Delivering QOS in XML Data Stream Processing Using Load Shedding

In recent years, we have witnessed the emergence of new types of systems that deal with large volumes of streaming data. Examples include financial data analysis on feeds of stock tickers, sensorbased environmental monitoring, network track monitoring and click stream analysis to push customized advertisements or intrusion detection. Traditional database management systems (DBMS), which are very good at managing large volumes of stored data, are not suitable for this, as streaming needs low-latency processing on live data from push-based sources. Data Stream Management Systems (DSMS) are fast emerging to address this new type of data, but faces challenging issues, such as unpredictable data arrival rate. On bursty mode, processing need surpasses available system capacity affecting the Quality of Service (QoS) adversely. The system overloading is even more acute in XML data streams compared to relational streams due to its extra resource requirements for data preparation and result construction. The main focus of this paper is to find out suitable ways to process this high volume of data streams dealing with the spikes in data arrival gracefully, under limited or fixed system resources in the XML stream context.