A distributed database system is a combination of sub-database separated over many sites communicated through a network. Deadlock is one of the most common problems that occur in distributed database implementation. Deadlock occurs when a multiple transaction locks the same data sources and every transaction waits for other to release. Deadlock detection and resolution is not easy in a distribu...

We characterize the number of remote accesses and the number of distinct remote sites accessed by global transactions in a distributed database environment. Both measures are important in determining the overhead of distributed transaction processing and are affected by the distribution of transaction size (number of distinct objects accessed by a transaction) and the distribution of remote acc...

In recent years research in the area of distributed transaction management has been growing rapidly. While designing concurrency control components for such systems, the most common techniques have involved locking and timestamping and variations of these. In spite of their heavy use in the designing of distributed systems, each of the above techniques has its own specific disadvantages and ove...

Map Reduce (Hadoop) is a popular framework for conducting data processing in a parallel manner. It requires us to take the data "out of" the database in order to process it efficiently, yet promises to achieve high-performance and scalable data processing by exploiting the power of a compute cluster (cloud) with ease. More recently, a marriage of MapReduce with DBMS Technologies has been touted...