Cloud Based Information System Samples †MyAssignmenthelp.com

Question: Discuss about the Cloud Based Information System. Answer: Introduction Headspace is an organization which works with the people belonging to the age group of 12 to 25. This organization works for mental ill health. Depression and anxiety are the two most common mental health problems, although there are many other problems. One of the problems related with this aspect is that a young person with a mental illness might see multiple professional before they can get the help they need. Each time they need to tell their own story (Allison, Bastiampillai Goldney, 2016). Soon the young people calm up and say very little as a result of which it becomes very difficult for the professional to help them. The main aim of the report is to design a vision document for a project. The project would capture the story the first time it is told. As a result of which the professional will get access so that the case notes and the story can be assisted more appropriately. Non functional Requirement Non functional Requirement in a software system describes not about what the software will do but how the software will do it. For example critical system qualities, system interfaces, user interface requirement and system constraints. Critical System Qualities Critical system are either integrated complex system which can be termed as system of system, or small sub system (components) the performance and quality of performance and quality with the projects development. Putting emphasis on the definition which is applicable to the critical system of a particular project the type of focus needed and the task can be varied accordingly (Kumar, Yadav Khan, 2017). With respect to the designing of the critical system it should be taken into consideration the account manufacturing and obsolescence of the component and the hardware related aspect. In a critical system error can occur from anywhere. Failure can result in a huge economic loss, physical damage or threads. Critical system failure can be divided mainly into three parts as; Safety Critical system: This can result in loss of life, injury or damage to the environment Mission Critical System: This is a failure result in failure of some goal directed activity. Business Critical System: This type of failure result in high economic losses. System Dependability For critical system, it is usually the case that the most important system property is the dependability of the system. The dependability of the system reflects the degree of trust with the user in the particular system (Shah, 2016). It basically reflects the extent of the users confidence that the system would operate as the user expects it to do and that it would not fail in normal use. Usefulness and trustworthiness is not the same thing all together (Hawkins et al., 2016). A system does not have to be trusted to be useful. System is not dependable and is unreliable, insecure and unsafe may be rejected by the user. In this context the cost of the system failure may be very high. The cost related to critical system failure is so high that with aspect to development methods alternative methods are used that are not cost effective for other types of system (Nurmi et al., 2016). Some example of development methods are formal methods of software, static analysis and external quality assurance. Usability Software usability can be described as how the end user of the system can use, learn and control the system. Maintainability (or Flexibility / Testability) The definition of maintainability implies much the code change is brittle. Because of this implementation the tie can be termed together with flexibility and testability into the overall maintainability of the project. Scalability In times of increase usage the ability of the system to gracefully meet the demand of the stress can be termed as scalability Availability The time the system is up and running and the mean time between failures (MTBF) is known as the availability of program. Functional requirement vs. non functional requirement Functional and non functional requirement need to be selected carefully in order to ensure that it makes sense in the context of the projects final outcome (Li et al., 2016). Requirement generally can be broadly divided into two parts Functional and non functional. Functional requirement: With emphasis on the system what it is intended to do can be termed as functional requirement. In order words functional requirement describes a particular behavior of the function related to the system when certain conditions are meeting (Abidin et al., 2016). Typical function requirement includes the following: Transaction correction, cancellation and adjustment Authentication Authorization levels External interfaces Non Functional Requirement: The performance of the system with regards to a specific fuction it is intended to perform can be termed as non functional requirement. In simple way it can be described as how the system should behave with regards to the limit how their functionality are. It is mainly intended in describing the systems characteristics and its attributes. (Filipe-Ribeiro et al., 2016). Typically non functional requirements include: Scalability Capacity Reliability Maintainability It is very much important to correctly state the non functional requirement since it would directly affect the users experience when interacting with the system. Review of Cloud Based System Strengths The strength with consideration of the environment in which it is deployed is: Cost reduction: Cloud computing eliminates certain conventional costs such as hardware, electric bills, paper work and IT staff. Scalability: The payment method is related to the resources and services it provides. It eliminates the worry about hardware and software upgrades as they grow along with the business. Up to date technology: With regards to small organization cloud computing offers the latest and sophisticated technologies from being up to par with the competition. The Weaknesses with consideration of the environment in which it is deployed is: Risk of unavailability: It is always possible that the vendors resource can shut down. In such a situation there is no option than to wait for the service to roll back. Data ownership and mobility: Service provider cannot guarantee 100% in order of delivering back the store data or other services. Data authenticity: Due to the limitation of not being present along the data limitation can be applied in order of authenticity and validity. Researchers have put forward 6 must considered factor before implementing into cloud computing service provider. The factors are: Confidentiality: The data should be kept secured. Integrity: The data should be unaltered without proper permission. Availability: The data should be accessible from anywhere and at any time to those who have the authority to access the data. Security Aspect or Threats The European Network and information security provides a basic description of the potential risk involved in the implementation and the working of cloud computing. The security aspect or the threads can be divided into following categories. Loss or theft of intellectual property: Companies store sensitive information in the cloud. When a cloud service is breached, the sensitive information can be accessed by the cyber criminals. In the scenario of absence of breach certain service can even pose the risk in their term and the condition ownership claim of the data uploaded by the user. Loss of control over the end user: when the workers work with cloud they can access the information and no one would know it. This can lead to the access to sensitive information which should be dangerous with the view point of the company. Diminished customer trust: when there is a data breach it would directly affect the trust of the customer. It has been recorded the largest breach was the stealing of over 40 million customer credit and debit card numbers. SDLCApproach Predictive Analysis: Pros Predictive analysis can be applied to a wide range of business and it plays as a vital player in search advertising and recommendation engine. Predictive analysis has multiple forms or techniques like predictive modeling, optimization and decision analysis, predictive search and transaction profiling (Hwang, 2017). These techniques can be provided by the managers and executives with decision making tools in order to influence sales, up selling and revenue forecasting, optimization of the process of the business and even implementing new strategies. Thought Predictive analysis is beneficial it is not for everyone (Xia et al., 2016). Cons A company that wishes to utilize the data driven decision making needs in order to access substantial data which are relevant from the activity range , and sometimes big data sets are hard to come by (Munasinghe Perera, 2016). Time also plays a vital role in this sphere on how the technique works. Adaptive Analysis Pros Adaptive Analysis does not need to invest time in it and effort. With emphasis on the documentation part, the documentation is crisp and to the point to save time. The end result is the high quality software in least possible time duration with the satisfaction of the client (Beloglazov, Abawajy Buyya, 2017). Cons In case of some software deliverables especially the software which are large it is very much difficult to access the effort required at the beginning of the software development life cycle. In this technique lack of emphasis is put on the designing and documentation which is a very important aspect in any project. The project can sometimes go out of track if the need of the customer is not known and what would be the final product is not known (Yadav Doke, 2016). Recommendation In the above scenario Predictive analysis would be the recommendation because of the reason that the approach has various forms and technique which can be implemented according with the need of the project. Different project have different needs and specification so in such a technique different approaches can be implemented accordingly. Conclusion From the above report it can be concluded that the organization in order of the implementation of the technique has different techniques in hand. The project being a part of the health care issue it should be taken care that the software would work according to the requirement and emphasis should be given on the quality of the software and well as the reliability of the software. The different aspect like functional requirement as well as the non functional requirement should be kept in mind in order for the software to work properly. The recommendation of the implementation of the software development tool is Predictive analysis which gives a wider prospective to the development of the software. References Abidin, M. Z. Z., Nawawi, M. K. M., Kasim, M. M. (2016, October). Research design of decision support system for team sport. In AIP Conference Proceedings (Vol. 1782, No. 1, p. 040001). AIP Publishing. Allison, S., Bastiampillai, T., Goldney, R. (2016). Australias national youth mental health initiative: Is headspace underachieving?. Australian and New Zealand Journal of Psychiatry, 50(2), 111-112. Beloglazov, A., Abawajy, J., Buyya, R. (2017). Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future generation computer systems, 28(5), 755-768. Filipe-Ribeiro, L., Milheiro, J., Matos, C. C., Cosme, F., Nunes, F. M. (2017). Data on changes in red wine phenolic compounds, headspace aroma compounds and sensory profile after treatment of red wines with activated carbons with different physicochemical characteristics. Data in Brief, 12, 188-202. Hawkins, S., Wang, H., Liu, Y., Garcia, A., Stringfield, O., Krewer, H., ... Goldgof, D. (2016). Predicting malignant nodules from screening CT scans. Journal of Thoracic Oncology, 11(12), 2120-2128. Hwang, K. (2017). Cloud and Cognitive Computing: Principles, Architecture, Programming. MIT Press. Kumar, S., Yadav, D. K., Khan, D. A. (2017). A novel approach to automate test data generation for data flow testing based on hybrid adaptive PSO-GA algorithm. International Journal of Advanced Intelligence Paradigms, 9(2-3), 278-312. Li, T., Guo, Y., Hu, H., Zhang, X., Jin, Y., Zhang, X., Zhang, Y. (2016). Determination of volatile chlorinated hydrocarbons in water samples by static headspace gas chromatography with electron capture detection. Journal of separation science, 39(2), 358-366. Munasinghe, B., Perera, P. L. M. (2016). Adopting SDLC in Actual Software Development Environment: A Sri Lankan IT Industry Experience. Nurmi, D., Wolski, R., Grzegorczyk, C., Obertelli, G., Soman, S., Youseff, L., Zagorodnov, D. (2017, May). The eucalyptus open-source cloud-computing system. In Proceedings of the 2017 9th IEEE/ACM International Symposium on Cluster Computing and the Grid (pp. 124-131). IEEE Computer Society. ztrk, V. (2016). Flexible and Adaptive Life Cycle Framework for Software Development. JSW, 11(9), 943-951. Shah, U. S. (2016). An Excursion to Software Development Life Cycle Models: An Old to Ever-growing Models. ACM SIGSOFT Software Engineering Notes, 41(1), 1-6. Xia, Z., Wang, X., Zhang, L., Qin, Z., Sun, X., Ren, K. (2016). A privacy-preserving and copy-deterrence content-based image retrieval scheme in cloud computing. IEEE Transactions on Information Forensics and Security, 11(11), 2594-2608. Yadav, D. S., Doke, K. (2016). Mobile Cloud Computing Issues and Solution Framework.