Modeling and Database Design
Data Modeling Database Design The databases significantly improve organization's ability access, share, apply relevant information. However, wealth data a database, greatly dependent design. Designing a database creates logical data relationships end

Data modeling and database design

Making use of database systems would benefit a health care organization as information regarding a patient can be retrieved easily and quickly Choobineh & Lo, 2004.

The initial design of the database is vital to ensure that information is accurate and logical. Patient information can be easily retrieved and shared with other professionals. Information sharing becomes easy, and health care workers can collaborate with others irrespective of location. Using different descriptions for the same data in a database will create confusion especially when the information has to be exported to another system. The database system would treat the description differently and create a new field for storing the data. This would result in…...

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References

Choobineh, J., & Lo, A.W. (2004). CABSYDD: Case-Based System for Database Design. Journal of Management Information Systems, 21(3), 281-314. doi: 10.2307/40398710

Dekleva, S.M. (1992). The Influence of the Information Systems Development Approach on Maintenance. MIS Quarterly, 16(3), 355-372. doi: 10.2307/249533

Mantha, R.W. (1987). Data Flow and Data Structure Modeling for Database Requirements Determination: A Comparative Study. MIS Quarterly, 11(4), 531-545. doi: 10.2307/248983

Parsons, J. (2002). Effects of Local vs. Global Schema Diagrams on Verification and Communication in Conceptual Data Modeling. Journal of Management Information Systems, 19(3), 155-183. doi: 10.2307/40398597

It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases" (Palace, 1996).
One common example of using data mining is that of store inventory, such as a Midwest grocery chain called Kroger that used the data mining capacity software to discover: "that when men bought diapers on Thursdays and Saturdays, they also tended to buy beer. Further analysis showed that these shoppers typically did their weekly grocery shopping on Saturdays. On Thursdays, however, they only bought a few items. The retailer concluded that they purchased the beer to have it available for the upcoming weekend. The grocery chain could use this newly discovered information in various ways to increase revenue. For example, they could move the beer display closer to the diaper…...

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Works Cited

Amber, Scott. (2002). "Data Modeling 101." Agile Data. Retrieved 167 Jul 2007 at  http://www.agiledata.org/essays/dataModeling101.html 

McGinn, Dan. (1 May 2001). "McDonald's Case Study: Burger Time."

The Jungle Magazine.

Retrieved 17 Jul 2007 at http://www.jungleonline.com/magazine.cfm?INC=inc_article.cfm&artid=1503&template=1&date=May2001&refid=g3

In the contemporary business environment, the accounting profession has undergone a fundamental change, and the traditional accounting systems are no longer adequate to satisfy a recent accounting domain. Thus, a call has been made to expand the scope of accounting information systems. In response to the call, the REA data model was launched to support the accounting information systems. The REA data model is identified as the conceptual modeling tool that assists in designing the AIS (accounting information system) database. The REA model is specifically good to develop an invoicing and billing process.
The objective of this project is to use the REA data model to develop the database for the billing and invoicing process.

Entity Relation Diagram

The entity relation diagram is required for the database of the billing and invoicing process. The entity relation diagram is a graphical illustration to portray the database schema. The ER diagrams show the entities and…...

Data Warehousing: A Strategic Weapon of an Organization.
Within Chapter One, an introduction to the study will be provided. Initially, the overall aims of the research proposal will be discussed. This will be followed by a presentation of the overall objectives of the study will be delineated. After this, the significance of the research will be discussed, including a justification and rationale for the investigation.

The aims of the study are to further establish the degree to which data warehousing has been used by organizations in achieving greater competitive advantage within the industries and markets in which they operate. In a recent report in the Harvard Business eview (2003), it was suggested that companies faced with the harsh realities of the current economy want to have a better sense of how they are performing. With growing volumes of data available and increased efforts to transform that data into meaningful knowledge that can…...

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References

Agosta, L. (2003). Ask the Expert. Harvard Business Review, 81(6), 1.

Database: Business Source Premier.

Babcock, Charles (1995). Slice, dice & deliver. Computerworld, 29, 46, 129 -132.

Beitler, S.S., & Lean, R. (1997). Sears' EPIC Transformation: Converting from Mainframe Legacy Systems to Online Analytical Processing (OLAP). Journal of Data Warehousing (2:2), 5-16.

From Supply Chain Efficiency to Customer Segmentation Focus

Because of this focus on supply chain forecasting accuracy and efficiency, the need for capturing very specific customer data becomes critical. The case study portrays the capturing of segmentation data as focused on growing each of the brands mentioned that VF relies on this data to base marketing, location development and store introductions, and pricing strategies on. In reality, the data delivered for these marketing programs and location-based analyses is also providing an agile and scalable platform for VF to more effectively manage and mitigate its supply chain risk as well.

elying on Alteryx for data analysis as it has superior capability to Microsoft Access and Excel in conjunction with the use of SC Software for geo-demographic analysis, VF has created a workflow for translating data warehouses into the basis of marketing and supply chain strategies. The strategic goal of getting the right product…...

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References

Adnan, M., Longley, P., Singleton, a., & Brunsdon, C. (2010). Towards Real-Time Geodemographics: Clustering Algorithm Performance for Large Multidimensional Spatial Databases. Transactions in GIS, 14(3), 283-297.

Paul Sheldon Foote, & Malini Krishnamurthi. (2001). Forecasting using data warehousing model: Wal-Mart's experience. The Journal of Business Forecasting Methods & Systems, 20(3), 13-17.

Yang-Im Lee, & Peter R.J. Trim. (2006). Retail marketing strategy: The role of marketing intelligence, relationship marketing and trust. Marketing Intelligence & Planning, 24(7), 730-745.

Lewis, M., Hornyak, R., Patnayakuni, R., & Rai, a.. (2008). Business Network Agility for Global Demand-Supply Synchronization: A Comparative Case Study in the Apparel Industry. Journal of Global Information Technology Management, 11(2), 5-29.

SQL and ig Data
Gaining greater insights into terabytes of unstructured and structured data organizations have been collecting in many cases for decades across diverse computing and storage platforms are increasingly being unified through advanced data and system architectures. ig Data is the term used to define very large, diverse data sets that contain both structured and unstructured data that defy analysis using conventional database management and analytics applications (International Journal of Micrographics & Optical Technology, 2010). ig Data is an area generating much interest in enterprises as this collection of data analysis, aggregation and extraction techniques continue to deliver valuable insights into how companies can become more competitive (Datskovsky, 2013). Structured Query Language (SQL) is a widely accepted approach to querying databases, aggregating and analyzing data and creating useful reports that guide decision making in organizations (Rys, 2011). Enterprise software companies are creating ig Data analytics applications that include…...

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Bibliography

Baker, B. 2013, "Enterprise Analytics: Optimize Performance, Process and Decisions Through Big Data," Quality Progress, vol. 46, no. 6, pp. 68.

Datskovsky, Galina, PhD., C.R.M. 2013, "Harnessing Big Data for Competitive Advantage," Information Management, vol. 47, no. 1, pp. 1.

Ferguson, R.B. 2013, "The Big Deal About a Big Data Culture (and Innovation)," MIT Sloan Management Review, vol. 54, no. 2, pp. 1-5.

Ferguson, R.B. 2012, "The Storage and Transfer Challenges of Big Data," MIT Sloan Management Review, vol. 53, no. 4, pp. 1-4.

Stationarity of Data
The panel data stationarity test has a severe size distortion inconsistent with the null hypothesis. Stationary is vague since the mean and variance of the data is not constant. The most appropriate resolution in this case is to merge ailing banks or let strongly financed banks purchase bad debts in accordance with market mechanisms and securitization in Portugal. In view of that, weak banks should be merged or be acquired by stronger banks that appear in the panel data.

Causality Test

The influence of NPL on technical competence is close to zero and not significant. n the other hand, the effect of technical efficiency on loan Loss is positive and significant at 5% level, once more indicating that the causality would run from bank efficiency to non-performing loans. Turning to the allocative efficiency case, the performance is poor and there is no significant response to variation in NPL. Consequently, the…...

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Output growth rate is realized to be significant for commercial bank at 1% significance level throughout the years used in the panel data. This portrayed a positive relationship with the dependent variable showing income elasticity of 0.197 -- 0.21.

The employment rate at the commercial banks in Portugal is significant at 10% significance level in random effects of the panel data model. This is in contrary to the significance at FGLS specification. Consequently, the finding portrayed a negative sign of the coefficient, which do not correspond, to the assumption of automatic stabilizer.

The transformation in output gap is significant at 12% significance level. Additionally, income elasticity is very similar in all specifications and is equal to -0.19 -- (-0.2). It is therefore observed that increase in the difference between loan loss provision and trend real GDP at 1% could lead to 0.2% deterioration on average income in Portugal commercial banks under consideration. From the results, it can be revealed that 1% increase in inflation rate could also lead to increase in Loan Loss provisions by 0.01%. Thus, expenditures rise in general, with the rise in inflation rate. Generally speaking, 2010 and 2011 portrayed 10% significance level both in FGLS and random effect specification. This could mean that Portugal government improved in budget by 1.4% of GDP.

Visualize Your Data
In defining a system to be used for visualizing case information and also capable of supporting geographic information systems (GIS) crime and incident analysis, standardizing on the ESI ArcGIS Server and its Law Enforcement Data Model would provide a scalable, agile foundation for data visualization and future growth. The components or modules that need to be included in the system include Crime Analysis, Data Fusion/Intelligence Analysis, Community Corrections, COMPSTAT statistics module, esource Management, Process outing and MDT/MDC integration. Taken together these components and ArcGIS Server platform would provide for greater criminal analysis visualization and prediction over time. The use of data visualization in crime prediction and prevention, coupled with pattern and data analysis, has shown to have significant effects on reducing crime in targeted regional and metro areas (Xu, Chen, 2005).

Advantages Of Including A Data Visualization Component

There are many advantages of having a data visualization software platform and…...

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References

Heather Havenstein. (2006, January). Data Analysis May Help LAPD Fight Terrorism. Computerworld, 40(3), 4.

Pike, W., Bruce, J., Baddeley, B., Best, D., Franklin, L., May, R., Rice, D., Riensche, R., & Younkin, K.. (2009). The Scalable Reasoning System: Lightweight visualization for distributed analytics. Information Visualization: Special Issue on Visual Analytics, Science and Technology, 8(1), 71-84.

Post, F.. (2011). Data Visualization: Featuring Interactive Visual Analysis. Computer Graphics Forum, 30(2), xxiii-xxiii.

Jennifer Xu, & Hsinchun Chen. (2005). Criminal network analysis and visualization. Association for Computing Machinery. Communications of the ACM, 48(6), 100-107.

Newell's Simplified Car-Following Model
Drivers tend to display oscillatory paths that are characterized with cycles of regular acceleration or deceleration because of traffic oscillations. The term traffic oscillations are used to describe the stop-and-go driving situations that are common in overcrowded traffic. Generally, conventional wisdom postulates that traffic oscillations are brought by instabilities in longitudinal car interactions. As a result of increased traffic oscillations, especially in congested traffic, numerous car-following models have been developed and proposed in the recent past. These models have been developed to duplicate oscillations through assumption of probabilistic headways during accelerations. In addition, car following models are the most significant reflections of traffic flow dynamics based on single vehicles. An example of the recently proposed or developed car-following model is the Simplified Car-Following Model by Newell.

The Model's Assumptions

Newell's car-following model is arguably the simplest model that was recently developed as part of the microscopic models whose dynamics…...

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References

Ahn, S., Cassidy, M. J. & Laval, J. (2004). Verification of a Simplified Car-Following Theory.

Transportation Research Part B: Methodological, 38(5), 431-440.

Chen, D., Laval, J., Zheng, Z. & Ahn, S. (2012). A Behavioral Car-Following Model that

Captures Traffic Oscillations. Transportation Research Part B: Methodological, 46(6), 744-761.

DNP POJECT : DATA COLLECTION AND ANALYSISImplementation Plan/ProceduresPhase 1: Program Development (Months 1-3) Conduct comprehensive literature review on evidence-based practices for culturally tailored hypertension self-management Collaborate with community stakeholders and minority health organizations to understand sociocultural determinants and barriers Design culturally relevant, linguistically appropriate education curriculum with interactive multimedia resources ecruit and train a diverse team of bilingual, culturally competent nurses and community health workersPhase 2: Participant ecruitment (Month 4) Establish partnerships with community organizations, faith-based institutions, and healthcare providers serving minorities Conduct informational sessions to raise awareness about the program Screen and enroll 300-400 minority adults with hypertension residing in Tulsa Obtain informed consent and administer baseline assessments (blood pressure, SF-36 survey)Phase 3: Program Implementation (Months 5-7) Week 1: Introduction to hypertension and importance of self-management Weeks 2-3: Skills training (BP monitoring, medication adherence, dietary education, cooking demos) Week 4: Physical activity promotion and goal-setting Week 5: Mid-program BP…...

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ReferencesEsubalew, H., Belachew, A., Seid, Y., Wondmagegn, H., Temesgen, K., & Ayele, T. (2024). Health-Related Quality of Life Among Type 2 Diabetes Mellitus Patients Using the 36-Item Short Form Health Survey (SF-36) in Central Ethiopia: A Multicenter Study. Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy, 17, 1039–1049.HIPAA Retention Requirements. (2024). The HIPAA Journal. Retrieved from hipaajournal.com/hipaa-retention-requirements/.Koh, E. T., Leong, K. P., Tsou, I. Y. Y., Lim, V. H., Pong, L. Y., Chong, S. Y., & Seow, A. (2016). The reliability, validity and sensitivity to change of the Chinese version of SF-36 in oriental patients with rheumatoid arthritis. Rheumatology, 45(8), 1023–1028.Neuman, W. L. (2018). Social Research Methods: Qualitative and Quantitative. Pearson.Peek, M. K., Ray, L., & Patel, K. (2014). Reliability and Validity of the SF-36 Among Older Mexican Americans. Gerontologist, 44(3), 418.Wu, Q., Chen, Y., Zhou, Y., Zhang, X., Huang, Y., & Liu, R. (2023). Reliability, validity, and sensitivity of short-form 36 health survey (SF-36) in patients with sick sinus syndrome. Medicine, 102(24), e33979.https://www.

Dictionaries
Neo-looked confused. "Programs hacking programs?" To which the Oracle replied, "At some time, a program was written to govern everything. The only time you see a program is when it's malfunctioning, and needs to be replaced, or exiled." Jack, I know you are new in the IT department, but I can't emphasize enough the need for you to take the Oracle's advice. We want our software, CRM modules, and sales processing databases to function without being seen. We don't need any programs fowling up other programs. I don't want to have to fight any 'vampires' or 'ghost twins' because you have allowed the database data dictionary to get corrupted.

The data dictionary is a tool which keeps the other programs up-to-date, and feeds information to the other querying programs as to what to expect from the data they are receiving. The data dictionary is a separate, and important indexing database…...

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Bibliography

Tribunella, T. (2002) Designing Relational Database Systems. The CPA Journal, Vol. 72.

Weber, A. (2000, Oct. 1) 14 No-Fail Steps to Building a Database. Target Marketing;

Community Dementia Care and the Chronic Care Model
nd-Stage Dementia valuation Proposal

Health Promotion Plan for Community nd-Stage Dementia Care: The Chronic Care Model

Health Promotion Plan for Community nd-Stage Dementia Care: The Chronic Care Model.

In 2013 an estimated 5.0 million Americans over the age of 65 suffered from Alzheimer's disease (Alzheimer's Association, 2013). Although the U.S. Centers for Disease Control and Prevention (CDC) considers dementia/Alzheimer's to be the fifth leading cause of death among adults 65-years of age or older, careful examination of Medicare claims data revealed that dementia is probably right behind cardiovascular disease as the second leading cause of death for this age group (Tinetti et al., 2012). Most of these patients would prefer to die at home, not only because of comfort concerns, but due to the higher quality of care that tends to be provided by informal and paid caregivers in this setting (reviewed by Teno et al.,…...

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Eloniemi-Sulkava and colleagues (2009) evaluated patients at baseline using the Barthel Index and Neuropsychiatric Inventory (NPI) (see Appendix). The Barthel Index (Stone, Ali, Auberleek, Thompsell, & Young, 1994; University of Iowa Healthcare, n.d.) and NPI (Cummings et al., 1994) were administered again at 6 and 12 months into the study and will be used in the current study to track ADL and BPSDs using the same intervals. PQOL will represent a composite score obtained using the Color Analog Scale for pain (Santos & Castanho, 2013) and the Quality at the End of Life Scale (QUAL-E) (National Palliative Care Research Center, 2005) (see Appendix). In cases of severe cognitive impairment, completion of the QUAL-E may depend on family caregivers. FCQOL will be evaluated using the Zarit Burden Scale (Regional Geriatric Program Central, 2014) (see Appendix). The success of the intervention, as perceived by family caregivers and providers, will be assessed using the questionnaires developed by Morita and colleagues (2013). The goal of these questionnaires will be to evaluate how effective the community palliative intervention was in improving the knowledge and skills of palliative care, increasing access to specialized services, coordinating care services, and increasing deaths at home. This evaluation will be performed following the death of the patient or the end of the study period, whichever comes first. The validity and reliability of the questionnaires developed by Morita et al. (2013) have not been evaluated, but should prove informative and provide context for the other findings.

Discussion

A review of interventions designed to improve the quality of community palliative care has revealed mixed findings, but the trend is in the desired direction of reducing the number of patients dying in hospital wards, ICUs, and hospice facilities. CCM has garnered the interest of researchers interested in improving palliative care outcomes for patients, family caregivers, and providers alike, and have begun to study the efficacy and quality of interventions, including CCM. This proposal provides justification for implementing CCM for end-stage dementia patients residing at home and details an evaluation strategy that can be implemented to determine the efficacy, effectiveness, and quality of the care provided. In contrast to many other studies, however, this proposal places equal value on the experiences of patients, family caregivers, and providers alike, in addition to the more common outcome measures of BPSDs and institutional admissions. The methods of data gathering will involve the review of patient records and several instruments designed

Measures
Targets

nitiatives

Profitable Growth

Return on nvested Capital

Return on Equity

Only accept strong NPV projects

15% ROC

20% ROE

Simplify the organization structure

Provide an open environment for idea generation and brainstorming

ndustry leading innovation

Update product upgrade cycle. Refresh or introduce a product at least once every two years.

Highest Quality products and services

Higher Gross Margin

nvest heavily in R and D with excess Free Cash Flow

Establish strong customer and brand loyalty

Adopt the net promoter score and customer satisfaction rating survey

4% Market Share growth per year

Rewards programs and partnerships with other service providers

Establish a well recognized brand

Strong brand recognition

Become the number 1 or number 2 rated brand in each product category

nvests heavily in marketing and advertising

Question 2

A major retailer such as Wal-Mart would be best served by using a transactional database. Wal-Mart unlike many other retailers has a strong competitive advantage relative to peers in the industry. t competes primarily based on price and value. As a low cost producer, Wal-Mart…...

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I would use linear regression as it allows a practitioner to see clusters of date scattered around a particular area. Although many problems can persist with linear regression, I believe it provides the best means of explaining the overall relationship between loans and default risk. The practitioner must first eliminate non-stationary variables in addition to co dependence. Variable that depend on the proceeding variable can cause problems and errors in the overall regression analysis. However, solutions such as use of the adjusted R squared metric, the Dickey-Fuller test and others can help eliminate these concerns. Regressions, through the use of the R squared metric can help an analyst better determine what percentage of the loan defaults can be explained by variables such as income, debt, or other variables. Regressions are also flexible allowing for multiple variables to be used in an explanatory fashion.

The data mining items I would need to conduct a regression analysis are varied. For example, I would need variables relating to debt levels on and individual basis. I would also need income, education, and demographic information. For example, homes in the New York will be more expensive than homes in North Dakota. As a result, a loan will be much higher in New York. With the higher loan amount, the possibility of default and capital loss is also higher. With a higher default risk, the bank will demand higher collateral, more money down, etc. The bank must be sure that the collateral backing the loan is appropriately priced given the market conditions that are prevailing and will prevail in the future.

In 2008, regression analysis failed at financial institutions because they failed to see or account for "tail risk." These risks are those that are three standard deviations away from the mean. Their regressions didn't take these occurrences into account because they were very rare, or had never happened before. By omitting these risks from the regression analysis, the outputs were in error. In particularly, a wave of massive loan defaults occurred that nearly crippled the United States financial system. Due to these occurrences, regressions must take into all the variables, no matter how farfetched or rare they may be.

Sundborg et al. (2012) conducted a quantitative study, which examined the preparedness of nurses to provide care for women who are exposed to intimate partner violence (p.1). The study was carried out on the premise that intimate partner violence (IPV) has significant effect on women's health. Therefore, nurses need adequate preparations to identify such victims and provide suitable interventions. While the study provides significant insights relating to nurses' preparedness in handling such patients, there are some drawbacks associated with it as demonstrated in this critique.
The sample for this study was randomly selected from the 174 primary health care centers that employ approximately 1,200 active nurses in Stockholm County (Sundborg et al., 2012, p.3). Since the sample was selected randomly, a probability sampling method was utilized by the researchers. Every individual in the sampling population had equal chance of being included in the study. The results from this sampling method were…...

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Reference

Sundborg, E.M., Saleh-Stattin, N., Wandell, P. & Tornkvist, L. (2012, January 10). Nurses' Preparedness to Care for Women Exposed to Intimate Partner Violence: A Quantitative Study in Primary Health Care. BMC Nursing, 11(1), 1-11.

ecurity Management Plan
John's Hospital

Privacy of client information is an assurance that every patient wants and this assurance is what the hospital can build patient confidence on. The lack of it therefore may have consequences such as loss of confidence in the hospital, loss of clientele and the emergence of a poor reputation. This paper looks at the t. John's Hospital which has experienced the leakage of confidential information a problem that needs to be addressed. It highlights the steps the hospital must take in its management plan. In the first step, hospital must identify how widespread the problem is and where exactly there are weaknesses in the system. econdly, the hospital's staff must receive adequate training in methods to deal with confidential information especially its destruction. A culture must be developed to deal with this information discreetly. In this same breadth breach must be understood by all staff as far…...

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Shred it (2013), Security Breach, Shred --It making sure it is secure,   (Retrieved 16/11/2015)http://www.shredit.com/en-us/document-destruction-policy-protect-your-business 

Scallan T. (2013), Disaster recovery solutions underscore the importance of security, Health Management Technology,   (Retrieved 16/11/2015)http://www.healthmgttech.com/disaster-recovery-solutions-underscore-the-importance-of-security.php 

U.S. Department of Health and Human Services (HHS) (2000), Health information privacy, HHS.gov,   (Retrieved 16/11/2015)http://www.hhs.gov/ocr/privacy/hipaa/understanding/srsummary.html 

Research data analysis is the process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains.

Data analysis techniques vary with the type of data, the intended purpose of the analysis, and the user's capabilities and resources. Some techniques have been automated into mechanical processes, but most analysis requires some human intervention.

Data analysis is a process that involves several steps:

1. Data collection: The first step in....