Nutrient enrichment in water and sediments due to excessive anthropogenic activities in recent years has caused excessive algal growth in the Caspian Sea. The current study was conducted to determine the abundance of phytoplankton community, the dominant species and chlorophyll-a [Chl-a] concentration during algal blooms in the Iranian coastal waters of Caspian Sea through four seasons from 201...

A Bloom filter is a compact data structure that supports membership queries on a set, allowing false positives. The simplicity and the excellent performance of a Bloom filter make it a standard data structure of great use in many network applications. In reducing the false positive rate of a Bloom filter, it is well known that the size of a Bloom filter and accordingly the number of hash indice...

Bloom filters are a well known data-structure that supports approximate set membership queries that report no false negatives. Each element in the universe represented by the bloom filter is associated with k random bits in the structure. Traditional bloom filters, therefore, require k non-local memory operations to insert an element or perform a lookups. For very large bloom filters, these k l...

Nutrient enrichment in water and sediments due to excessive anthropogenic activities in recent years has caused excessive algal growth in the Caspian Sea. The current study was conducted to survey the abundance of phytoplankton, its dominant species and chlorophyll-a [Chl-a] concentration in algal bloom aspect in the Iranian coastal water of Caspian Sea during four seasons from 2013 to 2014. Th...

Probabilistic data structures are so popular in membership queries, network applications, and so on. Bloom Filter and Cuckoo Filter are two popular space efficient models that incorporate in set membership checking part of many important protocols. They are compact representation of data that use hash functions to randomize a set of items. Being able to store more elements while keeping a reaso...

A Bloom filter is a simple data structure supporting membership queries on a set. The standard Bloom filter does not support the delete operation, therefore, many applications use a counting Bloom filter allowing the deletion. This paper proposes a generalization of the counting Bloom filters approach, called “autoscaling Bloom filters”, which allows elastic adjustment of its capacity with prob...

Bloom filters are probabilistic data structures commonly used for approximate membership problems in many areas of Computer Science (networking, distributed systems, databases, etc.). With the increase in data size and distribution of data, problems arise where a large number of Bloom filters are available, and all them need to be searched for potential matches. As an example, in a federated cl...

Cyanobacteria blooms are a major environmental issue worldwide. Our understanding of the biophysical processes driving cyanobacterial proliferation and the ability to develop predictive models that inform resource managers and policy makers rely upon the accurate characterization of bloom dynamics. Models quantifying relationships between bloom severity and environmental drivers are often calib...