Towards HCI-aware Data Management: Bridging the Chasm Between HCI and Data Management


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OVERVIEW

Visual query interface design and devising efficient query processing techniques 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 database 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, DB 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. DB 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 visual querying and data management services to end users. 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 DB (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 (referred to as HCI-aware data management) in the context of querying graph-structured data. Specifically, we focus on building an HCI-aware visual graph data management framework called HumanDB that aims to encapsulate 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 visual interface construction and data management.

The project is partially funded by NTU Tier 1 grant and MOE Tier 2 grant.

PROJECT TEAM

  • Sourav S Bhowmick (Faculty)
  • Byron Choi (Faculty, HKBU)
  • Curtis Dyreson (Faculty, Utah State)
  • Huey Eng Chua (Post Doc)
  • Jia Shi (Research Asst)
  • Yinglong Song (Research Asst)

ALUMNI

  • Kai Huang (Research Assistant)
  • Miao Xie (Research Assistant)
  • Rachit Dubey (Research Assistant)
  • Changjiu Jin (Research Assistant)
  • Ba Quan Truong (Research Assistant)
  • Chaohui Wang (Research Assistant)

 

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