The Practical Benefits of Personal Skills Essay

The Practical Benefits of Personal Skills - Essay Example The four skills that I have chosen are coordination, active learning, active listening, and critical thinking. Possession of these life skills is said to be very beneficial and eases difficult conditions, both in terms of personal matters and in terms of those that deal with the workplace. Indeed, when faced with life's problems, which are natural however, one should possess the basic life skills so that he may be armed in defeating all life's foes. My chosen skills are beneficial to a person in a variety of ways that one can imagine. Its importance is seen in the tendency of a suicidal patient to end his life, which is a certain manifestation of hardship in dealing with a current problem. This condition only mirrors the truth that this person has not equipped himself effectively enough with life's basic skills. The reason may perhaps be because he is not aware that these are important skills that must be developed and carried on in life, or it may be that he did not have the opportunity to develop them in the first place, the environment he is in is not one conducive enough for such development, or he simply did not have the knowledge of how important they are to his own advantage. Below are the four chosen basic skills and their benefits: Coordinatio Coordination Skills Coordination is adjusting actions in relation to others' action.1 It implies one's ability to adapt to situations and the people around it. Getting something through whatever means possible is contrary to the development of this basic skill, since one has to consider others - their ideas, feelings, and impacts of an undertaking or a decision - and unite them with one's ideas in making things work out the most possible and desirable way. Adjusting one's actions with those of others in a pursuit to achieve an individual or a group goal is the best way of dealing with differences.2 The benefits that this basic skill extends to an individual are massive. The results are manifested in a healthy work environment, a give-and-take relationship, and new and renewed friendships. People will also appreciate one's consideration and ability of involving them in his worldview, a rare capacity that must be developed, especially in the workplace. This mere appreciation is a benefit in itself, for it leads to the extension of one's sphere of influence and linkage building, necessary in making certain goals achievable and with ease. Coordination skill is one that I have acquired recently and I am reaping various positive outcomes as a result. Developing and sustaining new friendships is one benefit that I directly experience. Coordination has made me develop my receptivity as a person, a trait, which I think is important especially that people always want to have their ideas and insights be considered and being repulsive to them will scare them just the same. It does not mea n however, that this receptivity is just simply allowing people get their way. Rather, it is more of digesting their insights, weighting them with mine, considering them if they are attuned to my values and ethics, and informing people the reasons why I do not consider them in case I

Literature Review and Empirical Essay Example for Free

Literature Review and Empirical Essay From the early eighties to the nineties, the Standard Rate increased modestly, but is still below its mid-seventies level. While researchers have identified many reasons for the low UI recipiency rates over the past twenty years, many questions remain as to the causes behind the low rate and steps that policy and program officials might take to increase it. While the Standard Rate is the most commonly used measure to evaluate the effectiveness of the UI program, researchers have developed alternative UI recipiency rates to address some of the limitations of the standard measure. The standard measure is expressed as the ratio of the insured unemployed (i. e. , the number of regular UI claimants) to the total number unemployed. Alternative measures have been designed to better capture the effectiveness of the UI program by including the full range of UI programs available to the unemployed (beyond the regular program) and by more accurately defining the UI target population (a subset of unemployed workers). Purpose and Methodology The purpose of this report is to examine why the Standard Rate, as well as alternative recipiency rates, declined sharply in the early eighties and continued to remain well below their midseventies level in the early nineties. We critically reviewed the findings from the research literature to explore the factors others have identified to explain the drop in the UI recipiency rate. The literature review enabled us to identify factors for inclusion in our empirical analysis and to assess the effects of factors that could not be included in our own analysis. Our empirical analysis is based primarily on the methodology used by Burtless and Saks (1984) and focuses only on changes in the UI recipiency rate over recessionary periods. It is important to compare similar economic periods because the UI recipiency rate is higher during recessionary periods and lower during periods of economic expansion. We first replicated the analysis from Burtless and Saks, estimating the effects of various factors that influenced the rate used in their original analysis from the seventies recession (1975-76) to the eighties recession (1981-83). We then extended their earlier analysis by testing the effects of additional factors during that period. Next, we updated the analysis to include data from the most recent recessionary period in the nineties (1991-92). We chose the period in the nineties to be consistent with the periods of rising unemployment rates selected by Burtless and Saks. Finally, we extended their analysis by using the Standard Rate and two additional measures of UI recipiency selected to measure the performance of the UI programs during recessionary periods. Our conclusions about the effects of various factors on the UI recipiency rate are based on the findings from both the critical review of the literature and our empirical analysis. We also present evaluation design options to address some of the limitations of current knowledge. The Lewin Group, Inc. E-1 156059 Executive Summary C. UI Recipiency Rate Measures Four UI recipiency rate measures were selected for the empirical analysis. Standard Rate: number of weekly claims for regular program unemployment insurance benefits, as a proportion of all unemployed workers;1 All Programs Rate: number of weekly claims for all program (regular, extended and federal) unemployment insurance benefits, as a proportion of all unemployed workers; Standard Short-term Rate: number of weekly claims for regular program unemployment insurance benefits, as a proportion of job losers unemployed less than 27 weeks; and All Programs Job Loser Rate: number of weekly claims for all program (regular, extended and federal) unemployment insurance benefits, as a proportion of all job losers. The final three UI recipiency rates deviate from the Standard Rate by changing the definition of UI claimants, unemployed workers, or both. Because the All Programs Rate and the All Programs Job Loser Rate include all UI program claimants, Wandner and Stengle (1996) argue that they are generally better measures of UI coverage during recessionary periods when extended benefit programs are provided. The All Programs Job Loser Rate differs from the All Programs Rate because it targets a subset of unemployed workers (i. e. , job losers) who would be most likely to qualify for UI benefits. The Standard Short-term Rate only includes regular program claimants and the general â€Å"target population† for the regular state program, job losers unemployed less than 27 weeks. This final measure was used in the original Burtless and Saks analysis. All three alternative rates are larger than the Standard Rate because they use either a more expansive definition of UI claimants and/or a more restrictive definition of unemployed workers. From the seventies to the eighties, all four recipiency rates declined sharply (Exhibit 1). The largest reductions are for the All Programs Rate and the All Programs Job Loser Rate. These rates declined by more than the Standard Rate because of the large cutbacks in the extended benefit programs that were implemented in the early eighties. From the eighties to the nineties, the Standard Rate increased slightly. There is not, however, a large change in either the All Programs or All Programs Job Loser rates over this period, due to the small number of extended claimants. If, however, the analysis were extended to periods following March 1992, there would be an increase in both of these rates because of the extension of benefits through the Emergency Unemployment Compensation (EU3) program. 2 The Standard Short-term Rate follows the same general pattern as the Standard Rate, though there is a much sharper drop-off in the Standard Short-term rate in the early eighties that corresponds with fewer short term job losers receiving regular program benefits.

Popular culture

Popular culture What is popular culture? Introduction (John Storey, 2006, p.1) â€Å"popular culture is always defined, implicitly or explicitly, in contrast to other conceptual categories: folk culture, mass culture, dominant culture†. â€Å"Popular† and â€Å"Culture† Before we look into more depth about the term ‘popular culture we must first determine what it actually means. Firstly to break the term down, Raymond Williams says culture is â€Å"one of the two or three most complicated words in the English language† (cited storey 2006). To understand what William means we will take a look at his definitions. William refers to culture in three ways, firstly he states culture could be seen as â€Å"a general process of intellectual, spiritual and aesthetic development†.   The examples storey gives are â€Å"great philosophers, great artists and great poets† which noticeably the word great is used giving a sense of high culture. Secondly â€Å"a particular way of life, whether a people, a period or a group† (ibid). - Popular Culture Now we have determined what the terms popular and culture mean we can now look at the word as a whole. Storey states that Popular culture â€Å"is the culture that is left over after we have decided what is high culture† (Storey 2009, p. 6). Popular culture is therefore seen as â€Å"inferior culture† or ‘low culture (ibid). When you think about popular culture in this way there comes a problem, who determines which category it falls into. Storey states â€Å"to be real culture, it has to be difficult† therefore creating a division to who can and who can not. Pierre Bourdieu argues that â€Å"cultural distinctions of this kind are often used to support class distinctions. Taste is a deeply ideological category: it functions as a marker of class† (Bourdieu 1984).   Popular culture is often taken from culture, diluted and then distributed to the masses for personal gain. Sub cultures are created to help a group of people who feel left out from society, find there place. When looking at sub cultures, they are often associated with the youth of society. It could be argued that sub cultures are made possible because the youth within society have much more leisure time. (Lifestyle) The reason for this is said to be because of the generation gap. Some sub cultures create a moral panic because it challenges the norm and goes against how the people should think and what they should be doing. In doing so they create there ideology according to how they feel about society which is often opposed to the elite. If this is the case then it suggests a political dimension to what popular culture is so therefore it is not just about leisure or entertainment. Popular culture and the mass media go hand in hand because the media creates a distorted image of how the world is and the masses are said to passively consume it. Popular culture is also argued to divert people away from what is important which is said to be beneficiary for the elite, an example of this is capitalism. (Storey 2006) Ideology conceals the reality of domination from those in power: the dominant class dont see themselves as exploiter or oppressors†. More importantly though it works in favour of the elite as Storey states â€Å"Ideology conceals the reality of subordination from those who are powerless: the subordinate class dont see themselves as exploited or oppressed†. One of the most significant ideological practices in recent years has been the categorical dividing of what is popular and what is classical. Storey states â€Å"the division between high and popular culture†¦is absolutely clear†¦and transhistorical-fixed for all time.† Up until recent years the divions of the two were quite prominent but with groups like Escala (who were on Britains got talent) the divide becomes blurry. Groups like Escala play classical music in a different way to the norm but which is still classical and since they were on a popular show they made classical music popular. But that does not mean it will stay that way because if you look at some of the Cds they are bringing out for classical music now with such titles as â€Å"Pure Classical† which may seem innocent but could be argued that the elite are trying to police the boundaries. The real question what has been discussed is, value. As we have look in the first part of this essay, culture is not a fixed thing, it changes over time. A prime example of this would be opera. It started off for working class people but over time soon change and now is for the elite in society. If you were to look at the price it started off at it was only a few pennies but now the price of a ticket is quite high which therefore excludes people from taking part. Another example of this would be of you were to take a look at Pavarotti who had top selling albums and chart toppers, who then went to put on a performance in Hyde park for free and a woman attending give a comment to say â€Å"I cant afford to go to the posh opera houses and folk out  £100 for a ticket†. Yet again you could see some of the elite trying to police the boundaries by giving bad reviews saying â€Å"the park is no place for opera†. When you take that review and think about what they are really mean, they are saying it should stay in the opera hous e and keep its exclusiveness. Ideologies gain persuasive powers to either directly or indirectly influence the way people live, how they behave and how they relate to each other. Ideology can be understood by a set of values and ideas that a certain group shares.

Dionysus :: Ancient Rome Roman History

Dionysus Dionysus, also known by his Roman name Bacchus, which he appears to have two different origins. Dionysus was the god of wine, agriculture and fertility of nature, but on the other hand he also represents the mystery in religions. Scholars believe that this god came about later in pre-history, unlike other gods. Almost all barbarian nations had their own versions of Dionysus under many names such as, Bacchus, Zagreus, Sabazius, Adonis, Antheus, Zalmoxis, Pentheus, Pan, Liber Pater, or simply "the liberator." His symbol was the thyrsus, (which is a staff tipped with a pinecone, sometimes with ivy leaves) Dionysus was born of fire, he was good and gentle to those who honored him, but he also brought madness and destruction to those who ignored him or the rituals of his cult. "One day, Zeus was traveling on Earth. He wore a disguise; because undisguised no mortal could look at him and live. He came to Thebes, and Ancient City of Greece where he fell in love with Semele, the daughter of King Cadmus. Their love was immense, and before they knew she became pregnant. Semele wanted nothing else but to look into the eyes of her true love. Her nurse, who happened to be the disloyal Hera in disguise, influenced her cruelly. Semele could not stand it any longer and finally, she asked Zeus to give her a good fortune. He was deeply in love with the young women and did anything she pleased. He swore an unbreakable promise on the river Styx that she could have anything she wished for. Meaning no harm, Semele asked to see the god of thunderbolt, Zeus was frightened. He knew that this would mean her guaranteed death. "NO" he cried in torment. "Anything but that. You do not know what you're asking for." She insisted and Zeus miserably kept his word and granted her wish. As he reveled his burning appearance, the unlucky Semele was almost completely destroyed by the fire, except her womb, which she had wrapped with some ivy. Zeus was angry, he quickly took the fetus from the womb, cut open his own thigh, and put the child into it. The child continued to grow in Zeus's thigh, when development in the womb was complete, Zeus gave birth to the baby, Dionysus. The child of fire had been born with a brand new force to be discovered.

A Data Warehouse Appliance Can Have a Huge Positive Impact on Businesses and Organizations Essay

Businesses and organizations of all sizes are becoming increasingly dependent on data analytics, and data warehouses or business analytic infrastructure has become a business critical application for many (if not most) companies. Indeed, these companies have always searched for better ways to understand their customers, and anticipate their needs. They have longed to improve the speed and accuracy of operational decision-making. Equally important as timeliness is the depth of the data analysis. Generally, the companies want to decipher all secrets hidden within the massive amounts of ever-increasing data. A data warehouse appliance, which is an integrated collection of hardware and software designed for a specific purpose typically involving the high throughput of data and analytic functions, can be used by organizations to optimize various areas of data processing. Its main intent is to supplant conventional business intelligence functions, such as warehousing, extract-transform-load (ETL), analysis and reporting. Due to its cost-effectiveness and efficiency, the data warehouse appliance has become an important segment of the data warehousing market. In this paper, I will examine the data warehouse appliances and describe its positive impact on business enterprises. Introduction Since introduced in the early 1990s, data warehouse (DW) has proven to be the key platform for strategic and tactical decision support systems in the competitive business environment today. See more: Analysis of Starbucks coffee company employees essay It has become a major technology for building data management infrastructure, and resulted in many benefits for various organizations, including providing â€Å"a single version of the truth, better data analysis and time savings for users, reductions in head count, facilitation of the development of new applications, better data, and support for customer-focused business strategies† (Rahman, 2007). The technology has become extremely important in an environment where increasing competition, unpredictable market fluctuations, and changing regulatory environments are putting pressure on business organizations. Data warehouses are also becoming the central repositories of organization/company information for data, which is obtained from a variety of operational data sources. Business applications will find data warehouses more beneficial and rely on them as the main source of information as they progress. These applications are able to perform all sorts of data analysis, with increasing customer demands for having the most up-to-date information available in data warehouses. Improving data freshness within short time frames is essential to meeting such demands. According to Hong et al, virtually all Fortune 1000 companies, today, have data warehouses, and many medium and small sized firms are developing them. The desire to improve decision-making and organizational performance is the fundamental business driver behind data warehouses. DW help managers easily discover problems and opportunities sooner, and widen the scope of their analysis. Hong also mentions that data warehouse is user-driven, meaning that users are allowed to be in control of the data and will have the responsibility of determining and finding the data they need. But however, the data warehouses have to be designed and evaluated from the user perspective in order to motivate users to be responsible for finding the data they need. Data warehouse is said to be â€Å"one of the most powerful decision-support tools to have emerged in the last decade† (Ramamurthy, 2008). They are developed by firms to help managers answer important business questions which require analytics including data slicing and dicing, pivoting, drill-downs, roll-ups and aggregations. And these analytics are best supported by online-analytical processing (OLAP) tools. A data warehouse appliance, which is the main topic of discussion in this research, is referred to as an integrated collection of hardware and software designed for specific purposes involving the high throughput of data and analytic functions. Data warehouse appliance has become an important segment of the data warehousing market, due to its cost-effectiveness and efficiency. A business or organization can use a data warehouse appliance to optimize various areas of data processing. In general, the main purpose of the DW appliance is to supplant conventional business intelligence (BI) functions including warehousing, extract, transform, load (ETL), analysis, and reporting. A data warehouse appliance can have a huge positive impact on a business enterprise. Large organizations are able to staff their data warehouse more efficiently, while assisting mid-level companies in solving business intelligence challenges. Data warehouse is fundamentally changing the way the businesses operate, as they are increasingly adopted across various companies. The purpose of this paper is to present the data warehouse appliances and how they impact businesses and organizations. In the next sections, I present a brief overview of data warehousing and the current state of BI, then I define and discuss DW appliances including its benefits, after which I describe the positive impact of DW appliances on businesses. Data Warehousing A data warehouse can basically be defined a subject-oriented, integrated, non-volatile, and time-variant collection of data in support of management’s decisions. Unlike the on-line transaction processing (OLTP) database systems, data warehouses are organized around subjects storing historical/summarized data for business requirement purposes. According to O’Brien and Marakas, a data warehouse is a central source of data which have been cleaned, transformed and cataloged so they are usable by managers/business professionals for data mining, online analytical processing, market research, and decision support. These stored data are usually extracted from various operational, external, and other database management system of an organization. DW can be sub-divided into data marts, holding subsets of data from the warehouse that focus on specific aspects, such as department, of a company. In general all data warehouse systems comprises of the following layers; data source, data extraction, staging area, ETL, data storage, data logic, data presentation, metadata, and system operations layer. But the four major components include the multi-dimensional database, ETL, OLAP, and metadata. The dimensional database applies the concept of standard star-schema including dimension and fact tables, hierarchies for drill-down, role models, aggregates and snow flaking. It optimizes database design for better performance. The ETL process involves the extraction, transformation and loading of data with appropriate ETL tools. Data integration is one of the most important aspects of data warehouse, whereby data is extracted from multiple heterogeneous source systems and placed in a staging area where it is cleaned, transformed, pruned, reformatted, standardized, combined, and summarized before loading into the warehouse. OLAP (online analytical processing) tool provides the front-end analytical capabilities including slice and dice, drill up, drill down, drill across, pivoting, and trend analysis across time. And metadata stores information (or data) about the data in the warehouse system. The components of a complete data warehouse architectural system are illustrated in Figure 1 below. Figure 1 An important characteristic about the data in a data warehouse is that they are static, unlike a typical database with constant changes. Once the data are gathered up, formatted for storage, and stored in the data warehouse, they will never change. The restriction is such that complex patterns or historical trends can be searched for, and analyzed, by queries. Data warehouses are also non-volatile in the sense that end-users cannot update the data directly, thereby being able to maintain a history of the data. A major use of the data warehouse databases is data mining, in which the data are analyzed to reveal hidden patterns and trends in historical business activity. Such analysis could be used to help managers make decisions about strategic changes in business operations in order to gain competitive advantages in the marketplace. Data warehousing is a relatively new technology that â€Å"brings the vision of an entirely new (customer-centric) way of conducting business to reality†, and can provide â€Å"environments promising a revolution in organizational creativity and innovation† (Ramamurthy, 2008). Ramamurthy also mentioned that data warehouse generally serves as an IT infrastructure technology, focused on data architecture, as it provides a foundation for integrating a diverse set of internal and external data sources, enabling enterprise-wide data access and sharing, enforcing data quality standards, providing answers to business questions, and promoting strategic thinking through CRM, data mining, and other front-end BI applications. Users of the data warehouses are from virtually every business unit, amongst which information systems, marketing and sales, finance, production and operations, are the heaviest users. Current State of Business Intelligence Business Intelligence are computer based techniques used in identifying, extracting and analyzing business data. Sales revenue by products, department, time, region or income are such examples. The BI technologies provide historical, current and predictive views of business operations. Some common functions of BI technologies include reporting, online analytical processing, analytics, data mining, text-mining and predictive analytics. As BI aims to support better business decision-making, they can also be referred to as a decision support system. BI applications often use data gathered from data warehouses or data marts, however, not all BI applications require a data warehouse. With sources from Wikipedia, business intelligence can be applied to business purposes in order to drive business value. Amongst these business purposes include measurement, analytics, reporting, collaboration, and knowledge management. BI is widely used today, mainly to describe analytic applications. According to Watson, BI is currently the top-most priority of many chief information officers. In a survey of 1,400 CIOs, from Gartner Group, it was discovered that BI projects were the number one technology priority for 2007. Watson further informs that the BI is a process which basically consists of two primary activities; â€Å"getting data in and getting data out†. Getting data in, also referred to as data ware housing, delivers limited value to a business enterprise. Organizations realize the full value of data from data warehouses only when users and applications access the data and use it to make decisions. Getting data out receives the most attention, as it consists of business users/applications accessing data from DW to perform enterprise reporting, OLAP, querying and analytics. The business intelligence framework is depicted in figure 2. Current BI infrastructure is a patchwork of hardware, software and storage that is growing ever more complex. Figure 2 – BI framework BI is continuing to evolve, and several recent developments are generating widespread interest, including real-time BI, business performance management, and pervasive BI. Data Warehousing Appliance A data warehouse is developed to support a broad range of organizational tasks. It can be referred to as an organized collection of large amounts of structured data, designed and intended to support decision making in organizations. The import of information and knowledge from a data warehouse is a complex process that requires understanding of the logical schema structure and the underlying business environment. According to Hinshaw, a data warehouse appliance, applied to business intelligence, â€Å"is a machine capable of retrieving valuable decision-aiding intelligence from terabytes of data in seconds or minutes versus hours or days†. The appliances represent the difference between decision-making using either stale data or the freshest information possible. With sources from Wikipedia, a more standard definition of the data warehouse appliance is an integrated collection of hardware and software designed for a specific purpose that typically involves the high throughput of data and analytic functions. It typically consists of integrated set of servers, operating systems, data storage facilities, database management systems (DBMS), and software that is pre-installed and pre-optimized for data warehousing. DW appliances provide solutions for the mid-to-large volume data warehouse market, offering low-cost performance usually on data volumes within the terabyte range. Due to its cost-effectiveness and efficiency, the data warehouse appliance has become a critical segment of the data warehousing market. A business or an organization can use a data warehouse appliance to optimize various areas of data processing. The main purpose of a DW appliance, in general, is to supplant conventional business intelligence functions, such as warehousing, extract, transform, load (ETL), analysis, and reporting. A true DW appliance is defined as one that does not require fine-tuning, indexing, partitioning, or aggregating, whereas, some other DW appliances use languages such as SQL to facilitate interaction with the appliance at a database request level. With reference to Wikipedia, most data warehouse appliance vendors use massive parallel processing (MPP) architectures to provide high query performance and platform scalability. The MPP architectures consist of independent processors or servers executing in parallel, implementing a â€Å"shared nothing architecture† which provides an effective way to combine multiple nodes within a highly parallel environment. A DW appliance is capable of deploying up to thousands of query processing nodes in one ppliance package, compared to traditional solutions where the cost and complexity of each additional node prevents a high level of hardware parallelism. Leveraging fully integrated data warehouse architecture, a data warehouse appliance can deliver a significant performance advantage, performing up to 100 times faster than general-purpose data warehousing systems. Maturation With reference to Hinshaw, data warehouse appliance is specifically designed for the streaming workload of business intelligence and is built based on commodity components. It integrates hardware, DBMS and storage into one opaque device and combines the best elements of SMP and massively parallel processing (MPP) approaches into one that allows a query to be processed in the best possible optimized way. A data warehouse appliance is fully compatible with existing BI applications, tools and data, through standard interfaces. It is simple to use and has an extremely low cost of ownership. The development of standardized interfaces, protocols and functionality is one of the most important trends in BI. In comparison to about a decade ago, there are a wealth of tools and applications using these standardized interfaces including MicroStrategy, Business Objects, Cognos, SAS and SPSS. And these are coupled with ETL tools having standardized interfaces such as Ab Initio, Ascential and Informatica. The appliances work seamlessly with these tools and other in-house applications. A data warehouse appliance is truly scalable. The bottlenecks are the speeds of the internal buses, internal networks, and disk transfer in BI, whereas in transactional workloads, scalability is limited primarily by CPU. Reliability, which is provided by the homogenous nature of an appliance – all parts of the system coming from a vendor, is also critical. A data warehouse appliance also provides simplicity for the administrators, in that it allows administrators spend a more productive time in troubleshooting complex database systems. And DBAs can be deployed to assist end users doing real-time BI. A data warehouse appliance offers the lowest cost of ownership as it has one source and one vendor, thereby reducing costs associated with support. Businesses and organizations will run more efficiently with the simple, efficient solution provided by a data warehouse appliance. Benefits Data warehouse appliances provide freedom to the business user. With patch-work systems, users are limited in the queries they can run due to the time required to run them. And with the time required to run a complex query reduced to seconds, users can not only run their old analysis with more iterations, but have the time to devise and run entirely new sets of analysis on granular data. With sources from Wikipedia, some researched benefits of DW appliance are briefly discussed as follows; Reduction in costs – As a data warehouse grows, the total cost of ownership of the data warehouse consists of initial entry costs, maintenance costs, and the cost of changing capacity. DW appliances offer low entry and maintenance cost. Parallel performance – DW appliances provide a compelling price/performance ratio. The vendors use several distribution and partitioning methods to provide parallel performance. With high performance on highly granular data, DW appliances can address analytics that could previously not meet performance requirements. Reduced Administration – DW appliances can provide a single vendor solution, taking ownership for optimizing the parts and software within the appliance, thereby eliminating the customer’s costs for integration and regression testing of the DBMS, OS and storage on a terabyte scale. DW appliance reduces administration via automated space-allocation, reduced index-maintenance and reduced tuning and performance analysis. Scalability – DW appliances scale for both capacity and performance. In massive parallel processing architectures, adding servers increases performance as well as capacity. Built-in high availability – Massive parallel processing DW appliance vendors provide built-in high availability via redundancy on components within the appliance. Warm-standby servers, dual networks, dual power-supplies, disk mirroring with fail-over and solutions for server failure are offered by many. Increasingly, business analytics are expected to be used to improve the current cycle, and DW appliances provide quick implementations without the need for regression and integration testing. Also, DW appliances provide solutions for many analytic application uses. Some of these applications include; enterprise data warehousing, super-sized sandboxes isolating power users with resource intensive queries, pilot projects, off-loading projects from the enterprise data warehouse, applications with specific performance or loading requirements, data marts that have outgrown their present environment, turnkey data warehouses, solutions for applications with high data growth and high performance requirements, and applications needing data warehouse encryption. Impact of Data Warehouse Appliances on Businesses and Organization Demand for data warehouse appliances is increasing, and businesses taking advantage of the benefits of this hardware range from a world-wide large-scale business to the smallest individual business. Data virtualization could be a useful partner to appliances, providing a single view of information across multiple appliances. Data virtualization is also useful because it provides a stable reporting layer during normal migration exercises, such as the circumstances during addition of data warehouse appliances to the information infrastructure. As businesses today continue to process extremely large volumes of data, there is always the need to keep data warehousing costs under control while ensuring a superior BI and application performance. Scalability, flexibility, and affordability are essential requirements for designing an infrastructure capable of supporting next-generation BI performance. When asked why the demand for data warehouse appliance is increasing, during an interview, Robert Eve (executive vice president of marketing for Composite Software Inc. ) stated that it is the confluence of three primary drivers at the macro level. The first is â€Å"the well-reported information explosion, and the technical challenges involved in making this information accessible in forms that business decision-makers can easily use†. Secondly, data warehouse appliances are more affordable and appealing, as the costs per terabyte and for support are coming down. And finally, recent advancements in analytics technology, notably in predictive analytics, promise to concur with the massive data volumes. Data warehouse appliances offer numerous advantages some of which are similar to benefits. Amongst the advantages include; more reporting and analytical capabilities – data warehouse appliance are able to handle bigger and more complex query workload, if it executes queries, Cost reductions – data warehouse appliance requires a minimal amount of tuning and optimization of the database server and database design. It is also able to run most queries with a quick speed, Flexibility – it will be easier to implement new user requests if less tuning and optimization is needed. With other database servers, a new query might lead to quite a number of technical changes, such as creating and dropping indexes, repartitioning tables, etc. Sometimes, decision is made not to implement the new request at all, due to the overwhelming work. The need for these additional technical changes is less with a data warehouse appliance. Data warehouse appliances helps support impressive BI deployments. With reference to Hinshaw, real world application examples of the positive impact of DW appliance on businesses are discussed. The rapid growth of call detail records, in the telecommunications industry, creates an imposing amount of data, which makes it difficult for companies to quickly and efficiently analyze customer and call plan information. And traditional approaches have been inefficient in processing queries on even a month’s data, seriously hampering an organization’s ability to perform trend analysis to reduce customer churn and generate timely reports. However, with a DW appliance, the telecom user can analyze customer activity down to the call detail record level over a full year’s worth of detailed data. Another industry where data warehouse appliances have begun to prove their worth, and are poised to play a bigger role in the future, is the retail. Hinshaw states that Brick-and-mortar and online retailers are capturing great amounts of customer transaction and supply chain information, creating a data explosion that threatens to overwhelm an average retail organization and its current IT infrastructure. But data warehouse appliances enable these retailers to manage and analyze the terabytes of information in near-real time. They are able to use the information to effectively forecast buying patterns, quickly generate targeted promotions and optimize their inventory and supply chain. Business intelligence remains the foundation for the success of decision making in any company. And BI, itself, relies on the underlying database architecture. Eve also presents other real world examples of positive business impact among a broad range of industries. A leading worldwide convenience foods business uses data warehouse appliances and analytic applications to acquire major business benefits in two specific areas. One of which the company optimizes its international network of delivery routes, making the system more efficient and ensuring timely delivery of its products. Secondly, it continuously refines its merchandizing mix daily, on a retail basis, in order to maximize sales and margins. Major League Baseball captures information about every pitch, at-bat, and fielding play within a data warehouse appliance, using this data to predict players’ future on-field performance. This can help teams to evaluate current and free-agent talent, refine coaching and development methods, and determine salaries, hence maximizing their wins. Also, a global freight, transportation, and logistics company uses data warehouse appliances to identify behavioral patterns that indicate potential dissatisfaction within its existing customer base. The customer care group then proactively takes steps to improve satisfaction before they lose their customers. Currently, smaller data warehouse appliance vendors seem to be focusing on adding functionality to their products in order to compete with the mega-vendors. However, it is anticipated that all appliance vendors will be impacted by the trend toward an inexpensive, high-performance, and scalable virtualized data warehouse implementations which use regular hardware and open source software. Conclusion In general, data warehouse appliance is a combination hardware and software product specifically designed for analytical processing. In a traditional data warehouse implementation, the database administrator can spend a significant amount of time tuning and putting structures around the data to get the database to perform well for large sets of users. But with a data warehouse appliance, it is the vendor who is responsible for simplifying the physical database design layer and making sure that the software is tuned for the hardware. In this research, a comprehensive examination/review of the data warehouse appliances, their benefits, and how they positively impact businesses and organizations, was presented. Based on this research, the negative impact of DW appliances on businesses are negligible compared to its positive impact. And there is an increasing demand for DW appliances. I believe that, in the near future, the DW appliances will become the sole platform for all business intelligence applications and requirements. I gained much knowledge and insights from researching this topic, and I intend to further my research on future impacts of DW appliance on businesses.

Reading between the Lines, Interpreting Music

Reading between the Lines, Interpreting Music Listening to music is a fabulous way to spend time. However, learning about the way this music was creates is a nonetheless exciting experience, and When We Were Good by Robert S. Cantwell, as well as The Rose the Briar: Death, Love and Liberty in the American Ballad by Greil Markus provide such an opportunity. Which is even more exciting, the two books offer a lot of food for thoughts and raise many interesting questions.Advertising We will write a custom essay sample on Reading between the Lines, Interpreting Music specifically for you for only $16.05 $11/page Learn More The best thing about When We Were Good is that it helps the readers recall some of their first music experiences, which must have started with the introduction to the world of animated movies. It was rather surprising to find out that famous Joan Baez and Bob Dylan worked on one of the soundtracks to Disney’s masterpiece, Lady and the Tramp, playing the part of the Siamese cats. No t only did this news expand my understanding of the genres in which Baez and Dylan performed, but also helped realize how important media is in terms of shaping one’s culture, and how well child impressions can be cemented in one’s memory. After reading When We Were Good, a number of people can hear the famous line â€Å"We are Siamese, if you please† somewhat differently. One of the things that made me wonder, though, is the fact that in his essay, â€Å"We Did Them Wrong,† Cecil Brown makes a clear statement about â€Å"Frankie and Albert† being based off of a single incident. To be more exact, Brown links the ballad to the infamous murder of Allen Britt, which was committed by Frankie Baker earlier. On the one hand, everything from the names in the title of the song to the lyrics points at the fact that the song could be written to honor the memory of Britt and make the audience recall the incident. On the other hand, nothing in the song states explicitly that these are Allen Britt and Frankie Baker that the author is talking about; for what it is worth, the author could be talking about anyone of the same name, starting from his friends at up to fictional characters. Therefore, Brown’s bold statement about the way in which the song was created and what it was inspired by somehow rubs the wrong way. However, when it comes to analyzing the author’s original intent and idea, the discussion might turn rather heated, since there is no rule on how to interpret a work. Dave Marsh’s Barbara Allen, for instance, begs a similar question, making one guess whether the title of the most widespread folk song in the U.S. is actually praise or a statement of the song being slowly worn out. There are different schools of thoughts, each offering its own idea of the author’s intent. Some say that the author’s vision is the only valid one out there; others claim that once the work is open to discussion, it is free to be interpreted any which way the audience sees as possible. Thus, it is reasonable to admit that Brown’s idea of what the song is about has the right to exist, as well as the rest of the opinions on the issue, yet it still can be taken with a grain of salt.Advertising Looking for essay on art and design? Let's see if we can help you! Get your first paper with 15% OFF Learn More Offering a lot of food for thoughts, The Rose the Briar: Death, Love and Liberty in the American Ballad helps the readers discover the aspects of the world of music that they have never heard of, revealing the readers new shades of meanings of the songs and compositions that used to seem a well-trodden path.