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Search Ranking for Heterogeneous Data over Dataspace
Traditional relational database systems queries works over structured data, whereas information retrieval systems are designed for additional versatile and flexible ranked keyword queries, works over unstructured data, Semi-structured, Streamed data, Social networking data and data without any format, known as heterogeneous data. However, several new and emerging applications need data management capabilities that mix the advantages both approaches. In this paper, we have proposed and initiate steps to combine heterogeneous statistics and information retrieval systems over Dataspace, which are the collection heterogeneous data, data from various sources and in different format. In several enterprise, the heterogeneity among information at different levels has becomes a difficult job. In an organization, data exist in structured, semi-structured or unstructured format or combination of all these. The existing heterogeneous data management systems are unsuccessful to deal with such information in efficient manner. Dataspace approach gives the solution of the problem of presence of heterogeneity in information and a variety of drawbacks of the existing systems. The main motive of this paper is to explain searching ranking mechanism in Dataspace. We also investigate how structured, semi structured or unstructured data can be take advantages for ranking of search on Web and Dataspace with their research challenges.
Algorithms, Dataspace, Information Retrieval, Internet of Thing, Ranking, Ranking of Database, Search Engines Algorithms.
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