Design and Implementation of EFSC Algorithm for Hybrid Representation of Distributed Databases

In distributed database management systems, fragmenting base connections increases concurrency and hence system throughput for query processing. User queries use hybrid fragmentation methods focused on vector bindings, and deductive database implementations lack query-access-rule dependence. As a result, for hierarchical deductive information implementations, a hybrid fragmentation solution is used. The method considers the horizontal partition of base relations based on the bindings placed on user requests, then produces vertical fragments of the horizontally partitioned relations, and finally clusters rules based on attribute affinity and query and rule access frequency. The suggested fragmentation approach makes distributed deductive database structures easier to develop.

A Newly Proposed Cloud Computing Based Strengthen Distributed Database System Model and Its Applications in Database Modelling

In the modern era of today’s exceptional Information age, the day to day transactions of huge sensitive data sets, which is in the form of PBs (Peta-Bytes) 250 bytes and YBs (Yotta – Bytes) 280 bytes are drastically increases with enormous speed on CLOUD data storage environment. CLOUDs data storage environment are one of the most superior and reliable platform for storing a large sets of data both at enterprise level or local level. Because CLOUD provides online data fetching capability to restore or fetching data at any geographical locations through login their correspondent credentials. But to enhancement or spread of these large data sets are becomes also very complex with respect to maintenance of these data with take concern of consistency and data security, because to maintain these large data sets with full of consistency and integrity are really a very typical and rational tasks, so here In this paper we proposed a distributed database management systems for CLOUD interface also preserves or to take concern data security features with full restoration of CIA (Confidentiality, Integrity, Availability or Authenticity) trade of Information Security. Here we also improvised the mechanisms of traditional distributed database management systems because the tendency to preserves information and recover ability after any misconceptions happens that we restore data which belongs to similar person may have to be stored at different locations, but this newly proposed distributed database systems architecture contains all information or record which belong to similar person are stored in one database rather restore it different databases but the location of these data have to be changes mean while that the content or data which resides in one databases have to be moved to some other database and also preserves the security features, and this model also have capability to run older traditional methodology based distributed database management systems using this model. So the detailed description about these models and communication infrastructure among different CLOUDs are append in the upcoming sections of this paper. Keywords: Cloud based Distributed Database system model, Distributed system, Distributed Database model of CLOUD, Cloud Distributed Database, CLOUD based database systems

Distributed Database Management With Integration of Blockchain and Long Short-Term Memory

Supply chain management is the broad range of activities required to plan, control, and execute the flow of a product. As a less corruptible and more automated alternative to traditional databases, blockchains are well suited to the complicated record-keeping. However distributed database management system is a centralized software system; the blockchain technology can overcome the problem of synchronization between multiple databases; it also ensures that integrity problems are solved. In the proposed model, Ethereum blockchain is used to solve a few major supply chain problems to manage a distributed database. The model has incorporated techniques to predict the rise and fall of the demand for the medicine in the market by using machine learning algorithms such as linear regression and LSTM; also, the trend predicted by both the models has been compared. The result shows that while using linear regression the predicted trend is not very accurate and cannot trace the actual trend closely whereas BLSTM has performed well in predicting the trends of time series data.

Building a Retail Sysem Based on Distributed Databases Model

In recent years, distributed databases have become an important field of processing information, overcoming some limitations of centralized database such as overloading, bottlenecking while accessing, availability/ reliability of low fault tolerance. Our article proposes to build a distributed system (functions and databases) for POS (point of sale) retailers, data will be distributed across different locations but can still be linked together when required. At each location retailers can sell directly on the system (online or offline) so they can administer local databases and execute their local applications (business). Here we deploy the system on the distributed database management system based on Client-Server model. Therefore, aside from local management of data at clients (POS), there is also a server (manager) that stores data, manages and controls the entire system.