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Wessén, J., Carlsson, M., Schulte, C., Flener, P., Pecora, F. & Matskin, M. (2023). A constraint programming model for the scheduling and workspace layout design of a dual-arm multi-tool assembly robot. Constraints, 28(2), 71-104
Open this publication in new window or tab >>A constraint programming model for the scheduling and workspace layout design of a dual-arm multi-tool assembly robot
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2023 (English)In: Constraints, ISSN 1383-7133, E-ISSN 1572-9354, Vol. 28, no 2, p. 71-104Article in journal (Refereed) Published
Abstract [en]

The generation of a robot program can be seen as a collection of sub-problems, where many combinations of some of these sub-problems are well studied. The performance of a robot program is strongly conditioned by the location of the tasks. However, the scope of previous methods does not include workspace layout design, likely missing high-quality solutions. In industrial applications, designing robot workspace layout is part of the commissioning. We broaden the scope and show how to model a dual-arm multi-tool robot assembly problem. Our model includes more robot programming sub-problems than previous methods, as well as workspace layout design. We propose a constraint programming formulation in MiniZinc that includes elements from scheduling and routing, extended with variable task locations. We evaluate the model on realistic assembly problems and workspaces, utilizing the dual-arm YuMi robot from ABB Ltd. We also evaluate redundant constraints and various formulations for avoiding arm-to-arm collisions. The best model variant quickly finds high-quality solutions for all problem instances. This demonstrates the potential of our approach as a valuable tool for a robot programmer.

Place, publisher, year, edition, pages
Springer Nature, 2023
Keywords
Assembly manufacturing, Constraint programming, Robot planning and scheduling, Workspace layout design
National Category
Robotics Computer Sciences
Identifiers
urn:nbn:se:kth:diva-338539 (URN)10.1007/s10601-023-09345-4 (DOI)001032500800001 ()2-s2.0-85164960102 (Scopus ID)
Note

QC 20231114

Available from: 2023-11-14 Created: 2023-11-14 Last updated: 2023-11-14Bibliographically approved
Nikolov, N., Solberg, A., Prodan, R., Soylu, A., Matskin, M. & Roman, D. (2023). Container-Based Data Pipelines on the Computing Continuum for Remote Patient Monitoring. Computer, 56(10), 40-48
Open this publication in new window or tab >>Container-Based Data Pipelines on the Computing Continuum for Remote Patient Monitoring
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2023 (English)In: Computer, ISSN 0018-9162, E-ISSN 1558-0814, Vol. 56, no 10, p. 40-48Article in journal (Refereed) Published
Abstract [en]

The emerging concept of big data pipelines provides relevant solutions and is one of the main enablers of telemedicine. We present a data pipeline for remote patient monitoring and show a real-world example of how data pipelines help address the stringent requirements of telemedicine.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Keywords
Patient monitoring, Telemedicine, Pipelines, Big Data
National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:kth:diva-338966 (URN)10.1109/MC.2023.3285414 (DOI)001077539800007 ()2-s2.0-85174332029 (Scopus ID)
Note

QC 20231101

Available from: 2023-11-01 Created: 2023-11-01 Last updated: 2023-11-01Bibliographically approved
Layegh, A., Payberah, A. H., Soylu, A., Roman, D. & Matskin, M. (2023). ContrastNER: Contrastive-based Prompt Tuning for Few-shot NER. In: Proceedings - 2023 IEEE 47th Annual Computers, Software, and Applications Conference, COMPSAC 2023: . Paper presented at 47th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2023, Jun 26 2023 - Jun 30 2023, Hybrid, Torino, Italy (pp. 241-249). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>ContrastNER: Contrastive-based Prompt Tuning for Few-shot NER
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2023 (English)In: Proceedings - 2023 IEEE 47th Annual Computers, Software, and Applications Conference, COMPSAC 2023, Institute of Electrical and Electronics Engineers (IEEE) , 2023, p. 241-249Conference paper, Published paper (Refereed)
Abstract [en]

Prompt-based language models have produced encouraging results in numerous applications, including Named Entity Recognition (NER) tasks. NER aims to identify entities in a sentence and provide their types. However, the strong performance of most available NER approaches is heavily dependent on the design of discrete prompts and a verbalizer to map the model-predicted outputs to entity categories, which are complicated undertakings. To address these challenges, we present ContrastNER, a prompt-based NER framework that employs both discrete and continuous tokens in prompts and uses a contrastive learning approach to learn the continuous prompts and forecast entity types. The experimental results demonstrate that ContrastNER obtains competitive performance to the state-of-the-art NER methods in high-resource settings and outperforms the state-of-the-art models in low-resource circumstances without requiring extensive manual prompt engineering and verbalizer design.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Keywords
Contrastive learning, Language Models, Named Entity Recognition, Prompt-based learning
National Category
Language Technology (Computational Linguistics) Robotics
Identifiers
urn:nbn:se:kth:diva-336748 (URN)10.1109/COMPSAC57700.2023.00038 (DOI)001046484100028 ()2-s2.0-85168863373 (Scopus ID)
Conference
47th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2023, Jun 26 2023 - Jun 30 2023, Hybrid, Torino, Italy
Note

Part of proceedings ISBN 9798350326970

QC 20231031

Available from: 2023-09-19 Created: 2023-09-19 Last updated: 2023-11-02Bibliographically approved
Khan, A. Q., Nikolov, N., Matskin, M., Prodan, R., Roman, D., Sahin, B., . . . Soylu, A. (2023). Smart Data Placement Using Storage-as-a-Service Model for Big Data Pipelines. Sensors, 23(2), Article ID 564.
Open this publication in new window or tab >>Smart Data Placement Using Storage-as-a-Service Model for Big Data Pipelines
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2023 (English)In: Sensors, E-ISSN 1424-8220, Vol. 23, no 2, article id 564Article in journal (Refereed) Published
Abstract [en]

Big data pipelines are developed to process data characterized by one or more of the three big data features, commonly known as the three Vs (volume, velocity, and variety), through a series of steps (e.g., extract, transform, and move), making the ground work for the use of advanced analytics and ML/AI techniques. Computing continuum (i.e., cloud/fog/edge) allows access to virtually infinite amount of resources, where data pipelines could be executed at scale; however, the implementation of data pipelines on the continuum is a complex task that needs to take computing resources, data transmission channels, triggers, data transfer methods, integration of message queues, etc., into account. The task becomes even more challenging when data storage is considered as part of the data pipelines. Local storage is expensive, hard to maintain, and comes with several challenges (e.g., data availability, data security, and backup). The use of cloud storage, i.e., storage-as-a-service (StaaS), instead of local storage has the potential of providing more flexibility in terms of scalability, fault tolerance, and availability. In this article, we propose a generic approach to integrate StaaS with data pipelines, i.e., computation on an on-premise server or on a specific cloud, but integration with StaaS, and develop a ranking method for available storage options based on five key parameters: cost, proximity, network performance, server-side encryption, and user weights/preferences. The evaluation carried out demonstrates the effectiveness of the proposed approach in terms of data transfer performance, utility of the individual parameters, and feasibility of dynamic selection of a storage option based on four primary user scenarios.

Place, publisher, year, edition, pages
MDPI AG, 2023
Keywords
storage-as-a-service, big data pipelines, data locality, data placement strategies, software containers
National Category
Computer Sciences
Identifiers
urn:nbn:se:kth:diva-323915 (URN)10.3390/s23020564 (DOI)000916405600001 ()36679360 (PubMedID)2-s2.0-85147046022 (Scopus ID)
Note

QC 20230227

Available from: 2023-02-27 Created: 2023-02-27 Last updated: 2023-06-26Bibliographically approved
Khan, A. Q., Nikolov, N., Matskin, M., Prodan, R., Bussler, C., Roman, D. & Soylu, A. (2023). Towards Cloud Storage Tier Optimization with Rule-Based Classification. In: Service-Oriented and Cloud Computing: 10th IFIP WG 6.12 European Conference, ESOCC 2023, Proceedings. Paper presented at 10th IFIP WG 6.12 European Conference on Service-Oriented and Cloud Computing, ESOCC 2023, Larnaca, Cyprus, Oct 24 2023 - Oct 25 2023 (pp. 205-216). Springer Nature
Open this publication in new window or tab >>Towards Cloud Storage Tier Optimization with Rule-Based Classification
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2023 (English)In: Service-Oriented and Cloud Computing: 10th IFIP WG 6.12 European Conference, ESOCC 2023, Proceedings, Springer Nature , 2023, p. 205-216Conference paper, Published paper (Refereed)
Abstract [en]

Cloud storage adoption has increased over the years as more and more data has been produced with particularly high demand for fast processing and low latency. To meet the users’ demands and to provide a cost-effective solution, cloud service providers (CSPs) have offered tiered storage; however, keeping the data in one tier is not a cost-effective approach. Hence, several two-tiered approaches have been developed to classify storage objects into the most suitable tier. In this respect, this paper explores a rule-based classification approach to optimize cloud storage cost by migrating data between different storage tiers. Instead of two, four distinct storage tiers are considered, including premium, hot, cold, and archive. The viability and potential of the approach are demonstrated by comparing cost savings achieved when data was moved between tiers versus when it remained static. The results indicate that the proposed approach has the potential to significantly reduce cloud storage cost, thereby providing valuable insights for organizations seeking to optimize their cloud storage strategies. Finally, the limitations of the proposed approach are discussed along with the potential directions for future work, particularly the use of game theory to incorporate a feedback loop to extend and improve the proposed approach accordingly.

Place, publisher, year, edition, pages
Springer Nature, 2023
Keywords
cloud, cloud storage, optimization, StaaS, Storage tiers
National Category
Communication Systems
Identifiers
urn:nbn:se:kth:diva-339691 (URN)10.1007/978-3-031-46235-1_13 (DOI)2-s2.0-85175853881 (Scopus ID)
Conference
10th IFIP WG 6.12 European Conference on Service-Oriented and Cloud Computing, ESOCC 2023, Larnaca, Cyprus, Oct 24 2023 - Oct 25 2023
Note

Part of ISBN 9783031462344

QC 20231116

Available from: 2023-11-16 Created: 2023-11-16 Last updated: 2023-11-16Bibliographically approved
Khan, A. Q., Nikolov, N., Matskin, M., Prodan, R., Bussler, C., Roman, D. & Soylu, A. (2023). Towards Graph-based Cloud Cost Modelling and Optimisation. In: Proceedings: 2023 IEEE 47th Annual Computers, Software, and Applications Conference, COMPSAC 2023. Paper presented at 47th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2023, Hybrid, Torino, Italy, Jun 26 2023 - Jun 30 2023 (pp. 1337-1342). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Towards Graph-based Cloud Cost Modelling and Optimisation
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2023 (English)In: Proceedings: 2023 IEEE 47th Annual Computers, Software, and Applications Conference, COMPSAC 2023, Institute of Electrical and Electronics Engineers (IEEE) , 2023, p. 1337-1342Conference paper, Published paper (Refereed)
Abstract [en]

Cloud computing has become an increasingly popular choice for businesses and individuals due to its flexibility, scalability, and convenience; however, the rising cost of cloud resources has become a significant concern for many. The pay-per-use model used in cloud computing means that costs can accumulate quickly, and the lack of visibility and control can result in unexpected expenses. The cost structure becomes even more complicated when dealing with hybrid or multi-cloud environments. For businesses, the cost of cloud computing can be a significant portion of their IT budget, and any savings can lead to better financial stability and competitiveness. In this respect, it is essential to manage cloud costs effectively. This requires a deep understanding of current resource utilization, forecasting future needs, and optimising resource utilization to control costs. To address this challenge, new tools and techniques are being developed to provide more visibility and control over cloud computing costs. In this respect, this paper explores a graph-based solution for modelling cost elements and cloud resources and potential ways to solve the resulting constraint problem of cost optimisation. We primarily consider utilization, cost, performance, and availability in this context. Such an approach will eventually help organizations make informed decisions about cloud resource placement and manage the costs of software applications and data workflows deployed in single, hybrid, or multi-cloud environments.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Keywords
cloud, cost, graph, optimisation
National Category
Computer Systems
Identifiers
urn:nbn:se:kth:diva-336769 (URN)10.1109/COMPSAC57700.2023.00203 (DOI)001046484100193 ()2-s2.0-85168880680 (Scopus ID)
Conference
47th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2023, Hybrid, Torino, Italy, Jun 26 2023 - Jun 30 2023
Note

Part of ISBN 9798350326970

QC 20230920

Available from: 2023-09-20 Created: 2023-09-20 Last updated: 2023-10-24Bibliographically approved
Roman, D., Prodan, R., Nikolov, N., Soylu, A., Matskin, M., Marrella, A., . . . Kharlamov, E. (2022). Big Data Pipelines on the Computing Continuum: Tapping the Dark Data. Computer, 55(11), 74-84
Open this publication in new window or tab >>Big Data Pipelines on the Computing Continuum: Tapping the Dark Data
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2022 (English)In: Computer, ISSN 0018-9162, E-ISSN 1558-0814, Vol. 55, no 11, p. 74-84Article in journal (Refereed) Published
Abstract [en]

The computing continuum enables new opportunities for managing big data pipelines concerning efficient management of heterogeneous and untrustworthy resources. We discuss the big data pipelines lifecycle on the computing continuum and its associated challenges, and we outline a future research agenda in this area.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2022
Keywords
Pipelines, Big Data, Data integrity, Database systems, Information integrity
National Category
Computer Sciences
Identifiers
urn:nbn:se:kth:diva-321623 (URN)10.1109/MC.2022.3154148 (DOI)000873821800021 ()2-s2.0-85142373648 (Scopus ID)
Note

QC 20221122

Available from: 2022-11-22 Created: 2022-11-22 Last updated: 2023-06-08Bibliographically approved
Tahmasebi, S., Layegh, A., Nikolov, N., Payberah, A. H., Dinh, K., Mitrovic, V., . . . Matskin, M. (2022). DATACLOUDDSL: Textual and Visual Presentation of Big Data Pipelines. In: Leong, HV Sarvestani, SS Teranishi, Y Cuzzocrea, A Kashiwazaki, H Towey, D Yang, JJ Shahriar, H (Ed.), 2022 IEEE 46TH ANNUAL COMPUTERS, SOFTWARE, AND APPLICATIONS CONFERENCE (COMPSAC 2022): . Paper presented at 46th Annual IEEE-Computer-Society International Computers, Software, and Applications Conference (COMPSAC) - Computers, Software, and Applications in an Uncertain World, JUN 27-JUL 01, 2022, ELECTR NETWORK (pp. 1165-1171). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>DATACLOUDDSL: Textual and Visual Presentation of Big Data Pipelines
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2022 (English)In: 2022 IEEE 46TH ANNUAL COMPUTERS, SOFTWARE, AND APPLICATIONS CONFERENCE (COMPSAC 2022) / [ed] Leong, HV Sarvestani, SS Teranishi, Y Cuzzocrea, A Kashiwazaki, H Towey, D Yang, JJ Shahriar, H, Institute of Electrical and Electronics Engineers (IEEE) , 2022, p. 1165-1171Conference paper, Published paper (Refereed)
Abstract [en]

This paper describes the DATACLOUDDSL language and the DEF-PIPE tool for describing Big Data pipelines. DATACLOUDDSL has both a textual and a visual form and supports requirements obtained both from analyzing existing data pipeline specification tools and from interviews with relevant industrial actors. Particularly, DATACLOUDDSL supports (i) separation of concerns between design and run-time issues, (ii) reuse of previously developed pipeline steps and pipelines in designing new pipelines, (iii) flexible data transfer between pipelines steps and containerization of pipelines and pipeline steps, and (iv) integration of description and simulation components in Big Data pipeline orchestration systems. Additionally, it provides an interface to the discovery and deployment tools of the DataCloud toolbox.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2022
Keywords
Domain Specific Languages, Computing Continuum, Big Data Pipelines, Visual Pipeline Description
National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:kth:diva-319702 (URN)10.1109/COMPSAC54236.2022.00183 (DOI)000855983300175 ()2-s2.0-85136915589 (Scopus ID)
Conference
46th Annual IEEE-Computer-Society International Computers, Software, and Applications Conference (COMPSAC) - Computers, Software, and Applications in an Uncertain World, JUN 27-JUL 01, 2022, ELECTR NETWORK
Note

Part of proceedings: ISBN 978-1-6654-8810-5

QC 20221027

Available from: 2022-10-27 Created: 2022-10-27 Last updated: 2023-01-16Bibliographically approved
Khan, A. Q., Nikolov, N., Matskin, M., Prodan, R., Song, H., Roman, D. & Soylu, A. (2022). Smart Data Placement for Big Data Pipelines: An Approach based on the Storage-as-a-Service Model. In: 2022 IEEE/ACM 15TH INTERNATIONAL CONFERENCE ON UTILITY AND CLOUD COMPUTING, UCC: . Paper presented at IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) / 9th International Conference on Big Data Computing, Applications and Technologies (BDCAT), DEC 06-09, 2022, Vancouver, WA (pp. 317-320). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Smart Data Placement for Big Data Pipelines: An Approach based on the Storage-as-a-Service Model
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2022 (English)In: 2022 IEEE/ACM 15TH INTERNATIONAL CONFERENCE ON UTILITY AND CLOUD COMPUTING, UCC, Institute of Electrical and Electronics Engineers (IEEE) , 2022, p. 317-320Conference paper, Published paper (Refereed)
Abstract [en]

The development of big data pipelines is a challenging task, especially when data storage is considered as part of the data pipelines. Local storage is expensive, hard to maintain, comes with several challenges (e.g., data availability, data security, and backup). The use of cloud storage, i.e., Storageas-a-Service (StaaS), instead of local storage has the potential of providing more flexibility in terms of such as scalability, fault tolerance, and availability. In this paper, we propose a generic approach to integrate StaaS with data pipelines, i.e., computation on an on-premise server or on a specific cloud, but integration with StaaS, and develop a ranking method for available storage options based on five key parameters: cost, proximity, network performance, the impact of server-side encryption, and user weights. The evaluation carried out demonstrates the effectiveness of the proposed approach in terms of data transfer performance and the feasibility of dynamic selection of a storage option based on four primary user scenarios.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2022
Series
International Conference on Utility and Cloud Computing, ISSN 2373-6860
Keywords
Storage-as-a-service, big data pipelines, data locality, data placement strategies, software containers
National Category
Computer Sciences
Identifiers
urn:nbn:se:kth:diva-328773 (URN)10.1109/UCC56403.2022.00056 (DOI)000977247400045 ()2-s2.0-85148321483 (Scopus ID)
Conference
IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) / 9th International Conference on Big Data Computing, Applications and Technologies (BDCAT), DEC 06-09, 2022, Vancouver, WA
Note

QC 20230613

Available from: 2023-06-13 Created: 2023-06-13 Last updated: 2023-06-13Bibliographically approved
Tahmasebi, S., Payberah, A. H., Paragraph, A. S., Roman, D. & Matskin, M. (2022). TRANSQL: A Transformer-based Model for Classifying SQL Queries. In: Wani, MA Kantardzic, M Palade, V Neagu, D Yang, L Chan, KY (Ed.), 2022 21ST IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS, ICMLA: . Paper presented at 21st IEEE International Conference on Machine Learning and Applications (IEEE ICMLA), DEC 12-14, 2022, Nassau, BAHAMAS (pp. 788-793). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>TRANSQL: A Transformer-based Model for Classifying SQL Queries
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2022 (English)In: 2022 21ST IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS, ICMLA / [ed] Wani, MA Kantardzic, M Palade, V Neagu, D Yang, L Chan, KY, Institute of Electrical and Electronics Engineers (IEEE) , 2022, p. 788-793Conference paper, Published paper (Refereed)
Abstract [en]

Domain-Specific Languages (DSL) are becoming popular in various fields as they enable domain experts to focus on domain-specific concepts rather than software-specific ones. Many domain experts usually reuse their previously-written scripts for writing new ones; however, to make this process straightforward, there is a need for techniques that can enable domain experts to find existing relevant scripts easily. One fundamental component of such a technique is a model for identifying similar DSL scripts. Nevertheless, the inherent nature of DSLs and lack of data makes building such a model challenging. Hence, in this work, we propose TRANSQL, a transformer-based model for classifying DSL scripts based on their similarities, considering their few-shot context. We build TRANSQL using BERT and GPT-3, two performant language models. Our experiments focus on SQL as one of the most commonly-used DSLs. The experiment results reveal that the BERT-based TRANSQL cannot perform well for DSLs since they need extensive data for the fine-tuning phase. However, the GPT-based TRANSQL gives markedly better and more promising results.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2022
Keywords
SQL Classification, BERT, GPT
National Category
Computer Systems
Identifiers
urn:nbn:se:kth:diva-328418 (URN)10.1109/ICMLA55696.2022.00131 (DOI)000980994900121 ()2-s2.0-85152213359 (Scopus ID)
Conference
21st IEEE International Conference on Machine Learning and Applications (IEEE ICMLA), DEC 12-14, 2022, Nassau, BAHAMAS
Note

QC 20230613

Available from: 2023-06-13 Created: 2023-06-13 Last updated: 2023-06-13Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-4722-0823

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