The Bloom filter is a space efficient randomized data structure for representing a set and supporting membership queries. Bloom filters intrinsically allow false positives. However, the space savings they offer outweigh the disadvantage if the false positive rates are kept sufficiently low. Inspired by the recent application of the Bloom filter in a novel multicast forwarding fabric, this paper...

Modern databases tailored to highly distributed, fault tolerant management of information for big data applications exploit a classical data structure for reducing disk and network I/O as well as for managing data distribution: The Bloom filter. This data structure allows to encode small sets of elements, typically the keys in a key-value store, into a small, constant-size data structure. In or...

We provide a simple method for improving the performance of the recently introduced learned Bloom filters, by showing that they perform better when the learned function is sandwiched between two Bloom filters.

A Bloom filter is a space-efficient randomized data structure representing a set for membership queries. Faults in Bloom filters, however, cannot guarantee no false negatives. In this paper, we present a simple redundancy scheme for detecting false negatives and tolerating false positives induced by faults in Bloom filters during normal operation. A spare hashing unit with a simple coding techn...

We present the Bitwise Bloom Filter, a data structure for maintaining counts for a large number of items. The bitwise filter is an extension of the Bloom filter, a space-efficient data structure for storing a large set efficiently by discarding the identity of the items being held while still being able to determine whether it is in the set or not, with high probability. We show how this idea c...

Regarding the fact that, there are large number of resources in cloud computing system. Developing effective solutions for their queries are very important. The success of cloud computing systems fundamentally relies on the efficient use of resources. Discovering and managing resources is one important step in the system effecting. In this paper, a method of Agent-based resource discovery using...

In recent work, the authors introduced a data structure with the same functionality as a counting Bloom filter (CBF) based on fingerprints and the d-left hashing technique. This paper describes dynamic bit reassignment, an approach that allows the size of the fingerprint to flexibly change with the load in each hash bucket, thereby reducing the probability of a false positive. This technique al...

Content Centric Network (CCN) is an emerging information centric network design. There are a lot of studies focusing on caching, applications and so on to make CCN getting perfect, while the scalability of Pending Interest Table (PIT) is still one of the primary concerns of high-speed forwarding. In this paper, we make an attempt to compress PIT with bloom filter. This paper has two contributio...

The unreliability data reading such as noise, missed reading and duplicate reading that were produced by RFID reader has become the primary factor limiting the widespread adoption of RFID technology. It is compulsory to filter the raw data to maintain its reliability of data reading because a slight decreasing of effective read rate will reduce the accuracy and reliability of RFID. One of the a...

Privacy-preserving record linkage with Bloom filters has become increasingly popular in medical applications, since Bloom filters allow for probabilistic linkage of sensitive personal data. However, since evidence indicates that Bloom filters lack sufficiently high security where strong security guarantees are required, several suggestions for their improvement have been made in literature. One...