DATA PROCESSING IN COULD WITH AVL STRUCTURE AND BOOTYSTRAP ACCESS CONTROLINTORDUCTION:-The corporate networks such as supply chain networks have to share relevant information among the companies which are in collaboration in the same industry sector. So, they have to store, retrieve and process a huge amount of data. This requires huge databases and servers. Hence they choose third party data warehouse to store their data. But this has several threats like information disclosure, quality of service and data leaks.
Also a data warehouse is static that is its storage procedure is constant. With the advent of cloud computing. so we are moving to the AVL structure for storage process and map reduce algorithm for efficient retrieval of the well-structured stored data, which will minimizes the retrieving time of the dataABSTRACT:-Corporate networks store their data in the shared knowledge plane which is cloudy.
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But they have to decide which part of the data would be visible for which users. To accomplish this task, the bootstrap access control mechanism is used which decides the accessibility of the user while entering the database. Also, if the data are processed and stored in perfect data structure, then data mining and data processing would be easier. So, the data are processed with map reduce algorithm and stored in a Balanced Overlay Network (BATON). BATON uses AVL tree structure for storing data.
Also, it supports peer to peer system in which a separate server is provided for each company in the corporate network and in each server, the required redundancy is maintained. “Pay as you go” pricing policy is followed in this system which is a two way (Service provider and corporate network) beneficial pricing policy. In this policy, the user will be charged by categorizing his needs based on a range of memory required, duration and security policy.PROBLEM STATEMENT:- The corporate networks stores their bulk data in a third party data warehouse which may have security threats and high cost.
Also access control mechanism is not well defined for various categories of users like suppliers, manufacturers and retailers. The data are stored in an unstructured manner so that retrieval of data and processing them are tedious. There is no processing like sorting or clustering before storing the data from the server.
So, while retrieving, the complexity of the system will be increased because it has to search for the whole database. The scalability of the system is very low since it cannot scale up to thousands of participants. The storage of data in the data warehouse system entails non trivial costs, including hardware/software investment and high maintenance cost. The inside processing of data marts and classification of fact tables and dimension tables is complex tasks when we store data in the data warehouse. The system is not supported for heterogeneous environment that is participants of the network using different platforms cannot be supported.