Abstract
Nowadays there is an abundance of tools and systems to analyze large graphs. In general, the goal is to summarize the graph and discover interesting patterns hidden in the graph. On the other hand, there is a lot of data stored on DBMSs that can be potentially analyzed as graphs. External graph data sets can be quickly loaded. It is feasible to load data quickly and that SQL can help prepare graph data sets from raw data. In this paper, we show SQL queries on a graph stored in relational form as triples can reveal many interesting properties and patterns on the graph in a more flexible manner and efficient than existing systems. We explain many interesting statistics on the graph can be derived with queries combining joins and aggregations. On the other hand, linearly recursive queries can summarize interesting patterns including reachability, paths, and connected components. We experimentally show exploratory queries can be efficiently evaluated based on the input edges and it performs better than Spark. We also show that skewed degree vertices, cycles and cliques are the main reason exploratory queries become slow.
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Al-Amin, S.T., Ordonez, C., Bellatreche, L. (2018). Big Data Analytics: Exploring Graphs with Optimized SQL Queries. In: Elloumi, M., et al. Database and Expert Systems Applications. DEXA 2018. Communications in Computer and Information Science, vol 903. Springer, Cham. https://doi.org/10.1007/978-3-319-99133-7_7
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