Free Essays on Obsenity, Media Law

, pictures and motions we either utilize or decide to disregard or abstain from relying upon what meaning we have received for these things into our own lives. As we take a gander at the world from a nearsighted perspective you would think disgusting pictures, thoughts, artistic creations, compositions, etc†¦ would handily stand out. Anyway when you utilize a more extensive perspective on the world you understand that there are billions of individuals on the substance of the earth, and all of them has their own view and feeling and no two are actually the equivalent. This being the situation what one may discover profane and inappropriate, another may discover adequate, and let us not disregard each one of the individuals who stay in the vast shades of dark on some random subject. Webster’s characterizes obs... Free Essays on Obsenity, Media Law Free Essays on Obsenity, Media Law Romper bomper stomper boo disclose to me reveal to me let me know do enchantment reflect reveal to me today which media law subject should my paper spread today? Why don’t we talk about f#@*%n profanity? That sounds great to me. It likewise seems like the enchantment reflect needs its mouth cleaned out with cleanser, this being only my supposition. Shockingly the enchantment reflect has just shown just a single gathering of what is viewed as foulness. Revolting language is absolutely one issue I believe I am encircled by ordinary, both as a client and a collector, however there are likewise pictures and signals which are viewed as vulgar. Presently a conspicuous picture of indecency is erotic entertainment, yet shockingly even the bosses of craftsmanship have been some despite everything blame them for making indecent pictures. Presently I am not a stick in the mud in any way shape or form, I have been known to utilize language that would make my mom slap the rear of my head. I should state however that I use it in the trusts of assigned zones where it is all the more promptly acknowledged. As we tell kids as they develop up,† you have an indoor voice and an open air voice.† I would state, as we make our character we have a choice of words and pictures we permit at better places and times. One of these choices of words and pictures contain disgusting words, pictures and signals we either utilize or decide to overlook or abstain from relying upon what meaning we have received for these things into our own lives. As we take a gander at the world from a nearsighted perspective you would think disgusting pictures, thoughts, artworks, compositions, etc†¦ would handily stand out. Anyway when you utilize a more extensive perspective on the world you understand that there are billions of individuals on the essence of the earth, and all of them has their own view and feeling and no two are actually the equivalent. This being the situation what one may discover foul and ill-advised, another may discover worthy, and let us not disregard every one of the individuals who abide in the unbounded shades of dark on some random subject. Webster’s characterizes obs...

Film Essay Example | Topics and Well Written Essays - 1500 words - 1

Film - Essay Example The designers mix the differing highlights of the film viably accordingly making a strong film that conveys adequately through altering. City of God is a case of a film that depicts the intensity of altering among other film improvement strategies in making films. Altering gives a powerful path to the engineers of the film to mix assorted variety yet accomplish a level of both attachment and intelligence in the film as the conversation beneath depicts. Altering alludes to the way toward killing mistakes. In any case, the definition is more extensive in the advancement of movies than it is in writing. In film advancement, altering doesn't just kill mistakes yet in addition furnishes the engineers of the film with a chance to include explicit highlights that would improve the association of the different scenes along these lines making a strong and lucid plot equipped for imparting explicit topics. Various scenes in the film are studio manifestations while others are shot on the spot. The combination between the two makes immaculate mix that recounts to a firm story, one that armatures can't tell the distinctions in the areas. The executive of the film utilized proficient editors who depict their altering virtuoso as they include impacts among other altering components to accomplish the durable film that imparts viably. Altering assumes an indispensable job in the formation of the film. Such highlights and components as lighting, camera shots, camera edges, music and designs among numerous others help include decent variety in a film. In utilizing such, film designers must watch such essential ideas as equalization and agreement so as to make a durable film. The equivalent is the situation in the film, City of God. While a few scenes in the film were shot from areas, others are studio manifestations. Making scenes in the studio requires proper altering procedures so as to adjust the different highlights. In

Comprehensive Guide on Data Mining (and Data Mining Techniques)

Comprehensive Guide on Data Mining (and Data Mining Techniques) © Shutterstock.com | ScandinavianStockJust hearing the phrase “data mining” is enough to make your average aspiring entrepreneur or new businessman cower in fear or, at least, approach the subject warily. It sounds like something too technical and too complex, even for his analytical mind, to understand.Out of nowhere, thoughts of having to learn about highly technical subjects related to data haunts many people. Many cave in and just opt to find other people to take care of that aspect for them. Worse, in other cases, they pay little attention to it, thinking they can get away with not having anything to do with data mining in their business.Once they try to understand what data mining really is, they will realize that it is something that cannot be ignored or overlooked, since it is part and parcel of the management of a business or organization.Businesses cannot do away with implementing or applying various business intelligence methodologies, applications and technologies in order to gather and analyze data providing relevant information about the market, the industry, or the operations of the business. It just so happens that data mining is one of the most important aspects of business intelligence.WHAT IS DATA MINING?Forget about any highly technical definition you may associate with data mining and let us look at it for the relatively simple concept that it truly is. Data mining is basically the process of subjecting available data to analysis by looking at it from different perspectives, to convert it into information that will be useful in the management of a business and its operations.A simple way to describe data mining is that it is a process that aims to make sense of data by looking for patterns and relationships, so that it can be used in making business decisions.For the longest time, many people have associated data mining with the image of a set of high-end computers utilizing equally high-end software and technology to obtain data and p rocess them. This isn’t entirely wrong, because technology is definitely a huge and integral part of data mining. However, data mining is actually a broader concept, not just limited to the use of technology and similar tools.Perhaps one of the biggest reasons why many are intimidated by the very mention and idea of data mining is the fact that it involves more than one or two disciplines. When we talk of data mining, we are talking about database management and maintenance, which automatically means the involvement or use of database software and technologies. Thus, it also often entails machine learning and heavy reliance on information science and technology.Further, the analysis of data, especially of the numerical kind, is bound to make use of statistics, which is another area that some people find complicated. This will also demand a lot in terms of visualization.In short, being involved in data mining implies dipping one’s fingers and toes in more than a few rivers, so to speak, since it entails the use or application of multiple disciplines. This is what often makes data mining a challenge in the eyes of most people.We can gain a deeper understanding of what data mining is by talking about its five major elements.Extraction, transformation and uploading of the data to a data warehouse system.Data storage and management in a database system.Data access to analysts and other users.Data analysis using various software, tools and technologies.Data presentation in a useful and comprehensible format.IMPORTANCE OF DATA MININGBusinesses, organizations and industries share the same problems when it comes to data. Either they aren’t able the find the data that they require or, even if they know where to find it, they have difficulty actually getting their hands on it. In other cases, they may have access to the data, but they cannot understand it. Worse, the data may be readily available to them, and they may be able to have comprehension of it.However, fo r some reason or another, they find that they are unable to use the data.This is where data mining comes in.The main reason why data mining is very important is to facilitate the conversion of raw data into information that, in turn, will be converted into knowledge applicable for decision-making processes of businesses.Data mining has become increasingly important, especially in recent years, when nearly all industries and sectors all over the world are facing problems on data explosion. All of a sudden, there is simply too much data, and this rapid rise in the amount of data demands a corresponding increase in the amount of information and knowledge. Thus, there is a need to quickly, efficiently and effectively process all that data into usable information, and data mining offers the solution. In fact, you could say that data mining is the solution.You will find data mining to be most often used or applied in organizations or businesses that maintain fairly large to massive databa ses. The sheer size of their databases and the amount of information contained within them require more than a small measure of organization and analysis, which is where data mining comes in. Through data mining, users are able to look at data from multiple perspectives in their analysis. It will also make it easier to categorize the information processed and identify relevant patterns, relationships or correlations among the various fields the data or information belong to.Therefore, we can deduce that data mining involves tasks of a descriptive and predictive nature. Descriptive, because it involves the identification of patterns, relationships and correlations within large amounts of data, and predictive, because its application utilizes variables that are used to predict their future or unknown values. APPLICATIONS OF DATA MININGThe application of data mining is apparent across sectors and industries.Retail and ServiceThe sale of consumer goods and services in the retail and ser vice industries results in the collection of large amounts of data. The primary purpose of using data mining in these industries is to improve the firm’s customer relationship management, its supply chain management and procurement processes, its financial management, and also its core operations (which is sales).The most common areas where data mining becomes highly effective among retail and service provider companies include:Promotion Effectiveness Analysis, where the company will gather and analyze data on past successful (and unsuccessful or moderately successful) campaigns or promotions, and the costs and benefits that the campaigns provided to the company. This will give the firm an insight on what elements will increase the chances of a campaign or promotion being successful.Customer Segmentation Analysis, where the firm will take a look at the responses of the customers â€" classified in appropriate segments â€" to shifts or any changes in demographics or some other segme ntation basis.Product Pricing, where data mining will play a vital role in the firm’s product pricing policies and price models.Inventory Control, where data mining is used in monitoring and analyzing the movements in inventory levels with respect safety stock and lot size. Lead time analysis also greatly relies on data mining.Budgetary Analysis, where companies will need to compare actual expenditures to the budgeted expenses. Incidentally, knowledge obtained through data mining will be used in budgeting for subsequent periods.Profitability Analysis, where data mining is used to compare and evaluate the profitability of the different branches, stores, or any appropriate business unit of the company. This will enable management to identify the most profitable areas of the business, and decide accordingly.ManufacturingEssentially, the areas where data mining is applied in manufacturing companies are similar to those in retail and service companies. However, manufacturing businesses also use data mining for its quality improvement (QI) initiatives, where data obtained through quality improvement programs such as Six Sigma and Kaizen, to name a few, are analyzed in order to solve any issues or problems that the company may be having with regards to product quality.Finance and InsuranceBanks, insurance companies, and other financial institutions and organizations are also actively using data mining in its business intelligence initiatives. Risk Management is generally the area where data mining is most utilized. This time, data mining is used to recognize and subsequently reduce credit and market risks that financial institutions are almost always faced with. Other risks assessed with the help of data mining include liquidity risk and operational risk.For example, banks and credit card companies use data mining for credit analysis of customers. Insurance companies are mostly concerned with gaining knowledge through claims and fraud analysis.Telecommunication and UtilitiesOrganizations engaged in providing utilities services are also recipients of the benefits of data mining. For example, telecommunication companies are most likely to conduct call record analysis. Electric and water companies also perform power usage or consumption analysis through data mining.The global popularity of cellular phones in almost all transactions has made it a playground for many hackers and security threats. This spurred Coral Systems, a Colorado-based company, to create FraudBuster, which is described to be able to “track” down the types of fraud through data mining, specifically through analysis of cellular phone usage patterns in relation to fraud.TransportIn the transport industry, it is mainly all about logistics, which is why that is the area where data mining is most applied. Thus, logistics management benefits greatly from data mining. State or government transport agencies are also using data mining for its various projects, such as road construc tion and rehabilitation, traffic control, and the like.PropertyThe real estate industry heavily relies on information gleaned from property valuations which, in turn, resulted from the application of data mining. The focus is not entirely on the bottomline or the sales. Instead, data on property valuation trends over the years, as well as comparison on appraisals, are tackled.Healthcare and Medical IndustryEvery day, researches, studies and experiments are conducted in the healthcare and medical industry, which implies that there are tons of data being generated every single day. Data mining is often an integral part of those researches and studies.STEPS IN DATA MININGData mining is a process, which means that anyone using it should go through a series of iterative  steps or phases. The number of steps vary, with some packing the whole process within 5 steps. The one below involves 8 steps, primarily because we have broken down the phases into smaller parts. For example, steps #2 th rough #5 are lumped by other sources as a single step, which they call “Data Pre-processing”.For purposes of this discussion, however, let us take each step one at a time.Step #1: Defining the ProblemBefore you can get started on anything, you have to define the objectives of the data mining process you are about to embark on. What do you hope to accomplish with the data mining process? What problems do you want to address? What will the organization or business ultimately obtain from it as benefit?Step #2: Data IntegrationIt starts with the data, or the raw tidbit about an item, event, transaction or activity.The goal is to provide the users (those who are performing data mining) a unified view of the data, regardless of whether they are from single or multiple sources.This step involves:Identification of all possible sources of data. Chances are high that the initial list of sources will be quite long and heterogeneous. Integrating these data sources will save you a lot of tim e and resources later on in the process.Collection of data. Data are gathered from the sources previously identified and integrated. Usually, data obtained from multiple sources are merged.Data integration aims to lower the potential number and frequency of data redundancy and duplications in the data set and, consequently, improve the efficiency (speed) and effectiveness (accuracy) of the data mining process.Step #3: Data SelectionAfter the first step, it is highly probable that you will be faced with a mountain of data, a large chunk of which are not really relevant or even useful for data mining purposes. You have to weed out those that you won’t need, so you can focus on the data that will be of actual use later on.Create a target data set. The target data set establishes the parameters of the data that you will need or require for data mining.Select the data. From all the data gathered, identify those that fall within the data set you just targeted. Those are the data you wil l subject to pre-processing.Step #4: Data CleaningAlso called “data cleansing” and “data scrubbing”, this is where the data selected will be prepared and pre-processed, which is very important before it can undergo any data mining technique or approach.Some data mining processes refer to data cleaning as the first of a two-step data pre-processing phase.Data obtained, in their raw form, have a tendency to contain errors, inaccuracies and inconsistencies. Some may even prove to be incomplete or missing some values. Basically, the quality of the data is compromised. It is for these reasons that various techniques are employed to “clean” them up. After all, poor or low quality data is unreliable for data mining.One of the biggest reasons for these errors is the data source. If data came from a single source, the most common quality problems that require cleaning up are:Data entry errors, mostly attributed to ‘human’ factor, or error of the person in charge of the input of data into the data warehouse. They could range from simple misspellings to duplication of entries and data redundancy.Lack of integrity constraints, such as uniqueness and referential integrity. Since there is only one source of data, there is no way of ascertaining whether the data is unique or not. In the same way, duplication and inconsistency may arise due to the lack of referential integrity.Similarly, data obtained from multiple sources also have quality problems.Naming conflicts, often resulting from the fact that there are multiple sources of the same data, but named differently. The risk is that there may be data duplication brought about by the different names. Or it could be the other way around. More than one or two sources may use the same name for two sets of data that are completely unrelated or different from each other.Inconsistent aggregating, or contradictions arising from data being obtained from different sources. Duplications of data may result to them cance ling each other out.Inconsistent timing, where data may tend to overlap among each other, resulting to more confusion. The data then becomes unreliable. For example, data on shopping history of a customer may overlap when sourced from various shopping sites or portals.Cleaning up data often involves performing data profiling, or examining the available data and their related statistics and information, to determine their actual content, quality and structure.Other techniques used are clustering and various statistical approaches. Once the data has been cleaned, there is a need to update the record with the clean version.Step #5: Data TransformationThis is considered to be the second data pre-processing step. Other authors even describe data transformation as part of the data cleaning process.Despite having “cleaned” the data, they may still be incapable of being mined. To make the clean data ready for mining, they have to be transformed and consolidated accordingly. Basically, t he source data format is converted into “destination data”, a format recognizable and usable when using data mining techniques later on.The most common data transformation techniques used are:Smoothing. This method removes “noise” or inconsistencies in data. “Noise” is defined as a “random error or variance in a measured variable. Smoothing often entails performing tasks or operations that are also performed in data cleaning, such as:Binning. In this method, smoothing is done by referring to the ‘neighborhood’ of the chosen data value, and categorically distribute them in ‘bins’. This neighborhood essentially refers to the values around the chosen data value. Sorting the values in bins or buckets will smooth out the noise.Clustering. This operation is performed by organizing values into clusters or groups, ordinarily according to a certain characteristic or variable. In short, data values that are similar will belong to one cluster. This will smooth and remove any data noise.Regression. As a method for smoothing noise in data values, linear regression works by determining the best line to fit two variables and, in the process, improve their predictive value. Multiple regression, on the other hand, also works, but involves more than two variables.Aggregation. This involves the application of summarization tactics on data to further reduce its bulk and streamline processes. Usually, this operation is used to create a data cube, which will then be used later for analysis of data. A common example is how a retail company summarizes or aggregates its sales data periodically per period. Therefore, they have data on daily, weekly, monthly and annual sales.Generalization. Much like aggregation, generalization also leads to reduction of data size. The low-level or raw data are identified and subsequently replaced with higher-level data. An example is when data values on customer age is replaced by the higher level data concept of grouping them as pre-teen, teen, middle-aged, and senior. In a similar manner, raw data on families’ annual income may be generalized and transformed into higher-level concepts such as low-level, mid-level, or high-income level families.Normalization or Standardization. Data variations and differences can also have an impact of data quality. Large gaps can cause problems when data mining techniques are finally applied. Thus, there is a need to normalize them. Normalization is performed by specifying a small and acceptable range (the standard), and scaling the data in order to ensure they fall within that range.Examples of normalization tactics employed are Min-Max Normalization, Z-Score Normalization, and Normalization by Decimal Scaling.Step #6: Data MiningData mining techniques will now be employed to identify the patterns, correlations or relationships within and among the database. This is the heart of the entire data mining process, involving extraction of data patterns using various methods and operations.The choice on which data mining approach or operation to use will largely depend on the objective of the entire data mining process.The most common data mining techniques will be discussed later in the article.Step #7: Pattern EvaluationThe pattern, correlations and relationships identified through data mining techniques are inspected, evaluated and analyzed. Evaluation is done by using “interestingness” parameters or measures in figuring out which patterns are truly interesting and relevant or impactful enough to become a body of useful knowledge.The interpretation in this stepwill formally mark the transformation of a mere information into an entire “bag of knowledge”.Step #8: Knowledge PresentationThe knowledge resulting from the evaluation and interpretation will now have to be presented to stakeholders. Presentation is usually done through visualization techniques and other knowledge representation mechanisms. Once presented, the knowledge may, or will, b e used in making sound business decisions. DATA MINING TECHNIQUESOver the years, as the concept of data mining evolved, and technology has become more advanced, more and more techniques and tools were introduced to facilitate the process of data analysis. In Step #5 of the Data Mining process, the mining of the transformed data will make use of various techniques, as applicable.Below are some of the most commonly used techniques or tasks in data mining, classified whether they are descriptive or predictive in nature.Descriptive Mining TechniquesClustering or Cluster AnalysisClustering is, quite possibly, one of the oldest data mining techniques, and also one of the most effective and simplest to perform. As briefly described earlier, it involves grouping data values that have something in common, or have a similarity, together in a meaningful subset or group, which are referred to as “clusters”.The grouping or clustering in this technique is natural, meaning there are no predefi ned classes or groups where the data values are distributed or clustered into.Perhaps the most recognizable example of clustering used as a data mining tool is in market research, particularly in market segmentation, where the market is divided into unique segments. For instance, a manufacturer of cosmetic and skin care products for females may cluster its customer data values into segments based on the age of the users. Most likely the main clusters may include teens, young adults, middle age and mature.Association Rule DiscoveryThe purpose of this technique is to provide insight on the relationships and correlations that associate or bind a set of items or data values in a large database. Analysis of data is done mostly by looking for patterns and correlations.Customer behavior is a prime example of the application of Association Rules in data mining. Businesses analyze customer behavior in order to make decisions on key areas such as product price points and product features to b e offered.Incidentally, this technique may also be predictive, such as when it is used to predict customer behavior in response to changes. For example, if the company decides to launch a new product in the market, how will the consumers receive it? Association Rules may help in making hypotheses on how the customers will accept the new product.Sequential Pattern DiscoveryThis mining technique is slightly similar to the Association Rule technique, in the sense that the focus is on the discovery of interesting relationships or associations among data values in a database. However, unlike Association Rule, Sequential Pattern Discovery considers order or sequence within a transaction and even within an organization.Sequence Discovery or Sequence Rules is often applied to data contained in sequence databases, where the values are presented in order. In the example about customer behavior, this technique may be used to get a detailed picture of the sequence of events that a customer foll ows when making a purchase. He may have a specific sequence on what product he purchases first, then second, then third, and so on.Concept or Class DescriptionThis technique is straightforward enough, focusing on “characterization” and “discrimination” (which is why it is also referred to often as the Characterization and Discrimination technique. Data, or its characteristics, are generalized and summarized, and subsequently compared and contrasted.A data mining system is expected to be able to come up with a descriptive summary of the characteristics or data values. That is the data characterization aspect.For example, a company planning to expand its operations overseas is wondering which location would be most appropriate. Should they open an overseas branch in a county that experiences precipitation and storms for a greater half of the year, or should they pick a location that is mostly dry and arid throughout the year? Data characteristics on these two regions will be l ooked into for their descriptions, and then compared (or discriminated) for similarities and differences.Predictive Mining TechniquesClassificationThis method has several similarities with Clustering, which leads many to assume that they are one and the same. However, what makes them different is how, in Classification, there are already predetermined and pre-labeled instances, groups or classes. In clustering, the clusters are defined first, and the data values are put into the clusters they belong to. In classification, there are already pre-defined groups and, of course, it in these groups where the data values will be sorted into.In Classification, the data values will be segregated to the grouping or instances and be used in making predictions on how each of the data values will behave, depending on that of the other items within the class.An example is in medical research when analyzing the most common diseases that a country’s population suffers from. The classifications of diseases are already existing, and all that is left is for the researchers to collect data on the symptoms suffered by the population and classify them under the appropriate types of diseases.Nearest Neighbor AnalysisThis predictive technique is also similar to clustering in the sense that it involves taking the chosen data value in context of the other values around it. While clustering involves data values in extremely close proximity with each other, seeing as they belong to the same cluster, the nearest neighbor is more on the nearness of the data values being matched or compared to the chosen data value.In the cosmetic and skin care product manufacturing company example cited above, this technique may be used when the company wants to figure out which of their products are the bestsellers in their many locations or branches. If Product A is the bestseller in Location 1, and Location 10 is where Product J is selling like hot cakes, then the chances are greater that Location 2, which is nearer to Location 1 than Location 10 is, will also record higher sales for Product A more than Product J.RegressionRegression techniques come in handy when trying to determine relationships dependent and independent variables. It is a popular technique primarily because of its predictive capabilities, which is why you are likely to see it applied in business planning, marketing, budgeting, and financial forecasting, among others.Simple linear regression, which contains only one predictor (independent variable) and one dependent variable, resulting to a prediction. Presented graphically, the regression model that demonstrates a shorter distance or line between the X-axis (the predictor) and the Y-axis (the prediction or data point) will be the simple linear regression model to be used for predictive purposes.Multiple linear regression, which aims to predict the value of the responses or predictions with respect to multiple independent variables or predictors. Compared to th e simple regression, this is fairly more complicated and work-intensive, since it deals with a larger data set.Regression analysis is often used in data mining for purposes of predicting customer behavior in making purchases using their credit cards, or making an estimate of how long a manufacturing equipment will remain serviceable before it requires a major overhaul or repair. In the latter example, the company may plan and budget its expenditure on repairs and maintenance of equipment accordingly, and maybe even assess the feasibility of purchasing a new equipment instead of repeatedly spending more money on maintenance of the old one.So, now here is the fun stuff (hint: its the video :-). Decision TreesWhat makes this predictive technique very popular is its visual presentation of data values in a tree. The tree represents the original set of data, which are then segmented or divided into the branches, with each leaf representing a segment. The prediction is the result of a seri es of decisions, presented in the tree diagrams as a Yes/No question.What makes this model even more preferred is how the segments come with descriptions. This versatility â€" offering both descriptive and predictive value in an easy-to-understand presentation â€" is the main reason why decision trees are gaining much traction in data mining and database management, in general.Outlier AnalysisIn instances where there are already established models or general behavior expected from data objects, data mining may be done by taking a look at the exceptions or, in this case, what we call the “outliers”. These are the data objects that do not fall within the established model or do not comply with the expected general behavior. The result of these deviations may prove to be data that can be used as a body of knowledge later on.A classic example of applying outlier analysis is in credit card fraud detection. The shopping history of a specific customer already provides an e-tailer (onli ne retail store) a set of general behavioral data to base on. When trying to find if the fraudulent purchases have been made using the credit card of that customer, the focus of the analysis will be unusual purchases in his shopping history, such as surprisingly large amounts spent on a single purchase, or the unusual purchase of a specific item that is completely unrelated to all previous purchases.If the customer, for the past three years, has made a purchase at least once in every 2 months, a single month with the customer purchasing more than two or three times is enough to raise a red flag that his credit card may have been stolen and being improperly and fraudulently used.Evolution AnalysisWhen the data to be subjected to mining inherently changes or evolves over time, and the goal is to establish a clear pattern that will help in predicting the future behavior of the data object, a recommended approach is evolution analysis.Evolution analysis involves the identification, desc ription and modeling of trends, patterns and other regularities with respect to the behavior of data objects as they evolve or change. Thus, you will often find this applied the mining and analysis of time-series data. Stock market trends, specifically on stock prices in the stock market, are subjected to time-series analysis. The output will enable investors and stock market analysts to predict the future trend of the stock market, and this will ultimately guide them in making their stock investment decisions.There are a lot of other techniques used in data mining, and we named only a few of the most popular and the most commonly used approaches. Application of these techniques also require the use of other disciplines and tools, such as statistics, mathematics, and software management.The success of a business rides a lot on how good management is at decision-making. And let us not forget that a decision will only be as good as the quality of the information or knowledge tapped in to by the decision-makers. High quality information will rely heavily on how the collection, processing and evaluation of data. If data mining was unsuccessful or less than effective in the first place, then there is a great chance that the resulting “bag of knowledge” will not be as accurate and effective as well, and poor business decisions may be arrived at.

Digital Journalism And Its Impact On Society - 2418 Words

Digital journalism, or rather known as online journalism too, is modernizing the way news is reported and delivered. The rise of the Internet has endlessly altered the way society interacts with news. Articles and stories are published the second they break and readers routinely access news sources for them. (Jim Hall, 2001) Digital journalism was not always a welcome incorporation to the academic curriculum or the news industry, but in today’s day and age many will agree that online journalism is a vital and durable platform for the global communications landscape and that it will have as noteworthy an influence on society as news from traditional mediums has done, such as newspapers, magazines, television, etc. (Kevin Kawamoto, 2003) With digital journalism advancing, how sure are we that the Internet is actually helping news agencies transmit their news more efficiently, giving opportunities to writers and editors to showcase their work on a broader horizon. Or is it just a nother platform for news agencies to earn big bucks? Critical political economists have argued that quality journalism costs a lot. That is also one of the key challenges to some forms of online journalism, the constant need to attract resources. Jack Shafer, a media writer for Reuters, wrote a post on how online news has never really made money, and is unlikely to either. As the traditional news industry has struggled with the constant weakening of its conventional business and the surfacing of newShow MoreRelatedThe General Term Of Journalism1176 Words   |  5 Pages In our contemporary society, we consume massive amount of public affairs every day, all around the world, without being a personal witness of them. We are able to receive this important information through the news media, which includes print-based media such as newspapers, magazines, books, and audio visual such as films, television, and lastly, Internet. 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Slavery Is Like An Electric Fence - 1776 Words

â€Å"The topic of slavery is like an electric fence. Touch it and people will react.† The history of slavery in Puerto Rico is rather particular as the demand for slaves, and by extension slave labour, developed later than in other regions already dependent on forced labour in the plantations. The purpose of this paper is to offer a comparative critical review of two articles, which examined slavery and its abolition in Puerto Rico during the 19th century. Through a comparative analysis about the causes and effects of the abolition in Puerto Rico presented in Freedom in the making: the slaves of hacienda La Esperanza, Manatà ­, Puerto Rico, on the eve of abolition, 1868-76 by Astrid Cubano Iguina and The End of Slavery and the End of Empires: Slave Emancipation in Cuba and Puerto Rico by Christopher Schmidt-Nowara, the respective merits of each article will be considered. The strengths and weaknesses of each author’s arguments will also be assessed in order to deciph er which of the two articles is the strongest. To begin, although both articles explore slavery and its steps towards abolition in Puerto Rico they utilize different methodologies and present diverging causes and effects for its eradication in the 19th century. Iguina’s piece examines slavery from a social perspective by revealing the experiences of slaves on the sugar plantation La Esperanza, whereas Nowara analyzes slavery through rather economic and political lenses. Freedom in the Making argues that the drivingShow MoreRelatedChallenges And Impacts Of Robots870 Words   |  4 Pagesmachine, and electric grills. Q: Are robots the answer to run a shop in the United States to keep costs down? I am on the fence with this question. 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The Hunters Phantom Chapter 34 Free Essays

string(114) " emotion wil ingly rejected, wil draw back to them the life the phantom has stolen from their thoughts and deeds\." We didn’t weaken it, not enough!† Meredith shouted to her friends over Jealousy’s shouts. The phantom, if anything, appeared stronger as it crossed the garage in one great leap and backhanded Meredith across the face. Meredith felt a searing pain, saw a bright flash of light, and felt herself slam against the wal . We will write a custom essay sample on The Hunters: Phantom Chapter 34 or any similar topic only for you Order Now Dazed, she staggered back onto her feet. The phantom was coming toward her again. More slowly this time, with a smile of anticipation. The spell must be doing something then, Meredith thought groggily, or it wouldn’t care if I finished my part or not. Meredith gripped her fighting stave. She wasn’t going down easily, not if she could prevent it. Alaric had cal ed her a superhero. Superheroes kept fighting, even when the odds were stacked against them. She sliced out viciously, expertly, with the end of the fighting stave. Al those hours of practice paid off, because the phantom didn’t seem to expect the blow, and rather than the stave passing harmlessly through mist, Meredith caught the phantom in its solid form, just above the rose in its chest. The blade at the end opened a deep wound in the phantom’s chest, and when Meredith pul ed it back for a second blow, viscous green fluid dripped from the end of her weapon. As she swung again, Meredith’s luck ran out. The phantom reached out toward her, its hand moving so fast that Meredith didn’t see it until the phantom was holding the other end of the stave. Sharp as the stave was, poisonous as the coating of al those bits of silver and wood and iron were, the phantom held it lightly and easily, and pulled. Meredith went skidding across the garage floor toward the phantom, fast and helpless, and the phantom reached out lazily with its other hand to catch her, a sneer of contempt and anger on its glassy face. Oh no, Meredith’s internal voice babbled, not like this. It can’t end like this. Just before it touched Meredith, though, the phantom’s face changed, suddenly blossoming into an expression of confusion. It let go of the stave, and Meredith yanked herself back and caught her balance, wobbling furiously, gasping for breath. The phantom stared past her, Meredith forgotten, at least for the moment. The phantom’s glassy teeth were bared, and there was an expression of terrible rage on its greentinted face. As Meredith watched, the muscles in its icysolid arms seemed to strain, then dissolve to swirls of armshaped mist, then solidify again, stil in the same tense stil ness. She can’t move, Meredith realized. She turned to look behind her. Mrs. Flowers stood straight and tal , her blazing blue eyes fixed on the phantom. She held out her hands in front of her, her face set in strong, determined lines. Several strands of her gray hair had escaped from her bun, standing out in al directions as if caught by static electricity. Mrs. Flowers’s lips moved soundlessly, and, as the phantom strained to move, Mrs. Flowers strained, too, looking as if she was struggling to support something cripplingly heavy. Their eyes, cool intent blue and glacierclear green, were locked together in silent battle. Mrs. Flowers’s eyes were steady, but her arms were shaking violently, and Elena didn’t know how much longer the older woman would be able to hang on and keep the phantom under control. Not long, she suspected. The battle with the kitsune had taken a lot out of Mrs. Flowers, and she hadn’t recovered ful y yet. She wasn’t ready for a new fight. Elena’s heart was thumping like crazy, and she could n’t stand to look at the bloody figures of Damon and Stefan on the other side of the garage, because the one thing she knew she couldn’t do right now was panic. She needed to be able to think. â€Å"Meredith,† Elena said crisply, with such a tone of authority that her friends al turned away from watching the struggle between Mrs. Flowers and the phantom to look at her. â€Å"Finish your part of the ceremony.† Meredith looked at Elena blankly for a moment and then snapped into gear. That was one of the many wonderful things about Meredith: She could always be relied upon, no matter what, to pul herself together and get on with the job. â€Å"I have fed the phantom of jealousy,† Meredith said, looking down at the floor where her brown candle stil burned, â€Å"but now I cast my jealousy away.† Meredith’s words rang with truth, and the candle went out. The phantom flinched and grimaced, flexing its fingers angrily. The deep red of the rose in its chest dul ed to a dark pink for a moment before flushing back to crimson. But†¦ it didn’t seem like it was defeated; it seemed merely irritated. Its eyes never left Mrs. Flowers’s, and its ice-sculpted muscles stil were straining forward. Almost al the candles were out. Only two flames were flickering, from the blue and red candles, only two victims feeding the phantom with their jealousy. So, with almost al its victims torn away from it, shouldn’t the phantom be weaker? Shouldn’t it be sick and struggling? Elena turned to Alaric. â€Å"Alaric,† she whispered. â€Å"What did the book say? Shouldn’t the spel be starting to kil the phantom by now?† Alaric was watching the silent showdown between Mrs. Flowers and the phantom again, his own fists clenched and his body straining as if he could somehow lend Mrs. Flowers his strength, and it took a little time – time we don’t have, thought Elena furiously – for him to drag his attention to Elena. When he did and she repeated her question, he turned a more analytical gaze on the phantom, and a new worry dawned in his eyes. â€Å"I’m not entirely sure,† he said, â€Å"but the book did suggest†¦ the book said something like, ‘Every word truly spoken by its victims, each dark emotion wil ingly rejected, wil draw back to them the life the phantom has stolen from their thoughts and deeds. You read "The Hunters: Phantom Chapter 34" in category "Essay examples" The creature wil crumble with every honest word spoken against it.’ It could be just rhetoric, or maybe the person who wrote down the spel had heard about the ritual without seeing it performed, but it sounds†¦Ã¢â‚¬  He hesitated. â€Å"It sounds like the spel ought to be kil ing the phantom by now,† said Elena flatly. â€Å"It sounds like this isn’t working right.† â€Å"I don’t know what’s going wrong,† said Alaric unhappily. The world shifted and everything snapped into focus. â€Å"I do,† said Elena. â€Å"It must be because this is an Original, not an ordinary phantom. We didn’t create it with our emotions, so we can’t destroy it just by taking them away. I think we’re going to need to try something else.† Stefan and Damon were stil locked in combat. They were both bloody and battered. His hurt arm dangling at an unnatural angle, Stefan moved as though something inside him had been damaged, but they were both stil attacking each other viciously, Stefan no less than Damon. Elena reasoned that they must be fighting on their own initiative now. The phantom, absorbed in its battle with Mrs. Flowers, was no longer muttering poisonous encouragement to them. If Damon and Stefan weren’t being seduced by Jealousy’s voice, maybe they could be persuaded to listen to someone else. Elena, trying not to catch the phantom’s attention, eased her way toward the fighters. Damon was bleeding from his neck and a long cut on his head, and the skin around both his eyes was bruising up. He was limping, but he was clearly gaining the upper hand. Stefan, circling warily now just out of arm’s reach, was not only curled forward to protect whatever was injured inside him but had a long strip of torn skin hanging from his cheek. Damon was grinning savagely at him, moving closer with every shift of his feet. There was an alertness to Damon’s eyes that spoke only of the predator within, of his joy in the hunt and in the kil . Damon must have forgotten in the pleasure of the fight who he was battling, Elena told herself. He would never forgive himself, once he was himself again, if he real y seriously hurt Stefan, or even kil ed him. Although, something inside her whispered, part of him has always wanted this. She shoved the thought aside. Part of Damon might want to hurt Stefan, but the real, whole Damon did not. If there was anything that fighting the phantom had shown her, it was that the dark emotions everyone hid in their depths weren’t al of who they real y were. They weren’t their true selves. â€Å"Damon,† she shouted. â€Å"Damon, think! The phantom is influencing you! It’s making you fight.† She heard her voice rise pleadingly. â€Å"Don’t let it beat you. Don’t let it destroy you.† Damon didn’t seem to hear her, though. He stil wore that feral smile, and prowled a little closer to Stefan, edging him farther and farther toward the corner of the garage. Pretty soon Stefan would be trapped, boxed in and unable to run. And, catching a glimpse of the defiant expression on Stefan’s poor, battered face, Elena realized with a sinking heart that Stefan wouldn’t run, even if Damon gave him the chance. The part of Stefan that hated Damon was in control of him now. Stefan bared his teeth in a ferocious snarl. Damon pul ed back his fist to deliver a powerful blow, his canines extending in anticipation of drinking his brother’s lifeblood. More quickly than she had ever moved before, at least as a human, Elena flung herself between them as Damon’s fist swung forward. Eyes squeezed closed, she threw her arms wide to protect Stefan and awaited the impact. Damon was moving so fast by the time she jumped in front of him that momentum was carrying his whole body forward. With his inhuman strength, it was a punch that would break her bones and crush her face. But Damon stopped in time, as only a vampire could. She could feel the rush of displaced air from the blow, even the brush of his knuckles against her face, but there was no pain. Gingerly Elena opened her eyes. Damon stood poised, coiled to strike, one arm stil raised. He was breathing hard, and his eyes glittered strangely. Elena returned his gaze. Was there a tiny bit of relief shining in Damon’s eyes? Elena thought so. The question was, was it relief that he had stopped himself before he kil ed her, or that she had stopped him from kil ing Stefan? Surely Damon could have thrown her out of the way by now and attacked Stefan again, if that was what he real y wanted. Elena took a chance and reached out toward Damon’s fist, folding those battered knuckles within her own smal er hand. He didn’t resist as she lowered his fist to his side, passively al owing himself to be moved. â€Å"Damon,† she said softly. â€Å"Damon, you can stop now.† His eyes narrowed and she knew he could hear her, but his mouth was tight and fierce, and he didn’t answer. Without letting go of Damon’s hand, Elena turned toward Stefan. He was close behind her, his eyes fixed on Damon. He was panting rapidly, and he wiped the back of his hand absently against his mouth, smearing blood across his face. Elena reached out and took his hand, sticky as it was with blood. Damon’s hand tensed in hers, and she glanced at him to see he was staring at her other hand, the one that was holding Stefan’s. Stefan saw where Damon was looking, too, and the corners of his swol en mouth drew up in a bitter little smile. Behind them, the phantom snarled as it fought Mrs. Flowers’s power. It sounded louder, fiercer. â€Å"Listen,† she said urgently, looking from one brother to the other. â€Å"The phantom’s not focusing on you now, so you can think for yourselves. But Mrs. Flowers won’t be able to hold her for long. So you need to do it; you need to start thinking now, instead of just acting. I need to tel you†¦ um.† She cleared her throat uncomfortably. â€Å"I never told you this, but when Klaus was keeping me prisoner, after Katherine’s death, he used to show me†¦ images. Memories, I guess, Katherine’s memories. How you both were with her, back when you were human. When you were young and alive and loved her. How much you loved her. I hated it, seeing how real that love was. And I knew that you noticed me at first only because of the love you had for her then. It’s always bothered me a little bit, even though I know your love for me now is deeper.† Both brothers were looking at Elena now, and Stefan’s lips parted to speak. Elena shook her head briskly and went on. â€Å"No, let me finish. It’s bothered me a little bit. It hasn’t destroyed me, and it hasn’t changed what I feel†¦ for either of you. Because I also know that you might have noticed me for Katherine’s sake, but that once you got past it, you both saw me, Elena. You don’t see Katherine in me anymore.† She had to venture into dangerous territory now, so she proceeded cautiously, trying to lay out her argument with logic and sensitivity. â€Å"So, I know that, right? But when the phantom spoke to me, it dredged up that old jealousy and made it burn inside me again. And the other things the phantom said to me are partly true, too. Yes, I’m jealous sometimes of girls with† – she smiled despite herself – â€Å"normal love lives. But in my most centered moments, I know I wouldn’t want to be them. What I’ve got is amazing, even if it’s hard.† Elena swal owed. â€Å"And so I know that what the phantom said to you is partly true. You’re jealous of each other. You’re angry about things from the past, and you’re upset that I love both of you. But I also know that’s not all there is. It’s not the most important thing, either. Not anymore. Things have changed since the days when jealousy and anger were the only emotions between you. You’ve worked together, and you’ve protected each other. You’ve become brothers again.† She gazed into Damon’s eyes, searching for a response. â€Å"Damon, Stefan was devastated when he thought you were dead. You’re his brother, and he loves you, and he didn’t know what to do with you gone. You’re a big part of his life – past and present. You’re the only one who’s been there with him throughout his history.† She swung to look at Stefan. â€Å"Stefan, Damon didn’t hide from you the fact that he was alive because he wanted to make you suffer, or to be free of you, or whatever the phantom was convincing you of. He wanted to be able to come back in a way and at a time that he could show you things were going to be different. That he was capable of changing. And you were the person he wanted to change for. Not me. You. You’re his brother and he loves you, and he wanted things to be better between you.† Elena paused for breath, and to gauge what effect, if any, her speech was having on the brothers. At least they weren’t currently trying to kil each other. That had to be a good sign. They stared at each other now, their faces unreadable. Damon licked the blood from his lips. Stefan reached up and careful y ran his free hand over the torn skin on his face and chest. Neither one said a word. Was there a connection left between them? Damon was looking at the cuts on Stefan’s neck with an almost soft expression in his black eyes. Elena let go of them and threw up her hands. â€Å"Fine,† she said. â€Å"If you can’t forgive each other, then just think about this. The phantom wants you to fight. It wants you to kil each other, to hate each other. Your jealousy is what’s feeding it. One thing I know about you – about both of you – is that you’ve never given your enemies anything they wanted, not even if it would have saved you. Are you going to give in to what this phantom, this manipulative monster, wants? Is it going to control you, or are you going to control you? Does either of you real y want to murder your brother for someone else?† At the same exact moment, Damon and Stefan blinked. After a few seconds, Stefan cleared his throat awkwardly. â€Å"I’m glad you’re not dead after al ,† he offered. The corner of Damon’s mouth twitched. â€Å"I’m relieved I didn’t manage to kil you today, little brother,† he answered. Apparently, that was al they had to say. They held each other’s eyes for a beat longer, then turned to Elena. â€Å"So,† said Damon, and he was beginning to smile, a wild, reckless smile that Elena recognized. Damon the unstoppable, Damon the antihero, was back. â€Å"How do we kil this bitch?† Mrs. Flowers and the phantom were stil locked in their silent, almost motionless battle. Mrs. Flowers was beginning to lose ground to the phantom, though. The phantom’s stance was wider; its arms had spread out. It was gradual y gaining the power to move, and Mrs. Flowers’s hands and arms were shaking with strain. Her face was pale, and the lines of age around her mouth seemed deeper. â€Å"We have to hurry,† Elena said to Damon and Stefan. They skirted around Mrs. Flowers and the phantom, and joined the others who, white-faced and wary, were watching them approach. In front of them, only two candles stil burned. â€Å"Stefan,† Elena said. â€Å"Go.† Stefan stared down at the dark blue candle stil burning on the floor of the garage. â€Å"I’ve been jealous of everyone lately, it seems,† he said, the shame evident in his tone. â€Å"I’ve been jealous of Matt, whose life seems so simple and good to me, who I know could have taken Elena out of the shadows and given her the uncomplicated life she deserves. I was jealous of Caleb, who seemed like the kind of golden boy who would be a good match for Elena, so much so that I distrusted him even before I had reason to, because I thought he was after her. And especial y, I was jealous of Damon.† His gaze left the candle and settled on his brother’s face. Damon looked back at him with an inscrutable expression. â€Å"I suppose I’ve always been jealous of him. The phantom was tel ing the truth when she said that. When we were alive, he was older, faster, stronger, more sophisticated than I was. When we died† – Stefan’s lips curled up in a bitter smile of remembrance – â€Å"things only got worse. And, even more recently, when Damon and I found we could work together, I’ve resented how close he was to Elena. He has a piece of her that I’m not a part of, and it’s hard not to be jealous of that.† Stefan sighed and rubbed the bridge of his nose between his thumb and forefinger. â€Å"The thing is, though, I love my brother. I do.† He looked up at Damon. â€Å"I love you. I always have, even when we were at our worst. Even when al we wanted to do was kil each other. Elena’s right: We’re more than the bad parts of ourselves. I have fed the phantom of jealousy, but now I cast my jealousy away.† The blue candle flickered and went out. Elena was watching the phantom closely, and saw the rose in its torso dul for a moment. The phantom flinched and snarled, then renewed its struggle against Mrs. Flowers’s spel . As it gave a powerful twist, the older woman staggered backward. â€Å"Now!† Elena muttered quietly to Damon, looking at him meaningful y and wishing more than ever that she had her powers of telepathy. Distract her, she hoped her eyes said. Damon nodded once, as if to say he understood her message, then cleared his throat theatrical y, drawing every eye to him, and picked up the dark red candle, the last one burning in the line. He dabbed a line of his blood down its length and spent a few seconds posed with his head lowered pensively, his long, dark eyelashes brushing his cheeks. He was milking the moment for every drop of drama. Once every eye was fixed on him, Elena touched Stefan and indicated for him to help her approach the phantom from either side. â€Å"I have been jealous,† Damon intoned, staring down at the flame of the candle he held. He flicked his eyes up quickly at Elena, and she nodded encouragingly. â€Å"I have been jealous,† he repeated, frowning. â€Å"I have coveted that which my brother has, over and over again.† Elena slipped closer to the phantom, coming up beside it on its right side. She could see that Stefan was inching nearer on its left. Mrs. Flowers saw them, too. Elena could tel , because the older woman raised her eyebrows fractional y and began to mutter her spel more loudly and fiercely. Damon’s voice rose, too, everyone in the room competing for Jealousy’s attention, to keep it from noticing Stefan and Elena’s machinations. â€Å"I don’t need to go into every single detail of my past,† Damon said, his familiar smirk appearing on his battered face, a smirk that Elena found oddly reassuring. â€Å"I think there’s been enough of that here today. Suffice it to say there are things I†¦ regret. Things that I would like to be different in the future.† He paused dramatical y for a moment, his head thrown back proudly. â€Å"And so I admit that I have fed the phantom of jealousy. And now I cast jealousy out.† In the moment that Damon’s candle went out – and thank God it had gone out, Elena thought; Damon was apt to cling to his worst impulses – the rose in the phantom’s chest dul ed again to a dark pink. Jealousy snarled and wobbled ever so slightly on its feet. At that same instant, Stefan lunged for the cut across the phantom’s chest and got his hand inside it, inside the phantom’s torso, and grabbed for the rose. A gout of green, viscous fluid spurted from the wound as Stefan squeezed the rose, and then the phantom screamed, a long, unearthly howl that made al the humans flinch. Bonnie clapped her hands over her ears, and Celia moaned. For a moment, Elena thought they were going to win that easily – that by attacking the rose at the phantom’s heart, Stefan had defeated it. But then the phantom steadied itself and, with a huge flexing of muscle, pul ed suddenly out of Mrs. Flowers’s control, and in one smooth motion ripped Stefan away from its side, his hand coming empty out of its chest, and threw him across the garage. Stefan hit the wal with a muffled thump, slid to the floor, and lay stil . Evidently exhausted by her battle with the phantom, Mrs. Flowers also sagged backward, and Matt rushed to catch her in his arms before she hit the ground. The phantom smiled slowly at Damon, showing its sharp teeth. Its glacier-clear eyes glittered. â€Å"It’s time to go, Damon,† Jealousy said softly. â€Å"You’re the strongest one here. The best of al of them, the best of anyone. But they’l always fawn over Stefan, the weakling, the brat, your useless baby brother. No matter what you do, no one wil ever care for you the way these mortals do for him. The way everyone, for hundreds of years, has always cared for Stefan. You should leave them behind. Make them suffer. Why not leave them in danger? They’d do the same to you. Elena and her friends traveled through dimensions, faced slavery, braved the greatest perils, to save Stefan, but they left you lying dead, far from home. They came back here and were happy without you. What loyalty do you owe them?† Damon, his face in shadow now that al the candles were out, gave a dark, bitter little laugh. His black eyes gleamed in the dimness, fixed on the phantom’s clear ones. There was a long silence, and Elena’s breath caught in her throat. Damon stepped forward, stil holding his candle. â€Å"Don’t you remember?† he said, his voice cool. â€Å"I cast you out.† And with superhuman quickness, before anyone could even blink, he lit his candle again with a flick of Power and threw it, straight and true, directly into the phantom’s face. How to cite The Hunters: Phantom Chapter 34, Essay examples

Max weber free essay sample

1. Weber sought to develop a better understanding of the dynamics of social organization by focusing on how social control operates in different types of social contexts. To start, he distinguished power and authority: †¢Power is defined simply as the ability to get someone to do something despite resistance. There are many sources of power, which we will address when we talk about social control and leadership, but of primary interest here is the consideration that power is socially expensive. To work effectively, power depends upon observation and enforcement, both of which need to paid for. †¢Alternatively authority is simply defined as legitimate power. It is a socially recognized agreement process between a superordinate (i. e. , the administrator or leader) and a subordinate (i. e. , the employee or follower) that articulates a range of activities over which the leader can tell specific followers what to do. Its appeal lies in that it requires neither observation nor enforcement, and therefore is much more efficient and reliable. We will write a custom essay sample on Max weber or any similar topic specifically for you Do Not WasteYour Time HIRE WRITER Only 13.90 / page 2. Weber distinguishes three types of authority: †¢Traditional authority is based in the person (the classic example is the King/Queen). The traditional authority is an ascribed status, (received through birthright), and it defines a social relationship between the lord and the vassal based on personal loyalty or fealty. In return for the fidelity of the vassal, the lord promises protection or other resources that the lord controls. The relationship is thus one of mutual obligation and is dependent upon the legitimate recognition of authority in the person of the lord. The system works well under conditions where organization is relatively small or where decision making is not under severe time constraints. Weber regards bureaucracy as an â€Å"ideal-type,† meaning not the best of form of organization, but rather a theoretical abstraction of social control. In a bureaucracy, authority is rationalized so that everyone is treated the same. Bureaucracy is described through six characteristics: †¢ 1. The organization of activities in each position is based on rules. These are consistent and universal. 2. Each position is specifies a â€Å"sphere of influence† which organizes related activities. These positions define a functionally related division-of-labor. 3. Organizational positions are organized into a hierarchical system which directs communication and control. This system allows the delegation of tasks into a hierarchy of organizational relationships. 4. The positions in each office may carry technical qualifications that require suitable training. Hiring and promotion is thus based on merit. 5. Each position is compartmentalized into a distinct office, organized by function, not by the person who does the job. 6. Administrative acts and decisions are formulated recorded in writing. These written rules become the files on which organizational activity is based. Under bureaucracy, a more efficient and rationally consistent organization emerges. But bureaucracies often do not work as well as they are designed. Under what conditions do bureaucracies work best? †¢Charismatic authority is often misidentified as a set of individually held attributes. Instead, Weber defines Charisma as a set of attributes that are socially prescribed as related to leadership positions (e. g. , a priest or cult leader). Followers or disciples may endow the charismatic leader with â€Å"supernatural† or exceptional powers that are not accessible to the ordinary person.