Tutorial outline

Section 1. State of the Art in User Group Analytics (UGA)

Topic 1. Introduction to User Group Analytics (UGA): Motivations, Definitions, and Challenges

• What is User Group Analytics (UGA)?
• What is user data and user groups?
• Real use cases in various application domains such as advertisement, program committee formation and quantified-self
• Overview of UGA challenges

Topic 2. UGA Components: Discovery, Exploration, and Visualization

• User group discovery: defining the group discovery process, and categorizing related work into attribute-based and action-based discovery
• User group exploration: defining the group exploration process, and categorizing related work into by-query, by-facet, by-example, and by-analytics exploration
• User group visualization: defining the building blocks of group visualization and mapping functions, and categorizing related work into graph-based and map-based visualization

Topic 3. UGA Evaluation: Challenges and Measures

• Challenges and measures for evaluating each UGA component

Section 2. New Challenges in Combining Components of User Group Analytics

Topic 1. Combining Discovery and Visualization

• Challenges of combining discovery and visualization
• Discussion of approaches for combining discovery and visualization in the following categories: distribution-based, facet-based, relation-based, and time-based
• Discussion of visualization approaches for representing discovered groups, such as self-organizing maps, belt charts, and regression heatmaps

Topic 2. Combining Discovery and Exploration

• Challenges of combining discovery and exploration
• Discussion of approaches for combining discovery and exploration
• Discussion of different approaches of explicit and implicit feedback capture to enable exploratory discovery

Topic 3. Combining Exploration and Visualization

• Challenges of combining exploration and visualization
• Discussion of approaches for combining exploration and visualization (aka, Visual Analytics)

Topic 4. All-in-One UGA: Challenges and Research Directions

• Benefits of building an all-in-one system where all UGA components are integrated
• Challenges of integrating UGA components
• Discussion of recently established literature of all-in-one UGA
• Discussion of future directions towards a full-fledged integration

Important References

Group Discovery

• Mahashweta Das, Sihem Amer-Yahia, Gautam Das, and Cong Yu. MRI: meaningful interpretations of collaborative ratings. In PVLDB 2011.
• Jure Leskovec, Kevin J Lang, and Michael Mahoney. Empirical comparison of algorithms for network community detection. In WWW 2010.
• Rakesh Agrawal, Johannes Gehrke, Dimitrios Gunopulos, and Prabhakar Raghavan. Automatic subspace clustering of high dimensional data for data mining applications. In SIGMOD 1998.

Group Exploration

• Sihem Amer-Yahia, Sofia Kleisarchaki, Naresh Kumar Kolloju, Laks V. S. Lakshmanan, and Ruben H. Zamar. Exploring rated datasets with rating maps. In WWW 2017.
• Francesco Bonchi, Fosca Giannotti, Claudio Lucchese, Salvatore Orlando, Raffaele Perego, and Roberto Trasarti. Conquest: a constraint-based querying system for exploratory pattern discovery. In ICDE 2006.
• Ning Yan, Chengkai Li, Senjuti B Roy, Rakesh Ramegowda, and Gautam Das. Facetedpedia: enabling query-dependent faceted search for wikipedia. In CIKM 2010.
• Behrooz Omidvar-Tehrani, Sihem Amer-Yahia, and Alexandre Termier. Interactive user group analysis. In CIKM 2015.

Group Visualization

• Jeffrey Heer and Joseph M Hellerstein. Tutorial on data visualization and social data analysis. In VLDB 2009.
• Ivan Herman, Guy Melançon, and M Scott Marshall. Graph visualization and navigation in information visualization: A survey. In TVCG 2000.

Combining Discovery and Visualization

• Huiqi Xu, Zhen Li, Shumin Guo, and Keke Chen. Cloudvista: interactive and economical visual cluster analysis for big data in the cloud. In VLDB 2012.
• Josua Krause, Adam Perer, and Harry Stavropoulos. Supporting iterative cohort construction with visual temporal queries. In TVCG 2016.

Combining Discovery and Exploration

• Arnab Nandi and HV Jagadish. Guided interaction: Rethinking the query-result paradigm. In VLDB 2011.
• Mario Boley, Michael Mampaey, Bo Kang, Pavel Tokmakov, and Stefan Wrobel. One click mining: Interactive local pattern discovery through implicit preference and performance learning. In IDEA Workshop in KDD 2013.
• Ioannis Arapakis, Mounia Lalmas, and George Valkanas. Understanding within-content engagement through pattern analysis of mouse gestures. In CIKM 2014.

Combining Exploration and Visualization

• Arvind Satyanarayan, Dominik Moritz, Kanit Wongsuphasawat, and Jeffrey Heer. Vega-lite: A grammar of interactive graphics. In T VCG 2017.
• Tarique Siddiqui, Albert Kim, John Lee, Karrie Karahalios, and Aditya Parameswaran. Effortless data exploration with zenvisage: an expressive and interactive visual analytics system. In VLDB 2016.

All-in-One UGA

• Çağatay Demiralp, Peter J Haas, Srinivasan Parthasarathy, and Tejaswini Pedapati. Foresight: recommending visual insights. In VLDB 2017.
• Eugene Wu, Leilani Battle, and Samuel R Madden. The case for data visualization management systems: vision paper. In VLDB 2014.