An Expressive Language and Efficient Execution System for Software Agents

Software agents can be used to automate many of the tedious, time-consuming information processing tasks that humans currently have to complete manually. However, to do so, agent plans must be capable of representing the myriad of actions and control flows required to perform those tasks. In addition, since these tasks can require integrating multiple sources of remote information ? typically, a slow, I/O-bound process ? it is desirable to make execution as efficient as possible. To address both of these needs, we present a flexible software agent plan language and a highly parallel execution system that enable the efficient execution of expressive agent plans. The plan language allows complex tasks to be more easily expressed by providing a variety of operators for flexibly processing the data as well as supporting subplans (for modularity) and recursion (for indeterminate looping). The executor is based on a streaming dataflow model of execution to maximize the amount of operator and data parallelism possible at runtime. We have implemented both the language and executor in a system called THESEUS. Our results from testing THESEUS show that streaming dataflow execution can yield significant speedups over both traditional serial (von Neumann) as well as non-streaming dataflow-style execution that existing software and robot agent execution systems currently support. In addition, we show how plans written in the language we present can represent certain types of subtasks that cannot be accomplished using the languages supported by network query engines. Finally, we demonstrate that the increased expressivity of our plan language does not hamper performance; specifically, we show how data can be integrated from multiple remote sources just as efficiently using our architecture as is possible with a state-of-the-art streaming-dataflow network query engine

Use of a weighted matching algorithm to sequence clusters in spatial join processing

One of the most expensive operations in a spatial database is spatial join processing. This study focuses on how to improve the performance of such processing. The main objective is to reduce the Input/Output (I/O) cost of the spatial join process by using a technique called cluster-scheduling. Generally, the spatial join is processed in two steps, namely filtering and refinement. The cluster-scheduling technique is performed after the filtering step and before the refinement step and is part of the housekeeping phase. The key point of this technique is to realise order wherein two consecutive clusters in the sequence have maximal overlapping objects. However, finding the maximal overlapping order has been shown to be Nondeterministic Polynomial-time (NP)-complete. This study proposes an algorithm to provide approximate maximal overlapping (AMO) order in a Cluster Overlapping (CO) graph. The study proposes the use of an efficient maximum weighted matching algorithm to solve the problem of finding AMO order. As a result, the I/O cost in spatial join processing can be minimised