good morning !! good morning !! advanced internet applications development | georgia institute of...

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Advanced Internet Applications Development | Georgia Institute of Technology | Spring 2009

SANDHAISANDHAI


HarikrishnaVijay

PraneshSiva

AGENDAAGENDAIntroductionMotivationUse casesFeaturesArchitecture and DesignSystem EvaluationChallengesWhat’s Next ??Questions


What is Sandhai ?What is Sandhai ?Sandhai = a common market

place in Tamil [South Indian Language] where one can buy or sell anything

An e-shopping search engine system


What makes a good Online What makes a good Online Shopping Site ??Shopping Site ??

Wide range of Products

Attractive deals

Highly intuitive user interface


What makes Sandhai ?What makes Sandhai ?

Better part of most of the Online Shopping services out there

Several other built in features like social network recommendations, auto shopping helps shoppers to get the right product in time


MotivationMotivation First motivation for this project is cost and time

effective search. ◦ Usually

Step #1 : Search for a product in one or two online shopping sites, look for the best deal among the interested ones

Step #2 : Buy the deal that suits best Step #3 : After ordering the product, come to know that there were even better

deals than the one you bought Step #4 :

◦ Why this happens ? Lack of time to make a wider search Lack of time to compare different deals available

◦ What user needs ? Wider Searching capacity Find the best available deal fast and effective


MotivationMotivation Secondly, there are several e-shopping search

engines available and the main challenge that they face is providing support for new e-shopping services. [ Prof. Ling Lu ]◦ We thought if we could somehow reduce the work of

supporting new services by coming up with a highly flexible generic framework, it will be great

Another motivation is, social networking seems to be the hotspot in recent years and we thought we could use it to our advantage by making use of the social network infrastructure in helping users find the right product.


Use case illustrationUse case illustration


Motivation (contd.)Motivation (contd.) 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. Let 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 buying to all the subscribed friends. The existing social network infrastructure is used to accomplish this.


Core IdeaCore IdeaCome up with a framework for

seamless integration◦When a new e-commerce API needs

to be supported all the admin needs to do is make a small addition in the System DB and a config file explaining the API and interested data tags.

Make user find what he wants in much faster time.


Popular e-com Search Popular e-com Search EnginesEngineswww.shopzilla.com

www.kelkoo.com

www.MyShoppingpal.com

www.thefind.com


FeaturesFeaturesEasy integration of new web

services

Support for both SOAP and REST based product search APIs.

User customized search tuning◦Get information about users

preferences and interests [say his favorite online shops, his favorite brands, his favorite color etc]


Features (contd.)Features (contd.)Pricing Trend Analysis

Social Networking based product recommendations

Basic wish-list pubsub system

Auto shopper



Result Correlation and User Preference Filters

Web Service 1 Web Service 2

Web Service 3 Web Service 4

Social Network

Sandhai DB

Recommendation System

Trend Analysis

Presentation Layer

Architecture and DesignArchitecture and Design

What to look for in any E-Commerce Service ?

Product data: Product data includes information about product availability and pricing for items in the catalog.

Content from customers: Content from customers include reviews and product lists

Seller information: Seller information includes general information and customer feedback about the wide range of vendors


Architecture and Design Architecture and Design (Contd.)(Contd.)The 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.


Recommendation SystemRecommendation SystemSocial Recommendation – Huge Dataset Item Recommendation - Deeper analysis of

dataSocial Tagging - Huge User base

Our ApproachSocial Networking Recommendation using Twitter and FacebookDirect Feedback from user


E-Com Services supported E-Com Services supported Sandhai currently supports the

following APIs :


Testing and EvaluationTesting and EvaluationHow to test the effectiveness of a

service aggregation system as a whole?◦Highly dependent on individual sub

systems

Bringing up a Sandhai Pilot system and asking many users to perform search in it will help in evaluating the system.


Testing and Evaluation Testing and Evaluation (contd.)(contd.) Performance of the system at various stages of

integration The QOS parameters are Speed Number of Results Preference

Simulating Web services to profile the integration framework code◦ Created simple web service mockups◦ Integrated these mock up services into Sandhai ‘s

framework◦ Triggered custom searches and calculated the

request response times for various ranges of queries.


Testing and Evaluation Testing and Evaluation (contd.)(contd.)Defining Quality of Service ParametersQ : Search QueryWS1,WS2,…..WSn : WebServicesT1 ,T2,……Tn : Time Taken for SearchAT : Aggregator Framework time

We aim to achieve a performance in which the sandhai ‘s search time is always better than the slowest product search engine among the integrated engines.


Testing and Evaluation Testing and Evaluation (contd.)(contd.)


ChallengesChallengesTrying to aggregate different web

services with a generic framework.

Performance Evaluation was a challenge


What’s Next ?What’s Next ? Integrating and supporting more e-

commerce sites to provide users with a wider search range

Supporting a full fledged recommendation system for the user profiles in the system

Independent wish-list publisher subscriber system


What’s Next ? (contd.)What’s Next ? (contd.)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 all services at a single place.

Data mining and trend analysis based on product searches made by different user profiles


ReferencesReferencesReferences

◦Amazon API http://docs.amazonwebservices.com/AWSECommerceService/2008-03-03/GSG/

◦Masand, Spiliopoulou, Srivastava, Ziane: “Web Mining for Usage Patterns & Profiles”, WEBKDD 2002.

◦Rayid Ghani, Carlos Soares: “Data Mining for Business Applications”, KDD – 2006.


ReferencesReferenceshttp://developer.ebay.com/

DevZone/shopping/docs/HowTo/JS_Shopping/JS_SearchGS_NV_JSON/JS_SearchGS_NV_JSON.html


Questions ??


Some FactsSome Facts$28 billions were spent online in the

year 2000According to the Census Bureau company report, in the year 2000 the Internet users spent $28 billions. This amount has exceeded the similar stats for 1999 ($17,3 billions) and 1998 ($7,7 billions). Users spent most of their money by purchasing air tickets ($7,8 billions).$5,1 billions were spent by buying personal computers and making hotel reservations - $2,1 billions. 24% of all sales of the computer equipment were done through Internet. Users also spent $1,3 billions on the computer software. | TheWorldJournal.com


Questions ?


good morning !! good morning !! advanced internet applications development | georgia institute of...

Documents

product cost

right product

product p

online shopping services

online services

best deal

good deal

user needs