Graph data is found in numerous domains such as for the analysis of social networks, sensor networks, bioinformatics, industrial systems, and chemistry. Analyzing graphs to identify useful and interesting patterns is an important research area. It helps understanding graphs, and hence support decision making. Since two decades, many graph mining algorithms have been proposed to identify patterns such as frequent subgraphs, paths, cliques, and trees. But most of them assume that graphs are static. This simplifying assumption makes it easy to design algorithms but discard information about how graphs evolve. This article provides a detailed survey of techniques for mining interesting patterns in dynamic graphs, which can serve both as an introduction and as a guide to recent advances and opportunities in this research area. The main tasks related to mining patterns in dynamic graphs are reviewed such as discovering frequent subgraphs, evolution rules, motifs, subgraph sequences, recurrent and triggering patterns, and trend sequences. In addition, an overview of strategies and approaches to solve dynamic graph mining problems is presented, and their advantages and limitations are highlighted. Various extensions are also discussed such as to discover patterns in data streams and big data. Finally, the article mentions several research opportunities.
Most published research on system behavior and workload characterization has been based on either Unix systems or large, usually IBM, mainframe systems. It is reasonable to believe that user behaviors and workloads are di erent for PC systems. Further, the aspects of system design and most needing study have c hanged from the mainframes dominant in the 1960s and 1970s, and the Unix systems that became so popular in the 1980s to the PCs that seem to be rapidly taking over many o r most aspects of computing. Our analysis focuses instead on Windows95, which is currently the most widely used computer operating system; the newly released Windows98 is very similar. In this paper, we describe our workload analysis based on 36 sets of traces collected from Intel Pentium based PCs running the Microsoft Windows95 operating system. Following the discussion of our Windows95 trace data, we present some descriptive and statistical characterization of this data, directed principally at user behavior and le system behavior.
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