Toward A New Model Of Indexing Big Uncertain Data

Nowadays, due to the growth of technology, there is a mass production of data (of large volume), available in a digital form. These currently available data are not unified but appear in different formats and types. The diversity of the data is based on the type of information, they contain, such as text, image, video and audio documents and also on their sources, such as data from sensors (high variety). In addition, with the expansion of the Internet and the World Wide Web, the majority of these data become the publicity available for a wide range of users (at high speed). The main objective of this work is to propose an efficient Big Uncertainty Web Data Services Indexing Model able to reasoning in uncertain data environment. More concretely, the proposed approach is based on two main phases: the first one consists on processing uncertain data in the syntactic indexing phase and the second one consists on the semantic indexing phase. These two phases are presented as two algorithms syntactic and semantic.