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In-Depth Information
introduced these concepts. This chapter will illustrate how they can also be
applied with a time dimension, using Influent.
Influent is an open source tool for transaction analysis designed for
enterprise data system integration. Influent is web-based, and,
conveniently, a number of live demonstrations are available for exploration
online using public transaction data sets. This example uses the small
microloan demo available for download on the Influent website. The demo
uses an anonymized sample of data published by Kiva, and subsequently
curated for research use.
Note
You can find Influent documentation, demos, and download online at
http://influent.org
.
To find out more about the Kiva organization,
including how you can participate, visit
http://kiva.org
.
Kiva is a leading non-government organization that facilitates the
international crowd-sourcing of microloans, primarily to individuals in
second- and third-world countries. Participants fall into three classes:
•
Borrowers
who apply for a loan
•
Partners
, which are the financial institutions that administer the loan
locally, dispensing and collecting payments
•
Lenders
who contribute to loans
Each loan has a single partner, acting as a local broker. Loans almost always
have many lenders collectively contributing the funding. Lenders typically
give small amounts of money to each of many loans.
To protect the system from abuse and encourage the trust of lenders, Kiva's
policy is that all financial transaction activity of borrowers and partners
be an open book. In addition to transaction activity, detailed descriptive
information is published about both borrowers and partners. Lenders also
self-publish information about themselves and the loans they support as
endorsement and encouragement for others to do the same.
The Kiva data set is typical of many financial transaction data sets in nature
and scale. Kiva has millions of participants and hundreds of millions of
transactions, making for an ideal test case for approaches to understanding
patterns of behavior.
