SIGCSE is packed with teaching insights and inspiration. However, we get these insights and inspiration from hearing our colleagues talk about their teaching. Why not watch them teach? This session does exactly that! Six exceptional educators will present innovative content just as they would to their students. The moderator, Colleen Lewis, will describe their pedagogical moves and how they connect to education research. The goal of the session is to inspire SIGCSE attendees by highlighting innovative instruction by exceptional educators. Attendees can adopt the content and/or pedagogical moves from each microteaching example.

Hybrid human-machine query processing systems, such as crowd-powered database systems, aim to broaden the scope of questions users can ask about their data by incorporating human computation to support queries that may be subjective and/or require visual or semantic interpretation. A common type of query involves filtering data by several criteria, some of which need human computation to be evaluated. For example, filtering a set of hotels for those that both (1) have great views from the rooms, and (2) have a fitness center. Criteria can differ in the amount of human effort required to decide if data satisfy them, due to criterion's subjectivity and difficulty. There is potential to reduce crowdsourcing costs by ordering the evaluation of each of the criteria such that criteria needing more human computation are not processed for data that have not satisfied the less costly criteria. Unfortunately, for queries specified on-the-fly, the information about subjectivity and difficulty is unknown a priori. To overcome this challenge, we present Dynamic Filter, an adaptive query processing algorithm that dynamically changes the order in which criteria are evaluated based on observations while the query is running. Using crowdsourced data from a popular crowdsourcing platform, we show that Dynamic Filter can effectively adapt the processing order and approach the performance of a "clairvoyant" algorithm.

Hybrid human/computer database systems promise to greatly expand the usefulness of query processing by incorporating the crowd for data gathering and other tasks. Such systems raise many implementation questions. Perhaps the most fundamental issue is that the closed world assumption underlying relational query semantics does not hold in such systems. As a consequence, the meaning of even simple queries can be called into question. Furthermore, query progress monitoring becomes difficult due to non-uniformities in the arrival of crowd-sourced data and peculiarities of how people work in crowd-sourcing systems. To address these issues, we develop statistical tools that enable users and systems developers to reason about query completeness. These tools can also help drive query execution and crowd-sourcing strategies. We evaluate our techniques using experiments on a popular crowd-sourcing platform.

Hybrid human/computer database systems promise to greatly expand the usefulness of query processing by incorporating the crowd for data gathering and other tasks. Such systems raise many implementation questions. Perhaps the most fundamental question is that the closed world assumption underlying relational query semantics does not hold in such systems. As a consequence the meaning of even simple queries can be called into question. Furthermore, query progress monitoring becomes difficult due to non-uniformities in the arrival of crowdsourced data and peculiarities of how people work in crowdsourcing systems. To address these issues, we develop statistical tools that enable users and systems developers to reason about query completeness. These tools can also help drive query execution and crowdsourcing strategies. We evaluate our techniques using experiments on a popular crowdsourcing platform.

Large-scale websites are increasingly moving from relational databases to distributed key-value stores for high request rate, low latency workloads. Often this move is motivated not only by key-value stores' ability to scale simply by adding more hardware, but also by the easy to understand predictable performance they provide for all operations. While this data model works well, lookups are only done by primary key. More complex queries require onerous, explicit index management and imperative data lookups by the developer. We demonstrate PIQL, a Performance Insightful Query Language that allows developers to express many of the queries found on these websites, while still providing strict bounds on the number of I/O operations for any query.

We describe Dispute Finder, a browser extension that alerts a user when information they read online is disputed by a source that they might trust. Dispute Finder examines the text on the page that the user is browsing and highlights any phrases that resemble known disputed claims. If a user clicks on a highlighted phrase then Dispute Finder shows them a list of articles that support other points of view.