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Frequent Word Combinations Mining and Indexing on HBase

Frequent Word Combinations Mining and Indexing on HBase. Hemanth Gokavarapu Santhosh Kumar Saminathan. Introduction. Many projects use Hbase to store large amount of data for distributed computation

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Frequent Word Combinations Mining and Indexing on HBase

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  1. Frequent Word Combinations Mining and Indexing on HBase HemanthGokavarapu Santhosh Kumar Saminathan

  2. Introduction • Many projects use Hbase to store large amount of data for distributed computation • The Processing of these data becomes a challenge for the programmers • The use of frequent terms help us in many ways in the field of machine learning • Eg: Frequently purchased items, Frequently Asked Questions, etc.

  3. Problem • These projects on Hbase create indexes on multiple data • We are able to find the frequency of a single word easily using these indexes • It is hard to find the frequency of a combination of words • For example: “cloud computing” • Searching these words separately may lead to results like “scientific computing”, “cloud platform”

  4. Objective • This project focuses on finding the frequency of a combination of words • We use the concept of Data mining and Apriori algorithm for this project • We will be using Map-Reduce and HBase for this project.

  5. Survey Topics • Apriori Algorithm • HBase • Map – Reduce

  6. Data Mining What is Data Mining? • Process of analyzing data from different perspective • Summarizing data into useful information.

  7. Data Mining How Data Mining works? • Data Mining analyzes relationships and patterns in stored transaction data based on open – ended user queries What technology of infrastructure is needed? Two critical technological drivers answers this question. • Size of the database • Query complexity

  8. Apriori Algorithm • Apriori Algorithm – Its an influential algorithm for mining frequent item sets for Boolean association rules. • Association rules form an very applied data mining approach. • Association rules are derived from frequent itemsets. • It uses level-wise search using frequent item property.

  9. Algorithm Flow

  10. Apriori Algorithm & Problem Description If theminimum support is 50%, then {Shoes, Jacket} is the only 2- itemset that satisfies the minimum support. If the minimum confidence is 50%, then the only two rules generated from this 2-itemset, that have confidence greater than 50%, are: Shoes  Jacket Support=50%, Confidence=66% Jacket  Shoes Support=50%, Confidence=100%

  11. Database D L1 C1 Scan D C2 C2 L2 Scan D L3 C3 Scan D Apriori Algorithm Example Min support =50%

  12. Apriori Advantages & Disadvantages • ADVANTAGES: Uses larger itemset property Easily Parallelized Easy to Implement • DISADVANTAGES: Assumes transaction database is memory resident Requires many database scans

  13. HBase What is HBase? • A Hadoop Database • Non - Relational • Open-source, Distributed, versioned, column-oriented store model • Designed after Google Bigtable • Runs on top of HDFS ( Hadoop Distributed File System )

  14. Map Reduce • Framework for processing highly distributable problems across huge datasets using large number of nodes. / cluster. • Processing occur on data stored either in filesystem ( unstructured ) or in Database ( structured )

  15. Map Reduce

  16. Mapper and Reducer • Mappers • FreqentItemsMap • -Finds the combination and assigns the key value for each combination • CandidateGenMap • AssociationRuleMap • Reducer • FrequentItemsReduce • CandidateGenReduce • AssociationRuleReduce

  17. Flow Chart Start Find Frequent Items Find Candidate Itemsets Find Frequent Items No Set Null? Yes Generate Association Rules

  18. Schedule • 1 week – Talking to the Experts at Futuregrid • 1 Week – survey of HBase, Apriori Algorithm • 4 Weeks -- Kick start on implementing Apriori Algorithm • 2 Weeks – Testing the code and get the results.

  19. Results

  20. Conclusion • The execution takes more time for the single node • As the number of mappers getting increased, we come up with better performance • When the data is very large, single node execution takes more time and behaves weirdly

  21. Screenshot

  22. Known Issues • When the frequency is very low for large data set the reducer takes more time • Eg: A text paragraph in which the words are not repeated often.

  23. Future Work • The analysis can be done with Twister and other platform • The algorithm can be extended for other applications that use machine learning techniques

  24. References • http://en.wikipedia.org/wiki/Text_mining • http://en.wikipedia.org/wiki/Apriori_algorithm • http://hbase.apache.org/book/book.html • http://www2.cs.uregina.ca/~dbd/cs831/notes/itemsets/itemset_apriori.html • http://www.codeproject.com/KB/recipes/AprioriAlgorithm.aspx • http://rakesh.agrawal-family.com/papers/vldb94apriori.pdf

  25. Questions?

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