Interactive Graph Stream Analytics in Arkouda

Data from emerging applications, such as cybersecurity and social networking, can be abstracted graphs whose edges are updated sequentially in the form of a stream. The challenging problem interactive graph stream analytics is quick response queries on terabyte beyond data end users. In this paper, succinct efficient double index structure designed to build sketch meet general queries. A single pass model, which includes building, distributed based analysis algorithms regression approximation solution generation, developed, typical algorithm—triangle counting—is implemented evaluate proposed method. Experimental results power law normal distribution streams show that our method generate accurate (mean relative error less than 4%) with high performance. All methods code have been an open source framework, Arkouda, available GitHub repository, Bader-Research. This work provides large rapidly growing Python community powerful way handle using their laptops.