The Community for Technology Leaders
Green Image
Issue No. 04 - April (2016 vol. 27)
ISSN: 1045-9219
pp: 1212-1225
Yu Hua , Wuhan National Laboratory for Optoelectronics, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China
Hong Jiang , Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE
Dan Feng , Wuhan National Laboratory for Optoelectronics, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China
ABSTRACT
The challenges of handling the explosive growth in data volume and complexity cause the increasing needs for semantic queries. The semantic queries can be interpreted as the correlation-aware retrieval, while containing approximate results. Existing cloud storage systems mainly fail to offer an adequate capability for the semantic queries. Since the true value or worth of data heavily depends on how efficiently semantic search can be carried out on the data in (near-) real-time, large fractions of data end up with their values being lost or significantly reduced due to the data staleness. To address this problem, we propose a near-real-time and cost-effective semantic queries based methodology, called FAST. The idea behind FAST is to explore and exploit the semantic correlation within and among datasets via correlation-aware hashing and manageable flat-structured addressing to significantly reduce the processing latency, while incurring acceptably small loss of data-search accuracy. The near-real-time property of FAST enables rapid identification of correlated files and the significant narrowing of the scope of data to be processed. FAST supports several types of data analytics, which can be implemented in existing searchable storage systems. We conduct a real-world use case in which children reported missing in an extremely crowded environment (e.g., a highly popular scenic spot on a peak tourist day) are identified in a timely fashion by analyzing 60 million images using FAST. FAST is further improved by using semantic-aware namespace to provide dynamic and adaptive namespace management for ultra-large storage systems. Extensive experimental results demonstrate the efficiency and efficacy of FAST in the performance improvements.
INDEX TERMS
Data analysis, Semantics, Real-time systems, Correlation, Accuracy, Cloud computing, Smart phones
CITATION

Y. Hua, H. Jiang and D. Feng, "Real-Time Semantic Search Using Approximate Methodology for Large-Scale Storage Systems," in IEEE Transactions on Parallel & Distributed Systems, vol. 27, no. 4, pp. 1212-1225, 2016.
doi:10.1109/TPDS.2015.2425399
504 ms
(Ver 3.3 (11022016))