# Content-Based Approach for Improving Bloom Filter Efficiency
## 3 Proposed Approach  
We propose a content-based solution for taking advantage of the content bits and using them to reduce the false positives in the BFs. Many solutions use a large size of BF to reduce the false positives. However, we keep the BF smaller and reduce the false positives by introducing just a few extra bits (content bits). These bits take into account the content value of the data and thus produce different hashes that can differentiate between the two content values having the same hash output. For making it easy to understand the methodology, we proceed as follows:  
The problem with the traditional BF is two-fold:  
1. First, a significant number of false positives, leading to incorrect content being returned when queried. 2. Second, the large size of the BF. The large size of the BF has two issues, firstly the querying speed and secondly the large memory usage. To counter the aforementioned problems, we propose an approach founded on content bits. The proposed content-based approach for processing text data in this paper is a multistep process that includes several key elements as described next:  
1. Load and extract the text data, where the raw text data are loaded from the source and extracted for further processing. 2. Remove the punctuation from the text data. This is performed to remove any unnecessary characters that might affect the processing of text data. 3. Convert the data to lowercase. This ensures that all the text data are in the same case and improves the consistency of processing. 4. Tokenize the text. This involves breaking down the textual data into individual words or tokens for further analysis. 5. Remove the stop words. Stop words are commonly used words such as \"and\" or \"the\" that do not contain much semantic value and can be removed to reduce the size of the text data and improve the efficiency of processing. 6. Remove words having two or fewer characters. This step is to make sure that the processed data contain only meaningful words. 7. Normalize the words. This step is to make sure that the words are in their base form and to remove any variations that might affect the processing of text data. For example, if the word \"running\" is present in the text data it will be normalized to its base form \"run\". 8. Generate processed sentences (content). This step involves creating meaningful sentences from the processed text data that can be used for further analysis. 9. Prior to storing the content in a BF, acquire its binary representation, referring to encoding data into a series of binary digits, zeroes and ones. 10. Take a few bits of the content (2 or 3), which are called content bits, and store them in a separate array, which points to the same location as the BF. 11. Generate hash values for the content. The hash values are generated using the following functions: \(functionhash=string2 hash(str,maxSize)\) \(str=double(str)\); \(hash=maxSize*ones(size(str,1),1)\); \(for\ i=1:size(str,2)\), \(hash=mod(hash*33+str(:,i),maxSize)+1\); \(end\) end
12. Store the content value in the BF. 13. The BF and the check bits can point to similar content before retrieval, and this reverse process makes sure that, even if the same hash functions output is obtained for different content, the content bits will play a role in generating uniqueness, thus reducing the dependency on the hash functions alone and reducing the chance of false positives by approximately 50%. The overall steps of the proposed methodology are also shown and summarized in Figure 1. Furthermore, the pseudocode of the algorithm is given in Algorithm 1. Figure 1: Overall summary of the proposed content-based methodology steps. ```
1:procedureMain
2:Text \(\leftarrow\) Read Input
3:Processed_Text \(\leftarrow\) Content_Analysis(Text)
4: Convert Processed_Text to binary form
5: Choose an appropriate number of hash functions and determine the offset of content bits.