IEEE SMDS 2021 - Technical Program

SMDS Technical Program

Conference code: SMD

All times are listed in UTC time. To convert UTC time to your time, use the UTC Time Zone Converter.

Monday 9/6
16:30 - 17:50 pm UTC time

Panel Discussion: Digital Twin & Digital Threading: Current Practice & Future Trends
Joint panel with the Blockchain Symposium

A digital twin, being an exact digital replica of a given tangible physical asset or process, a digital twin is expected to play a vital role in Industry 4.0. Capturing and integrating the asset, production, and performance data into a corresponding digital twin is commonly referred to as digital threading.

Today’s methods for enabling digital twins are based on centralized architectures and do not natively provide trusted data provenance, audit, and traceability. In contrast, blockchain-based digital threading ensures secure and trusted traceability, accessibility, immutability of transactions, logs through data provenance, which is native to the blockchain protocols. In addition to the blockchain, several other technologies such as the Internet of Things, artificial intelligence, big and streaming data analytics are commonly used to enable seamless synchronization between digital twins and the processes they represent. The panel session will discuss ongoing innovations and further research efforts needed to resolve blockchain-based digital threading challenges associated with scalability, data privacy, interoperability, energy consumption, and integration with legacy systems.

Moderator: Dragan Boscovic (Arizona State University)
Dragan Boscovic is a Research Professor of Computer Science and Engineering at ASU and is serving as the Director of ASU's Blockchain Research Lab and the co-Director of ASU's Center for Assured and Scalable Data Engineering (CASCADE). He is a seasoned ITC industry executive and senior technology expert with proven track record in conceiving strategies and managing development, investment and innovation efforts as related to advanced technologies, products and mobile services. Based on extensive international experience uniquely positioned to release latent potentials and extract value within a global operation. Lateral thinker with broad exposure to different information and communication technologies/systems and very comfortable with attacking complex and multifaceted problems. Currently leading a consortium of to jointly address new business opportunities as related to Smart Cities, Smart Energy/Grid and Smart Health applications and services.


Jan Veuger (Saxion University) is a Professor of Blockchain at the Saxion University of Applied Sciences, Schools of Finance & Accounting, Creative Technology and School of Governance, Law and Urban Development. He is also the President of the Academic Board at FIBREE, editor of the Real Estate Quarterly Research The Netherlands, member of four Super Advisory Boards and the Scientific Editorial Board at CIRRE. His research focuses on Blockchain and its impact on the new economy and society.

Khaled Salah (Kalifa University) is a full professor at the Department of Electrical Engineering and Computer Science, Khalifa University, UAE. He has over 190 publications and three US patents and is the Track Chair of IEEE Globecom 2018 on Cloud Computing. He is also an Associate Editor of IEEE Blockchain Tech Briefs, International Journal of “Blockchain in Healthcare”, and a member of the IEEE Blockchain Education Committee.

Mic Bowman (Intel) is a senior principal engineer in Intel Labs and leads the decentralized computing research group. Mic has spent over 20 years working on large-scale databases and distributed systems. Among other roles he served as a member of the Hyperledger Technical Steering Committee for several years contributing to various aspects of architecture definition and evaluation of technologies for privacy and confidentiality. He is currently working on methods for improving the security, scalability, and privacy of distributed ledgers.

Date/Time Session Session Chair Presentation
Mon 9/6

18:10 - 19:30 pm
UTC time
SMD 13 RuoCheng Guo, Arizona State University Causal Learning Special Session
Tues 9/7

1:00 - 2:20 am
UTC time
SMD 1: Graphs, Knowledge Graphs and AI I Mudhakar Srivatsa, IBM TJ Watson Research Center SMD_REG_14
Towards a Reinforcement Learning-based Exploratory Search for Mashup Tag Recommendation
Richard Anarfi, Benjamin Kwapong and Kenneth Fletcher

Chinese stock trend prediction based on multi-feature learning and model fusion
Shanyan Lai, Chunyang Ye, Hongyu Jiang and Hui Zhou

Graph Convolutional Network-Strengthened Topic Modeling for Scientific Papers
Jia Zhang, Junhao Shen, Beichen Hu, Nivedha Rajaram, Rahul Ramachandran, Tsengdar Lee, Kwo-Sen Kuo, Manil Maskey and Seungwon Lee
Tues 9/7

2:30 - 3:50 am
UTC time
SMD 2: Smart Data Management I Huawei Huang, Sun Yat-Sen University SMD_INV_42
Federated Process Mining: Exploiting Event Data Across Organizational Boundaries
Wil van der Aalst

AC2M: An Automated Consent Management Model for Blockchain Financial Services Platform
Zhiyu Xu, Tengyun Jiao, Ziyuan Wang, Sheng Wen and Shiping Chen

Data Readiness Report
Shazia Afzal, Rajmohan C, Manish Kesarwani, Sameep Mehta and Hima Patel
Wed 9/8

1:00 - 2:20 am
UTC time
SMD 3: Data Computing at Edge Shiping Chen, CSIRO SMD_WIP_26
Cognitive Advisory Agent
Shubhi Asthana and Shikhar Kwatra

Using Blockchain for Enhancing Collaboration among Independent Enterprises:A Knowledge-based Approach
Niranjan Marathe, Haan Johng, Tom Hill and Lawrence Chung

Here, There, Anywhere: Profiling-Driven Services to Tame the Heterogeneity of Edge Applications
Manish Pandey, Breno Cruz, Minh Le, Young-Woo Kwon and Eli Tilevich
Wed 9/8

2:30 - 3:50 am
UTC time
SMD 4: Data Computing at Edge Yucong Duan, Hainan University SMD_REG_37
Ship trajectory anomaly detection based on multi-feature fusion
Guanbin Huang, Shanyan Lai, Chunyang Ye and Hui Zhou
Wed 9/8
19:40 - 21:00 pm UTC time
SMD 12
Panel Discussion: The Role of Smart Data and IoT in Building Energy Automation
This panel focuses on the current state-of-the-arts and international trends surrounding the emerging question of what is the Role of Smart Data and IoT in Building Energy Automation for smart buildings. The panel is formed with diversified panelists that include experts from major building automation industry, government research laboratories, and academia. Needs, gaps, and challenges from data schema, data analytics, and real field application perspectives are will be discussed.


Dr. Zheng O'Neill (TAMU) is an Associate Professor of Mechanical Engineering at TAMU. Her research (funded by NSF and DOE) focuses on exploring fundamental challenges and emerging technologies for smart and healthy buildings, including building/HVAC&R energy efficiency, intelligent building controls and optimization, grid-interactive efficient buildings, ground source heat pumps, and uncertainty quantification in building energy systems.

Dr. Jin Wen (Drexel University) is a Professor of Civil, Architectural and Environmental Engineering at Drexel. Her research impacts (also funded by NSF and DOE) includes building energy efficiency, building-grid integration, building control and operation, fault detection and diagnosis, human-building interactions, dynamic building system simulation, and big data for building operations.


Dr. Steve White (CSIRO) leads CSIRO's Energy Efficiency Research. He also leads the “Buildings to Grid Data Clearing House” Activity in the Affordable Heating and Cooling Innovation Hub (i-Hub). He is the Operating Agent for the International Energy Agency EBC Annex 81 “Data-Driven Smart Buildings”. Dr White has over 25 years of experience in energy end use efficiency and electricity industry demand side management. He has extensive experience in the application of research to support both government energy efficiency policy instruments and technology commercialization. He oversees the NatHERS benchmark software (referenced in the National Construction Code) and key national energy databases (NEAR, Australian Housing Database).

Dr. Youngchoon Park (Healing LLC) is the principal / co-founder of Healing LLC, a new start up to help smart community of future – experience platform and service for the next micro city. Before this, he served as the VP/GM Data Enabled Solution at Johns Controls and was the lead for data & AI innovation strategies and execution across the firm. He also led solution development of occupant experience, smart space, energy optimization, clean air, predictive maintenance, and logistic optimization.

Dr. Jan Drgnoa (PNNL) is a data scientist in the Physics and Computational Sciences Division (PCSD) at Pacific Northwest National Laboratory. His current research focus falls in the intersection of deep learning, constrained optimization, and model-based optimal control. Jan has a PhD in Control Engineering from Slovak University of Technology in Bratislava with focus on learning-based and model based optimal control of buildings. Before joining PNNL, Jan was a postdoc at KU Leuven, Belgium, where he was working on model predictive control (MPC) in real-world office buildings. He is the lead author of the paper: “All you need to know about model predictive control for buildings” that presents an overview of contemporary advanced control methods for smart buildings.

Dr. Young M Lee (Johnson Controls) is a distinguished fellow at Johnson Controls, thought leader, innovator, data scientist, mathematical modeler, research scientist, manager and master inventor with B.S./M.S./Ph.D. degrees from Columbia University and 30 years’ of combined industrial experience.

Date/Time Session Session Chair Presentation
Thurs 9/9

1:00 - 2:20 am
UTC time
SMD 5: Edge AI
Youngchoon Park, Healing LLC SMD_WIP_13
ML Model Change Detection and Versioning Service
Shubhi Asthana, Shikhar Kwatra and Sushain Pandit

Why Did You Turn On That Light?
Supratik Mukhopadhyay, Alimire Nabijiang, Chanachok Chokwitthaya, Yimin Zhu, Girish Rentala and Qun Liu

From Big Data to Smart Data-centric software architectures for city analytics: the case of the PELL smart city platform
Mubashir Ali, Patrizia Scandurra, Fabio Moretti, Laura Blaso, Mariagrazia Leccisi and Fabio Leccese
Thurs 9/9

2:30 - 3:50 am
UTC time
SMD 6: Smart Data Trustworthiness Katsunori Oyama, Nihon University SMD_REG_28
Turning a Curse into a Blessing: A General Approach to Resolve Endogeneity Problem in Data-Rich Environment
Xiliang Lin, Tho Le, Carlos Carrion and Zenan Wang

Improving Knowledge Based Detection of Soft Attacks Against Autonomous Vehicles with Reputation, Trust and Data Quality Service Models
Sergey Chuprov, Ilia Viksnin, Iuliia Kim, Timofey Melnikov, Leon Reznik and Igor Khokhlov
Thurs 9/9

18:10 - 19:30 pm
UTC time
SMD 7: Smart Data Management II Supratik Mukhopadhyay
Louisiana State University
Assessing the Effectiveness of the Shared Responsibility Model for Cloud Databases: the case of Google’s Firebase
Biniam Fisseha Demissie and Silvio Ranise

Targeted VAE: Variational and Targeted Learning for Causal Inference
Matthew J. Vowels, Necati Cihan Camgoz and Richard Bowden

Track Before Detect: A Novel Approach For Unsupervised Anomaly Detection In Time Series
Ralph Bou Nader, Nour Assy, Walid Gaaloul, Yehia Taher and Rafiqul Haque
Thurs 9/9

19:40 - 21:00 pm
UTC time
SMD 8: BlockChain Mengchu Zhou
New Jersey Institute of Technology
Blockchain Based RAN Data Sharing
Andreas Heider-Aviet, Danny Roswin Ollik, Van Thanh Le, Nabil El Ioini, Claus Pahl, Hamid R. Barzegar, Silvio Ranise, Roberto Carbone and Stefano Berlato

An Analysis of Transaction Handling in Bitcoin
Befekadu Gebraselase, Bjarne Emil Helvik and Yuming Jiang

Semantic Data Integration to Support Prosecutors in their Investigations: Lessons Learned and Challenges
Carlo Batini, Valerio Bellandi, Paolo Ceravolo, Federico Moiraghi, Matteo Palmonari and Stefano Siccardi
Fri 9/10

1:00 - 2:20 am
UTC time
SMD 9: Graphs, Knowledge Graphs and AI II Sachiko Yoshihama, IBM Research SMD_SHT_34
DynGraphTrans: Dynamic Graph Embedding via Modified Universal Transformer Networks for Financial Transaction Data
Toyotaro Suzumura, Shilei Zhang and Li Zhang

An Auxiliary Learning Task-Enhanced Graph Convolutional Network Model for Highly-accurate Node Classification on Weakly Supervised Graphs
Zengmei Zhuo, Xin Luo and Mengchu Zhou
Fri 9/10

2:30 - 3:50 am
UTC time
SMD 10: Graphs, Knowledge Graphs and AI III Toyotaro Suzumura, University of Tokyo SMD_REG_12
HOPE-Graph: A Hypothesis Evaluation Service considering News and Causality Knowledge
Futoshi Iwama, Miki Enoki and Sachiko Yoshihama

Nonnegative Latent Factor-Incorporated Fuzzy Double c-Means Clustering for Incomplete Data
Ming Li and Yan Song

Efficient Mobility Support Services for Highly Mobile Devices in 5G Networks
Zohar Naor