23-bit metaknowledge template towards Big Data knowledge discovery and management

Abstract

the global influence of Big Data is not only growing but seemingly endless. The trend is leaning towards knowledge that is attained easily and quickly from massive pools of Big Data. Today we are living in the technological world that Dr. Usama Fayyad and his distinguished research fellows discussed in the introductory explanations of Knowledge Discovery in Databases (KDD) [1] predicted nearly two decades ago. Indeed, they were precise in their outlook on Big Data analytics. In fact, the continued improvement of the interoperability of machine learning, statistics, database building and querying fused to create this increasingly popular science-Data Mining and Knowledge Discovery. The next generation computational theories are geared towards helping to extract insightful knowledge from even larger volumes of data at higher rates of speed. As the trend increases in popularity, the need for a highly adaptive solution for knowledge discovery will be necessary. In this research paper, we are introducing the investigation and development of 23 bit-questions for a Metaknowledge template for Big Data Processing and clustering purposes. This research aims to demonstrate the construction of this methodology and proves the validity and the beneficial utilization that brings Knowledge Discovery from Big Data.

Construction of FuzzyFind Dictionary using Golay Coding Transformation for Searching Applications

“…Yet the traditional use of GCTHT was hampering the ability to use fuzzy logic and we also realized the order of extracted metafeatures was critical for Big Data clusterization. Re-arranging the order altered the results of the output ultimately requiring us to either use another algorithm to derive the best order or manually reorder to achieve best the best results [1,2]. 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.…”

“…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.…”

“…This method is an implementation of Golay transformation in conjunction with 23 bits and allows for error correction with two hashes utilizing the overlapping hash values [14]. In previous experiments, we concluded using the hash table is the most efficient method way for accessing data in constant time [13], Since 2014 we have been able to use the GCTHT in constructing a 23-bit meta-knowledge template for Big Data Discovery allowing for meta-feature extraction for clustering Structured and Unstructured Data (text-based and multimedia) [1,2]. Yet the traditional use of GCTHT was hampering the ability to use fuzzy logic and we also realized the order of extracted metafeatures was critical for Big Data clusterization.…”

Novel Metaknowledge-Based Processing Technique for Multimediata Big Data Clustering Challenges

“…There are several challenges. The first challenge was to determine the specific classification algorithms applicable for our proposed ontology as noted in [7].…”

FSL-BM: Fuzzy Supervised Learning with Binary Meta-Feature for Classification

“…The Golay code generate hash table with six indices for labelling Binary Features (BF) as fuzziness labeled but FSL-BM is supervised learning is induced techniques of encoding and decoding into two labels or sometimes fuzzy logics classifiers by using probability or similarity. Between 2014 and 2015, the several studies addressed on using the Golay Code Transformation Hash table (GCTHT) in constructing a 23-bit meta-knowledge template for Big Data Discovery which allows for meta-feature extraction for clustering Structured and Unstructured Data (text-based and multimedia) [21], [19].…”