly translates to battery capacity for these devices. Streaming algorithms, such as the Very Fast Decision Tree (VFDT), are designed to run in such devices due to their high velocity and low memory requirements. However, they have not been designed with an energy efficiency focus. This paper addresses this challenge by presenting the nmin adaptation method, which reduces the energy consumption of the VFDT algorithm with only minor effects on accuracy. nmin adaptation allows the algorithm to grow faster in those branches where there is more confidence to create a split, and delays the split on the less confident branches. This removes unnecessary computations related to checking for splits but maintains similar levels of accuracy. We have conducted extensive experiments on 29 public datasets, showing that the VFDT with nmin adaptation consumes up to 31% less energy than the original VFDT, and up to 96% less energy than the CVFDT (VFDT adapted for concept drift scenarios), trading off up to 1.7 percent of accuracy.
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| 6. |
- Silva, Lakmal
(författare)
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On Identifying Technical Debt using Bug Reports in Practice
- 2023
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Context: In an era where every industry is impacted by software, it is vital to keep software costs under control for organizations to be competitive. A key factor contributing to software costs is software maintenance where a significant proportion is utilized to deal with different types of technical debt. Technical debt is a metaphor used to describe the cost of taking shortcuts or sub-optimal design and implementation that compromises the software quality. Similar to financial debt, technical debt needs to be paid off in the future.Objective: To be in control of technical debt related costs, organizations need to identify technical debt types and quantify them to introduce solutions and prioritize repayment strategies. However, the invisible nature of technical debt makes its identification challenging in practice. Our aim is to find pragmatic ways to identify technical debt in practice, that can be supported by evidence. Once technical debt types that are significant have been identified, we aim to propose suggestions to mitigate them.Method: We used design science as a methodological framework to iteratively improve the technical debt identification methods. We utilized bug reports, which are artifacts produced by software engineers during the development and operation of the software system as the data source for technical debt identification. Software defects reported through bug reports are considered as one of the key external quality attributes of a software system which supports us in our evidence based approach. Throughout the design science iterations, we used the following research methods: case study and sample study.Results: We produced three design artifacts that support technical debt identification. The first artifact is a systematic process to identify architectural technical debt from bug reports. The second is an automated bug analysis and a visualization tool to support our research as well as to support practitioners to identify components with hot spots in relation to the number of defects. The third is a method for identifying documentation debt from bug reports.Conclusion: Based on the findings from this thesis, we demonstrated that bug reports can be utilized as a data source to identify technical debt in practice by identifying two types of technical debt; architectural technical debt and documentation debt. Compared to the identification of documentation debt, architectural technical debt identification still remains challenging due to the abstract nature of the architecture and its boundaries. Therefore, our future work will focus on evaluating the impact of reducing the sources of documentation debt on the frequency of bug reports and overall project cost.
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| 7. |
- Annavarjula, Vaishnavi, et al.
(författare)
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Implicit user data in fashion recommendation systems
- 2020
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Ingår i: Developments of Artificial Intelligence Technologies in Computation and Robotics. - : World Scientific. - 9789811223334 - 9789811223341 - 9789811223327 ; , s. 614-621
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Konferensbidrag (refereegranskat)abstract
- Recommendation systems in fashion are used to provide recommendations to users on clothing items, matching styles, and size or fit. These recommendations are generated based on user actions such as ratings, reviews or general interaction with a seller. There is an increased adoption of implicit feedback in models aimed at providing recommendations in fashion. This paper aims to understand the nature of implicit user feedback in fashion recommendation systems by following guidelines to group user actions. Categories of user actions that characterize implicit feedback are examination, retention, reference, and annotation. Each category describes a specific set of actions a user takes. It is observed that fashion recommendations using implicit user feedback mostly rely on retention as a user action to provide recommendations.
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| 8. |
- Flyckt, Jonatan, et al.
(författare)
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Detecting ditches using supervised learning on high-resolution digital elevation models
- 2022
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Ingår i: Expert systems with applications. - : Elsevier Ltd. - 0957-4174 .- 1873-6793. ; 201
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Tidskriftsartikel (refereegranskat)abstract
- Drained wetlands can constitute a large source of greenhouse gas emissions, but the drainage networks in these wetlands are largely unmapped, and better maps are needed to aid in forest production and to better understand the climate consequences. We develop a method for detecting ditches in high resolution digital elevation models derived from LiDAR scans. Thresholding methods using digital terrain indices can be used to detect ditches. However, a single threshold generally does not capture the variability in the landscape, and generates many false positives and negatives. We hypothesise that, by combining the digital terrain indices using supervised learning, we can improve ditch detection at a landscape-scale. In addition to digital terrain indices, additional features are generated by transforming the data to include neighbouring cells for better ditch predictions. A Random Forests classifier is used to locate the ditches, and its probability output is processed to remove noise, and binarised to produce the final ditch prediction. The confidence interval for the Cohen's Kappa index ranges [0.655, 0.781] between the evaluation plots with a confidence level of 95%. The study demonstrates that combining information from a suite of digital terrain indices using machine learning provides an effective technique for automatic ditch detection at a landscape-scale, aiding in both practical forest management and in combatting climate change. © 2022 The Authors
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| 9. |
- Flyckt, Jonatan, et al.
(författare)
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Explaining rifle shooting factors through multi-sensor body tracking
- 2023
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Ingår i: Intelligent Data Analysis. - : IOS Press. - 1088-467X .- 1571-4128. ; 27:2, s. 535-554
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Tidskriftsartikel (refereegranskat)abstract
- There is a lack of data-driven training instructions for sports shooters, as instruction has commonly been based on subjective assessments. Many studies have correlated body posture and balance to shooting performance in rifle shooting tasks, but have mostly focused on single aspects of postural control. This study has focused on finding relevant rifle shooting factors by examining the entire body over sequences of time. A data collection was performed with 13 human participants carrying out live rifle shooting scenarios while being recorded with multiple body tracking sensors. A pre-processing pipeline produced a novel skeleton sequence representation, which was used to train a transformer model. The predictions from this model could be explained on a per sample basis using the attention mechanism, and visualised in an interactive format for humans to interpret. It was possible to separate the different phases of a shooting scenario from body posture with a high classification accuracy (80%). Shooting performance could be detected to an extent by separating participants using their strong and weak shooting hand. The dataset and pre-processing pipeline, as well as the techniques for generating explainable predictions presented in this study have laid the groundwork for future research in the sports shooting domain.
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| 10. |
- García Martín, Eva, et al.
(författare)
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Energy modeling of Hoeffding tree ensembles
- 2021
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Ingår i: Intelligent Data Analysis. - : IOS Press. - 1088-467X .- 1571-4128. ; 25:1, s. 81-104
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Tidskriftsartikel (refereegranskat)abstract
- Energy consumption reduction has been an increasing trend in machine learning over the past few years due to its socio-ecological importance. In new challenging areas such as edge computing, energy consumption and predictive accuracy are key variables during algorithm design and implementation. State-of-the-art ensemble stream mining algorithms are able to create highly accurate predictions at a substantial energy cost. This paper introduces the nmin adaptation method to ensembles of Hoeffding tree algorithms, to further reduce their energy consumption without sacrificing accuracy. We also present extensive theoretical energy models of such algorithms, detailing their energy patterns and how nmin adaptation affects their energy consumption. We have evaluated the energy efficiency and accuracy of the nmin adaptation method on five different ensembles of Hoeffding trees under 11 publicly available datasets. The results show that we are able to reduce the energy consumption significantly, by 21% on average, affecting accuracy by less than one percent on average.
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