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...

Bloom filters are used for answering queries on set membership. In this data structure, the whole element is not stored at the hashed address. Only a few bits are set in an array. Given a set S of cardinality n, we store it in an array of m bits using k hash functions h1(), . . . , hk(). Initially, all the cells in the array are set to 0. Then, for each element in the set, x ∈ S, for each 1 ≤ i...

A bloom filter is a probabilistic data structure used to test whether an element is a member of a set. The bloom filter shares some similarities to a standard hash table but has a higher storage efficiency. As a drawback, bloom filters allow the existence of false positives. These properties make bloom filters a suitable candidate for storing topological information in large-scale mobile ad hoc...

The large shallow eutrophic Lake Taihu in China has long suffered from eutrophication and toxic cyanobacterial blooms. Despite considerable efforts to divert effluents from the watershed, the cyanobacterial blooms still reoccur and persist throughout summer. To mitigate cyanobacterial bloom pollution risk, a large scale integrated monitoring and forecasting system was developed, and a series of...

The microbial communities associated with algal blooms play a pivotal role in organic carbon, nitrogen and phosphorus cycling in freshwater ecosystems. However, there have been few studies focused on unveiling the dynamics of bacterial and fungal communities during the outbreak and decline of algal blooms in drinking water reservoirs. To address this issue, the compositions of bacterial and fun...

Counting Bloom Filters (CBFs) are widely employed in many applications for fast membership queries. CBF works on dynamic sets rather than a static set via item insertions and deletions. CBF allows false positive, but not false negative. The Bh-Counting Bloom Filter (Bh-CBF) and Variable Increment Counting Bloom Filter (VI-CBF) are introduced to reduce the false positive probability, but they su...

In distributed hash tables, much communication traffic comes from multi-word searches. The aim of this work is to reduce the amount of traffic by using a bloom filter, which is a space-efficient probabilistic data structure used to test whether or not an element is a member of a set. However, bloom filters have a limited role if several sets have different numbers of elements. In the proposed m...

A computer science student is required to progress from a novice programmer to a proficient developer through the programming fundamentals sequence of subjects. This paper deals with the capturing and representation of learning progression. The key contribution is a web-based interactive tutorial that enables computer science tutors and lecturers to practice applying the Bloom Taxonomy in class...

In mobile ad hoc networks, it is challenging to implement efficient and scalable multicast because of multicast packet forwarding and difficulty in group membership management. To overcome this problem we propose an efficient geographic multicast protocol (EGMP).To achieve this a network wide zone based bidirectional tree is constructed. Position is determined by which the tree structure and ro...