Despite several advances in information retrieval systems and user interfaces, the specification of queries over text-based document collections remains a challenging problem. Query specification with keywords is a popular solution. However, given the widespread adoption of gesture-driven interfaces such as multitouch technologies in smartphones and tablets, the lack of a physical keyboard makes query specification with keywords inconvenient. We present BinGO, a novel gestural approach to querying text databases that allows users to refine their queries using a swipe gesture to either "like" or "dislike" candidate documents as well as express the reasons they like or dislike a document by swiping through automatically generated "reason bins". Such reasons refine a user's query with additional keywords. We present an online and efficient bin generation algorithm that presents reason bins at gesture articulation. We motivate and describe BinGo's unique interface design choices. Based on our analysis and user studies, we demonstrate that query specification by swiping through reason bins is easy and expressive.

Traditional database queries follow a simple model: they define constraints that each tuple in the result must satisfy. This model is computationally efficient, as the database system can evaluate the query conditions on each tuple individually. However, many practical, real-world problems require a collection of result tuples to satisfy constraints collectively, rather than individually. In this paper, we present package queries , a new query model that extends traditional database queries to handle complex constraints and preferences over answer sets. We develop a full-fledged package query system, implemented on top of a traditional database engine. Our work makes several contributions. First, we design PaQL, a SQL-based query language that supports the declarative specification of package queries. We prove that PaQL is at least as expressive as integer linear programming, and therefore, evaluation of package queries is in general NP-hard. Second, we present a fundamental evaluation strategy that combines the capabilities of databases and constraint optimization solvers to derive solutions to package queries. The core of our approach is a set of translation rules that transform a package query to an integer linear program. Third, we introduce an offline data partitioning strategy allowing query evaluation to scale to large data sizes. Fourth, we introduce S ketch R efine , a scalable algorithm for package evaluation, with strong approximation guarantees ((1 ± ε) 6 -factor approximation). Finally, we present extensive experiments over real-world and benchmark data. The results demonstrate that S ketch R efine is effective at deriving high-quality package results, and achieves runtime performance that is an order of magnitude faster than directly using ILP solvers over large datasets.

Crowdfunding websites like Kickstarter and Indiegogo offer project organizers the ability to market, fund, and build a community around their campaign. While offering support and flexibility for organizers, crowdfunding sites provide very little control to donors. In this paper, we investigate the idea of empowering donors by allowing them to specify conditions for their crowdfunding contributions. We introduce a crowdfunding system, Codo, that allows donors to specify conditional donations. Codo allow donors to contribute to a campaign but hold off on their contribution until certain specific conditions are met (e.g. specific members or groups contribute a certain amount). We begin with a micro study to assess several specific conditional donations based on their comprehensibility and usage likelihood. Based on this study, we formalize conditional donations into a general grammar that captures a broad set of useful conditions. We demonstrate the feasibility of resolving conditions in our grammar by elegantly transforming conditional donations into a system of linear inequalities that are efficiently resolved using off-the-shelf linear program solvers. Finally, we designed a user-friendly crowdfunding interface that supports conditional donations for an actual fund raising campaign and assess the potential of conditional donations through this campaign. We find preliminary evidence that roughly 1 in 3 donors make conditional donations and that conditional donors donate more compared to direct donors.

Two approaches to measuring the similarity between symbolically notated musical rhythms are compared with each other and with human judgments of perceived similarity. The first is the edit-distance, a popular transformation method, applied to the symbolic rhythm sequences. The second approach employs the histograms of the inter-onset-intervals (IOIs) calculated from the rhythms. Furthermore, two methods for dealing with the histograms are also compared. The first utilizes the Mallows distance, a transformation method akin to the Earth-Movers distance popular in computer vision, and the second extracts a group of standard statistical features, used in music information retrieval, from the IOI-histograms. The measures are compared using four contrastive musical rhythm data sets by means of statistical Mantel tests that compute correlation coefficients between the various dissimilarity matrices. The results provide evidence from the aural domain, that transformation methods such as the edit distance are superior to feature-based methods for predicting human judgments of similarity. The evidence also supports the hypothesis that IOI-histogram-based methods are better than music-theoretical structural features computed from the rhythms themselves, provided that the rhythms do not share identical IOI histograms.