Location-based queries are quickly becoming ubiquitous. However, traditional search engines perform poorly for a significant fraction of location-based queries, which are nonfactual (i.e., subjective, relative, or multi-dimensional). As an alternative, we investigate the feasibility of answering locationbased queries by crowdsourcing over Twitter. More specifically, we study the effectiveness of employing location-based services (such as Foursquare) for finding appropriate people to answer a given location-based query. Our findings give insights for the feasibility of this approach and highlight some research challenges in social search engines.

Abstract. This paper describes the design, implementation and deployment of LineKing (LK), a crowdsourced line wait-time monitoring service. LK consists of a smartphone component (that provides automatic, energy-efficient, and accurate wait-time detection), and a cloud backend (that uses the collected data to provide accurate wait-time estimation). LK is used on a daily basis by hundreds of users to monitor the wait-times of a coffee shop in our university campus. The novel wait-time estimation algorithms deployed at the cloud backend provide mean absolute errors of less than 2-3 minutes.

This article describes LineKing, a crowdsensing system for monitoring and forecasting coffee shop line wait times. LineKing consists of a smartphone component that provides automatic and accurate wait-time detection, and a cloud backend that uses the collected data to provide accurate wait-time estimation. LineKing is used on a daily basis by hundreds of users to monitor the wait-times of a coffee shop in the University at Buffalo, SUNY. The novel wait-time estimation algorithms of LineKing deployed at the cloud backend provide median absolute errors of less than 3 minutes. © 2002-2012 IEEE

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