Membership classification using Integer Bloom Filter

Due to the large quantity of digital information now available, information search engines provide a popular and important Internet service. Issues involved in the improvement of digital content search efficiency include: keyword filtering, inefficient search filtering, and existence search queries. Internet services are currently focusing on improving efficiency and accuracy. Through a pre-processing filter of inefficient search contents, a waste of Internet resources can be avoided and search efficiency can be improved. This study proposes an Integer Bloom Filter (IBF) that combines the concepts of a Bloom Filter (BF) and an artificial neutral network. It is based on the basic structure of the Bloom Filter so that multiple attribute existence algorithms can be developed. The algorithm´s characteristics include: error-detected ratio, parallel computing, multiple attribute identification, non-fixed length string sample applications, as well as dynamic sample addition. With the non-fixed length string sample, the research results show that under proper conditions, the error-detected ratio has a very satisfactory performance and an on-line/off-line application field demonstrates its stable and highly efficient performance.