Research On XML Ordered Regular Tree Pattern Optimization Method

With the advent of the era of big data,the popularity of cloud computing in various industries,the demand for stream data processing from various real-time systems such as Web application,real-time tracking monitoring,business intelligence data management and has become more and more complicated.Such big data not only has the characteristics of large data volume,data organization and semantic complexity,but also has less valuable data.But if you aggregate them and analyze them by specific rules,you can generate a lot of valuable information.Since the value of most real-time data will decrease over time,this requires that the real-time system needs to accurately and efficiently identify the target information in the event flow according to the detection rules.The goal of complex event processing technology is to timely and accurately detect event sequences in the event stream that conform to a specific pattern.Therefore,the event detection pattern matching algorithm,which can obtain more efficient and can solve structures and semantically more complex queries,becomes the mainstream method of processing stream data.Obviously,event stream processing is a generalization of stream data processing,so the analysis and processing techniques of complex events can use stream data query methods,so various stream data query modes can be used to describe the patterns of complex events.Therefore,the development of event patterns that describe more capable capabilities and improved event detection efficiency has become the mainstream method of processing streaming data.Today,complex events consist of a series of events.There are multiple relationships between events.Operators such as continuous,disjunctive,and Kelin closures are used to specify the connection between events,through formal pattern matching.Detect the occurrence of complex events.However,streaming data often exhibits semi-structured characteristics in real-time operating systems.This feature complicates events.The complexity of this stream data structure also leads to the complexity of events.For complex events from semi-structured data streams,a class of event patterns named regular tree patterns and corresponding pattern matching algorithms were proposed.It can not only constrain the structural relationships between events,but also can constrain timing relationships and have a strong ability to describe complex events.So as to improve the efficiency of complex event detection in stream data,this paper introduces an event pattern and its pattern matching algorithm called ordered regular tree pattern to detect stream data with semi-structured properties,but the existence of ordering After the generation of redundancy and the unnecessary backtracking caused by the operator's matching process,so as to further improve the query processing efficiency of the ordered regular tree pattern,this paper proposes astructural and temporal constraint on ordered regular tree patterns using DTD's structural information and semantic information,minimization of tree models and optimization of overwrite optimization methods.This method uses DTD filtering to eliminate invalidation and redundancy tree pattern,and the reduction of the candidate path in the matching process by converting the ancestor generation relationship into a determined parent-child relationship path.And according to the experimental results the optimization method can improve the execution efficiency of the regular tree pattern query.