Visual interface design and devising efficient techniques for interactive search and exploration are traditionally independent to each other for decades. This is primarily due to the fact that the two key enablers of these efforts, namely HCI and data management, have evolved into two disparate and vibrant scientific fields, rarely making any systematic effort to leverage techniques and principles from each other towards superior realization of these efforts. Specifically, data management researcher has traditionally focused on "under-the-hood" techniques such as indexing, query processing, and transactions. On the hand, the HCI community has focused on "outside-the-hood" issues such as user task modeling, menu design models, human factors, etc. Data management researchers have a tendency to shy away from outside-the-hood challenges with HCI flavors whereas the HCI researchers are reluctant to look at under-the-hood challenges that may influence the way they build visual interfaces among others. We believe that this chasm between these two vibrant fields sometimes create obstacles in providing superlative human interaction and experience with underlying data. For instance, on the one hand, as visual query interface construction process is traditionally data-unaware, it may fail to generate flexible, portable, and user-friendly query interface. On the other hand, traditionally query processing techniques are only invoked once a user has completed her visual query formulation as the former is completely decoupled from the latter.

In this research, we question the traditional reluctance of the data management (resp. HCI) community to embark on seemingly non-DB-ish (resp. non-HCI-ish) grand challenges. We explore the vision of bridging the long-standing chasm between traditional data management and HCI in the context of network data. Such data is widespread nowadays in various domains and applications such as biology, cheminformatics, social science, machine learning and AI. We refer to this new arena as human-graph interaction. Specifically, we aim to explore several novel and intriguing research challenges toward the grand goal of bridging this chasm. Realization of these challenges entails significant rethinking of several long-standing strategies for human interaction with data management and analytics systems.

A picture is worth a thousand words. An interface is worth a thousand pictures.

- Ben Shneiderman

This research is partially funded by NTU Tier 1 grant and MOE Tier 2 grant. Results of our research have appeared in premium venues such as SIGMOD, VLDB, ICDE, VLDB Journal, and TKDE.


The list of publications related to this project can be found in ResearchGate.

Key Achievements

  • PRAGUE (IEEE ICDE 2012),GBLENDER (ACM SIGMOD 2010, ACM SIGMOD 2011), QUBLE (ACM SIGMOD 2013, VLDB J 2014), and BOOMER (ACM SIGMOD 2018, ACM SIGMOD 2020) are the world's first frameworks that realize the paradigm of blending visual graph query formulation and query processing.
  • PICASSO (VLDB 2017) and FERRARI (IEEE ICDE 2019, VLDB J 2020)are the world's first frameworks for visual exploratory subgraph search on graph databases.
  • VISUAL (IEEE ICDE 2015, IEEE TKDE 2017) is the world's first framework to simulate visual graph query formulation process using an HCI-inspired model. This framework can be used to automate performance benchmarking of visual graph query-based applications.
  • DAVINCI (IEEE ICDE 2015, VLDB 2016) and AURORA (ACM SIGMOD 2019, ACM SIGMOD 2020) are the world's seminal framework to realize a data-driven approach for constructing visual graph query interface. Prior to DAVINCI, visual graph query interfaces were created by writing relevant code manually.
  • AUTOG (VLDB 2016, IEEE TKDE 2017) is the world's first framework that supports subgraph suggestions during visual graph query formulation.