TY - GEN
T1 - Fuzzy-Based Conversational Recommender for Data-intensive Science Gateway Applications
AU - Chandrashekara, Arjun Ankathatti
AU - Talluri, Radha Krishna Murthy
AU - Sivarathri, Sai Swathi
AU - Mitra, Reshmi
AU - Calyam, Prasad
AU - Kee, Kerk
AU - Nair, Satish
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - Neuro-scientists are increasingly relying on parallel and distributed computing resources for analysis and visualization of their neuron simulations. Although science gateways have democratized relevant high performance/throughput resources, users require expert knowledge about programming and infrastructure configuration that is beyond the repertoire of most neuroscience programs. These factors become deterrents for the successful adoption and the ultimate diffusion (i.e., systemic spread) of science gateways in the neuroscience community. In this paper, we present a novel intuitionistic fuzzy logic based conversational recommender that can provide guidance to users when using science gateways for research and education workflows. The users interact with a context-aware chatbot that is embedded within custom web-portals to obtain simulation tools/resources to accomplish their goals. In order to ensure user goals are met, the chatbot profiles a user's cyberinfrastructure and neuroscience domain proficiency level using a 'usability quadrant' approach. Simulation of user queries for an exemplary neuroscience use case demonstrates that our chatbot can provide step-by-step navigational support and generate distinct responses based on user proficiency.
AB - Neuro-scientists are increasingly relying on parallel and distributed computing resources for analysis and visualization of their neuron simulations. Although science gateways have democratized relevant high performance/throughput resources, users require expert knowledge about programming and infrastructure configuration that is beyond the repertoire of most neuroscience programs. These factors become deterrents for the successful adoption and the ultimate diffusion (i.e., systemic spread) of science gateways in the neuroscience community. In this paper, we present a novel intuitionistic fuzzy logic based conversational recommender that can provide guidance to users when using science gateways for research and education workflows. The users interact with a context-aware chatbot that is embedded within custom web-portals to obtain simulation tools/resources to accomplish their goals. In order to ensure user goals are met, the chatbot profiles a user's cyberinfrastructure and neuroscience domain proficiency level using a 'usability quadrant' approach. Simulation of user queries for an exemplary neuroscience use case demonstrates that our chatbot can provide step-by-step navigational support and generate distinct responses based on user proficiency.
KW - Conversational Recommenders
KW - Guided User Interfaces
KW - Intutionistic Fuzzy Logic
KW - Mamdani Inference
KW - Neuroscience Workflows
KW - Science Gateways
KW - Virtual Agents
UR - http://www.scopus.com/inward/record.url?scp=85062628660&partnerID=8YFLogxK
U2 - 10.1109/BigData.2018.8622046
DO - 10.1109/BigData.2018.8622046
M3 - Conference contribution
AN - SCOPUS:85062628660
T3 - Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
SP - 4870
EP - 4875
BT - Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
A2 - Abe, Naoki
A2 - Liu, Huan
A2 - Pu, Calton
A2 - Hu, Xiaohua
A2 - Ahmed, Nesreen
A2 - Qiao, Mu
A2 - Song, Yang
A2 - Kossmann, Donald
A2 - Liu, Bing
A2 - Lee, Kisung
A2 - Tang, Jiliang
A2 - He, Jingrui
A2 - Saltz, Jeffrey
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Big Data, Big Data 2018
Y2 - 10 December 2018 through 13 December 2018
ER -