Metadata-Driven Cloud Analytics Intelligence Framework
Keywords:
Cloud Analytics,Metadata-Driven Architecture,Scalable Data Operations,Intelligent Data Frameworks,Unified Data Management,Cloud Data Intelligence,Analytics Automation,Operational Scalability,Metadata Integration,Distributed Cloud Computing.Abstract
The Intelligence Layer for Cloud Analytics Operations supports scalable analytics operations over large volumes of industrial metadata for a wide range of users. It harnesses the processing and storage power of cloud systems, integrating globally distributed information. The Intelligence Layer transforms metadata into business intelligencetasks by making it intelligible for decision making by enabling familiarizations through browsing as well as diversity in ,choosing different analytics for generating preferred results. Metadata+-based intelligence tasks can include business processes, organizations, locations, data provenance, archives, visualizations and GST. The analytics intelligence operations are made available to users through a service catalog aligned with the service model in cloud computing. The analytics on nearchat-service in cloud computing enables users to get information on Global Service Tax (GST) in the country. It provides GST-related information such as activities liable to GST, percentage of GST, location of GST payments and a free-service area for browsing various rules and regulations relating to GST. Metadata+-based business-intelligence operations support complete business-processtransport operations for an organization.
The Intelligence Layer for Cloud Analytics Operations provides scalability for mining intelligent information from metadata. These analytics operations enable users to get information about the Metadata+ repository and to generate business intelligence using different pipelines. The key approaches for scaleable cloud-analytics operations are distributed-processing and distributed-storage approaches, where cloud computing supports elastic scaling by providing the required processing, storage and network assets within a portal under a pay-per-use model. The distribution of big data over multiple data stores using a data-storage distribution scheme enables faster operation execution through Hadoop and HBase on the data stored in HDFS or HBase, respectively.
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