An explosive growth of spatial data has been demanding to Spatial Data Mining (SDM) technology, emerging as a innovative area for spatial data analysis. Geographical Information System (GIS) contains heterogeneous data from multidisciplinary sources in different formats. Geodatabase is the repository of GIS data, representing spatial attributes, with respect to location. Rapidly increasing satellite imagery and geodatabases generates huge data volume related to real world and natural resources such as soil, water, temperature, vegetation, forest cover etc. Inferring information from geodatabases has gained value using computational algorithms. The intent of this paper is to introduce with GIS, and spatial data mining, GIS and SDM tools, algorithmic approaches, issues and challenges, and role of spatial association rule mining in big data of GIS.

Resource scheduling in a cloud computing environment is noteworthy for scientific workflow execution under a cost-effective deadline constraint. Although various researchers have proposed to resolve this critical issue by applying various meta-heuristic and heuristic approaches, no one is able to meet the strict deadline conditions with load-balanced among machines. This article has proposed an improved genetic algorithm that initializes the population with a greedy strategy. Greedy strategy assigns the task to a virtual machine that is under loaded instead of assigning the tasks randomly to a machine. In general workflow scheduling, task dependency is tested after each crossover and mutation operators of genetic algorithm, but here the authors perform after the mutation operation only which yield better results. The proposed model also considered booting time and performance variation of virtual machines. The authors compared the algorithm with previously developed heuristics and metaheuristics both and found it increases hit rate and load balance. It also reduces execution time and cost.

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S U M M A R YThe geological utility of satellite magnetic observations is limited by orbital altitude variations which may be as large as a few hundred kilometres. This study investigates the use of fast and elegant statistical procedures for altitude normalization and gridding of magnetic anomaly data as an alternative to more commonly used equivalent source inversion procedures involving computationally extensive and complex least-squares matrix methods.A standard statistical approach for gridding satellite magnetic anomalies is to recompute numerically averaged values from three-dimensionally distributed observations which are within two standard deviations of an initially determined averaged anomaly estimate. The errors of this procedure for geological analysis are investigated using orbital anomaly simulations of lithospheric sources over a spherical earth. The simulations suggest that numerical averaging errors constitute small and relatively minor contributions to the total error-budget of higher orbital estimates (2400 km), whereas for lower orbital estimates the error of averaging may increase substantially.A more complex statistical procedure involving least-squares collocation in 3-D is found to produce substantially more accurate anomaly estimates as the elevation of prediction is decreased towards the lithospheric sources. Moreover, 3-D collocation is computationally much more efficient and faster to apply than equivalent source inversion methods for altitude-normalizing and gridding magnetic anomaly data. Application of this procedure to MAGSAT magnetic observations of South America demonstrates its utility for producing accurately gridded magnetic anomalies at constant elevation for geological analysis.

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