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:

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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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.