Scalable data summarization on big data

Scalable Keyword Search on Big RDF Data

Keyword search is a useful tool for exploring large RDF datasets. Existing techniques either rely on constructing a distance matrix for pruning the search space or building summarization from the RDF graphs for query processing. In this work, we show that existing techniques have serious limitations in dealing with realistic, large RDF graphs with tens of millions of triples. Furthermore, the e...

Scalable Data Cube Analysis over Big Data

Data cubes are widely used as a powerful tool to provide multidimensional views in data warehousing and On-Line Analytical Processing (OLAP). However, with increasing data sizes, it is becoming computationally expensive to perform data cube analysis. The problem is exacerbated by the demand of supporting more complicated aggregate functions (e.g. CORRELATION, Statistical Analysis) as well as su...

Knowledge Summarization for Scalable Semantic Data Processing

Scalable semantic data processing has become a crucial issue for practical applications of the Semantic Web. In this paper, we propose an approach of scalable semantic data processing by knowledge summarization. The main idea is to express scalable semantic data on different abstraction and summarization levels to reduce their cardinalities, so that they can be processed efficiently. The notion...

Big Data Summarization Using Semantic Feture for IoT on Cloud

Data management is a crucial aspect in the Internet of Things (IoT) on Cloud. Big data is about the processing and analysis of large data repositories on Cloud computing. Big document summarization method is an important technique for data management of IoT. Traditional document summarization methods are restricted to summarize suitable information from the exploding IoT big data on Cloud. This...

Scalable Big Data Systems: Architectures and Optimizations