# Automated Recommendation of Aggregate Visualizations for Crowdfunding Data
## 2 Materials and Methods
### The LoanVis Visualization Recommendation System  
The process of visual data exploration is typically initiated by an analyst specifying an exploratory query \(Q\) on a database \(D\), as shown in Figure 1. The result of query \(Q\), denoted as \(R\), represents a subset of the database \(D\), which the analyst can further transform into data visualizations that might reveal some interesting insights. For instance, an analyst exploring the Kiva dataset using our LoanVis system might pose some specific queries that are based on the following general query structure \(Q\):  
\(Q\): SELECT * FROM \(D\) WHERE T;  
Where in the exploratory query \(Q\), \(D\) specifies the explored dataset (i.e., the Loans table) and \(T\) specifies a combination of predicates, which selects a subset of \(D\) for visual analysis. For instance, an analyst who is reproducing the results in [29] might want to study the disparity in loan distribution for projects in the _Entertainment_ sector, and in turn will pose a query \(Q\), in which \(T\) is specified as: sector = Entertainment. Similarly, exploratory analysis of the other attributes and values of the Kiva dataset can be conducted using alternative settings of the predicate \(T\) (e.g., gender = Female or country = Kenya AND sector = Food, etc.). A visual representation of the query \(Q\) is basically the process of generating different aggregate views of its result (i.e., \(R\)), which are then plotted using some of the popular data visualization representations (e.g., bar charts). An analyst typically examines those visualizations looking for insights, which are used as a springboard to decide their next analytical query. For instance, the results for a query in which \(T\) is sector = Entertainment might trigger further analysis based on gender, where an analyst would pose a subsequent query in which \(T\) is sector = Entertainment AND gender = Female. Accordingly, we employ a multi-dimensional data model of \(D\), which consists of a set of _dimension_ attributes \(\mathbb{A}\) (e.g., country, sector, etc.) and a set of _measure_ attributes \(\mathbb{M}\) (e.g., loan amount, lender count, etc.). Additionally, \(\mathbb{F}\) is the set of possible aggregate functions over the measure attributes \(\mathbb{M}\), such as SUM, COUNT, AVG, MIN, and MAX. \begin{table}
\begin{tabular}{l l} \hline \hline
**Attribute Name** & **Description** \\ \hline
**Borrower information** & \\ Country & The name of the country in which the loan was disbursed. \\ Borrower Genders & Comma separated list of _Male_, _Female_, where each instance represents a single male/female in the group. \\ \hline
**Loan Usage information** & \\ Sector & High-level category of the loan usage field. \\ Activity & Granular category of the loan usage field. \\ Use & Exact Usage of loan amount. \\ \hline
**Loan Dates** & \\ Posted Time & The time at which the loan is posted on Kiva by the field agent. \\ Funded Time & The time at which the loan posted to Kiva gets funded by lenders \\ Disbursed Time & The time at which the loan is disbursed by the field agent to the borrower. \\ \hline
**Loan Amount** & \\ Funded Amount & The amount disbursed by Kiva to the field agent (USD). \\ Loan Amount & The amount disbursed by the field agent to the borrower (USD). \\ Lender Count & The total number of lenders that contributed to this loan. \\ \hline
**Loan Repayment** & \\ Term in Months & \\ Repayment Interval & The duration for which the loan was disbursed in months. \\  & Loan repayment pattern - either monthly, irregular, or bullet (one \\  & time). \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The Schema for the Kiva Dataset. Hence, an aggregate view \(V_{i}\) over \(R\) is represented by a tuple \((A,M,F)\) where \(A\in\mathbb{A}\), \(M\in\mathbb{M}\), and \(F\in\mathbb{F}\), as shown in Figure 1. That is, \(R\) is grouped by dimension attribute \(A\) and aggregated by function \(F\) on measure attribute \(M\).