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Unstructured data mining has become topical recently due to the availability of high-dimensional and voluminous digital content (known as "Big Data") across the enterprise spectrum. The Relational Database Management Systems (RDBMS) have been employed over the past decades for content storage and management, but, the ever-growing heterogeneity in today's data calls for a new storage approach...
Analytics-as-a-Service (AaaS) has become indispensable because it affords stakeholders to discover knowledge in Big Data. Previously, data stored in data warehouses follow some schema and standardization which leads to efficient data mining. However, the Big Data epoch has witnessed the rise of structured, semi-structured, and unstructured data, a trend that motivated enterprises to employ the NoSQL...
One of the major challenges of the "Big Data" epoch is unstructured data mining. The problem arises due to the storage of high-dimensional data that has no standard schema. While knowledge discovery in database (KDD) algorithms were designed for data extraction, the algorithms best fit for structured data storages. Moreover, today, at the data storage level, NoSQL databases have been deployed...
The major challenge that the big data era brings to the services computing landscape is debris of unstructured data. The high-dimensional data is in heterogeneous formats, schemaless, and requires multiple storage APIs is some cases. This situation has made it almost impractical to apply existing data mining techniques which are designed for schema-based data sources in a knowledge discovery in database...
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