Interacting with graphs using queries has emerged as an important research problem for real-world applications that center on large graph data. Given the syntactic complexity of graph query languages (e.g., SPARQL, Cypher), visual graph query interfaces make it easy for non-programmers to query such graph data repositories. In this book, we present recent developments in the emerging area of visual graph querying paradigm that bridges traditional graph querying with human computer interaction (HCI). Specifically, we focus on techniques that emphasize deep integration between the visual graph query interface and the underlying graph query engine. We discuss various strategies and guidance for constructing graph queries visually, interleaving processing of graph queries and visual actions, visual exploration of graph query results, and automated performance study of visual graph querying frameworks. In addition, this book highlights open problems and new research directions. In summary, in this book, we review and summarize the research thus far into the integration of HCI and graph querying to facilitate user-friendly interaction with graph-structured data, giving researchers a snapshot of the current state of the art in this topic, and future research directions.
Sourav S. Bhowmick is an Associate Professor in the School of Computer Science and Engineering (SCSE) at Nanyang Technological University. He leads the data management research group (DANTe) in SCSE. His research has appeared in top-tier venues in data management and analytics such as SIGMOD, VLDB, VLDB Journal, TKDE, WWW, and KDD. Sourav has been keynote and tutorial speaker for several international conferences including SIGMOD and VLDB. 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. His work on influence maximization was nominated for the best paper award in ACM SIGMOD 2015. Sourav has served as a PC member of premium data management and data mining conferences (e.g., SIGMOD, VLDB, KDD) and a reviewer for various premium journals (e.g., VLDB Journal).Byron Choi is an Associate Professor in the Department of Computer Science at Hong Kong Baptist University (HKBU). He obtained his Ph.D in Computer and Information Science from the University of Pennsylvania in 2006. His research interests include graph-structured databases, database usability, and database security. Byron's publications have appeared in premium venues such as TKDE, VLDBJ, SIGMOD, and VLDB. He has served as a program committee member or reviewer of premium conferences and journals including PVLDB, VLDBJ, TKDE and TOIS. He has served as the director of a Croucher Foundation Advanced Study Institute (ASI), titled ""Frontiers in Big Data Graph Research"" in 2015. He was a recipient of the HKBU President's Award for Outstanding Young Researcher in 2016.
Chengkai Li is an Associate Professor in the Department of Computer Science and Engineering at the University of Texas at Arlington. He received his Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign in 2007. Chengkai's research interests are in database, data mining, web data management, and natural language processing. He is conducting research on computational journalism, crowdsourcing and human computation, data exploration by ranking (top-k), skyline and preference queries, database testing, entity query, and usability challenges in querying graph data. Chengkai's papers have appeared in prestigious database, data mining, and web conferences (e.g., SIGMOD, VLDB, CIDR, KDD, WWW, WSDM) and journals (e.g., TODS, TKDD, TKDE). He has served as General Co-Chair and Program Co-Chair of IEEE IPCCC, and he has also served on the organizing committee of SIGMOD. He served on the program committees of premier conferences (e.g., SIGMOD, VLDB, KDD, WWW, IJCAI). He has also been a reviewer for prestigious journals (e.g., TODS, TOIS, TKDE, VLDB Journal). Chengkai is a recipient of the 2011 and 2012 HP Labs Innovation Research Award.
