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Distributed stream processing systems must function efficiently for data streams that fluctuate in their arrival rates and data distributions. Yet repeated and prohibitively expensive load re-allocation across machines may make these systems ineffective, potentially resulting in data loss or even system failure. To overcome this problem, we instead propose(More)
Complex Event Processing (CEP) has emerged as a technology of choice for high performance event analytics in time-critical decision-making applications. Yet it is becoming increasingly difficult to support high-performance event processing due to the rising number and complexity of event pattern queries and the increasingly high velocity of event streams.(More)
This demonstration presents the Redoop infrastructure, the first fullfledged MapReduce framework with native support for recurring big data queries. Recurring queries, repeatedly being executed for long periods of time over evolving high-volume data, have become a bedrock component in most large-scale data analytic applications. Redoop is a comprehensive(More)
Distributed stream processing systems must function efficiently for data streams that fluctuate in their arrival rates and data distributions. Yet repeated and prohibitively expensive load reallocation across machines may make these systems ineffective, potentially resulting in data loss or even system failure. To overcome this problem, we propose a(More)
With the increasing complexity of data-intensive MapReduce workloads, Hadoop must often accommodate hundreds or even thousands of recurring analytics queries that periodically execute over frequently updated datasets, e.g., latest stock transactions, new log files, or recent news feeds. For many applications, such recurring queries come with user-specified(More)
Displaying large-scale 3D vector data in landscape map is very important, as 3D vector data can provide many important information, such as: precise geographic boundaries, areas, 3D text marks, different attributes identity, precise path and many important invisible information in real world (e.g.: underground things) and etc. In this paper, we present a(More)
Complex event processing is a popular technology for continuously monitoring high-volume event streams from health care to traffic management to detect complex compositions of events. These event compositions signify critical “application contexts” from hygiene violations to traffic accidents. Certain event queries are only appropriate in particular(More)
Using texture information in image classification is usually an extensive solution to obtain enhanced accuracy. However, the whole mass of texture information is not all that useful in improving the accuracy of the classified image. On the other hand, it brings a lot of noise from the texture information. Traditionally, Principle Components Analysis (PCA)(More)
Event processing applications from financial fraud detection to health care analytics continuously execute event queries with Kleene closure to extract event sequences of arbitrary, statically unknown length, called Complete Event Trends (CETs). Due to common event sub-sequences in CETs, either the responsiveness is delayed by repeated computations or an(More)