Good Morning !!

1. Good Morning !! Advanced Internet Applications Development | Georgia Institute of Technology | Spring 2009

2. SANDHAI Vijay Harikrishna Siva Pranesh Advanced Internet Applications Development | Georgia Institute of Technology | Spring 2009

3. AGENDA • Introduction • Motivation • Use cases • Features • Architecture and Design • System Evaluation • Challenges • What’s Next ?? • Questions Advanced Internet Applications Development | Georgia Institute of Technology | Spring 2009

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

5. What makes a good Online Shopping Site ?? • Wide range of Products • Attractive deals • Highly intuitive user interface Advanced Internet Applications Development | Georgia Institute of Technology | Spring 2009

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

7. Motivation • 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 Advanced Internet Applications Development | Georgia Institute of Technology | Spring 2009

8. Motivation • 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. Advanced Internet Applications Development | Georgia Institute of Technology | Spring 2009

9. Use case illustration Advanced Internet Applications Development | Georgia Institute of Technology | Spring 2009

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

11. Core Idea • Come 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. Advanced Internet Applications Development | Georgia Institute of Technology | Spring 2009

12. Popular e-com Search Engines • www.shopzilla.com • www.kelkoo.com • www.MyShoppingpal.com • www.thefind.com Advanced Internet Applications Development | Georgia Institute of Technology | Spring 2009

13. Features • Easy 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] Advanced Internet Applications Development | Georgia Institute of Technology | Spring 2009

14. Features (contd.) • Pricing Trend Analysis • Social Networking based product recommendations • Basic wish-list pubsub system • Auto shopper Advanced Internet Applications Development | Georgia Institute of Technology | Spring 2009

15. Presentation Layer Result Correlation and User Preference Filters Sandhai DB Social Network Recommendation System Trend Analysis Web Service 1 Web Service 2 Web Service 3 Web Service 4 Advanced Internet Applications Development | Georgia Institute of Technology | Spring 2009

16. Architecture 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 Advanced Internet Applications Development | Georgia Institute of Technology | Spring 2009

17. Architecture and Design (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. Advanced Internet Applications Development | Georgia Institute of Technology | Spring 2009

18. Recommendation System • Social Recommendation – Huge Dataset • Item Recommendation - Deeper analysis of data • Social Tagging - Huge User base • Our Approach • Social Networking Recommendation using Twitter and Facebook • Direct Feedback from user Advanced Internet Applications Development | Georgia Institute of Technology | Spring 2009

19. E-Com Services supported • Sandhai currently supports the following APIs : Advanced Internet Applications Development | Georgia Institute of Technology | Spring 2009

20. Testing and Evaluation • How 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. Advanced Internet Applications Development | Georgia Institute of Technology | Spring 2009

21. Testing and Evaluation (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. Advanced Internet Applications Development | Georgia Institute of Technology | Spring 2009

22. Testing and Evaluation (contd.) • Defining Quality of Service Parameters • Q : Search Query • WS1,WS2,…..WSn : WebServices • T1 ,T2,……Tn : Time Taken for Search • AT : 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. Advanced Internet Applications Development | Georgia Institute of Technology | Spring 2009

23. Testing and Evaluation (contd.) Advanced Internet Applications Development | Georgia Institute of Technology | Spring 2009

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

25. Challenges • Trying to aggregate different web services with a generic framework. • Performance Evaluation was a challenge Advanced Internet Applications Development | Georgia Institute of Technology | Spring 2009

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

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

28. References • References • 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. Advanced Internet Applications Development | Georgia Institute of Technology | Spring 2009

29. References • http://developer.ebay.com/DevZone/shopping/docs/HowTo/JS_Shopping/JS_SearchGS_NV_JSON/JS_SearchGS_NV_JSON.html Advanced Internet Applications Development | Georgia Institute of Technology | Spring 2009

30. Questions ?? Advanced Internet Applications Development | Georgia Institute of Technology | Spring 2009

31. Some 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 Advanced Internet Applications Development | Georgia Institute of Technology | Spring 2009

32. Questions ? Advanced Internet Applications Development | Georgia Institute of Technology | Spring 2009