Weight based approach to extract top-k pages

1. A project stage presentation onExtraction of Top-k List From The Web Using Threshold Algorithm By Mahesh V. Dabade (Exam No-6602) Guide Mr. Shriniwas Gadage Dept. of Computer Engineering

2. AGENDA • Introduction • Literature Survey • Motivation • Problem Statement • Proposed System • System Architecture • Mathematical Module • UML Diagram • GUI • System Requirements • Conclusion • References

3. INTRODUCTION • We proposed the method in which user fires “Top-k” query and gets multiple links as output. • Extracting useful information from web is called as web mining. • System will give user direct top-k list as result when user fire top-k list as query.

4. Literature Survey

5. Literature Survey

6. MOTIVATION • All data available on web is not in same format • Many times structured -information is available in tabular form. Again the question arises, “is this tabular data is valuable?" Many times the answer is NO. User may get huge tables on web but inside those tables only small amount of information is valuable. • We proposed the method in which user fires Top-K list or any other query, user get multiple links as output

7. PROBLEM STATMENT • Most of the information on the web is unstructured text in natural language, and extracting knowledge from natural language text is very difficult. Since some information on the web exists is the form of structured or semi-structured. Therefore, we study here about the information extraction from the top-k web pages, which describes the top k instance which is of general interests.

8. PROPOSED SYSTEM ARCHITECTURE

9. PROPOSED SYSTEM • In proposed system, we can make use of extracted Top-k lists to act as a background knowledge for the system to answer Top-k related queries. • To prepare such knowledge we used a technique to aggregate a number of similar or related lists into a more comprehensive one. • One of the most well known technique is known as Threshold Algorithm.

10. PROPOSED SYSTEM • Threshold Algorithm utilizes aggregate functions to combine the scores of the items in each list and then compute the Top-k items based on the combined score.

11. MATHEMATICAL MODULE • Let S, be a system such that, S = {I, e, In, X,Y, T, fme, DD, NDD, ffriend, MEMshared, CPUCoreCnt, ф} • Where, • S- Proposed System • I- Initial state at T<init> i.e. User enter the query for searching the top k list. • e- End state is schema definition of top k list. • X- Input of System i.e. Query • Y- Output of System i.e. Schema Definition of top k list. • T- Set of serialized steps to be performed in pipelined machine cycle. In a given system serialized steps are search Query, Candidate Picker, Title Classifier, Top k-ranker, etc.

12. MATHEMATICAL MODULE • fme- Main algorithm resulting into outcome Y, mainly focus on success defined for the solution. Threshold Algorithm. • DD- Deterministic Data , it helps identifying the load-store function or assignment function. e.g. i= {return i}. Such function contributes in space complexity. In a given system deterministic data will be title classifier and candidate picker. • NDD- Non Deterministic Data of the system to be solved. These being computing function or CPU time or ALU time function contribute in time complexity. In a given system we need to find time required to find top k list. • Ffriend- Set of user query. • MEMshared- Memory required to process all these operations, memory will allocated to every running process. • CPUCoreCnt- More the number of count double the speed and performance. • Ф- Null value if any.

13. USE CASE DIAGRAM

14. SEQUENCE DIAGRAM

15. POSSIBLE GUI

16. TITLE CLASSIFIER

17. CANDIDATE PICKER

18. TRY IT OUT

19. SYSTEM REQUIREMENT • Hard Disk – 1 GB • RAM – 256 MB • Processor – Intel Pentium 4 or above • Technology – Core Java • Tools - Netbeans • Operating System – windows xp or above

20. ADVANTAGE • Different goals: The goal of previous approaches is to indiscriminatingly extract all lists or tables from a web page, while ours is to extract one specific list from a special kind of page while purging all other lists. • In other systems, Top-k list is chosen from the set of candidate lists which are manually composed. But our system will generate an automatic Top-k list. • No Manual Intervention is required.

21. CONCLUSION • The system solves interesting problem of extracting top-k list from web, which aims at recognizing, extracting and understanding top-k list from web pages. • We would like to conclude that compared to other structure data top-k list are cleaner, easier to understand and more interesting for human consumption and therefore are an important source for data mining and knowledge discovery.

22. REFERENCES • Zhixian Zhang, Kenny Q. Zhu , Haixun Wang, Hongsong Li , “Automatic Extraction of Top-k Lists from the Web”, IEEE , ICDE Conference, 2013, 978-1-4673-4910-9. • F. Fumarola, T. Weninger, R. Barber, D. Malerba, and J. Han, “Extracting general lists from web documents: A hybrid approach”, in IEA/AIE (1), 2011, pp. 285-294 • G. Miao, J. Tatemura, W.-P. Hsiung, A. Sawires, and L. E. Moser, “Extracting data records from the web using tag path clustering”, in WWW, 2009, pp. 98190.

23. REFERENCES • M. J. Cafarella, E. Wu, A. Halevy, Y. Zhang, and D. Z. Wang, “Webtables: Exploring the power of tables on the web”, in VLDB Auckland, New Zealand, 2008. • Zhixian Zhang, Kenny Q. Zhu , Haixun Wang , Hongsong Li, “A system for Extracting Top-K List from the Web”, KDD'12, August 12-16, 2012, Beijing, China, ACM 978-1-4503-1462-6/12/08. • http://techtrickle.com/new-android-marshmallow-features-to-check-out/

24. Thank You !!!