This paper presents a novel approach that transforms the feature space into a new feature space such that a range query in the original space is mapped into an equivalent box query in the transformed space. Since box queries are axis aligned, there are several implementational advantages that can be exploited to speed up the retrieval of query results using R-Tree [9] like indexing schemes. For two dimensional data, the transformation is precise. For larger than two dimensions, we propose a space transformation scheme based on disjoint planer rotation and a new type of query, pruning box query, to get the precise results. Experimental results with large synthetic databases and some real databases show the effectiveness of the proposed transformation scheme. These experimental results have been corroborated with suitable mathematical models. In disjoint planer rotation, additional computation time is required to remove the false positives produced due to the bounding box not being precise. A second topological transformation scheme is presented based on optimized bounding box, which reduces the amount of false positives. The amount of this reduction is more with increasing dimensions. Optimized bounding box for higher dimensions is computed based on a novel approach of simultaneous local optimal projections.

Multi agent systems (MAS) are expected to be involved in futuristic technologies. Agents require some execution environment in which they can publish their service interfaces and can provide services to other agent. Such execution environment is called agent platform (AP). From a technical point of view any abnormal behavior of platform can distress agents residing on that platform. That's why it is necessary to provide a suitable architecture for the AP which should not only provide fault tolerance but also scalability features. There also exist some management components within the platform, which provide services to application agents. All the agents within MAS are managed by agent management system (AMS) which is the mandatory supervisory authority of any AP. To be more scalable, a single agent platform can be distributed over several machines which not only provides load balancing but also fault tolerance depending upon the distributed architecture of the AP. In existing systems, AMS is centralized i.e. it exists on one machine. With centralized AMS, this infrastructure lacks fault tolerance, which is a key feature of high assurance. Absence of fault tolerance is the main reason for the small number of deployments of MAS. Failure of AMS leads towards abnormal behavior in the distributed platform. This paper proposes virtual agent cluster (VAC) paradigm which strongly supports decentralized AMS to achieve fault tolerance in distributed AP. VAC provides fault tolerance by using separate communication layers among different machines. Experiments show that it improves performance, brings autonomy and supports fault recovery along with load balancing in distributed AP.