Abstract
Unlike relational databases and XML documents, most of graphs are not given their own schemas. If we can extract a schema from a graph efficiently, we can take advantage of the extracted schema for query optimization, structure browsing, and so on. In this paper, we consider extracting schemas from large graphs by using utility function. Although reasonable schemas can be extracted by the utility function, the major problem of the utility function is its computation cost. In this paper, we propose a schema extraction algorithm based on (a) a novel utility function called local utility function and (b) parallelization. Experimental results show that our algorithm can extract schemas from graphs more efficiently without losing quality of schemas.
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This work was supported by JSPS KAKENHI Grant Number JP17K00150.
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Sekine, Y., Suzuki, N. (2018). Extracting Schemas from Large Graphs with Utility Function and Parallelization. In: Liu, C., Zou, L., Li, J. (eds) Database Systems for Advanced Applications. DASFAA 2018. Lecture Notes in Computer Science(), vol 10829. Springer, Cham. https://doi.org/10.1007/978-3-319-91455-8_13
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DOI: https://doi.org/10.1007/978-3-319-91455-8_13
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