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Publications (4 of 4) Show all publications
Boodaghian Asl, A. & Gokan, M. (2019). An Empirical Study On GUI-ii Interview Methods In Participatory Design. In: Katherine Blashki and Yingcai Xiao (Ed.), IADIS (International association for developement of the information society) digital library: . Paper presented at MCCSIS 2019, 13th Multi Conference on Computer Science and Information Systems 16 – 19 July 2019, Porto, Portugal (pp. 3-10). IADIS Press
Open this publication in new window or tab >>An Empirical Study On GUI-ii Interview Methods In Participatory Design
2019 (English)In: IADIS (International association for developement of the information society) digital library / [ed] Katherine Blashki and Yingcai Xiao, IADIS Press , 2019, p. 3-10Conference paper, Published paper (Refereed)
Abstract [en]

Graphical user interface interaction interview (GUI-ii), is a recently purposed method in which designers can remotely co-design and review GUI prototypes by eliminating the need for physical presence of co-designers. However, there are some concerns regarding the accuracy of such remote interview methods, as users do not have any physical interaction with the designers during their interview. In this work, for the first time, we compare GUI-ii methods with the traditional face-to-face interview processes to study their effectiveness in various design phases. The result shows that GUI-ii method is most effective when used in Ozlab.    

Place, publisher, year, edition, pages
IADIS Press, 2019
Keywords
Human-Computer Interaction, Participatory Design, Interview Methods, Graphical User Interface, Prototype
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-74859 (URN)978-989-8533-91-3 (ISBN)
Conference
MCCSIS 2019, 13th Multi Conference on Computer Science and Information Systems 16 – 19 July 2019, Porto, Portugal
Available from: 2019-09-26 Created: 2019-09-26 Last updated: 2020-01-10Bibliographically approved
Boodaghian Asl, A. & Gokan Khan, M. (2019). Studying the Effect of Online Medical Applications on Patients Healing Time and Doctors Utilization Using Discrete Event Simulation. In: : . Paper presented at IEEE International Conference on e-Health and Bioengineering (EHB) 2019.
Open this publication in new window or tab >>Studying the Effect of Online Medical Applications on Patients Healing Time and Doctors Utilization Using Discrete Event Simulation
2019 (English)Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

Online Medical Applications (OMA) has evolved dramatically in the last few years, and, consequently, the number of patients using it has also grown exponentially. Patients who are seeking non-emergency but immediate medical services or consultant may save time and money by approaching online doctors through OMAs instead of visiting them physically in healthcare centers. Additionally, medical doctors quantity contributing to OMAs growth, which can affect patients average waiting time and the queue size in healthcare centers. In this paper, We have developed a Discrete Event Simulation (DES) model to study the effects of using OMA on the patients healing time and doctors utilization by comparing it with the same process in healthcare centers. Additionaly, we compared patients average queue size, maximum number of patients in the queue, and total number of healed patients in our study. The results of this simulation showed that the healing process in OMA could serve the same number of patients in ~46% shorter time compared to healthcare centers with ~5.7% less doctors’ utilization.

Keywords
discrete event simulation, ehealth, modeling, queuing theory, telemedicine
National Category
Computer and Information Sciences
Research subject
Public Health Care Administration; Computer Science; Public Health Care Administration; Public Health Science
Identifiers
urn:nbn:se:kau:diva-75998 (URN)
Conference
IEEE International Conference on e-Health and Bioengineering (EHB) 2019
Available from: 2019-12-16 Created: 2019-12-16 Last updated: 2020-01-10
Gokan Khan, M., Taheri, J., Kassler, A. & Darula, M. (2018). Automated Analysis and Profiling of VirtualNetwork Functions: the NFV-Inspector Approach. In: 2018 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN): . Paper presented at IEEE Conference on Network Function Virtulization and Software defined Networks, Verona, Italy, 27-29 November 2018. IEEE
Open this publication in new window or tab >>Automated Analysis and Profiling of VirtualNetwork Functions: the NFV-Inspector Approach
2018 (English)In: 2018 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN), IEEE, 2018Conference paper, Published paper (Refereed)
Abstract [en]

Discovering insights about Virtual Network Function (VNFs) resource demand characteristics will enable cloud vendors to optimize their underlying Network Function Virtualization (NFV) system orchestration and dramatically mitigate CapEx and OpEx spendings. However, analyzing large-scale NFV systems, especially in mobile network environments, is a challenging task and requires tailor-made approaches for each particular application. In this demo, we showcase NFV-Inspector, an open source and extensible VNF analysis platform that is capable of systematically benchmark and profile NFV deployments. Based on its pluggable framework, NFV-Inspector classifies VNFs resource demand characteristics and correlate their Key Performance Indicators (KPIs) with system-level Quality of Service (QoS) measurements. 

Place, publisher, year, edition, pages
IEEE, 2018
Keywords
Classification, Network Function Virtualization, Platform, Profiling, Quality of Service
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-71388 (URN)10.1109/NFV-SDN.2018.8725697 (DOI)000475896900023 ()978-1-5386-8281-4 (ISBN)978-1-5386-8282-1 (ISBN)
Conference
IEEE Conference on Network Function Virtulization and Software defined Networks, Verona, Italy, 27-29 November 2018
Projects
NFV Optimizer, 5276
Funder
Knowledge Foundation, 20160182
Note

Available from: 2019-02-28 Created: 2019-02-28 Last updated: 2019-08-06Bibliographically approved
Gokan Khan, M., Bastani, S., Taheri, J., Kassler, A. & Deng, S. (2018). NFV-Inspector: A Systematic Approach to Profile and Analyze Virtual Network Functions. In: 2018 IEEE 7th International Conference on Cloud Networking (CloudNet): . Paper presented at 7th IEEE International Conference on Cloud Networking, CloudNet 2018, 22 October 2018 through 24 October 2018 (pp. 1-7). IEEE
Open this publication in new window or tab >>NFV-Inspector: A Systematic Approach to Profile and Analyze Virtual Network Functions
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2018 (English)In: 2018 IEEE 7th International Conference on Cloud Networking (CloudNet), IEEE, 2018, p. 1-7Conference paper, Published paper (Refereed)
Abstract [en]

Network Function Virtualization (NFV) focuses on decoupling network functions from proprietary hardware (i.e., middleboxes) by leveraging virtualization technology. Combining it with Software Defined Networking (SDN) enables us to chain network services much easier and faster. The main idea of using these technologies is to consolidate several Virtual Network Functions (VNFs) into a fewer number of commodity servers to reduce costs, increase VNFs fluidity and improve resource efficiency. However, the resource allocation and placement of VNFs in the network is a multifaceted decision problem that depends on many factors, including VNFs resource demand characteristics, arrival rate, configuration of underlying infrastructure, available resources and agreed Quality of Services (QoS) in Service Level Agreements (SLAs). This paper presents a bottom-up open-source NFV analysis platform (NFV-Inspector) to (1) systematically profile and classify VNFs based on resource capacities, traffic demand rate, underlying system properties, placement of VNFs in the network, etc. and (2) extract/calculate the correlation among the QoS metrics and resource utilization of VNFs. We evaluated our approach using an emulated virtual Evolved Packet Core platform (Open5GCore) to showcase how complex relation among various NFV service chains can be systematically profiled and analyzed.

Place, publisher, year, edition, pages
IEEE, 2018
Series
IEEE International Conference on Cloud Networking, ISSN 2374-3239
Keywords
Classification, Network Function Virtualization, Profiling, Quality of Service, Software Defined Networking, Classification (of information), Open source software, Open systems, Outsourcing, Transfer functions, Virtual reality, Decoupling network, Evolved packet cores, Resource efficiencies, Resource utilizations, Service level agreement (SLAs), Software defined networking (SDN), Virtualization technologies
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-71277 (URN)10.1109/CloudNet.2018.8549333 (DOI)000465081600016 ()2-s2.0-85060215258 (Scopus ID)9781538668313 (ISBN)
Conference
7th IEEE International Conference on Cloud Networking, CloudNet 2018, 22 October 2018 through 24 October 2018
Projects
NFV Optimizer, 5276
Funder
Knowledge Foundation, 20160182
Available from: 2019-02-21 Created: 2019-02-21 Last updated: 2019-07-03Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-4825-8831

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