| 1. |
- Abdeen, Waleed
(author)
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Taxonomic Trace Links Recommender : Context Aware Hierarchical Classification
- 2023
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In: CEUR Workshop Proceedings. - : CEUR-WS.
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Conference paper (peer-reviewed)abstract
- In the taxonomic trace links concept, the source and target artifacts are connected through knowledge organization structure (e.g., taxonomy). We introduce in this paper a recommender system that recommends labels to requirements artifacts from domain-specific taxonomy to establish taxonomic trace links. The tool exploits the hierarchical nature of taxonomies and uses requirements text and context information as input to the recommender. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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| 2. |
- Abdelraheem, Mohamed Ahmed, et al.
(author)
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Executing Boolean queries on an encrypted Bitmap index
- 2016
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In: CCSW 2016 - Proceedings of the 2016 ACM Cloud Computing Security Workshop, co-located with CCS 2016. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450345729 ; , s. 11-22
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Conference paper (peer-reviewed)abstract
- We propose a simple and efficient searchable symmetric encryption scheme based on a Bitmap index that evaluates Boolean queries. Our scheme provides a practical solution in settings where communications and computations are very constrained as it offers a suitable trade-off between privacy and performance.
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| 3. |
- Abghari, Shahrooz, et al.
(author)
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A Minimum Spanning Tree Clustering Approach for Outlier Detection in Event Sequences
- 2018
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In: The 17th IEEE International Conference on Machine Learning and Applications Special Session on Machine Learning Algorithms, Systems and Applications. - : IEEE. ; , s. 1123-1130
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Conference paper (peer-reviewed)abstract
- Outlier detection has been studied in many domains. Outliers arise due to different reasons such as mechanical issues, fraudulent behavior, and human error. In this paper, we propose an unsupervised approach for outlier detection in a sequence dataset. The proposed approach combines sequential pattern mining, cluster analysis, and a minimum spanning tree algorithm in order to identify clusters of outliers. Initially, the sequential pattern mining is used to extract frequent sequential patterns. Next, the extracted patterns are clustered into groups of similar patterns. Finally, the minimum spanning tree algorithm is used to find groups of outliers. The proposed approach has been evaluated on two different real datasets, i.e., smart meter data and video session data. The obtained results have shown that our approach can be applied to narrow down the space of events to a set of potential outliers and facilitate domain experts in further analysis and identification of system level issues.
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| 4. |
- Abghari, Shahrooz, et al.
(author)
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An Inductive System Monitoring Approach for GNSS Activation
- 2022
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In: IFIP Advances in Information and Communication Technology. - Cham : Springer Science+Business Media B.V.. - 9783031083365 ; , s. 437-449
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Conference paper (peer-reviewed)abstract
- In this paper, we propose a Global Navigation Satellite System (GNSS) component activation model for mobile tracking devices that automatically detects indoor/outdoor environments using the radio signals received from Long-Term Evolution (LTE) base stations. We use an Inductive System Monitoring (ISM) technique to model environmental scenarios captured by a smart tracker via extracting clusters of corresponding value ranges from LTE base stations’ signal strength. The ISM-based model is built by using the tracker’s historical data labeled with GPS coordinates. The built model is further refined by applying it to additional data without GPS location collected by the same device. This procedure allows us to identify the clusters that describe semi-outdoor scenarios. In that way, the model discriminates between two outdoor environmental categories: open outdoor and semi-outdoor. The proposed ISM-based GNSS activation approach is studied and evaluated on a real-world dataset contains radio signal measurements collected by five smart trackers and their geographical location in various environmental scenarios.
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| 5. |
- Abghari, Shahrooz, et al.
(author)
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District Heating Substation Behaviour Modelling for Annotating the Performance
- 2020
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In: Communications in Computer and Information Science. - Cham : Springer. - 9783030438869 ; , s. 3-11
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Conference paper (peer-reviewed)abstract
- In this ongoing study, we propose a higher order data mining approach for modelling district heating (DH) substations’ behaviour and linking operational behaviour representative profiles with different performance indicators. We initially create substation’s operational behaviour models by extracting weekly patterns and clustering them into groups of similar patterns. The built models are further analyzed and integrated into an overall substation model by applying consensus clustering. The different operational behaviour profiles represented by the exemplars of the consensus clustering model are then linked to performance indicators. The labelled behaviour profiles are deployed over the whole heating season to derive diverse insights about the substation’s performance. The results show that the proposed method can be used for modelling, analyzing and understanding the deviating and sub-optimal DH substation’s behaviours. © 2020, Springer Nature Switzerland AG.
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| 6. |
- Abghari, Shahrooz, et al.
(author)
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Higher order mining for monitoring district heating substations
- 2019
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In: 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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Conference paper (peer-reviewed)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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| 7. |
- Abghari, Shahrooz, et al.
(author)
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Multi-view Clustering Analyses for District Heating Substations
- 2020
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In: DATA 2020 - Proceedings of the 9th International Conference on Data Science, Technology and Applications2020. - : SciTePress. ; , s. 158-168
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Conference paper (peer-reviewed)abstract
- In this study, we propose a multi-view clustering approach for mining and analysing multi-view network datasets. The proposed approach is applied and evaluated on a real-world scenario for monitoring and analysing district heating (DH) network conditions and identifying substations with sub-optimal behaviour. Initially, geographical locations of the substations are used to build an approximate graph representation of the DH network. Two different analyses can further be applied in this context: step-wise and parallel-wise multi-view clustering. The step-wise analysis is meant to sequentially consider and analyse substations with respect to a few different views. At each step, a new clustering solution is built on top of the one generated by the previously considered view, which organizes the substations in a hierarchical structure that can be used for multi-view comparisons. The parallel-wise analysis on the other hand, provides the opportunity to analyse substations with regards to two different views in parallel. Such analysis is aimed to represent and identify the relationships between substations by organizing them in a bipartite graph and analysing the substations’ distribution with respect to each view. The proposed data analysis and visualization approach arms domain experts with means for analysing DH network performance. In addition, it will facilitate the identification of substations with deviating operational behaviour based on comparative analysis with their closely located neighbours.
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| 8. |
- Abghari, Shahrooz, et al.
(author)
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Outlier Detection for Video Session Data Using Sequential Pattern Mining
- 2018
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In: ACM SIGKDD Workshop On Outlier Detection De-constructed.
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Conference paper (peer-reviewed)abstract
- The growth of Internet video and over-the-top transmission techniqueshas enabled online video service providers to deliver highquality video content to viewers. To maintain and improve thequality of experience, video providers need to detect unexpectedissues that can highly affect the viewers’ experience. This requiresanalyzing massive amounts of video session data in order to findunexpected sequences of events. In this paper we combine sequentialpattern mining and clustering to discover such event sequences.The proposed approach applies sequential pattern mining to findfrequent patterns by considering contextual and collective outliers.In order to distinguish between the normal and abnormal behaviorof the system, we initially identify the most frequent patterns. Thena clustering algorithm is applied on the most frequent patterns.The generated clustering model together with Silhouette Index areused for further analysis of less frequent patterns and detectionof potential outliers. Our results show that the proposed approachcan detect outliers at the system level.
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| 9. |
- Abghari, Shahrooz, et al.
(author)
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Trend analysis to automatically identify heat program changes
- 2017
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In: Energy Procedia. - : Elsevier. ; , s. 407-415
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Conference paper (peer-reviewed)abstract
- The aim of this study is to improve the monitoring and controlling of heating systems located at customer buildings through the use of a decision support system. To achieve this, the proposed system applies a two-step classifier to detect manual changes of the temperature of the heating system. We apply data from the Swedish company NODA, active in energy optimization and services for energy efficiency, to train and test the suggested system. The decision support system is evaluated through an experiment and the results are validated by experts at NODA. The results show that the decision support system can detect changes within three days after their occurrence and only by considering daily average measurements.
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| 10. |
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