Network Alarms Dynamic Weighted Association Rule Mining Algorithm

In the area of communication network management, fault management is an important and challenging task. The sticking point in fault management is fault detection, which relies on the knowledge about faults, especially, about the relation between alarms and faults. This necessary knowledge can be acquired through analyzing and interpreting the alarm information. So fault location is the aim and alarm information analyzing is the approach. Currently the main measure for fault management is using alarm correlation system, which is an expertise system. But the complexity and dynamics of the communication network leads to acquiring necessary knowledge to construct a correlation system for a special net is very difficult. With the background of the project, the alarm correlation in communication networks based on data mining, that is supported by National Natural Science Foundation of China, this thesis focused on mining alarm association rules in networks, including the alarm's preprocess, incremental mining association rules, the large-itemset post-weighed, searching algorithn of rules and the simulation and validation of the system of mining weighted alarm association rules in networks.The alarm's preprocess is carried out by three kinds of functions. The problem of alarm's synchronization was settled by setting time window and slide length. The alarms in the same time windows were regarded as an alarm transaction. The attributes of the alarm that reflect the faults were picked out to form an item of an alarm transaction. The redundant alarms were gotten rid of by alarm compressing.In the end, thesis proposes what DataClean make contrbution to data mining procedure.Alarm correlation mining and its incremental mining is core research of thesis. Based on ready-made mining algorithm proposes an emumerate_tree structure to handle massive and dynamic alarm data which improve data mining effiency and could be useful to lacate network fault on time. The most important thing is that such an algorithm brings weighted procedure to full rein in postprocess.The weighted_process aim at sorting out large itemset according to specific network environment. Existing sequential data mining techniques address the task of identifying possible correlations in sequences of alarms. The output sequence sets, however, may contain sequences which are not plausible from the point of view of network topology constraints. This paper presents a topology-weighted approach which exploits network topology information to mine alarms and how to integrate such measurement into mining algorithm.As an efffectve tool, Alarm correlation rule searching algorithm provide an aproach to discover association. The algorithm takes advantage of graphic theory to search all the rules based on fixed precondition. The simulation and validation of the system of mining weighted alarm association rules in networks indicates that it can find out effective alarm rules which reflect the faults in networks rapidly. It is also validated that the system which provides the user the compact alarm rules with complete information is valuable and useful to the alarm correlation analysis and fault diagnosis and localization in networks.