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Data stream processing is becoming essential in most current advanced scientific or business applications as data production rates are increasing. Different companies compete to efficiently ingest high velocity data and apply some form of computation in order to make better business decisions. In order to successfully compete in this environment, companies(More)
The rapid growth of monitoring applications has led to unprecedented amounts of generated time series data. Data analysts typically explore such large volumes of time series data looking for valuable insights. One such insight is finding pairs of time series, in which subsequences of values exhibit certain levels of correlation. However, since exploratory(More)
Large graph datasets have caused renewed interest for graph partitioning. However, existing well-studied graph partitioners often assume that vertices of the graph are always active during the computation, which may lead to time-varying skewness for traversal-style graph workloads, like Breadth First Search, since they only explore part of the graph in each(More)
The use of smartphone devices over the past years seems to follow a growing trend. This great acceptance along with the endless possibilities that go hand to hand with having a mini computer at all times within reach, can explain this vast interest shown by solo developers and major companies in the mobile industry. As a result, many innovative applications(More)
The increasing popularity and ubiquity of various large graph datasets has caused renewed interest for graph partitioning. Existing graph partitioners either scale poorly against large graphs or disregard the impact of the underlying hardware topology. A few solutions have shown that the nonuniform network communication costs may affect the performance(More)
With data becoming available in larger quantities and at higher rates, new data processing paradigms have been proposed to handle high-volume, fast-moving data. Data Stream Processing is one such paradigm wherein transient data streams flow through sets of continuous queries, only returning results when data is of interest to the querier. To avoid the large(More)
Graph partitioning is an essential preprocessing step in distributed graph computation and scientific simulations. Existing well-studied graph partitioners are designed for static graphs, but real-world graphs, such as social networks and Web networks, keep changing dynamically. In fact, the communication and computation patterns of some graph algorithms(More)
With the explosion of large, dynamic graph datasets from various fields, graph partitioning and repartitioning are becoming more and more critical to the performance of many graph-based Big Data applications , such as social analysis, web search, and recommender systems. However, well-studied graph (re)partitioners usually assume a homogeneous and(More)