Implementation of Multi-dimensional Indexing using Pyramid Technique Presented by

1. Implementation of Multi-dimensional Indexing using Pyramid Technique Presented by Shunmuga Prabhu.A Sanjeetha Devi. S Retna. M. V.

2. Abstract Implementing Pyramid Techniques in Multi Dimensional database Concepts of PASS Bots and Search Engine Implementing these techniques with the help of Latest web programming languages and database.

3. Multi Dimensional Database A form of database that is designed to make the best use of storing and utilizing data To optimize online analytical processing (OLAP) and data warehouse applications Higher level of organization than the relational table

4. Implementing Pyramid Techniques d Dimensional range 2d 1-dimensional range B+ Tree

5. Data Space Partitioning Split the data space into 2d pyramids having the center point of the data space (0.5, 0.5, ..., 0.5) Each of the 2d pyramids is divided into several partitions each corresponding to one data page of the B+-tree

6. Definition 1: (Pyramid of a point v) A d-dimensional point v is defined to be located in pyramid pi, jmax if (vjmax < 0.5) j = (jmax+d) if (vjmax ≥ 0.5)

7. Definition 2: (Height of a point v) Given a d-dimensional point v. Let pi be the pyramid in which v is located according to Definition 1. Then, the height hvof the point v is defined as

8. Definition 3: (Pyramid value of a point v) Given: a d-dimensional point v Let pibe the pyramid in which v is located according to Definition 1. hvbe the height of v according to Definition 2. Then, the pyramid value pvvof v is defined as pvv = ( i + hv )

9. Existing System Most of the dimensional databases linked in the form of star schema Use of Online Transaction Processing (OLTP) Concepts Databases are not indexed

10. Star Schema

11. Proposed System Multi Dimensional Databases linked in the form of Snowflake Schema Use of Online Analytical Processing (OLAP) Concepts All the dimensional databases are indexed, So the query result will be faster than old.

12. Snowflake Schema

13. Why we choose PHP/MySQL with AJAX? PHP Standard CGI, FastCGI and Apache module support Access Control Extended Regular Expressions MySQL Massive data warehouses holding terabytes of information Strong support for specialized web functions High-performance query engine AJAX Asynchronous loading of content without page refresh Increased interactivity and responsiveness Reduced loading time and server traffic

14. PASS Bots Run automated tasks over the Internet An automated script fetches, analyses and files information from web servers at many times the speed of a human Our Bots search the link and its contents, title and description in rapidly and also collect the backup of the website Crawled 700 rows in URL table, 25000 rows in web content table

15. PASS Search Engine Query The search results are returned into fraction of a second The web server sends the query into the index server User Web Server Doc Server Index Server Snippets are generated to describe each search result. It tells which pages contains the words that match any particular query

16. Index Creation A database index is a data structure that improves the speed of operations in a table. Determine the pyramid value pvv of the point and insert the point into a B+-tree using pvv as a key. SQL Query Statement for Indexing Create index sweb using btree on webcontent (height);

17. Query Processing To examine which pyramids are affected by the query To determine the ranges inside the pyramids Determining all such objects is a one-dimensional indexing problem  if(count($searchword)>=1) {foreach($searchword as $key=>$value)  $search.="contents like '% " . trim($value) ." %' and “; } $sql='select * from webcontent use index (sweb) where '. $search; $res=$dbconn->query($sql) or die($dbconn->errorInfo());

18. Analytical Results

19. Performance Behavior – Data Space

20. Performance Behavior – DB Size

21. Screen Shots Home Page

22. Screen Shots Search Result Page

23. Thank You

24. Queries…?