Big geospatial raster data pose a grand challenge to data management technologies for effective big data query and processing. To address these challenges, various big data container solutions have been developed or enhanced to facilitate data storage, retrieval, and analysis. Data containers were also developed or enhanced to handle geospatial data. For example, Rasdaman was developed to handle raster data and GeoSpark/SpatialHadoop were enhanced from Spark/Hadoop to handle vector data. However, there are few studies to systematically compare and evaluate the features and performances of these popular data containers. This paper provides a comprehensive evaluation of six popular data containers (i.e., Rasdaman, SciDB, Spark, ClimateSpark, Hive, and MongoDB) for handling multi-dimensional, array-based geospatial raster datasets. Their architectures, technologies, capabilities, and performance are compared and evaluated from two perspectives: (a) system design and architecture (distributed architecture, logical data model, physical data model, and data operations); and (b) practical use experience and performance (data preprocessing, data uploading, query speed, and resource consumption). Four major conclusions are offered: (1) no data containers, except ClimateSpark, have good support for the HDF data format used in this paper, requiring time- and resource-consuming data preprocessing to load data; (2) SciDB, Rasdaman, and MongoDB handle small/mediate volumes of data query well, whereas Spark and ClimateSpark can handle large volumes of data with stable resource consumption; (3) SciDB and Rasdaman provide mature array-based data operation and analytical functions, while the others lack these functions for users; and (4) SciDB, Spark, and Hive have better support of user defined functions (UDFs) to extend the system capability.

In the research field of spatiotemporal data discovery, how to utilize the semantic characteristics of spatiotemporal datasets is an important topic. This paper presented a content-based recommendation method, and applied Bayesian networks and ontologies into the vocabulary recommendation process for spatiotemporal data discovery. The source data of this research was from the MUDROD (Mining and Utilizing Dataset Relevancy from Oceanographic Datasets) search platform. From the historical search log, major keywords were extracted and organized according to ontologies in a hierarchical structure. Using the search history, the posterior probability between each subclass and their super class in the ontologies was calculated, indicating a recommendation likelihood. We created a Bayesian network model for inference based on ontologies. This model can address the following two objectives: (1) Given one class in the ontology, the model can judge which class has the biggest likelihood to be selected for recommendation. 2) Based on the search history of a user, the Bayesian network model can judge which class has the biggest probability to be recommended. Comparison experimentation with existing system and evaluation experimentation with expert knowledge show that this method is specifically helpful for spatiotemporal data discovery.

Keywords: Precipitation, Kinetics, GIS, Spatio-temporal data model, Divergence, Rotation, Deformation Precipitation is a key type of dynamic geographic phenomena. With time changing, precipitation areas can move, merge, split or disappear. Based on existing research methods, this paper applies a kinetics-based GIS method to analyze precipitation patterns in US mainland area. Annual precipitation data of 1950 and 2010 in small spatial resolution are selected for analysis. Three major dynamic changing types are considered for analysis: divergence, rotation and deformation. Objects to analyze transitions are designed, and displacement of precipitation area is calculated. After applying kinetics-based algorithm in precipitation data, conclusions can be made that there are some significant pattern changes in some key areas of US, such as east coast, west coast and Mississippi river area. Dynamic precipitation pattern changes of divergence/convergence, rotation and deformation in different areas of US mainland are analyzed and compared.