| 1. |
- Abelev, Betty, et al.
(författare)
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Measurement of prompt J/psi and beauty hadron production cross sections at mid-rapidity in pp collisions at root s=7 TeV
- 2012
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Ingår i: Journal of High Energy Physics. - 1029-8479. ; :11
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Tidskriftsartikel (refereegranskat)abstract
- The ALICE experiment at the LHC has studied J/psi production at mid-rapidity in pp collisions at root s = 7 TeV through its electron pair decay on a data sample corresponding to an integrated luminosity L-int = 5.6 nb(-1). The fraction of J/psi from the decay of long-lived beauty hadrons was determined for J/psi candidates with transverse momentum p(t) > 1,3 GeV/c and rapidity vertical bar y vertical bar < 0.9. The cross section for prompt J/psi mesons, i.e. directly produced J/psi and prompt decays of heavier charmonium states such as the psi(2S) and chi(c) resonances, is sigma(prompt J/psi) (p(t) > 1.3 GeV/c, vertical bar y vertical bar < 0.9) = 8.3 +/- 0.8(stat.) +/- 1.1 (syst.)(-1.4)(+1.5) (syst. pol.) mu b. The cross section for the production of b-hadrons decaying to J/psi with p(t) > 1.3 GeV/c and vertical bar y vertical bar < 0.9 is a sigma(J/psi <- hB) (p(t) > 1.3 GeV/c, vertical bar y vertical bar < 0.9) = 1.46 +/- 0.38 (stat.)(-0.32)(+0.26) (syst.) mu b. The results are compared to QCD model predictions. The shape of the p(t) and y distributions of b-quarks predicted by perturbative QCD model calculations are used to extrapolate the measured cross section to derive the b (b) over bar pair total cross section and d sigma/dy at mid-rapidity.
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| 2. |
- Abelev, Betty, et al.
(författare)
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Underlying Event measurements in pp collisions at root s=0.9 and 7 TeV with the ALICE experiment at the LHC
- 2012
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Ingår i: Journal of High Energy Physics. - 1029-8479. ; :7
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Tidskriftsartikel (refereegranskat)abstract
- We present measurements of Underlying Event observables in pp collisions at root s = 0 : 9 and 7 TeV. The analysis is performed as a function of the highest charged-particle transverse momentum p(T),L-T in the event. Different regions are defined with respect to the azimuthal direction of the leading (highest transverse momentum) track: Toward, Transverse and Away. The Toward and Away regions collect the fragmentation products of the hardest partonic interaction. The Transverse region is expected to be most sensitive to the Underlying Event activity. The study is performed with charged particles above three different p(T) thresholds: 0.15, 0.5 and 1.0 GeV/c. In the Transverse region we observe an increase in the multiplicity of a factor 2-3 between the lower and higher collision energies, depending on the track p(T) threshold considered. Data are compared to PYTHIA 6.4, PYTHIA 8.1 and PHOJET. On average, all models considered underestimate the multiplicity and summed p(T) in the Transverse region by about 10-30%.
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| 3. |
- Corodescu, Andrei-Alin, et al.
(författare)
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Big Data Workflows : Locality-Aware Orchestration Using Software Containers
- 2021
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Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 21:24
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Tidskriftsartikel (refereegranskat)abstract
- The emergence of the edge computing paradigm has shifted data processing from centralised infrastructures to heterogeneous and geographically distributed infrastructures. Therefore, data processing solutions must consider data locality to reduce the performance penalties from data transfers among remote data centres. Existing big data processing solutions provide limited support for handling data locality and are inefficient in processing small and frequent events specific to the edge environments. This article proposes a novel architecture and a proof-of-concept implementation for software container-centric big data workflow orchestration that puts data locality at the forefront. The proposed solution considers the available data locality information, leverages long-lived containers to execute workflow steps, and handles the interaction with different data sources through containers. We compare the proposed solution with Argo workflows and demonstrate a significant performance improvement in the execution speed for processing the same data units. Finally, we carry out experiments with the proposed solution under different configurations and analyze individual aspects affecting the performance of the overall solution.
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| 4. |
- Khan, Akif Quddus, et al.
(författare)
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Cloud storage cost: a taxonomy and survey
- 2024
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Ingår i: World wide web (Bussum). - : Springer. - 1386-145X .- 1573-1413. ; 27:4
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Tidskriftsartikel (refereegranskat)abstract
- Cloud service providers offer application providers with virtually infinite storage and computing resources, while providing cost-efficiency and various other quality of service (QoS) properties through a storage-as-a-service (StaaS) approach. Organizations also use multi-cloud or hybrid solutions by combining multiple public and/or private cloud service providers to avoid vendor lock-in, achieve high availability and performance, and optimise cost. Indeed cost is one of the important factors for organizations while adopting cloud storage; however, cloud storage providers offer complex pricing policies, including the actual storage cost and the cost related to additional services (e.g., network usage cost). In this article, we provide a detailed taxonomy of cloud storage cost and a taxonomy of other QoS elements, such as network performance, availability, and reliability. We also discuss various cost trade-offs, including storage and computation, storage and cache, and storage and network. Finally, we provide a cost comparison across different storage providers under different contexts and a set of user scenarios to demonstrate the complexity of cost structure and discuss existing literature for cloud storage selection and cost optimization. We aim that the work presented in this article will provide decision-makers and researchers focusing on cloud storage selection for data placement, cost modelling, and cost optimization with a better understanding and insights regarding the elements contributing to the storage cost and this complex problem domain.
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| 5. |
- Khan, Akif Quddus, et al.
(författare)
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Smart Data Placement for Big Data Pipelines : An Approach based on the Storage-as-a-Service Model
- 2022
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Ingår i: 2022 IEEE/ACM 15TH INTERNATIONAL CONFERENCE ON UTILITY AND CLOUD COMPUTING, UCC. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 317-320
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 6. |
- Khan, Akif Quddus, et al.
(författare)
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Smart Data Placement Using Storage-as-a-Service Model for Big Data Pipelines
- 2023
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Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 23:2
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 7. |
- Khan, Akif Quddus, et al.
(författare)
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Towards Cloud Storage Tier Optimization with Rule-Based Classification
- 2023
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Ingår i: Service-Oriented and Cloud Computing. - : Springer Nature. ; , s. 205-216
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 8. |
- Khan, Akif Quddus, et al.
(författare)
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Towards Graph-based Cloud Cost Modelling and Optimisation
- 2023
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Ingår i: Proceedings. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 1337-1342
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 9. |
- Layegh, Amirhossein, et al.
(författare)
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ContrastNER : Contrastive-based Prompt Tuning for Few-shot NER
- 2023
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Ingår i: Proceedings - 2023 IEEE 47th Annual Computers, Software, and Applications Conference, COMPSAC 2023. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 241-249
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 10. |
- Mrazovic, Petar, 1989-
(författare)
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Crowdsensing-driven Route Optimisation Algorithms for Smart Urban Mobility
- 2018
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Urban mobility is often considered as one of the main facilitators for greener and more sustainable urban development. However, nowadays it requires a significant shift towards cleaner and more efficient urban transport which would support for increased social and economic concentration of resources in cities. A high priority for cities around the world is to support residents’ mobility within the urban environments while at the same time reducing congestions, accidents, and pollution. However, developing a more efficient and greener (or in one word, smarter) urban mobility is one of the most difficult topics to face in large metropolitan areas. In this thesis, we approach this problem from the perspective of rapidly evolving ICT landscape which allow us to build mobility solutions without the need for large investments or sophisticated sensor technologies.In particular, we propose to leverage Mobile Crowdsensing (MCS) paradigm in which citizens use their mobile communication and/or sensing devices to collect, locally process and analyse, as well as voluntary distribute geo-referenced information. The mobility data crowdsensed from volunteer residents (e.g., events, traffic intensity, noise and air pollution, etc.) can provide valuable information about the current mobility conditions in the city, which can, with the adequate data processing algorithms, be used to route and manage people flows in urban environments.Therefore, in this thesis we combine two very promising Smart Mobility enablers – MCS and journey/route planning, and thus bring together to some extent distinct research challenges. We separate our research objectives into two parts, i.e., research stages: (1) architectural challenges in designing MCS systems and (2) algorithmic challenges in MCS-driven route planning applications. We aim to demonstrate a logical research progression over time, starting from fundamentals of human-in-the-loop sensing systems such as MCS, to route optimisation algorithms tailored for specific MCS applications. While we mainly focus on algorithms and heuristics to solve NP-hard routing problems, we use real-world application examples to showcase the advantages of the proposed algorithms and infrastructures.
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| 11. |
- Nikolov, Nikolay, et al.
(författare)
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Conceptualization and scalable execution of big data workflows using domain-specific languages and software containers
- 2021
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Ingår i: INTERNET OF THINGS. - : Elsevier BV. - 2543-1536 .- 2542-6605. ; 16, s. 100440-
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Tidskriftsartikel (refereegranskat)abstract
- Big Data processing, especially with the increasing proliferation of Internet of Things (IoT) technologies and convergence of IoT, edge and cloud computing technologies, involves handling massive and complex data sets on heterogeneous resources and incorporating different tools, frameworks, and processes to help organizations make sense of their data collected from various sources. This set of operations, referred to as Big Data workflows, requires taking advantage of Cloud infrastructures' elasticity for scalability. In this article, we present the design and prototype implementation of a Big Data workflow approach based on the use of software container technologies, message-oriented middleware (MOM), and a domain-specific language (DSL) to enable highly scalable workflow execution and abstract workflow definition. We demonstrate our system in a use case and a set of experiments that show the practical applicability of the proposed approach for the specification and scalable execution of Big Data workflows. Furthermore, we compare our proposed approach's scalability with that of Argo Workflows - one of the most prominent tools in the area of Big Data workflows - and provide a qualitative evaluation of the proposed DSL and overall approach with respect to the existing literature.
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| 12. |
- Nikolov, Nikolay, et al.
(författare)
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Container-Based Data Pipelines on the Computing Continuum for Remote Patient Monitoring
- 2023
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Ingår i: Computer. - : Institute of Electrical and Electronics Engineers (IEEE). - 0018-9162 .- 1558-0814. ; 56:10, s. 40-48
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 13. |
- Roman, Dumitru, et al.
(författare)
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Big Data Pipelines on the Computing Continuum : Tapping the Dark Data
- 2022
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Ingår i: Computer. - : Institute of Electrical and Electronics Engineers (IEEE). - 0018-9162 .- 1558-0814. ; 55:11, s. 74-84
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 14. |
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| 15. |
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| 16. |
- Tahmasebi, Shirin, et al.
(författare)
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DATACLOUDDSL : Textual and Visual Presentation of Big Data Pipelines
- 2022
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Ingår i: 2022 IEEE 46TH ANNUAL COMPUTERS, SOFTWARE, AND APPLICATIONS CONFERENCE (COMPSAC 2022). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 1165-1171
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 17. |
- Tahmasebi, Shirin, et al.
(författare)
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TRANSQL : A Transformer-based Model for Classifying SQL Queries
- 2022
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Ingår i: 2022 21ST IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS, ICMLA. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 788-793
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 18. |
- Tahmasebi, Shirin, et al.
(författare)
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TRANSQLATION : TRANsformer-based SQL RecommendATION
- 2023
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Ingår i: Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 4703-4711
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Konferensbidrag (refereegranskat)abstract
- The exponential growth of data production emphasizes the importance of database management systems (DBMS) for managing vast amounts of data. However, the complexity of writing Structured Query Language (SQL) queries requires a diverse range of skills, which can be a challenge for many users. Different approaches are proposed to address this challenge by aiding SQL users in mitigating their skill gaps. One of these approaches is to design recommendation systems that provide several suggestions to users for writing their next SQL queries. Despite the availability of such recommendation systems, they often have several limitations, such as lacking sequence-awareness, session-awareness, and context-awareness. In this paper, we propose TRANSQLATION, a session-aware and sequence-aware recommendation system that recommends the fragments of the subsequent SQL query in a user session. We demonstrate that TRANSQLATION outperforms existing works by achieving, on average, 22% more recommendation accuracy when having a large amount of data and is still effective even when training data is limited. We further demonstrate that considering contextual similarity is a critical aspect that can enhance the accuracy and relevance of recommendations in query recommendation systems.
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