Beth Trushkowsky

Crowdsourced enumeration queries

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.

Highlighting disputed claims on the web

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. Dispute Finder builds a database of known disputed claims by crawling web sites that already maintain lists of disputed claims, and by allowing users to enter claims that they believe are disputed. Dispute Finder identifies snippets that make known disputed claims by running a simple textual entailment algorithm inside the browser extension, referring to a cached local copy of the claim database. In this paper, we explain the design of Dispute Finder, and the trade-offs between the various design decisions that we explored.

The New Database Architectures

The rise of big data and cloud computing significantly changed the database market. Here, the authors describe how developers decomposed the traditional database system and questioned the classical three-tier system architecture.

Answering enumeration queries with the crowd

Hybrid human/computer database systems promise to greatly expand the usefulness of query processing by incorporating the crowd. 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 nonuniformities 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.

Crowdsourcing Enumeration Queries: Estimators and Interfaces

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.

An architecture for a collaborative bibliographic database

CoBib is a system that will allow affinity groups to effectively collaborate to maximize the searching and browsing utility of an academic paper database. The system will facilitate the process of surveying literature in a specific field by using the communitys annotations and referrals. The database architecture for CoBib provides users within research communities the means to collaboratively index and annotate citations by supporting both searching and browsing behavior. This extensible architecture is a novel solution that is interoperable with existing data formats and systems and incorporates recommendations gathered from the community for the discovery of new citations.