Efficient processing of continuous spatial-textual queries over geo-textual data stream

Due to the extensive use of social media and mobile devices, unbounded and massive data is generated continuously. The need to process this big data is increasing day by day. The traditional data processing algorithms fail to cater to the need of processing data generated by various applications such as digital geo-based advertising, and recommendation systems. There has been a high demand to process continuous spatial fuzzy textual queries over data stream of spatial-textual objects with high density by present locationbased and social network-based service applications. For the spatialkeyword data stream, the performance plays a vital role as the geo information and keyword description matching is needed for every incoming streaming object. The various continuous geo-keyword query processing methods normally lack the support for fuzzy keyword matching when processing the objects from the geo-textual data stream. The edit distancebased approach with the adaptive partitioning tree index for the queries is used for fuzzy string matching and it outperforms than the existing approaches in storage cost and query performance cost