A host of software vendor technologies including database management systems, ArcGIS Desktop and ArcGIS Server software, Web services, and hardware operating systems—all integrated with existing legacy applications. Data (including business layers, basemap layers, and Imagery) and user applications are added to the integrated infrastructure environment to support the final implementation. The result is a very large mixed bag of technology that must work together properly and efficiently to support user workflow requirements.
A broad range of technical skills are required to build and maintain effective enterprise GIS operations. Key operational staffs participate in data management, planning and analysis, field mobility, and operational awareness disciplines. Key supporting positions include skills in application development and enterprise system administration. Figure 12.1 provides an overview of the variety of GIS workflows and key functional responsibilities required to support enterprise GIS operations.
Once you have completed your System Architecture Design and identified your target architecture solution, suggested guidance for deploying Enterprise design solutions are provided for each of the following implementation phases.
Figure 12.4 shows how you can prepare for your system architecture design. Business needs must be understood before you are ready to complete the system architecture design. Business requirements analysis includes a review of the enterprise vision, the existing business architecture, and the user workflow requirements. Each of these areas must be explored in some detail before you begin the design.
The SDSwiki provides a framework for completing the system architecture design. Once the user requirements and architecture solution are configured, the CPT completes the system architecture design loads analysis to identify network bandwidth and the platform target state design solution.
We performed a time-series analysis with the daily death count as the outcome variable and with each pollutant in turn as the main exposure. An over-dispersed Poisson regression was applied, controlling for time-varying confounders. Specifically, we chose the following terms as a priori confounders: time-trend of mortality, warm and cold temperatures separately, barometric pressure, influenza epidemics, and indicator variables accounting for population depletion during vacation and summer periods. We adjusted for long-term trends and seasonality by adding to the model a three-way interaction term of year, month, and day of the week. This method has been demonstrated to be equivalent to a case-crossover design with “time-stratified” approach for selecting control days (), and simulation studies have shown it to induce minimum bias under a wide range of different scenarios of data-generating processes (, ). We adjusted for warm temperatures by modeling mean apparent temperature on the same day and previous day (lag 0–1). We have used linear and quadratic terms for days with AT above the overall median, while AT was kept constant to the median value on days below (; ). Similarly, we adjusted for cold temperatures by modeling the mean temperature of the previous six days (lag 1–6) with a linear term for days below the median, and the variable was kept constant to the median value on days above. We modeled barometric pressure with linear and quadratic terms of the same day (lag 0) variable. We adjusted for influenza epidemics by modeling an indicator variable (coded as 1 for days during peak incidence periods, up to a maximum of 3 consecutive wk within each year, and 0 otherwise), based on weekly incidence data for the city of Rome provided by the National Health Service Sentinel System (; ). Finally, we modeled an indicator variable for holidays (assigned a value of 1 on national and city holidays and 0 on other days), and a three-level variable to account for population shifts during the summer (assigned a value of 2 for days during the 2-wk holiday period in mid-August, 1 for all other days during the period July 16–August 31, and 0 otherwise) (, ).
Our original search resulted in a total of 6,245 studies, of which 469 were duplicates (i.e., repeat entries of citations from multiple databases). Thus, we screened titles and/or abstracts of 5,776 studies and reviewed the full text of 62 articles (). The literature that we excluded based on title/abstract review included studies on waterborne disease outbreaks (including outbreaks caused by protozoan pathogens) and studies conducted in rural populations without access to a centralized water supply. Of the 62 articles we reviewed in full text, 20 studies were identified for inclusion in the systematic review, and 14 of these 20 studies had combinable data and were included in the meta-analysis. Ineligible studies reviewed in full text were excluded because a) they were reviews or general articles with no health outcomes (n = 18); b) contamination occurred prior to entry into the distribution system (at the water source or treatment plant), or there was not sufficient information to differentiate contamination in the distribution system from contamination at the source or plant (n = 17); c) exposure was not tap water or it was a mix of tap water and other sources (n = 6); or d) study authors did not report data on the association between the tap water exposures and GII outcomes (n = 1).
Conducting proper testing at the right time can contribute to implementation success. Functional component and system integration testing should be conducted for new technology during prototype development and before introduction into production. The primary focus during this testing is to make sure everything works. Performance targets established during the initial system design can be evaluated during early testing, paying close attention to map display performance and layer complexity (see Chapter 3 Software Performance). This is an opportunity to evaluate workflow functions and reduce processing overhead.
Martin, they describe how information technology is becoming more advanced as the years pass and that accounting information systems are becoming insufficient as a result.
A specific in vitro diagnostic instrument contained approximately 175,000lines of source code and approximately 1,600 software requirements that neededto be traced. While the division also has an automated traceability system(ATS) that allowed them to automate many of the tasks, it was the process andnot the tool that led to their success. The main purpose of the traceabilityprogram is to identify links and determine that the links are complete andaccurate. The traceability analysis consists of 4 aspects: forward requirementsanalysis, reverse requirements trace, forward test analysis, and reverse testtrace. These steps are used to trace each software requirement through itsdesign elements and test traces. The ATS can be used to design documentationmatrices and test matrices that is used to perform the different analysesrequired. The ATS is also able to give feedback about the design componentsthat are not yet implemented during the life cycle. In the test phase, the ATSgives input to what requirements are covered by the test cases. [Watkins94]
Due to the inadequacy that accounting information systems are becoming, accountants are being advised to focus on designing new accounting information systems to help businesses receive the information and support needed....