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## Novel hybrid approaches for big data recommendations

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Hybrid approaches (HAs) based on Fuzzy-Ontology are one of the core functions to efficiently handle and process massive dataset from diverse heterogeneous sources (DHS). HAs are becoming a noticeable trend in recent times due to its wide range of functionality to tackle all types of problem spaces. HAs are in the high demand for organisations to run their daily business operations as increasing numbers of numerous dataset occur every day. Therefore, big data communications are challenging in the traditional approaches to satisfy the needs of the consumer, as data are often not capturing into the database management systems (DBMS) in a seasonably enough fashion to enable their use subsequently. In addition, big data plays a vital role in containing a plenty of treasures for all the fields in the DBMS. However, one of the main challenges of HAs for the big data integration (DI) system is the inherent difficulty to coherently manage data from DHS, as different data sources have several standards and different major systems. It is practically challenging to integrate diverse data into a global schema to attain what looked forward to. The efficient management of HAs using an existing DBMS presents a challenge because of incompatibility and sometimes inconsistency of data structures. As a result, no common methodological approach is currently in existence to effectively solve every DI problem. The challenges of HAs raise the need to find a better way to efficiently integrate voluminous data from DHS. To handle and align massive dataset efficiently, the HAs algorithm with the logical combination of Fuzzy-Ontology along with big data analysis platform has shown the results in term of improved accuracy. The proposed novel HAs will combine the promising features of Fuzzy-Ontology to search, extract, filter, clean and integrate data to ensure that users can coherently create new consistent of datasets.

Chapter Contents:

• 5.1 Introduction
• 5.2 Context
• 5.3 The big data architecture
• 5.4 Different approaches to handle big data
• 5.4.1 Approaches to detect and reduce data inconsistency
• 5.5 Complexity and issues of big DI
• 5.5.1 Big data analysis and integration architecture
• 5.5.1.1 Combine two figures at work
• 5.5.1.2 Architectural patterns as development standards
• 5.6 Big DI using HAs based on Fuzzy-Ontology
• 5.7 Developing approaches for the crisp ontology
• 5.8 Developing HAs for Fuzzy-Ontology
• 5.9 Extracting the big data key business functions for the proposed HAs based on Fuzzy-Ontology
• 5.10 Identify the specification for the purpose HIDAs for big data
• 5.11 Real-world project: hypertension-specific diagnosis based on HIDAs
• 5.11.1 Data collection
• 5.11.2 Step 1: HIDAs contrivance and excellence
• 5.11.3 Step 2: determine and ascertain the necessity for fuzziness in hypertension diagnosis
• 5.11.4 Step 3: specify fuzzy-associated elements in hypertension data
• 5.11.5 Step 4: reusing the subsisting HIDAs resources
• 5.11.6 Step 5: reusing the subsisting Fuzzy-Ontology resources elements
• 5.11.7 Step 6: appropriate the subsisting of Fuzzy-Ontology elements
• 5.11.8 Step 7: identify appropriate Fuzzy-Ontology elements
• 5.11.9 Step 8: identify appropriate crisp ontology elements
• 5.11.10 Step 9: formalisation
• 5.11.11 Step 10: Fuzzy-Ontology result affirmation
• 5.11.12 Step 11: documentation and notes
• 5.12 Mathematical simulation of hypertension diagnosis based on Markov chain probability model
• 5.13 Analysis of result
• 5.14 Conclusion
• References

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