The process of transaction can guarantee the reliability of business applications. Locking resources is widely used in distributed transaction management (e.g; two phase commit, 2PC) to keep the system consistent. The locking mechanism, however, potentially results in various deadlocks. In service oriented architecture, the deadlock problem becomes even worse because multiple transactions try to lock shared resources in the unexpectable way due to the more randomicity of transaction requests, which has not been solved by existing research results. In this paper, we investigate how to prevent local deadlocks, caused by the resource competition among multiple sub-transactions of a gl obal transaction, and global deadlocks from the competition among different global transactions. We propose a replication based approach to avoid the local deadlocks, and a timestamp based approach to significantly mitigate the global deadlocks. A general algorithm is designed for both local and global deadlock prevention. The experimental results demonstrate the effectiveness and efficiency of our deadlock prevention approach. Further, it is also proved that our approach provides higher system performance than traditional resource allocation schemes.
It is a computer aided technique of searching and evaluating a bulk of data in order to obtain useful information.
This technique searches through the data to find hidden patterns and uses statistical methods to find relationship among data for finding predictive information and for classification of data.
This is a mixture of different disciplines which include machine learning, statistics and artificial intelligence and also some mathematical methods.
Often the useful information is extracted from enormous database using modeling technique which is used to build a model from instances of the data where the solution is known and later apply this model on the instances where the solution is unknown.
In unsupervised data mining, the data analysis is not done by using modeling technique. In other words the labels are provided in order not draw inferences and prediction from data sets. Example is clustering.
In supervised data mining the model is developed to make inferences and classification. Example is neural networks.
For example data mining is used in Medicine industry to provide more accurate diagnostics and treatments on the basis of patient's medical history , physical examination or different patient tests data.
Data mining also makes it possible to manage health resources more efficiently and cost-effectively by detecting risks, predicting diseases in certain sections of the population or predicting hospitalization duration.
