A strategy of finding surprising patterns over data stream without prior knowledge about surprising patterns requires comparing the newly arrived pattern with all kinds of patterns which have emerged. It means all kinds of patterns which have emerged should be stored in memory for the following comparison to ensure real-time response. The patterns needed to be stored in memory are potentially unbounded in size. But the memory resource is limited. To deal with the limited memory problem, we propose a strategy called diversity-based load shedding strategy in this paper. This strategy sheds load with the granularity of pattern and aims to maximize diversity. The experiments on real datasets containing millions of data items demonstrate the fea...
Maintaining frequency counts for data streams has attracted much interest among the research communi...
Abstract. Discovering frequent patterns over event sequences is an important data mining problem. Ex...
Frequent-pattern mining from databases has been widely studied and frequently observed. Unfortunatel...
Most algorithms that focus on discovering frequent patterns from data streams assumed that the machi...
International audienceMany applications generate data streams where online analysis needs are essent...
We consider the problem of resource allocation in mining multiple data streams. Due to the large vol...
AbstractLoad shedding is imperative for data stream processing systems in numerous functions as data...
Continuously identifying pre-defined patterns in a streaming time series has strong demand in variou...
Pattern management is an important task in data stream mining and has attracted increasing attention...
In diverse applications ranging from stock trading to traffic mon-itoring, popular data streams are ...
In this demo, we show that intelligent load shedding is essential in achieving optimum results in mi...
National audienceIn recent years the emergence of new real-world applications such as network traffi...
With increasing availability and power of parallel computational resources, attention is drawn to th...
In recent years the emergence of new real-world applications such as network traffic monitoring, int...
Traditional data mining techniques expect all data to be managed within some form of persistent data...
Maintaining frequency counts for data streams has attracted much interest among the research communi...
Abstract. Discovering frequent patterns over event sequences is an important data mining problem. Ex...
Frequent-pattern mining from databases has been widely studied and frequently observed. Unfortunatel...
Most algorithms that focus on discovering frequent patterns from data streams assumed that the machi...
International audienceMany applications generate data streams where online analysis needs are essent...
We consider the problem of resource allocation in mining multiple data streams. Due to the large vol...
AbstractLoad shedding is imperative for data stream processing systems in numerous functions as data...
Continuously identifying pre-defined patterns in a streaming time series has strong demand in variou...
Pattern management is an important task in data stream mining and has attracted increasing attention...
In diverse applications ranging from stock trading to traffic mon-itoring, popular data streams are ...
In this demo, we show that intelligent load shedding is essential in achieving optimum results in mi...
National audienceIn recent years the emergence of new real-world applications such as network traffi...
With increasing availability and power of parallel computational resources, attention is drawn to th...
In recent years the emergence of new real-world applications such as network traffic monitoring, int...
Traditional data mining techniques expect all data to be managed within some form of persistent data...
Maintaining frequency counts for data streams has attracted much interest among the research communi...
Abstract. Discovering frequent patterns over event sequences is an important data mining problem. Ex...
Frequent-pattern mining from databases has been widely studied and frequently observed. Unfortunatel...