In this study, we analyze an educational search engine log for shedding light on K-12 students' search behavior in a learning environment. We specially focus on query, session, user and click characteristics and compare the trends to the findings in the literature for general web search engines. Our analysis helps understanding how students search with the purpose of learning in an educational vertical, and reveals new directions to improve the search performance in the education domain.

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In this demo, we present DBPal, a novel data exploration tool with a natural language interface. DBPal leverages recent advances in deep models to make query understanding more robust in the following ways: First, DBPal uses novel machine translation models to translate natural language statements to SQL, making the translation process more robust to paraphrasing and linguistic variations. Second, to support the users in phrasing questions without knowing the database schema and the query features, DBPal provides a learned auto-completion model that suggests to users partial query extensions during query formulation and thus helps to write complex queries.

One of the search tasks in Web search is repeat search behaviour to find out documents that users once visited, which is called re-finding. Although there have been several works in the context of general-purpose Web search addressing the latter phenomena, the problem is usually overlooked for vertical search engines. In this work, we report re-finding and newfinding behaviours of users in an educational search context and compare results with the findings in the literature for general-purpose web search. Our analysis shows that re-finding pattern of students differs from web search drastically as only 26% of all queries indicate re-finding behaviour compared to 40% in Web.

Enabling interactive visualization over new datasets at "human speed" is key to democratizing data science and maximizing human productivity. In this work, we first argue why existing analytics infrastructures do not support interactive data exploration and outline the challenges and opportunities of building a system specifically designed for interactive data exploration. Furthermore, we present the results of building IDEA, a new type of system for interactive data exploration that is specifically designed to integrate seamlessly with existing data management landscapes and allow users to explore their data instantly without expensive data preparation costs. Finally, we discuss other important considerations for interactive data exploration systems including benchmarking, natural language interfaces, as well as interactive machine learning.