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Skyline queries, which return the objects that are better than or equal in all dimensions and better in at least one dimension, are useful in many decision making and monitor applications. With the number of dimensions increasing and continuous large volume data arriving, mining the approximate skylines over data stream under control of losing quality is a more meaningful problem. In this paper, firstly,...
Computer networks have become ubiquitous and integral part of the nation’s critical infrastructure. How to grasp the real-time overall situation of the network security is very noteworthy to study. An increasing number of network security systems have been deployed in the backbone and the gateways of enterprises, including various Netflow systems, IDS, VDS, VS and firewalls. These products make great...
Large-scale distributed monitor networks are in face of the challenge of tremendous data communication costs due to the resource restriction. Prediction models can be used to reduce communication cost over the networks. A framework is proposed which provides a mechanism to maintain adaptive prediction models that significantly reduce communication cost over the distributed environment while still...
In many applications, monitor equipments produce a large number of time series which need to be analyzed. Segments of time series demonstrating dominating advantages over others are often of particular interest. Based on the measurement of volume attribute, the existing research about interval skyline was proposed. The query returns the time series which are not dominated by any other time series...
Processing multi-extreme value queries efficiently over data streams is important for data analysis in real-time environment. Cost-efficient processing of continuous extreme values queries over sliding windows, especially about resource sharing, is considered. Firstly, an effective storage structure to minimize the number of elements to be kept for queries is given. We prove the average the cardinality...
Large-scaled distributed monitoring systems are in face of the challenge of massive data and resource restriction. Prediction models can be used to reduce communication cost over the Net. A framework is proposed which provides a mechanism to maintain adaptive prediction models that significantly reduce communication cost over the distributed environment while still guaranteeing sufficient precision...
In many data stream oriented application circumstances, frequency distribution of elements meets heavy-tailed distribution, which means most elements have small frequencies and few elements have high frequencies. Consequently, traditional counting bloom filters (CBF) and dynamic count filters (DCF) cannot represent the data effectively. In order to improve the data processing efficiency, this paper...
Emerging hardware and communication technologies enable new data stream applications that deal efficiently with very high rates of data updates. In this paper, we propose a novel index structure, termed the QDM-tree (quantized R*-tree with double memos), to support efficient similarity search over data streams. We integrate quantized minimum bounding spheres (QMBSs) and quantized minimum bounding...
Burst detection is an inherent problem for data streams, so it has attracted extensive attention in research community due to its broad applications. One of the basic problems in burst detection is how to count frequencies of all elements in data stream. This paper presents a novel solution based on Improved Counting Bloom Filter, which is also called BCBF+HSet. Comparing with intuitionistic approach...
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