Research Areas

Big Data Analytics and Management (BDAM)

The BDAM research group aims to facilitate secure, effective, and efficient use and management of big data under a wide variety of hardware, software, and organizational settings. The research topics include data analytics, blockchain, databases, data privacy and security, query processing, and graph/social/spatial data management.

Faculty Involved:

• Prof. CHOI, Byron Koon Kau
• Dr. HUANG, Xin
• Dr. MA, Jing
• Dr. PIAO, Chengzhi
• Dr. WANG, Qichen
• Prof. XU, Jianliang
• Dr. YANG, Renchi

Funded Research and Consultancy Projects in the Past Few Years:

An Exploratory Energy Study of the Smart Grid and Smart City Data from Australia
	Staff	Dr. CHOI, Byron Koon Kau
	Abstract	To identify climate variables related to the power consumption patterns in Australia To undertake an exploratory study for the effectiveness of energy tariff programs for different households To explore options of energy demand management for sustainable energy transitions

Exploratory Search on Graph Databases through Subgraph Query Feedback
	Staff	Dr. CHOI, Byron Koon Kau
	Abstract	To formally analyze the feedback (WIFSSQ and WNSSQ) and propose novel querying algorithms To efficiently integrate the feedback as a generic module into a GUI for graph platforms To formulate the optimal opportunities for delivering feedback To conduct comprehensive performance and usability evaluations

Privacy-preserving Linear Algebra Framework for Graph Query Algorithms for Massive Networks
	Staff	Dr. CHOI, Byron Koon Kau
	Abstract	To study a set of linear algebra operators such as set intersection/union, scalar product, matrix multiplication/addition, and propose the encoding and encryption for graph queries To apply privacy-preserving optimizations for the specific algebra operations To unify the operations and develop a publicly available tool (API)

Efficient Graph Search Algorithms for Public-Private Social Networks
	Staff	Dr. HUANG, Xin
	Abstract	To design public-private graph models, and collect real-life public-private graph datasets; To investigate efficient algorithms of community search and keyword search on public-private networks; To develop a prototype system to demonstrate the feasibility of public-private social network analysis;

Tracking the viral spread of incivility online: An interdisciplinary approach to studying profanity use in Chinese-language online platforms
	Staff	Dr. HUANG, Xin
	Abstract	To draw upon the advances in machine learning and natural language processing to tackle online incivility. To collect a large corpus of profane speech from a variety of Chinese-language online platforms.More To identify the mechanisms and processes behind the spread of nasty talk in social media at both individual and group levels, and have implications for the development of cost-effective long-term solutions to online uncivil behavior.

Keyword-Centric Community Search
	Staff	Dr. ZHANG, Zhiwei
	Abstract	To formally analyze the differences and benefits of a new framework based on graph contraction compared to existing frameworks. To design new algorithms for basic graph operations, including node centric graph operations and edge centric graph operations. To efficiently integrate these developed basic graph algorithms for graph applications.

Development and Evaluation of the Effectiveness of an Online Cognitive Behavioral Intervention Program for Hong Kong People with Social Anxiety Disorder
	Staff	Prof. XU, Jianliang
	Abstract	To develop an online Cognitive Behavioral Therapy (CBT) program (including both an online platform and mobile application) for Hong Kong people with Social Anxiety Disorder To evaluate the effectiveness of this online CBT program in reducing anxiety symptoms and psychological distress, and improving quality of life for Hong Kong people with Social Anxiety Disorder at post-treatmentMore To evaluate the maintenance effects of this online CBT program at 3- and 6-month follow-up tests To test the predictive effects of sociodemographic factors (e.g. age, education, marital and economic status) in the treatment effects

ImageProof: Enabling Authentication for Large-Scale Image Retrieval
	Staff	Prof. XU, Jianliang
	Abstract	To design novel frameworks and query authentication algorithms for the verification of large-scale image retrieval. To propose novel ADSs and several optimization techniques for robust and efficient authenticated top-k image queries. To evaluate the proposed techniques by combining theoretical analysis and empirical experiments.

Towards Searchable and Verifiable Blockchain
	Staff	Prof. XU, Jianliang
	Abstract	To design a framework for blockchain to alleviate the storage and computing costs of the user and support verifiable Boolean range queries to guarantee the results’ integrity. To design an efficient index structure that supports range queries with integrity assurance in a hybrid-storage blockchain framework.

Verifiable Attribute-Based Search over Shared Cloud Data
	Staff	Prof. XU, Jianliang
	Abstract	To design novel security primitives for supporting verifiable attribute-based access control over shared cloud data. To protect data content and access policy of outsourced databases in a zero-knowledge manner. To propose query authentication algorithms and optimization techniques for various query types.More To develop a demonstration system to show the robustness and efficiency of our proposed techniques.