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dc.contributor.authorSheshbolouki, Aida 15:27:35 (GMT) 15:27:35 (GMT)
dc.description.abstractThis thesis introduces two main-memory systems sGrapp and sGradd for performing the fundamental analytic tasks of biclique counting and concept drift detection over a streaming graph. A data-driven heuristic is used to architect the systems. To this end, initially, the growth patterns of bipartite streaming graphs are mined and the emergence principles of streaming motifs are discovered. Next, the discovered principles are (a) explained by a graph generator called sGrow; and (b) utilized to establish the requirements for efficient, effective, explainable, and interpretable management and processing of streams. sGrow is used to benchmark stream analytics, particularly in the case of concept drift detection. sGrow displays robust realization of streaming growth patterns independent of initial conditions, scale and temporal characteristics, and model configurations. Extensive evaluations confirm the simultaneous effectiveness and efficiency of sGrapp and sGradd. sGrapp achieves mean absolute percentage error up to 0.05/0.14 for the cumulative butterfly count in streaming graphs with uniform/non-uniform temporal distribution and a processing throughput of 1.5 million data records per second. The throughput and estimation error of sGrapp are 160x higher and 0.02x lower than baselines. sGradd demonstrates an improving performance over time, achieves zero false detection rates when there is not any drift and when drift is already detected, and detects sequential drifts in zero to a few seconds after their occurrence regardless of drift intervals.en
dc.publisherUniversity of Waterlooen
dc.subjectstreaming graphen
dc.subjectdata miningen
dc.subjectgraph analyticsen
dc.subjectsubgraph listingen
dc.subjectdrift detectionen
dc.subjectgraph modelen
dc.subjectmain memory systemen
dc.subjectexplainable analyticsen
dc.subjectgraph patternsen
dc.titleMining Butterflies in Streaming Graphsen
dc.typeDoctoral Thesisen
dc.pendingfalse R. Cheriton School of Computer Scienceen Scienceen of Waterlooen
uws-etd.degreeDoctor of Philosophyen
uws.contributor.advisorOzsu, M. Tamer
uws.contributor.affiliation1Faculty of Mathematicsen

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