A Speculative Concurrency Control in Real-Time Spatial Big Data Using Real-Time Nested Spatial Transactions and Imprecise Computation

Abstract

Real-time spatial applications have become more and more important. These applications are innovating in the way we live. As a result, there is a huge amount of real-time spatial data generated everyday, accessed simultaneously by two types of transactions, Update transactions and User transactions (continuous requests). In real-time spatial Big Data, the performance can be increased by allowing concurrent execution of transactions. This activity is called concurrency control. The concurrency control algorithm must be used to ensure serializability of transaction scheduling and to maintain data consistency.Several works have been done in this area, but without holding into account the existence of a huge volume of data. In this paper, we apply the technique of imprecise computation for real-time spatial nested transactions. We consider that imprecise transaction consist that a User transaction is decomposed logically into a one mandatory sub-transaction and one or more optional sub-transactions and an Update transaction consists only of a single mandatory sub-transaction. We propose an improvement of an existing Two-Shadow Speculative Concurrency Control (SCC-2S) with priority with the use of the imprecise real-time spatial transaction. Our main objectives are: to guarantee the data freshness, to enhance the deadline miss ratio even in the presence of conflicts and unpredictable workloads and finally to satisfy the requirements of users by the improving of the quality of service (QoS).