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Credit Card Fraud Detection Gist

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Summary: Add Credit Card Fraud Detection as a TCK feature.

Reviewers: matej.gradicek, teon.banek

Reviewed By: teon.banek

Subscribers: pullbot, buda

Differential Revision: https://phabricator.memgraph.io/D514
This commit is contained in:
Marko Budiselic 2017-06-28 12:59:00 +02:00
parent 592ba4df98
commit 5b1fde2a98
5 changed files with 118 additions and 0 deletions
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80
tests/qa/tck_engine/tests/cypher_graph_gists/features/credit_card_fraud_detection.feature Normal file
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Feature: Credit Card Fraud Detection
Scenario: Match all disputed transactions
Given graph "credit_card_fraud_detection"
When executing query:
"""
MATCH (victim:Person)-[r:HAS_BOUGHT_AT]->(merchant) WHERE r.status = "Disputed" RETURN victim.name AS `Customer Name`, merchant.name AS `Store Name`, r.amount AS Amount, r.time AS `Transaction Time` ORDER BY `Transaction Time` DESC
"""
Then the result should be:
| Customer Name | Store Name | Amount | Transaction Time |
| 'Olivia' | 'Urban Outfitters' | '1152' | '8/10/2014' |
| 'Olivia' | 'RadioShack' | '1884' | '8/1/2014' |
| 'Paul' | 'Apple Store' | '1021' | '7/18/2014' |
| 'Marc' | 'Apple Store' | '1914' | '7/18/2014' |
| 'Olivia' | 'Apple Store' | '1149' | '7/18/2014' |
| 'Madison' | 'Apple Store' | '1925' | '7/18/2014' |
| 'Madison' | 'Urban Outfitters' | '1374' | '7/10/2014' |
| 'Madison' | 'RadioShack' | '1368' | '7/1/2014' |
| 'Paul' | 'Urban Outfitters' | '1732' | '5/10/2014' |
| 'Marc' | 'Urban Outfitters' | '1424' | '5/10/2014' |
| 'Paul' | 'RadioShack' | '1415' | '4/1/2014' |
| 'Marc' | 'RadioShack' | '1721' | '4/1/2014' |
| 'Paul' | 'Macys' | '1849' | '12/20/2014' |
| 'Marc' | 'Macys' | '1003' | '12/20/2014' |
| 'Olivia' | 'Macys' | '1790' | '12/20/2014' |
| 'Madison' | 'Macys' | '1816' | '12/20/2014' |
Scenario: Identify the Point of Origin of the Fraud
Given graph "credit_card_fraud_detection"
When executing query:
"""
MATCH (victim:Person)-[r:HAS_BOUGHT_AT]->(merchant) WHERE r.status = "Disputed" MATCH (victim)-[t:HAS_BOUGHT_AT]->(othermerchants) WHERE t.status = "Undisputed" AND t.time < r.time WITH victim, othermerchants, t ORDER BY t.time DESC RETURN victim.name AS `Customer Name`, othermerchants.name AS `Store Name`, t.amount AS Amount, t.time AS `Transaction Time` ORDER BY `Transaction Time` DESC
"""
Then the result should be:
| Customer Name | Store Name | Amount | Transaction Time |
| 'Olivia' | 'Wallmart' | '231' | '7/12/2014' |
| 'Olivia' | 'Wallmart' | '231' | '7/12/2014' |
| 'Olivia' | 'Wallmart' | '231' | '7/12/2014' |
| 'Madison' | 'Wallmart' | '91' | '6/29/2014' |
| 'Madison' | 'Wallmart' | '91' | '6/29/2014' |
| 'Madison' | 'Wallmart' | '91' | '6/29/2014' |
| 'Paul' | 'Starbucks' | '239' | '5/15/2014' |
| 'Marc' | 'American Apparel' | '336' | '4/3/2014' |
| 'Marc' | 'American Apparel' | '336' | '4/3/2014' |
| 'Paul' | 'Just Brew It' | '986' | '4/17/2014' |
| 'Paul' | 'Just Brew It' | '986' | '4/17/2014' |
| 'Marc' | 'Amazon' | '134' | '4/14/2014' |
| 'Marc' | 'Amazon' | '134' | '4/14/2014' |
| 'Paul' | 'Sears' | '475' | '3/28/2014' |
| 'Paul' | 'Sears' | '475' | '3/28/2014' |
| 'Paul' | 'Sears' | '475' | '3/28/2014' |
| 'Marc' | 'Wallmart' | '964' | '3/22/2014' |
| 'Marc' | 'Wallmart' | '964' | '3/22/2014' |
| 'Marc' | 'Wallmart' | '964' | '3/22/2014' |
| 'Paul' | 'Wallmart' | '654' | '3/20/2014' |
| 'Paul' | 'Wallmart' | '654' | '3/20/2014' |
| 'Paul' | 'Wallmart' | '654' | '3/20/2014' |
| 'Madison' | 'Subway' | '352' | '12/16/2014' |
| 'Madison' | 'Subway' | '352' | '12/16/2014' |
| 'Madison' | 'Subway' | '352' | '12/16/2014' |
| 'Madison' | 'Subway' | '352' | '12/16/2014' |
| 'Madison' | 'MacDonalds' | '630' | '10/6/2014' |
| 'Madison' | 'MacDonalds' | '630' | '10/6/2014' |
| 'Madison' | 'MacDonalds' | '630' | '10/6/2014' |
| 'Madison' | 'MacDonalds' | '630' | '10/6/2014' |
| 'Olivia' | 'Soccer for the City' | '924' | '10/4/2014' |
| 'Olivia' | 'Soccer for the City' | '924' | '10/4/2014' |
| 'Olivia' | 'Soccer for the City' | '924' | '10/4/2014' |
| 'Olivia' | 'Soccer for the City' | '924' | '10/4/2014' |
# doesnt' work because of count(DISTINCT ...) and collect(DISTINCT ...)
Scenario: Zero in on the criminal
Given graph "credit_card_fraud_detection"
When executing query:
"""
MATCH (victim:Person)-[r:HAS_BOUGHT_AT]->(merchant) WHERE r.status = "Disputed" MATCH (victim)-[t:HAS_BOUGHT_AT]->(othermerchants) WHERE t.status = "Undisputed" AND t.time < r.time WITH victim, othermerchants, t ORDER BY t.time DESC RETURN DISTINCT othermerchants.name AS `Suspicious Store`, count(DISTINCT t) AS CountT, collect(DISTINCT victim.name) AS Victims ORDER BY CountT DESC
"""
Then the result should be:
| Suspicious Store | Count | Victims |
| 'Wallmart' | 4 | ['Olivia', 'Madison', 'Marc', 'Paul'] |

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tests/qa/tck_engine/tests/cypher_graph_gists/features/restaurant_recommendations.feature Normal file
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Feature: Restaurant Recommendations
Scenario: Match all disputed transactions
Given graph "restaurant_recommendations"
When executing query:
"""
MATCH (philip:Person {name:"Philip"})-[:IS_FRIEND_OF]-(person) RETURN person.name AS person ORDER BY person ASC
"""
Then the result should be:
| person |
| 'Andreas' |
| 'Emil' |
| 'Michael' |
Scenario: Restaurants in NYC and their cusines
Given graph "restaurant_recommendations"
When executing query:
"""
MATCH (nyc:City {name:"New York"})<-[:LOCATED_IN]-(restaurant)-[:SERVES]->(cusine) RETURN nyc, restaurant, cusine
"""
Then the result should be:
|nyc |restaurant| cusine|
|(:City {name:'New York'}) | (:Restaurant{name:'Zushi Zam'}) | (:Cuisine{name:'Sushi'}) |
|(:City {name:'New York'}) | (:Restaurant{name:'iSushi'}) | (:Cuisine{name:'Sushi'}) |
Scenario: Graph Search Recommendation
Given graph "restaurant_recommendations"
When executing query:
"""
MATCH (philip:Person {name:"Philip"}), (philip)-[:IS_FRIEND_OF]-(friend), (restaurant:Restaurant)-[:LOCATED_IN]->(:City {name:"New York"}), (restaurant)-[:SERVES]->(:Cuisine {name:"Sushi"}), (friend)-[:LIKES]->(restaurant) RETURN restaurant.name AS restaurant, collect(friend.name) as likers, count(*) as occurence ORDER BY occurence DESC
"""
Then the result should be:
| restaurant | likers | occurence |
| 'iSushi' | ['Andreas', 'Michael'] | 2 |
| 'Zushi Zam' | ['Andreas'] | 1 |

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tests/qa/tck_engine/tests/cypher_graph_gists/graphs/credit_card_fraud_detection.cypher Normal file
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tests/qa/tck_engine/tests/cypher_graph_gists/graphs/restaurant_recommendations.cypher Normal file
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CREATE (philip:Person {name:"Philip"})-[:IS_FRIEND_OF]->(emil:Person {name:"Emil"}), (philip)-[:IS_FRIEND_OF]->(michael:Person {name:"Michael"}), (philip)-[:IS_FRIEND_OF]->(andreas:Person {name:"Andreas"}) create (sushi:Cuisine {name:"Sushi"}), (nyc:City {name:"New York"}), (iSushi:Restaurant {name:"iSushi"})-[:SERVES]->(sushi),(iSushi)-[:LOCATED_IN]->(nyc), (michael)-[:LIKES]->(iSushi), (andreas)-[:LIKES]->(iSushi), (zam:Restaurant {name:"Zushi Zam"})-[:SERVES]->(sushi),(zam)-[:LOCATED_IN]->(nyc), (andreas)-[:LIKES]->(zam);

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tests/stress/create_match.py Normal file → Executable file
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