Spatial Data Infrastructure (SDI) is an information infrastructure providing access and enabling interoperability among spatial information based on standards, policies, regulations and coordination mechanisms (Groot R, 1997). The methods for creating SDI undergone radical changes in the recent years like the shift from the product-led model or data-producer-led model to process-led model or data-user model, etc. However, there are some limitations challenging the SDI growth like the lack of standards to handle linked geospatial data, etc.
The goal of this essay is to detail the concepts for the creation of an improved SDI in the year 2019. The intended SDI integrates the existing developments in the field to the relevant emerging trends and incorporates additional functionalities and technological advancements like cloud computing infrastructure. The essay also highlights the policy, standards and organizational requirements for overcoming the challenges of the current scenario.
The future SDI has all of its components moved to cloud environment which helps in scalability. The future SDI has the following key features categorized according to the components of a SDI:
• Databases, Metadata & Sources:
The geographic information is converted into linked geospatial data, for exposing, sharing and connecting resources in the web. For example, a geographical feature described in the data has a URI with links pointing to other geographical features based on their geospatial relation. The knowledge model and catalogue model of the datasets (metadata) is changed according to the developments in the field of semantic web to accommodate details that are necessary for pattern recognition and subsequent linking .The SDI should use open data...
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The future SDI increases the transparency in acquiring data and access to information which allows saving time, money and creates a platform to sustain increased efficiency and avoid duplication of efforts. This definitely impacts the society, especially involves more people during the spatial decision making process. For example, an analysis of a person can be challenged with more facts and in the similar way supported. With the increases of problems related to environment, SDIs like these can help more people to involve in the research and analysis. It might help solve some of those problems by identifying some things which went un-noticed, etc.
I believe, the future SDI described in this essay has the potential to cater the needs of 2019, except in an unlikely event of technologies mentioned here becomes outdated by that time, for example cloud environment.
The factors listed though impressive and quite thorough, lacks in three crucial factors we believe would make a deep influence in choosing a provider. They are future scalability and expandability, flexibility to adapt to probable changes during development and maturity of technology being used. Elaborating on why the above stated factors are important to be considered during the selection process, we believe that sufficient forethought and foresight needs to be put on to predict future demand and load on the software and evaluate if the provider can handle these predicted future requirements by upgrading and scaling their system. With a focus on agility, the SiL’K team needs to ensure if the providers are flexible enough to accommodate a change if one arises outside the scope of the initial requirement specification. This is more of a qualitative assessment. Lastly, the third factor stresses on evaluating the technology used in the provider’s implementation. Care must be taken to ensure though the technology can meet the current needs, it is of the modern age and has sufficient maturity to evolve over a period of time to enjoy advantages of new, upcoming enhancements.
Basically, a Browser/Server (B/S) model is adopted in the system design where nearly all computing load is located on the server side, while the client side is only responsible for displaying. In this project, SOA is used to facilitate data communication and interactive operations for the reason that each web service is an independent unit in SOA. The general structure of the web-based UMS using SOA is described as follows (Figure 2). In Figure 2, the server side is composed of GIS web service providers, an image cache server, a web server and a firewall.
There has long been argument over whether genetic modification holds the key to our future as a species, or if the risks and downsides of genetic modification outweigh all of the possible rewards . There have been an uncountable number of papers written on the subject, arguing both for and against. Ronald M. Green's article “Building Baby from the Genes Up” argues that genetic modification has many possible benefits to the human race, such as preventing deadly diseases, and eliminating fears that genetic modification would lead to the creation of a selective “master race” where babies are hand picked to be doctors and athletes by their parents. In contrast to Green is Richard Hayes' article “Genetically Modified Humans? No Thanks.” in which Hayes disagrees with Green, saying that genetic modification would no doubt lead to hand picked “designer” babies, which would destroy the free will and futures of children who were born into their destiny. Hayes' final point, saying that although it is a good thing to use genetic modification to eliminate diseases such and cancer and obesity, we shouldn't go any further than that when it comes to messing with the genes of unborn babies. Although both authors make some great points in their essays, Green definitely makes the stronger more persuasive argument than Hayes, who basically just gives his opinion without backing it up with anything.
Hellerstein, J. M., Stonebraker, M., & Caccia, R. (1999). Independent, open enterprise data integration. IEEE Data Eng. Bull., 22(1), 43-49.
The development of a big data strategy was initiated by the APS ICT Strategy 2012 – 2015 (ICT Strategy) which highlighted the need for a strategy to enhance cross-agency data analytic capability for improved policy and service delivery. The big data strategy is intended for agency senior executives and business program managers. It is designed to highlight key opportunities and challenges that big data will bring to government agencies. The strategy will aim to assist agencies to take advantage of these opportunities and realise the potential benefits of these new technologies.
information you will read about in this paper is what might become of the future.
The cloud storage services are important as it provides a lot of benefits to the healthcare industry. The healthcare data is often doubling each and every year and consequently this means that the industry has to invest in hardware equipment tweak databases as well as servers that are required to store large amounts of data (Blobel, 19). It is imperative to understand that with a properly implemented a cloud storage system, and hospitals can be able to establish a network that can process tasks quickly with...
Leebaert, Derek. Technology 2001. The Future of Computing and Communications. Edited by Derek Leebaert. The MIT Press, Cambridge, Massachusetts. Third printing, 1991.
Now days, companies are searching for new ways of gathering data so that they can get useful data in order to make well informed decisions regarding the market they are operating in. Google analytics is considered one of the best tools offers extensive amount of data to business owners for free. However, the success of business is highly depended on how well they can arrange data and customize their collected data corresponded to their business priorities. Google analytics provides beneficial information for companies regardless of their extent of operation. Google Analytics (Location data)
According to Haag (2013), the difference between the two decision support systems (DSS) is that the geographic information system (GIS) designed specifically to analyze spatial information. The Spatial information is any information shown in a map format. The learning team member was informative to elaborate that the GIS infrastructure often includes capturing location specific data to store, manipulate, analyze, and in some way make use of the data for a particular purpose. Learning the traffic control system was the most interesting because of the critical decision-making process to retrieve data to access traffic movement for optimal traffic control strategies The DSS would become an analytical process to determine the type of controller configuration for an efficient outcome objective. The discussion on cybersecurity was an initial consideration an area of interest. Developing a course of action to counteract cybersecurity from hacker and infrastructure breaches would need a structured process. Constructing a critical complex network infrastructure, the use of the DSS will help leaders and team members with searching retrieving, and analyzing data to summarize main
A SAN can be configured to provide a nearly infinite pool of storage that you can grow and move between servers as they need it. The storage can be added to and removed without requiring the server to be rebooted. The services provided by the server continue to operate without interruption.
Staff training in GIS technology, database operation and maintenance and good data documentation practises is essential.
Building a GIS system from the ground up is a very time consuming and extremely expensive venture. This is why only large metropolitan areas have developed or are developing GIS systems.
The issues relating to professional geographers include control of and access to information, privacy and misuse of data, and international considerations. All of these extend to electronic networks, electronic databases, and to professional geographers using geographic information systems. When working as a professional geographer, it is important to address any concerns to the appropriate department. Issues to report are theft, fraud, conflicts of interest, threat to employee safety, unauthorized access to customer or employee information, and unlawful practices.
Geographical information systems have their roots in cartography. Schuurman (2004), who broadly describes geographical information science as the theoretical basis for GIS, points to the instance of Ian McHarg in 1962, who was tasked with planning the route of a road through an area with several different types of land cover. He set out to pick the route that would cause the least amount of disturbance to the habitats in the area. In order to do this he devised a method called overlaying which involved drawing each piece of land cover on a seperate sheet of tracing paper and laying them over eachother. This formed the foundation for spatial analysis and provided the basis for what would later become the layers we now use today in GIS. Another early example mentioned by Shuurman is that of John Snow, who located the source of a cholora outbreak in London by making a dot density map of each individual case. The spatial data this provided him with allowed him to narrow down the source to specific water pumps in the city.