HumanDB: Bridging The Chasm Between HCI and Data Management
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 talk, 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 will highlight our research 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. Last but not the least, through this talk we would like to encourage DB community to look across the fence and get more engaged with these challenges which are outside the traditionally narrow boundaries of data management research.
Prof. Sourav S Bhowmick
Date & Time
27 Nov 2015 (Friday) 11:00 - 12:00
E11-4045 (University of Macau)
Department of Computer and Information Science
Sourav S Bhowmick is an Associate Professor in the School of Computer Engineering, Nanyang Technological University and the Director of Data Management & Analytics Lab. He is the co-founder of the Data Management Research Group at NTU (DANTE) and the Computational Systems Biology (COSBY) Research Group. He was a Visiting Professor at Massachusetts Institute of Technology and Fudan University. Sourav’s current research interest is multi-disciplinary in flavor, focusing primarily on bridging data management and HCI, computational social science, and in silico network biology. He has published more than 60 top-tier papers in major international data management, data mining, multimedia, bioinformatics, and systems biology conferences and journals such as VLDB, IEEE ICDE, ACM WWW, ACM SIGMOD, CIDR, ACM SIGKDD, ACM MM, IEEE TKDE, IEEE TSC, VLDB Journal, Bioinformatics, and Biophysical Journal. He has served as a program chair/co-chair of several international workshops and conferences such as DEXA 2008 and 2009, DASFAA 2013. Sourav has been tutorial speaker for several international conferences such as ER 2006, APWeb 2008, WAIM 2008, PAKDD 2009 and 2011, DASFAA 2011 and 2012, and ADMA 2012. He has received Best Paper Awards at ACM CIKM 2004 and ACM BCB 2011 for papers related to evolution mining and biological network summarization, respectively. In his free time, Sourav likes to paint.