Settlement Patterns - Studying Societys Evolution

Settlement Patterns - Studying Societys Evolution In the scientific field of archaeology, the term settlement pattern refers to the evidence within a given region of the physical remnants of communities and networks. That evidence is used to interpret the way interdependent local groups of people interacted in the past. People have lived and interacted together for a very long time, and settlement patterns have been identified dating back to as long as humans have been on our planet. Settlement pattern as a concept was developed by social geographers in the late 19th century. The term referred then to how people live across a given landscape, in particular, what resources (water, arable land, transportation networks) they chose to live by and how they connected with one another: and the term is still a current study in geography of all flavors. Anthropological Underpinnings According to archaeologist Jeffrey Parsons, settlement patterns in anthropology began with the late 19th-century work of anthropologist Lewis Henry Morgan who was interested in how modern Pueblo societies were organized. Julian Steward published his first work on aboriginal social organization in the American southwest in the 1930s: but the idea was first extensively used by archaeologists Phillip Phillips, James A. Ford and James B. Griffin in the Mississippi Valley of the United States during World War II, and by Gordon Willey in the Viru Valley of Peru in the first decades after the war. What led to that was the implementation of regional surface survey, also called pedestrian survey, archaeological studies not focused on a single site, but rather on an extensive area. Being able to systematically identify all the sites within a given region means archaeologists can look at not just how people lived at any one time, but rather how that pattern changed through time. Conducting regional survey means you can investigate the evolution of communities, and thats what archaeological settlement pattern studies do today. Patterns Versus Systems Archaeologists refer to both settlement pattern studies and settlement system studies, sometimes interchangeably. If there is a difference, and you could argue about that, it might be that pattern studies look at the observable distribution of sites, while system studies look at how the people living at those sites interacted: modern archaeology cant really do one with the other, but if youd like to follow through, see the discussion in Drennan 2008 for more information about the historical differentiation. History of Settlement Pattern Studies Settlement pattern studies were first conducted using regional survey, in which archaeologists systematically walked over hectares and hectares of land, typically within a given river valley. But the analysis only truly became feasible after remote sensing was developed, beginning with photographic methods such as those used by Pierre Paris at Oc Eo but now, of course, using satellite imagery. Modern settlement pattern studies combine with satellite imagery, background research, surface survey, sampling, testing, artifact analysis, radiocarbon and other dating techniques. And, as you might imagine, after decades of research and advances in technology, one of the challenges of settlement patterns studies has a very modern ring to it: big data. Now that GPS units and artifact and environmental analysis are all intertwined, how to do you analyze the huge amounts of data that are collected? By the end of the 1950s, regional studies had been performed in Mexico, the United States, Europe, and Mesopotamia; but they have since expanded throughout the world. Sources Balkansky AK. 2008. Settlement pattern analysis. In: Pearsall DM, editor. Encyclopedia of Archaeology. New York: Academic Press. p 1978-1980. doi: 10.1016/B978-012373962-9.00293-4 Drennan RD. 2008. Settlement system analysis. In: Pearsall DM, editor. Encyclopedia of Archaeology. New York: Academic Press. p 1980-1982. 10.1016/B978-012373962-9.00280-6 Kowalewski SA. 2008. Regional Settlement Pattern Studies. Journal of Archaeological Research 16:225–285. Parsons JR. 1972. Archaeological settlement patterns. Annual Review of Anthropology 1:127-150.

Multiculturalism Issues in Curriculum Assignment

Multiculturalism Issues in Curriculum - Assignment Example It shall also make recommendations and/or predictions for the future of this issue. Finally, it shall establish support for recommendations and/or predictions with this research. Body The multicultural curriculum has its roots in the history of multicultural education which follows the history of the US civil rights movement (Fillion, n.d). The desegregation practices during the 1950s were established in order to provide equal education for all individuals, regardless of race or any other demographic considerations. The 1960s and 1970s, desegregation practices expanded to include application in seeking equity of all students in terms of their human rights (Banks, 2000). With the focus on human rights, multicultural concerns in education were considered, recognizing the importance of establishing awareness in a culturally diverse community. The changes in the teaching curriculum came under the collective heading of multiculturalism. These changes were also apparent in Britain and in A ustralia just as they were unfolding in the US (Lynch, 1983). The educational authorities recognized the fact that the curriculum must come from the social and the ethical concepts being seen in the multicultural setting. Various references to the inherent value of all human beings were also highlighted, and this attached value was also seen in the human rights movement in the 1960s and 1970s (Lynch, 1983). In these states, the focus was on acknowledging the need for the multicultural curriculum and for teachers to support and promote such a program. Research was then carried out and workshops with teachers implemented. Such actions were able to establish that even as many resources were needed in order to â€Å"bring teachers to the level to the level of multicultural teaching acceptable to the proponents of multicultural curriculum, an emphasis must be placed on institutions of higher learning to require the incorporation of inclusionary multicultural methods and practice in the teacher certification process† (Fillion, n.d, p. 1). In effect, without any inclusionary methods in the institutions of higher learning, the multicultural curriculum could not find its place in the educational system. One of the issues in the multicultural curriculum revolves around the fact that the US is composed of different racial groups, but the educational system, including the curricula, the books, the resources, and other teaching materials are founded on White Anglo-Saxon Protestants, otherwise known as the mainstream Americans (Tiedt and Tiedt, 2000). This type of curriculum largely does not consider the experiences, the history, and the culture of other racial and ethnic groupings in the US (Banks, 1988). This type of curriculum has a negative impact on minorities as well as those belonging to the mainstream culture. This is because racism and ethnocentrism are protected and nourished under this mainstream-supported culture. A mainstream-focused curriculum has a neg ative impact on the mainstream students because it further supports their sense of superiority, misleading their understanding of their relationship with other members of society, especially the ethnic minorities (Banks, 1988). The lack of a multicultural curriculum also prevents the process of understanding concepts and information from the point of view of other cultures and races. Another issue with the

Scholarly Research Articles Essay Example | Topics and Well Written Essays - 1000 words

Scholarly Research Articles - Essay Example This article is within one’s chosen area of research on managing obesity in children, particularly as it discussed means of prevention as the first line of defense. There were other recommended treatments for childhood obesity which were consistent with the policy statements issued by the American Academy of Pediatrics (AAP). The strengths of the article lie in the clarity of structure and presentation of information, as well as the magnitude of scholarly and academic authoritative references that supported and validated the authors’ contentions. There were a total of 83 references with 1997 as the earliest research date and 2003 as the most current. The only weakness of the research was its failure to present an actual study comprising of a sample of participants to test theories, particularly the neuroendocrine control of weight, which, as indicated by the authors were still being investigated at the time of their report being published. Since most information were so urced from secondary sources, there was no new data or findings instigated from their current research. Evidence based management of childhood obesity: evidence based paediatrics By Laurel Edmunds, Elizabeth Waters, Elizabeth J. Elliot Edmunds, Waters and Elliot (2001) presented a research through citing a real case scenario of an obese child manifesting depression and finding out how to manage and address the dilemma through research literatures as evidenced based support. The theoretical approach of the authors identified the crucial role of secondary sources of information to support issues associated with obesity, particularly on areas of prevalence, psychological problems, current and future health problems, risk of obesity in adulthood, and family based weight reduction programs, among others (Edmunds, et.al, 1 – 3). The article is significant for one’s personal area of research focusing on the management of childhood obesity in terms of the information provided that supports frequently asked questions about obesity with specifically identified type of evidence (ibid, 5). The article’s strengths are found through the manner and clarity of structure, presentation and support from 25 authoritative and scholarly sources on the topic. By providing an actual case scenario, readers are given a clear understanding of reality as manifested through the points of views of both the child and the parents. Likewise, through the presentation of evidence, professionals, practitioners and clinicians are made aware of viable interventions that require the collaborative efforts of the family and the school. The only weakness of the article is its failure to identify research limitations and applicability of findings. Further, also school-based programs were presented as a promising approach to prevent obesity, it was clearly noted that â€Å"schools are not suitable locations for treatment due to stigmatization of children receiving treatment† (ibid, 5). Further studies must validate and determine the net effect of school based programs for the child’s proposed treatment for this chronic illness. The Impact of Physical Activity and Obesity on Academic Achievement Among Elementary Students By Jimmy Byrd The article written by Byrd and reviewed, accepted and

Comparing Human Resource Management in Multinational and Local Essay

Comparing Human Resource Management in Multinational and Local Companies in Hong Kong - Essay Example Moreover, there is a clear difference in how it is executed. All employees from the American company were divided into different groups, and in order to ensure efficient management of the employees, a manager was assigned to each group. The company also had a data base where each individual's sales performance and feedback from customers was encoded and saved. In these instances, in terms of employee performance, employees within the group were also allowed to put in their opinion of their colleagues. Using numerical totals from the database, an evaluation on how each person has been performing could easily be established. Moreover, the interesting part of this database is on how customers’ feedback was also accounted for in the overall evaluation of the employees. The fact that the employees formed groups to monitor one another was very much in keeping with the findings of Bjorkman (2003) on Chinese companies’ putting greater emphasis on team work. The Swiss company al so showed traces of an Appraisal system in place. They also went through a performance evaluation process for their employees. They mostly divided their staff into two groups: first, the lower level staff which were general operational staff, and second, the other staff which were usually higher level employees. The lower level staff usually underwent a two-way communication process using the Preference Evaluation (PE) form. Employees would then set objectives at the beginning of the year, with fulfillment of the objectives monitored throughout the year. By the end of the year, their managers usually evaluated the individuals by assessing whether or not they have achieved their objectives. A performance rating also followed the assessment. Among higher level staff, they also underwent a similar evaluation process, however, the review was called performance cycle, and was usually done through an online tool. These individuals also set objectives at the beginning of the year and were then monitored throughout the year. The difference in these two groups of employees is that towards the end of the year, there was a performance calibration session conducted by the managers on the employees. Such a session was not carried out on managers. This session consisted of an open forum where the managers discussed employee performance and then agree on a performance rating for each employee. This allowed a more unbiased evaluation of the employees. It also created a chance for cross department discussions and the sharing of opinions among these departments. In contrast, employees from the Hong Kong Company revealed that their company was using a more traditional appraisal system. Managers evaluated employees’ attitude in the workplace, and took into account the number of times each employee was late for work or if their work was done on time. The Hong Kong company does not have an evaluation system like the other two companies, and the employees’ evaluation i s mainly based on their performance and the perception of their managers. The Swiss company’s evaluating system shares some similarities with the American company. They both have a database for the easy evaluation of their employees. They also have groups which help with the evaluation process and which help illuminate the probability of bias. Both companies have traces of HR practices from the East and the

Support Vector Machine Based Model

Support Vector Machine Based Model Support Vector Machine based model for Host Overload Detection in Clouds Abstract. Recently increased demand in computational power resulted in establishing large-scale data centers. The developments in virtualization tech-nology have resulted in increased resources utilization across data centers, but energy efficient resource utilization becomes a challenge. It has been predicted that by 2015 data center facilities costs would contribute about 75%, whereas IT would contribute the remaining 25% to the overall operating cost of the data center. The Server consolidation concept has been evolved for improving the energy efficiency of the data centers. The paper focuses on support vector machine based novel approach to predict the overload and underload pattern of the servers for better data center reconfiguration. Keywords: Support vector machine, energy efficiency. 1 Introduction Virtualization plays an important role in cloud computing, since it permits appropriate degree of customization, security, isolation, and manageability that are fundamental for delivering IT services on demand. One of its striking features is the ability to utilize compute power more proficiently. Particularly, virtualization provides an opportunity to consolidate multiple virtual machine (VM) instances on fewer hosts depending on the host utilization, enabling many of computers to be turned-off, and thereby resulting in substantial energy savings. In fact, commercial products such as the VMware vSphere Distributed Resource Scheduler (DRS), Microsoft System Center Virtual Machine Manager (VMM), and Citirix XenServer offer VM consolidation as their chief functionality[1]. But with the rapid growth in computing demand, the number of datacenters grows with the need which leads to more number of servers active at a time. The high active servers’ ratio leads to more energy emission and production of Carbon dioxide (CO2). According to data centers’ study, the data centers are not utilized up to their maximum utilization level which leads to more active servers, everyone utilized to less than their total capacity. With this in mind, it is worthwhile to attempt to minimize energy consumption through any means available. Various research agencies and universities have contributed into the research and design of heat dissipation and control in the data center. Virtualization is a technology that contributes to the maximum u tilization of the servers by virtual machine (VM) consolidation and VM Migration. The decision of reallocation of virtual machine for VM consolidation depends on the host utilization behavior. The VMs from the under-utilized and over-utilized hosts are relocated to other hosts by packing the VMs on minimum number of hosts. The hosts having no virtual machine are shifted to the passive mode so that the total energy consumption can be reduced. Statistical methods played a great role in predicting the behavior of the host in dynamic manner. The author [3] has proposed various statistical methods for host overload and underload behavior of the hosts in his thesis. These algorithms take input as the previous or current utilization of the hosts and predict the future based on the previous or current state of the system. He has proposed Local Regression, Median Absolute Deviation, Robust Local Regression and Markov Chain model for predicting the overloaded hosts [3]. All statistical models cannot be applied to all the environments. The choice of the statistical methods d epends on the input data, because every statistical model is based on some assumptions. Markov chain model assumes that the data will be stationary but complex and dynamic environment like cloud, experience highly variable non-stationary workload. The author [3] in his thesis modified his model by using multisize sliding window workload estimation method so that it can be suitable for the cloud environment. In this paper we have proposed a prediction based model i.e. Support Vector Machine (SVM) to predict the host utilization to forecast the host overload and underload behavior of the host. The rest of the paper is organized as follows. Section 2 explains the basic concepts and modeling approaches of the Support Vector Machine. In section 3, the literature review related to Support Vector Machine is presented. In section 4 the model is applied to time series forecasting and its performance is compared with those of other forecasting models. Section 5 contains the concluding remarks. 2 Support Vector Machine Support vector machine is a novel technique based on neural network invented by Vapnik and his co-workers at AT T Bell Laboratories in 1995. The objective of SVM is to find a generalized decision rule through selecting some particular subset of training data, called support vectors. Training SVMs is equivalent to solving a linearly constrained quadratic programming problem. The quadratic equation is solved such that the solution of SVM is globally optimal and the quality complexity of the solution does not directly depend on the input space. Another key advantage of SVM is that SVMs tend to be resistant to over-fitting, even in cases where the number of attributes is greater than the number of observations. According to Vapnik there are three main problems in machine learning, e.g. Density Estimation Classification and Regression. In every case the main goal is to learn a function (or hypothesis) from the training data using a learning machine and then conclude general results base d on this knowledge. Time series is a series of data points S t à ¯Ã†â€™Ã… ½ R usually ordered in time. Time series analysis comprises the methods for analyzing the time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting models predicts future values based on the previously observed values. The main focus of this paper is to predict the overload and underload behavior of the hosts in cloud data centers based on the previous load pattern of the hosts in the datacenter. The time series prediction is affected by various factors like data is linearly separable or follows non-linear patterns, the learning is supervised learning or unsupervised learning and on support vector kernels. In Euclidean geometry linear separability is a geometric property of a pair of sets of points. The points are linearly separable or not are decided by visualizing the points in two dimensions plane by taking one set of points as being colored green and the other set of points as red. These two sets are linearly separable if there exists at least one line in the plane with all of the green points on one side of the line and all the red points on the other side. Usually in practical problems the data points are mapped to the high dimensional plane and the optimal separating hyper plane is constructed with the help of some special functions known as support vector Kernels in this new feature space. This method also resolves the problem where the training points are not separab le by a linear decision boundary. Because by using an appropriate transformation the training data points can be made linearly separable in the feature space. Figure1 (a) Linearly separable data1(b) non-linear patterns of data In supervisory learning, the training data is composed of input as well as the output vector (also called supervisory signals) whereas in un-supervisory learning the training data is composed of only input vectors. Supervisory learning produces better results because the output vector is already known and the predicted values by the SVM are compared with the output to learn better for the next step. In un-supervisory learning the output data points are not known and the training depends on the probability to drive better results out of it. SVM comes in the supervisory learning category and the kernel function makes the technique applicable for the linear as well as non-linear approximation. 3 Related Works In various practical domains time series modeling and forecasting has essential importance. A lot of research works is going on in this subject during several years. Many models have been proposed in literature for improving the accuracy and efficiency of time series modeling and forecasting. The author [1] has compared various time series prediction methods widely used these days. This paper investigated the application of SVM in financial forecasting. The autoregressive integrated moving average model(ARIMA), ANN, and SVM models were fitted to Al-Quds Index of the Palestinian Stock Exchange Market time series data and two-month future points were forecast. The results of applying SVM methods and the accuracy of forecasting were assessed and compared to those of the ARIMA and ANN methods through the minimum root-mean-square error of the natural logarithms of the data. Results proved that svm is better method of modeling and outperformed ARIMA and ANN. The author of [2] explains the time series concept and the various methods of predicting the future values based on ARIMA model, Seasonal ARIMA model, ANN model, time lagged ANN, seasonal ANN, SVM for regression, SVM for forecasting etc. they have also explained the forecast performance measure MFE (Mean Forecast Error), MAE (Mean Absolute error), MAPE (Mean absolute percentage error), MPE (Mean percentage error), MSE (Means squared error) etc. In paper [5], a model based on least squares support vector machine is proposed to forecast the daily peak loads of electricity in a month. In [5] the time series prediction was first used to forecast electricity load .In paper [4] the author has improved the method presented in [5] to derive more accurate results. The author has proposed dynamic least square support vector machine (DLS-SVM) to track the dynamics of nonlinear time-varying systems. The dynamic least square method works dynamically by replacing the first vector by the new input vector to obtain more accurate result.. The author in paper [9] has proposed the modified version on svm for time series forecasting. The algorithm performs the forecasting in phases. In the first phase, self-organizing map (SOM) is used to partition the whole input space into several disjointed regions. A tree-structured architecture is adopted in the partition to avoid the problem of predetermining the number of partitioned regions. Then, in the second phase, multiple SVMs, also called SVM experts, are constructed by finding the most appropriate kernel function and the optimal free parameters of SVMs. 4 Support Vector Machine Regression Formulations for Forecasting Host Overload Detection Host overload and underload detection is based on current utilization patterns of the host. The host utilization is a univariate time series. In univariate time series the future values are entirely based on past observations. The goal of the SVM regression is to find a function that presents the most deviation from the target values so the maximum allowed error is. The future values are predicted by splitting the time series data into training inputs and the training outputs. Given training data sets of N points, with input data and output data . Assume a non-linear function as given below: (1) w = weight vector, b=bias and is a non-linear mapping to a higher dimensional space. The optimization problem can be defined as: : (2) is a user defined maximum error allowed. The above equation (2) can be rewrite as: : (3) To solve the above equation slack variables needs to be introduced to handle the infeasible optimization problem. After introducing the slack variables the above equations takes the form as given below: : (4) The slack variables defines the size of the upper and the lower deviation as shown in the figure 2(a). Figure 2 (a) The Accurate points inside Tube 2(b) Slope decided by C For simplicity and for avoiding the case of infinite dimensionality of the weight vector w the optimization operation are performed in the dual space[4] the Lagrangian for the problem(a) is given by [2] (3) Here, where are the Lagrange multiples. Applying the conditions of the optimality, one can compute the partial derivatives of L with respect to equate them to zero and finally eliminating w and obtain the following linear system of equations (4) Here, and with is the kernel matrix. The LS-SVM decision function is thus given by [4] (5) The dynamic least square support vector machine is modified so that it is best suitable for the real world problems. The key feature of DLS-SVM is that it can track the dynamics of the non-linear time varying system by deleting one existing data point whenever a new observation is added, thus maintaining the constant window size. 4 Experiments We have used CloudSim for retrieving the utilization of the host based on the workload defined in the PlanetLab folder in CloudSim. It contains the daily virtual machine requirement and the utilization of the host is calculated based on the daily requirement of the virtual machines. After retrieving the utilization of the hosts LSSVMLabv1 toolbox is used for support vector regression and the results are compared with [10] and [5]. The comparison is based on MAPE (mean absolute percentage error) and Maximal error (ME). The chart shows that DLS-SVM produce better forecast for the load pattern of the hosts in the data centers. Figure2: Comparison of errors References Okasha, M. K.,Using Support Vector Machines in Financial Time Series Forecasting.International Journal of Statistics and Applications 2014, 4(1): 28-392. Adhikari, R., Agrawal, R. K. (2013). An Introductory Study on Time Series Modeling and Forecasting.arXiv preprint arXiv:1302.6613. Beloglazov, Anton. Energy-efficient management of virtual machines in data centers for cloud computing. (2013). Niu, D. X., Li, W., Cheng, L. M., Gu, X. H. (2008, July). Mid-term load forecasting based on dynamic least squares SVMs. InMachine Learning and Cybernetics, 2008 International Conference on(Vol. 2, pp. 800-804). IEEE. Bo-Jeun Chen, Ming-Wei Chang, and Chih-Jen LIN, â€Å"Load forecasting using support vector machines: A study on EUNITE competition 2001†, IEEE Trans. Power Syst., vol. 19, no. 4, pp. 1821-1830, Nov. 2004. Fan, Y., Li, P., Song, Z. (2006, June). Dynamic least squares support vector machine. InIntelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on(Vol. 1, pp. 4886-4889). IEEE. Kim, K. J. (2003). Financial time series forecasting using support vector machines.Neurocomputing,55(1), 307-319. Gui, B., Wei, X., Shen, Q., Qi, J., Guo, L. (2014, November). Financial Time Series Forecasting Using Support Vector Machine. InComputational Intelligence and Security (CIS), 2014 Tenth International Conference on(pp. 39-43). IEEE. Cao, L. (2003). Support vector machines experts for time series forecasting.Neurocomputing,51, 321-339. Haishan Wu, Xiaoling Chang. â€Å"Power load forecasting with least square support vector machines and Chaos Theory†, Proceedings of the 6th World Congress on Intelligent Control and Automation, Dalian, China, June 21-23, 2006. Rà ¼ping, S. (2001).SVM kernels for time series analysis(No. 2001, 43). Technical Report, SFB 475: Komplexità ¤tsreduktion in Multivariaten Datenstrukturen, Università ¤t Dortmund.

The Impact of Negro Spirituals on Todays Music Essay -- Exploratory E

The Impact of Negro Spirituals on Today's Music I believe that it would be difficult for someone to make the argument that Negro spirituals have not been influential in the field of music, much less the realm of gospel music today. However, church members often do not make the time to reflect on the heritage of a hymn or song to realize the meaning that the particular piece has carried with it through the decades, even centuries. With this in mind, I am going to look at the history of the Negro spiritual and then at specific hymns in the 1991 Baptist Hymnal, published by Convention Press, to see just what impact the Negro spiritual has had on today's church music. I believe that we will find that these songs have had a significant affect on our music, and that without it, we would not have many of the hymns that are now considered standard church music. An important observation regarding African music comes from Richard Jobson in The Golden Trade or a Discovery of the River Gambra [Gambia] and the Golden Trade of the Aethiopians. Although published in 1623, we learn a lot about the nature of African music when we read: "There is without a doubt, no people on the earth more naturally affected to the sound of musicke than these people; which the principal persons [that is, the kings and chiefs] do hold as an ornament of their state, so as when wee come to see them their musicke will seldome be wanting" (qtd. in Southern 4). By understanding that music was of utmost importance to the original slaves, we understand how the reverence of music was handed down through the many generations of slaves on the plantations. It is apparent that music was the highest form of expression for Africans, as well as... ...ital Schomburg African American Women Writers in the 19th Century Works Consulted Fisher, Miles Mark. Negro Songs in the United States. New York: Russell & Russell, 1968. Forbis, Wesley L. The Baptist Hymnal. Nashville: Convention Press, 1991. "God's gonna trouble the water: The essence of African American spirituality." U.S. Catholic. Nov. 1995. ProQuest. Online. 3 Aug. 1998. Maultsby, Portia K. Afro-American Religious Music: A Study in Musical Diversity. The Papers of the Hymn Society of America. 35. Springfield: The Hymn Society of America, n.d. Southern, Eileen. Readings In Black American Music. New York: WW Norton, 1971. ---. The Music of Black Americans: A History. New York: WW Norton, 1971. Thurman, Howard. Deep River and the Negro Spiritual Speaks of Life and Death. Richmond: Friends United Press, 1975.

Diploma in Business Communication Essay

Learning Outcomes for Unit 1. Understand different types of business information. 2. Be able to present business information effectively 3. Understand the issues and constraints in relation to the use of business information. 4. Know how to communicate business information using appropriate methods.| Grading criteria for unit| P1| Explain different types of business information their sources and purposes| P2| Present complex internal business information using 3 different methods appropriate to the user’s needs| P3| Produce corporate communications| P4| Evaluate the external corporate communications of an existing product or service| P5| Explain the legal and ethical issues in relation to the use of business information| P6| Explain the perational issues in relation to the use of business information| P7| Outline electronic and non-electronic methods for communicating business information, using examples for different types of audience| M1| Analyse different types of business information and their sources| M2| Analyse the legal ethical and operational issues in relation to the use of business information, using appropriate examples| D1| Evaluate the appropriateness of business information used to make strategic decisions.| D2| Evaluate the effectiveness of business information and its communication as key contributors to the success of an organisation, using examples to illustrate your points| Tasks| Grading criteria addressed| Part 1 – Task 1Using the research on HMV, prepare a written report which: a) Explain the types of information used, their sources and purposes(Make sure you include verbal, written, onscreen, multi-media, web-based) b) To achieve M1 your report should analyse the different types of information (make sure your explanation is detailed – break down each piece of information and look for connections etc) c) The next section of your report should evaluate the appropriateness of 2 sets of information used in making strategic decisions e.g. a marketing decision based on market research information or financial performance based on financial information gathered etc. (Find out what sort of information each function requires and uses) d) Complete the attached form and attach it to your written report, outlining the methods of communication used by the company for different types of ‘audience’ (different ages, ethnicity, gender, special needs etc) Consider electronic (email, internet, fax etc) and non-electronic (invoices, memos, letters etc) methods.| P1P2 – method 1M1D1P7| PART 2 – Task 1 a) Produce a leaflet or advert for customers giving information about products or services HMV offer. b) Choose a real example of an external corporate communication from HMV. In a Memo to your manager evaluate it as a means of corporate communication. (Remember not all such communications are effective) c) Then evaluate the effectiveness of the business organisation and its communication and the role it has played in the success of this company. You must include examples to illustrate your point.Tip; investigate other organisations that have excellent communication and information systems and use them for comparison with your organisation.| P3P4P2 – method 2D2| Part 2 – TASK 2Give a power point presentation a) Explaining the legal and ethical issues in relation to the use of business information at HMV such as protection of personal information and confidentiality b) Also explaining the operational issues in relation to the use of business information such as back up, health and safety, security of information etc. Make sure you explain how the organisation deals with these issues. c) An analysis (with appropriate examples) of the way HMV manages their business information legally, ethically and operationally. Try to look at reasons for policies and procedures that relate to business information and any benefits or drawbacks| P5P2 – method 3P6P2 – method 4M2| Personal Learning and Thinking Skills (PLTS)| * Opportunities for development| | Unit | Unit| Independent Enquirers| P1, P2, P5, P7| | Creative Thinkers| P1, P2, P4, P5, P6| | Reflective learners| P4| | Team Workers| | | Self Managers| P3| | Effective Participators| | | Unit assignment verified by | Signature of Internal Verifier| Date| | | |