Membership classification using Integer Bloom Filter

Cheng, Hung-Yu; Ma, Huadóng

doi:10.1109/icis.2013.6607871

Cited by 1 publication

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To search for objects in a database/very large set, we need searching techniques or methods and related to the checking of object membership in a set [3]. Techniques of object TELKOMNIKA ISSN: 1693-6930  searching are important to determine speed of the object sought [4]. The checking of object membership in a set can simply be done by checking one by one to the existing object until the object sought is found.…”

Section: Introductionmentioning

confidence: 99%

Bloom Filter Implementation in Cache with Low Level of False Positive

2017

View full text Add to dashboard Cite

Searching techniques significantly determine the speed of getting the information or objects. Finding an object in a set is related to membership checking. In the case of massive data, it needs an appropriate technique to search an object accurately and faster. This research implements searching methods, namely Bloom Filter and Sequential Search algorithms, to find objects in a set of data. It aims to improve our system getting a proper item. Due to the possibility of False-Positive existence as a result of Bloom filter technique, there is a potentially inaccurate representation to object sought. Some parameters are influencing False-Positive, namely the number of objects, available bits, and the number of mappedbit. A Combination of those parameters could decrease the level of False-Positive and improve their accuracy and faster accessibility. In this research, we use three data object variations with the biggest object size of 2000000. Cached objects used in our experiments are between 2 -20% of variation from the generated objects. The best results with the lowest False-Positive is a combination of bit = 8, mapped bit = 7, and 6% of cache size from 2000000 generated objects.

show abstract

Section: Introductionmentioning

confidence: 99%