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CS 8803 – Advanced Internet Application Development
Project Proposal
Sandhai – A Consolidated Intelligent e-Shopper
Harikrishna Narayanan
902533226
Pranesh Parimala Ranganathan
902505951
Vijay Ramakrishnan
902446624
{harikrishna, pranesh, v.ramakrishnan, siva.subbiah}@gatech.edu
Siva Subbiah
902538209
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Abstract and Motivation:
With more E-commerce services and more offers online, users find it difficult to get the right
deal for the products of their interest online. Different service providers have different interfaces for the
offers they have and the end result is uses have to spend a lot of time in finding a product through the
variety of web services and often end up in bad or average deals while better ones are still out there yet
to be found and booked. The motivation for this project is cost and time effective search. The proposed
system will allow users to do a single master search that will spawn itself across various e-commerce
players using their E-Shopping API interfaces and help users in getting the right product.
Proposed System:
1. User profile based e-shopping product search
2. Wish list and social network synchronization
3. Integrated Google Maps for buy and sell centers
The proposed System uses the following APIs :
Amazon ,
ebay,
DealsToBuy,
Craiglist.com
Sample Use case #1:
Consider a user U wish to buy a product P online. He has to search across several online e-commerce
services to find a best deal of his interest both in terms of cost and quality. The various factors that
might influence his buying are Product Cost, Free Offers, Shipment charges, Taxes. He has to spend a
considerable amount of time in finding a good deal for the product of his interest. Also the user has to
be aware of various services and also other information like Products of Category “C” are better offered
by Website W1 and Products of Category “D” are better offered by Website W2. Nearly half of people,
who fix deals of products through a web service online, find out a better offer of the same product by a
different service later.
Instead if there is a consolidated service or a system that can talk to several online services and find the
best offer amongst all, the user would be happy to use the system and can be very much satisfied with
the deal he found for himself.
Sample Use case #2:
The idea of buying new products, goods, gadgets spreads amongst friends circle when friends usually
meet or get together. Say A, B, C, D and E are friends and they get together once in a while for a dinner.
fLet user A buy a product P in a nice offer. When the friends meet and casually talk about the product
that A bought some of his friends might like the product and would wish to buy the same in a similar
offer, but unfortunately the offer might have expired or might have turned unfavorable in the time.
Instead If there existed a system where users can keep track of their wish list and once they buy one or
get one they check it with the details of the deal they used to buy it, his/her friends circle might be
notified by the same by a Pub Sub framework. So in our system users create and maintain their wish
lists. The friends circle can then subscribe themselves for a wish list item of their friend. Say now user A
buys a product in his wish list he fills out the wish list completion that will publish the details of his
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buying to all the subscribed friends. The existing social network infrastructure can also be used to
accomplish this.
APIs to be used:
Location based service
The location information is used to identify interesting stores in a customer location using a map quest
like locator service. For example, a customer could identify the nearest store sites within a specified
distance from the current location. We use the google maps API to map the nearest centers where a
user can find the best deal balancing the trade-off between the cost included in traveling to the site and
the overall cost otherwise. The Google maps API is a free web mapping service application and
technology provided by Google that powers many map-based services including the Google Maps
website, Google Ride Finder, Google Transit and embedded maps on third-party websites via the Google
Maps API. It offers street maps, a route planner for traveling by foot, bicycle, car, or public transport and
an urban business locator for numerous countries around the world. It also can help with finding
businesses.
API Technology
Our application uses major e commerce web players’ services to provide the best possible results. There
are several APIs for each of these major services. For example EBay has several APIs for trading,
shopping, merchandising and other research APIs. These APIs use either SOAP or REST protocol. The
APIs are also available in several programming paradigms.
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Amazon Associates Web Service provides APIs in a choice of languages to access the data used by
www.amazon.com, including the items for sale, customer reviews, seller reviews, and most of the
functionality, such as finding items, finding similar items, displaying customer reviews, and product
promotions. Amazon Associates Web Service operations open the doors to Amazon's databases to take
advantages of Amazon’s e-commerce data and functionality.
Resource Plan and Schedule:
Week 1: Reading related literature and Web Service APIs
Week 2: Design of Database and basic Classes for implementation
Week 3: Implementation of Shopping features to support various e-commerce services
Week 4: Implementation of wish list Pub Sub system
Week 5: Implementation of auxiliary services supported by the system
Week 6: Integration and System Testing
Week 7: Testing, Bug fixes and Regression
Week 8: Presentation, Release and Usage in Production
Extensions:
We would like to implement this idea mainly for e-commerce [buying and selling of online
goods] services and extend them to other service consolidations like web search services consolidation,
Social Network services consolidation and thus giving the user flexibility across services at a single place.
Evaluation of the System:
The system thus designed will be evaluated in terms of time and money it has saved by doing deals
through the consolidated search system. The evaluation is tricky here and will be done on a deal basis
to find the QOS of the deal received by the users on a deal by deal basis to arrive at a score.
Credits:
Shopping API Service Implementers: Harikrishna Narayanan and Vijay Ramakrishnan
Mining and system database Design : Pranesh Parimala Ranganathan
System Integration, Testing and Evaluation: Siva Subbiah
User Feedback and UAT: Vivek Swaminathan Sridharan
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References
1. Amazon API “http://docs.amazonwebservices.com/AWSECommerceService/2008-03-03/GSG/”
2. Google Maps API http://en.wikipedia.org/wiki/Google_Maps
3. Masand, Spiliopoulou, Srivastava, Ziane: “Web Mining for Usage Patterns & Profiles”, WEBKDD
2002.
4. Rayid Ghani, Carlos Soares: “Data Mining for Business Applications”, KDD – 2006.