Enhancing the Accuracy of Movie Recommendation System Based on Probabilistic Data Structure and Graph Database

2015 ◽  
Author(s):  
Ashish Sharma ◽  
Shalini Batra
Keyword(s):  
Data Structure ◽  
Recommendation System ◽  
Graph Database ◽  
Probabilistic Data ◽  
Movie Recommendation ◽  
Probabilistic Data Structure

scholarly journals These Are Not the K-mers You Are Looking For: Efficient Online K-mer Counting Using a Probabilistic Data Structure

PLoS ONE ◽  
2014 ◽  
Vol 9 (7) ◽  
pp. e101271 ◽  
Author(s):  
Qingpeng Zhang ◽  
Jason Pell ◽  
Rosangela Canino-Koning ◽  
Adina Chuang Howe ◽  
C. Titus Brown
Keyword(s):  
Data Structure ◽  
Probabilistic Data ◽  
Probabilistic Data Structure

scholarly journals Creating a Concurrent Overflowing Bloom Filter

2019 ◽  
Author(s):  
Alex Berliner ◽  
Brian Estes ◽  
Ebin Scaria
Keyword(s):  
Data Structure ◽  
Recent Literature ◽  
Bloom Filter ◽  
Bloom Filters ◽  
Probabilistic Data ◽  
Additional Element ◽  
Marginal Value ◽  
The Creation ◽  
Probabilistic Data Structure

Bloom filters are an efficient probabilistic data structure used to verify membership of an element inside of a set. There is diminishing marginal value for inserting each additional element into a Bloom filter, and so steps must be taken to maintain scalability. One such option is to create a secondary hash set for a particular hash set in a Bloom filter that has become full, known as an overflow area. At this time, there are no implementations of a Bloom filter that implement this overflow system while maintaining concurrency. In this paper, we demonstrate the creation of a concurrent overflow system for Bloom filters. We use the base Bloom filter presented in recent literature and replace their method of dynamically resizing the Bloom filters with our overflow table implementation, as outlined in one of their suggested areas for future exploration. We then compare the results of our Bloom filter with those from the previously mentioned implementation as well as a standard Bloom filter.

scholarly journals Integrated Probabilistic Data Structure For Accurate and Scalable Sequence Prediction

2020 ◽  
Vol 167 ◽  
pp. 2429-2436
Author(s):  
Soumonos Mukherjee ◽  
Uddipta Dutta ◽  
Jit Sarkar ◽  
Rajkumar R
Keyword(s):  
Data Structure ◽  
Probabilistic Data ◽  
Sequence Prediction ◽  
Probabilistic Data Structure

scholarly journals Creating a Concurrent Overflowing Bloom Filter

2019 ◽  
Author(s):  
Alex Berliner ◽  
Brian Estes ◽  
Ebin Scaria
Keyword(s):  
Data Structure ◽  
Recent Literature ◽  
Bloom Filter ◽  
Bloom Filters ◽  
Probabilistic Data ◽  
Additional Element ◽  
Marginal Value ◽  
The Creation ◽  
Probabilistic Data Structure

Bloom filters are an efficient probabilistic data structure used to verify membership of an element inside of a set. There is diminishing marginal value for inserting each additional element into a Bloom filter, and so steps must be taken to maintain scalability. One such option is to create a secondary hash set for a particular hash set in a Bloom filter that has become full, known as an overflow area. At this time, there are no implementations of a Bloom filter that implement this overflow system while maintaining concurrency. In this paper, we demonstrate the creation of a concurrent overflow system for Bloom filters. We use the base Bloom filter presented in recent literature and replace their method of dynamically resizing the Bloom filters with our overflow table implementation, as outlined in one of their suggested areas for future exploration. We then compare the results of our Bloom filter with those from the previously mentioned implementation as well as a standard Bloom filter.

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