Udoyara S. Tim

A Brief Review on Leading Big Data Models

Today, science is passing through an era of transformation, where the inundation of data, dubbed data deluge is influencing the decision making process. The science is driven by the data and is being termed as data science. In this internet age, the volume of the data has grown up to petabytes, and this large, complex, structured or unstructured, and heterogeneous data in the form of “Big Data” has gained significant attention. The rapid pace of data growth through various disparate sources, especially social media such as Facebook, has seriously challenged the data analytic capabilities of traditional relational databases. The velocity of the expansion of the amount of data gives rise to a complete paradigm shift in how new age data is processed. Confidence in the data engineering of the existing data processing systems is gradually fading whereas the capabilities of the new techniques for capturing, storing, visualizing, and analyzing data are evolving. In this review paper, we discuss some of the modern Big Data models that are leading contributors in the NoSQL era and claim to address Big Data challenges in reliable and efficient ways. Also, we take the potential of Big Data into consideration and try to reshape the original operationaloriented definition of “Big Science” (Furner, 2003) into a new data-driven definition and rephrase it as “The science that deals with Big Data is Big Science.”

Spatial decision support system for planning sustainable livestock production

Abstract Recent shifts toward intensive and large confined livestock production units to enhance economic growth coupled with increased concerns for air, soil, and water quality have necessitated the development of computer-based management decision support systems for selecting environmentally sound production sites and for planning sustainable production systems. An integral part of a sustainable livestock production system is the selection of appropriate land areas that meet several environmental, socio-economic, and aesthetic constraints. Traditionally, regulatory and zoning criteria, in conjunction with manual review and overlay of land cover, soils, and topographic maps, have been used to select sites for livestock production. This approach can be both time-consuming and expensive, and the land areas delineated by this method have been shown to be problematic from the odor nuisance and water pollution standpoint. A more rational approach that narrows down large areas under consideration to a finite set of optimal sites that satisfy the environmental protection goals is needed. This paper describes the development and application of an interactive spatial decision support system to delineate optimal land areas for locating a number of livestock production strategies. The spatial decision support system is based on the ARC/INFO geographic information system and incorporates the effects of land use, soil type, topography, proximity to roads and surface water bodies, and other aesthetic and political considerations, as well as multicriteria analysis techniques. The design and implementation of the system as well as an example application involving several alternative livestock production strategies are presented.

Interactive water quality modeling within a GIS environment

Abstract A number of water quality models have been developed to characterize the local, regional, and national extent of environmental pollution problems, and to assess the potential socio-economic impacts. These models provide an understanding of the processes involved in pollution generation from nonpoint sources and can be used to evaluate the effectiveness of alternative land management practices. However, a major problem facing modelers has been the inability to efficiently handle, manipulate, and manage large volumes of model parameters. Recent developments in geographic information systems (GIS) provide the opportunity and tools to spatially organize and effectively manage data for modeling. This paper presents an interactive water quality modeling system developed within a GIS environment. The system combines soil erosion and pollutant export models with ARC/INFO GIS software and a graphic user interface. In addition, the system is designed to allow efficient and cost-effective use of simplified water quality models for the analysis of nonpoint source pollution problems in watersheds. Implemented on a color-graphic engineering workstation, the interactive modeling system permits visualization of the spatial distribution of model inputs and of the spatio-temporal variation of simulated model outputs. The goal of developing the interactive modeling system, in general, was to provide a spatial decision support tool for targeting critical areas of water quality problems in a watershed and for agricultural production planning and management decision-making.

AutoConViz: automating the conversion and visualization of spatio-temporal query results in GIS

In recent years, large multifaceted spatial, temporal, and spatio-temporal databases have attained significant popularity and importance in the database community. In order to perform preliminary investigation, exploratory visual analysis of such data-sets is highly desirable. To facilitate the convenient and efficient visualization, scientists and practitioners often need to convert the spatial component of the data-set into a more usable format. Though among the various formats available today in spatial data science community, Geographical Markup Language (GML) adheres to its central position and is of our interest in this work. The development of a tool to satisfy the spatial format conversion needs tailored to every user’s needs from scratch is difficult, time-consuming, and requires skills not easy to possess. We developed AutoConViz, to solve the issue stated above. It accepts the spatial component in GML format, converts that into shapefile format, and facilitates informative and automated interaction with the data-sets. It supports basic query and geospatial analysis and visualization tasks and offers functionalities such as zooming, panning, and feature selection. Furthermore, our software leverages navigation to classical ArcGIS software interface for users interested in more intensive analysis. AutoConViz serves both the database and geographical information system communities to explore insights of spatio-temporal databases and will help to further geospatial research and development.

Geo-spatial Pattern Determination for SNAP Eligibility in Iowa Using GIS

U. S. Department of Agriculture (USDA) administers SNAP - to assist low income citizens at the federal level, delegating the benefits distribution to the states. To avail the SNAP benefits the target population should be 50% or more low income (185% of federal poverty level) defined.

Visual subsetting, conversion and complex query exploitation in large spatio-temporal databases

Abstract In recent years, large amounts of multidimensional data are being generated at a rapid pace, creating the need to: develop appropriate tools for visual data analytics and for enhancing accessibility and discoverability of the data; and new algorithms and methods for seamless integration, manipulation and interactive visualization of data. We describe an interactive tool and technique for data mining and visualization, called SubVizCon that integrates advanced concepts for spatio-temporal databases with interactive visualization capabilities of the geographic information system software. More specifically, we illustrate the development and implementation of the SubVizCon framework, which integrates geospatial data analysis, feature extraction, and visualization functions in ArcGIS with the advanced subsetting and querying functions of the Parametric Database Model. The seamless integration and interoperability of these software tools within SubVizCon framework enables users to not only visualize large amounts of data but also discover spatio-temporal patterns that might otherwise remain hidden.