Intelligent Software Defined Storage

Berkovich, Simon

doi:10.1109/com.geo.2014.31

Cited by 3 publications

(5 citation statements)

References 4 publications

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“…43 [2] 108 [3] 199 [4] 1920 [5] 3840 [6] 176236 [7] 176327 [8] 178048 [9] 179968 [10] 442567 [11] 444288 [12] 446208 [13] 819072 [14] 816896 [15] 7868160…”

Section: Resultsmentioning

confidence: 99%

“…202947 [3] 203166 [4] 204288 [5] 202496 [6] 442563 [7] 442782 [8] 443904 [9] 446208 [10] 799134 [11] 800256 [12] 802560 [13] 1697280 [14] 1699584 [15] 6295296 1000  00000000000001111101000 480  00000000000000111100000…”

Section: Resultsmentioning

confidence: 99%

“…The other problems of that project is they use traditional way of GCTHT for their works and authors [1,2] did not use the fuzzy logics of Golay Code, and another problem of these methods is the order of the features is critical for clustering the big data that means if we change the order of the features, the result will be changed in our output, so they need another algorithm for finding the best order of features or even they need to change the order of feature manually to find out best results. Intelligent Software Defined Storage is another example of usage of Golay Code Transformation Table [3].…”

Section: Related Workmentioning

confidence: 99%

“…43 [2] 108 [3] 199 [4] 1920 [5] 3840 [6] 176236 [7] 176327 [8] 178048 [9] 179968 [10] 442567 [11] 444288 [12] 446208 [13] 819072 [14] 816896 [15] 7868160 1000  [0] 5244416 [1] 202860 [2] 202947 [3] 203166 [4] 204288 [5] 202496 [6] 442563 [7] 442782 [8] 443904 [9] 446208 [10] 799134 [11] 800256 [12] 802560 [13] 1697280 [14] 1699584 [15] 6295296 1000  00000000000001111101000 480  00000000000000111100000 Figure 5 : Sample result of FuzzyFind Dictionary and shows that two indices with hamming distance of less or equal than two has at least one same mapping index.…”

Section: For Testing Fuzzyfind Dictionary We Needs To Test All Cases mentioning

confidence: 99%

See 3 more Smart Citations

Construction of FuzzyFind Dictionary using Golay Coding Transformation for Searching Applications

Kowsari¹,

Yammahi²,

Bari³

et al. 2015

ijacsa

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searching through a large volume of data is very critical for companies, scientists, and searching engines applications due to time complexity and memory complexity. In this paper, a new technique of generating FuzzyFind Dictionary for text mining was introduced. We simply mapped the 23 bits of the English alphabet into a FuzzyFind Dictionary or more than 23 bits by using more FuzzyFind Dictionary, and reflecting the presence or absence of particular letters. This representation preserves closeness of word distortions in terms of closeness of the created binary vectors within Hamming distance of 2 deviations. This paper talks about the Golay Coding Transformation Hash Table and how it can be used on a FuzzyFind Dictionary as a new technology for using in searching through big data. This method is introduced by linear time complexity for generating the dictionary and constant time complexity to access the data and update by new data sets, also updating for new data sets is linear time depends on new data points. This technique is based on searching only for letters of English that each segment has 23 bits, and also we have more than 23-bit and also it could work with more segments as reference table.

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“…43 [2] 108 [3] 199 [4] 1920 [5] 3840 [6] 176236 [7] 176327 [8] 178048 [9] 179968 [10] 442567 [11] 444288 [12] 446208 [13] 819072 [14] 816896 [15] 7868160…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: For Testing Fuzzyfind Dictionary We Needs To Test All Cases mentioning

confidence: 99%

See 2 more Smart Citations

Construction of FuzzyFind Dictionary using Golay Coding Transformation for Searching Applications

Kowsari¹,

Yammahi²,

Bari³

et al. 2015

ijacsa

View full text Add to dashboard Cite

show abstract

“…Intelligent software defined storage is also an example for application that takes advantage of finding the most frequent items by enhancing the resolution of multiple responses produced by FuzzyFind procedures [18].…”

Section: Majority Algorithms In the Stream Processingmentioning

confidence: 99%

A cyber-physical algorithm for selecting a prevalent element from big data streams

Alhudhaif¹,

Tong²,

Berkovich³

2015

GSTF J Comput

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Abstract-The paper presents a new algorithm for processing big data streams, which mimics the surmised physical design in the brain. The algorithm is very suitable for extracting prevalent information items, even at rather low frequencies of about several percents. The developing data driven process can be used to effectually realize various types of large-scale computational intelligence operations.

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A Cyber-Physical Stream Algorithm for Intelligent Software Defined Storage

Alhudhaif¹,

Yammahi²,

Tong³

et al. 2015

IJCA

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The paper presents a new Cyber Physical Stream algorithm for selecting a predominant item from very large collections of data. The algorithm effectively works for frequencies of the predominant items starting from about 2%. The algorithm is focused on querying massive data in Software-Defined Storage combined with Fuzzy indexing method. Experiment results show that Cyber Physical Stream algorithm improves the accuracy and efficiency over previous efforts.

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Intelligent Software Defined Storage

Cited by 3 publications

References 4 publications

Construction of FuzzyFind Dictionary using Golay Coding Transformation for Searching Applications

Construction of FuzzyFind Dictionary using Golay Coding Transformation for Searching Applications

A cyber-physical algorithm for selecting a prevalent element from big data streams

A Cyber-Physical Stream Algorithm for Intelligent Software Defined Storage

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