PDDL based workflow auto-modeling for spatial data mining

Abstract

Spatial Data Mining (SDM) has grown a lot during the last years. More and more data mining operators are brought forward to the SDM domain. A SDM problem can be typically confronted by separating into several steps that could be organized into a workflow which is consisted of several core SDM operators and some assistant operators. However, the large numbers of operators makes it difficult to organize a tailored workflow by a manual workflow modeling. In this paper, an Artificial Intelligence (AI) Planning method is introduced into SDM workflow modeling, while the critical processes and elements for modeling a complete SDM workflow is abstracted and analyzed, and the corresponding elements are formally described by using Planning Domain Definition Language (PDDL). After the PDDL description, a JavaFF planner is used to drive the SDM workflow automodeling. A demonstration application of workflow automodeling for spatial outlier mining is proposed as well, to test the feasibility and availability of automated workflow construction achieved by the presented AI Planning method. The goal of this work is to present an attempt for AutoModeling of SDM workflow, and to help users out of previously cumbersome manual design of workflow and meanwhile to lower the threshold by AI Planning.