Efficient structuring of data in big data

Unstructured data brings enormous challenges to Big data. This is a major reason why traditional relational databases cannot meet the needs of Big data. This becomes a concern particularly when unstructured data from multiple sources are integrated in a query. This paper aims to structure the unstructured data in a structured form so that the data can be queried efficiently. Our research harnesses both context and usage patterns of data items to extract individual pieces of data and determine relationships between the extracted data. The transformed data and relationships placed in a structured schema can help to ensure good performance. Our experimental results identify that efficient and user-friendly structured data can be constructed from unstructured data by using context and usage patterns.