HAMIDAH IBRAHIM is currently a professor in the Department of Computer Science at Universiti Putra Malaysia (UPM), Malaysia. She received her Ph.D. in Computer Science in 1998 from Cardiff University, UK, with a dissertation entitled Semantic Integrity Constraints Enforcement for a Distributed Database, guided by Prof. Alex Gray. She is a member of IEEE, IEEE Computer Society, and Association Computing Machinery (ACM). Her current research interests include databases (distributed, parallel, mobile, biomedical, XML) focusing on issues related to integrity maintenance/checking, ontology/schema/data integration, ontology/schema/data mapping, cache management, data security, transaction processing, query optimization, query reformulation, preference evaluation – context-aware, information extraction, concurrency control; and data management in mobile, grid, and cloud. She has received several research grants; among others are the Fundamental Research Grant Scheme (Ministry of Higher Education Malaysia), ScienceFund (Ministry of Science, Technology, and Innovation), and Research University Grant Putra. She has authored more than 300 scientific publications and book chapters. She serves as the editorial board member of several outstanding journals as well as reviewer of several scholarly journals like IEEE Access, Information Sciences, Journal of Supercomputing, etc.

• Invited Speech Abstract (Click)

  Speech Title: Energy-Efficient Skyline Query Processing in Wireless Sensor Networks

  Abstract: Query processing, a technique for retrieving objects from a database in a reliable and efficient way; has achieved tremendous success at both research and industry levels. It operates by retrieving only those data points that strictly satisfy the conditions specified in the query or returning an empty result if otherwise. Recent developments in query processing attempt to relax these stringent requirements, by retrieving the best, most preferred data points from a database. These queries known as skyline queries rely on the notion of Pareto dominance, have achieved significant success, as they are widely used in applications related to multi-criteria decision making; wherein several conflicting criteria need to be evaluated in the process of making decision. It is more challenging when there are too many criteria to be considered while the data points to be analysed are those generated and transmitted from sensing devices also known as sensing data. This group of sensors which constitutes a wireless sensor network monitors data points at different sites and transmits these data points to a central site for further analysis. Inevitably, the network lifetime is reduced due to energy consumption for transmitting these sensing data. Furthermore, with the growth of network sizes, the sensing data become massive. Intuitively, it is crucial to reduce the transmission energy consumption for energy- efficient of skyline query computation by filtering the unwarranted data points in the sensor networks. In this abstract, we surveyed the recent works that have been conducted in improving energy efficiency of skyline query computation of wireless sensors networks in terms of the total energy consumption, the maximum energy consumption, and the network lifetime. Hence, the following questions will be the main focus of the abstract: What are the challenges in computing skylines of sensing data particularly in wireless sensor networks? How to reduce the transmission energy consumption of wireless sensor networks for energy-efficient of skyline query computation? What are the limitations of existing energy-efficient models and the potential extension works that can be conducted in future?

Dr. Yixiang Fang is an Associate Professor at the School of Data Science in the Chinese University of Hong Kong, Shenzhen. He received his Ph.D. Degree from the University of Hong Kong in 2017 and worked as a research fellow in the University of Hong Kong during 2017-2018 and University of New South Wales, Australia during 2018-2020. Dr. Fang’s general research interests mainly focus on the areas of data management, data mining, and artificial intelligence over big data, such as data management, data mining, graph neural network, representation learning over big graph data, and keyword search, geo-social network mining, and trajectory query over big spatial data. He has published over 60 papers in the areas of database, data mining, and artificial intelligence, and most of them were published in top-tier conferences (e.g., PVLDB, SIGMOD, ICDE, NeurIPS, and IJCAI) and journals (e.g., TODS, VLDBJ and TKDE). Particularly, one of his representative papers was selected as the One of the four Best Papers in SIGMOD 2020 (~4/458) and awarded as the 2021 ACM SIGMOD Research Highlight Award. Dr. Fang is an editorial board member of the journal of Information & Processing Management (IPM). He has also served as program committee members for several top conferences (e.g., PVLDB, ICDE, KDD, AAAI, and IJCAI) and invited reviewers for top journals (e.g., TKDE and VLDBJ) in the areas of database and data mining.

• Invited Speech Abstract (Click)

  Speech Title: Cohesive Subgraph Search: From Theories to Algorithms and Real Applications

  Abstract: With the advent of a wide spectrum of recent applications (e.g., social media and knowledge bases), various big graphs are prevalent in many areas. An important component of these graphs is the cohesive subgraph, or subgraph containing vertices that are densely connected internally. Searching cohesive subgraphs, as a fundamental research topic in the network science, has found many real-world applications, such as friend recommendation, event organization, fraud detection, and network analysis. In this talk, I will present our recent series of works in cohesive subgraph search over big graphs. Particularly, I will focus on the topics of densest subgraph discovery, community search, and graph embedding-based subgraph search in this research area. The first one aims to efficiently discover the subgraph with the highest density, the second one queries dense communities from big graphs in an online manner, and the last one aims to exploit AI techniques to solve traditional graph data management problems. I will extensively discuss the challenges, theories, solutions, and applications for these topics. Moreover, I will point out a list of promising future research directions in this research area.

Xiaofang Xia is currently an associate professor with the School of Computer Science and Technology, Xidian University, China. She was selected for the 8th China Association for Science and Technology Young Talents Entrustment Project in 2022. She received her Ph.D. degree in Control Theory and Control Engineering from Shenyang Institute of Automation, Chinese Academy of Sciences, China, in 2019. She was a visiting scholar at the Department of Computer Science, University of Alabama, USA, from August 2016 to February 2018. Her research interests are mainly in cyber physical systems, smart grid security, database management system and anomaly detection. She serves as the Principal Investigator for nearly 10 research grants, including National Natural Science Foundation of China, China Postdoctoral Science Foundation, and multiple enterprise cooperation projects. In these areas, she has published over 20 papers in Proceedings of the IEEE, IEEE TIFS, IEEE TII, IEEE TFS, IEEE TASE, IEEE TNSE, IEEE TETC, IEEE IOTJ, IEEE JBHI, Computers & Security, etc.

• Invited Speech Abstract (Click)

  Speech Title: Group Testing based Methods for Efficient Electricity Theft Detection

  Abstract: Electricity theft is a widespread problem that causes tremendous economic losses for all utility companies around the globe. As many countries struggle to update their antique power systems to emerging smart grids, more and more smart meters are deployed throughout the world. Compared with analog meters which can be tampered with by only physical attacks, smart meters can be manipulated by malicious users with both physical and cyber-attacks for the purpose of stealing electricity. Thus, electricity theft will become even more serious in a smart grid than in a traditional power system if utility companies do not implement efficient solutions. Our goal is to identify all malicious users in a neighborhood area in a smart grid within the shortest detection time. We propose an adaptive binary splitting inspection (ABSI) algorithm and a Group Testing based Heuristic Inspection (GTHI) algorithm which adopt group testing methods to locate the malicious users. During the inspection process of our proposed scheme, the inspection strategy as well as the number of users in the groups to be inspected are adaptively adjusted. Simulation results show that the proposed ABSI and GTHI algorithms outperform existing methods.

Asst. Prof. Ronina R. Caoili-Tayuan is a Computer Engineering graduate from Mapua University and teaching in the Institute of Information and Computing Sciences (IICS)-Information Technology Department at the University of Santo Tomas, where she teaches courses under the Internet of Things (IoT), and Network and Security specializations. She is one of the Education eLearning Specialists of the University. She holds a Master’s Degree in Computer Science major in Software Development from Mapua University (formerly Mapua Institute of Technology), and a Microsoft Certified Professional in Systems Administration from Microsoft Corporation. She is currently taking up her Doctorate Degree in Doctor in Information Technology at De La Salle University-Manila specialized in Health Informatics.

She is an author of Living in the IT Era book published last 2019 and another book that is published early in 2021 entitled IT Application Tools in Business. She is currently involved with CHED-RECPE Grant Research entitled “UST-IICS Student Academic Performance Evaluation through Data Mining and Analytics as Suggestive Input to Curriculum Improvement”. She has been a Technical Committee Reviewer of International Research funded by Government in Gulf College, Sultanate of Oman.

• Invited Speech Abstract (Click)

  Speech Title: Safe Box: A Three-Factor Authentication Vault Version 3.0

  Abstract: Safe Box: A Three-factor Authentication Vault v3.0 intends to enhance the first version and second version of the project. This version uses facial recognition, PIN, RFID tag, and mobile application as the authentication methods for secure identification of authorized users. Every activity made from the Safe Box would be logged to the user’s mobile application. Any sign of unauthorized access would trigger the Raspberry Pi Camera to take a five-second video clip to send to the authorized user’s mobile application through Firebase and sound an alarm for five minutes. The average boot up time of the system is 40.497 seconds. 10 out of 10 times a valid face was recognized inside a well-lit room, and 4 out of 10 inside a dimly lit room for a valid face. This resulted in improving the facial recognition feature of the system. The average time that the safe box unlocks after pressing the unlock button on the mobile application takes 1.91 seconds with a strong internet signal. In conclusion, the Raspberry Pi can manage the whole system without the use of Arduino. The user would be able to choose between using RFID or the mobile application as the third authenticator for every time the user transaction for opening the safe box to further secure valuables from unauthorized access or theft. Included also in the mobile application is the status of the vault which made the user able to monitor the Safe Box. This had resulted in an improvement of the overall security delivered by the Safe Box for the valuables put in it by the users. The researchers recommend that the mobile application should be able to access real time footage from the vault’s camera and provide the safe box GPS to be able to locate it if an intruder has taken it away.