Cognitive Theorists Essay Example | Topics and Well Written Essays - 500 words

Cognitive Theorists - Essay Example In like manner, students in a vocational school who are required to finish certain skill within a period of time are expected to complete such task (2006). Furthermore, the participation of students and staffs in the attainment of the school’s goal reflects the other assumption of Tolman’s theory that behavior is analyzed through the actions of a larger group rather than the movements of individuals. Although, reward is not evident in the accomplishment of the school’s purpose, students still comply owing to their internal discrimination with the presentation of the goal. After all, the concept of Tolman’s theory indicates that learning and motivation is still possible even without a reward. Cognitive mapping of Tolman could also be applied to vocational trainings considering the performance of the participants. As the instructor teaches a skill, students paint an outline in their brain to indicate the route with how they could retain the sequence of skill s taught. Once a blueprint is fixed in their mind, they can repeat the skill even in the midst of changes or difficulties like when they are nervous.

Human Resource Management and Labor Relations Essay - 1

Human Resource Management and Labor Relations - Essay Example The main challenges faced by domestic and global companies involve the challenge become more efficient or competitive, to overcome diversity and cultural differences, to introduce effective culture and good morale, organizational structure and change. In home country, difficulties in communication can arise with produc ­tion systems where workers are stationed continuously at a particular point with limited freedom of movement. Even when opportunities exist for interaction with colleagues, physical conditions may limit effective commu ­nication. Communication has an impact of organizational structure which is on the surface the elements or patterns of rules, and social relations (encounters and face to face co-presence) are articulated by discourse and ideology. Rather than being dependent upon information, organizational communication is embedded in trust. Surface relations must be seen in terms of a deeper code or structure that makes sense of the often conflicting surface even ts. An environment from which communication flows is interpreted and constructed, regardless of the networks from which the communication emerges or arises (dense, close, loose, tight, redundant, etc) (Campbell 45). In a global context, workforce diversity has a tremendous impact on communication and relations between employees. All employees come from different cultural and social environment; they have different social statuses and class location, different religious beliefs and belong to different cultures. Differences are bound to exist, due simply to the physical characteristics of the employees, such as sex, nation race (Gesteland 76). These differences create a certain tension between employees which requires special intervention actions of HR department to manage workforce diversity. The main challenges in communication involve cultural and language differences.

Educational relevance of Vygotsky's theory Essay

Educational relevance of Vygotsky's theory - Essay Example The main idea in Vygotskys theory is his principle that biological and cultural development does not take place in isolation. Vygotsky held that development is a procedure that has to be examined, instead of a product to be attained (Social Development Theory, No date). This theory acknowledged that individuals learn through social interactions and their culture. They gain knowledge through dialogues in which individuals socially interact and communicate with others to discover the cultural values of the society. Vygotsky also believed that human activities take place in cultural settings and cannot be understood separately from these settings (Woolfolk, 2004). The second belief in Vygotsky’s perspective is that all superior mental functions begin in social activity, that is, the real formation of relations among humans. Development entails mental processes initially on the social level, among individuals, and after that on the individual basis, within the child. Thus, through these social interactions, an individual moves in the direction of more individualized thought process. The co-constructed progression consists of individuals interacting through common activities, typically to resolve a problem. When the child gets assistance through this process, he or she may be able to use enhanced strategies in the future, should a similar difficulty occur. The co-constructed dialogues directs to internalization, which then results in independent though process (Social Development Theory, No date). The development of language is considered to be the chief aspect of Vygotskys sociocultural theory. The language of a certain group of people signifies their cultural beliefs and value system. This suggests that children learn language in a similar fashion as learning of cognitive skills. Vygotsky affirms that individuals might have fabricated prejudices, regulations, and restrictions about language that limit the amount of possibilities

1984 Vs Brave New World :: compare contrast

Aldous Huxley’s Brave New World, and George Orwell’s 1984 and Animal Farm each make commentary regarding the governing of society. Each story involves a so called perfect society, or Utopia. The people are given what they want, only to discover it wasn’t really what they desired. It seems that both authors are telling us their idea of what’s wrong with society, and how extreme these wrongs could become if we government to think for us. The way in which each story gives its warning is different. I will explore this. Throughout the novel Winston tries to avoid the control o Big Brother, by committing many almost illegal crimes, such as keeping a diary or sneaking around with Julia, his mistress. Winston is eventually caught, and tortured by "the Party" for his crimes, until he is ‘restored’ and learns to love big brother. 1984 contains a warning about the future. Though not specifically about computers, since it was finished in 1948 "Big Brother" can be viewed as playing the role of a computer. The way big Brother, and tele - screens work can be linked to the commanding control computers have over society today. With the increasing amount of electronic business and the accelerated expansion of the Net the amount of personal information raging from tax file numbers to favourite colours available on line is aiding increasing governmental desire for a paperless world where you can be eliminated, monitored, or altered with the tap of a key. Although many similarities exist between Aldous Huxley's A Brave New World and George Orwell's 1984, the works books though they deal with similar topics, are more dissimilar than alike. A Brave New World is a novel about the struggle of Bernard Marx, who rejects the tenants of his society when he discovers that he is not truly happy. 1984 is the story of Winston who finds forbidden love within the hypocrisy of his society. In both cases, the main character is in quiet rebellion against his government which is eventually found to be in vain. Huxley wrote A Brave New World in the third person so that the reader could be allotted a more comprehensive view of the activities he presents. His characters are shallow and cartoon-like in order to better reflect the society in which they are entrapped. In this society traditional notions of love and what ideally should come out of it have long been disregarded and are now despised, "Mother, monogamy, romance.

Data Warehouses & Data Mining

DATA WAREHOUSES & DATA MINING Term-Paper In Management Support System [pic] Submitted By:Submitted To: Chitransh NamanAnita Ma’am A22-JK903Lecturer 10900100MSS ABSTRACT :- Collection of integrated, subject-oriented, time-variant and non-volatile data in support of managements decision making process. Described as the â€Å"single point of truth†, the â€Å"corporate memory†, the sole historical register of virtually all transactions that occur in the life of an organization.A fundamental concept of a data warehouse is the distinction between data and information. Data is composed of observable and recordable facts that are often found in operational or transactional systems. At Rutgers, these systems include the registrar’s data on students (widely known as the SRDB), human resource and payroll databases, course scheduling data, and data on financial aid. In a data warehouse environment, data only comes to have value to end-users when it is organized and p resented as information.Information is an integrated collection of facts and is used as the basis for decision-making. For example, an academic unit needs to have diachronic information about its extent of instructional output of its different faculty members to gauge if it is becoming more or less reliant on part-time faculty. [pic] INTRODUCTION :- â€Å"The data warehouse is always a physically separate store of data transformed from the application data found in the operational environment†. Data entering the data warehouse comes from operational environment in almost every case.Data warehousing provides architectures and tools for business executives to syste-matically organize ,understand ,and use their data to make stragetic decisions. A large number of organizations have found that data warehouse systems are valuable tools in today’s competive,fast-evolving world. In the last several years ,many firms have spent millions of dollars in building enterprise wide da ta warehouses. Many people feel that with competition mounting in every industry ,data warehousing is the latest must have marketing weapon –a way to keep customers by learning more about their needs.Data warehouses have been defined in many ways,making it difficult to formulate a rigorous definition. Loosely speaking , a data warehouse refers to a database that is maintened separately from an organization,s operational databases. Data warehouse systems allow for integration of a variety of applications systems . They support information processing by providing a solid platform of consolidated historical data for analysis. Data warehousing is a more formalised methodology of these techniques.For example, many sales analysis systems and executive information systems (EIS) get their data from summary files rather then operational transaction files. The method of using summary files instead of operational data is in essence what data warehousing is allabout. Some data warehousin g tools neglect the importance of modelling and building a datawarehouse and focus on the storage and retrieval of data only. These tools might havestrong analytical facilities, but lack the qualities you need to build and maintain a corporatewide data warehouse.These tools belong on the PC rather than the host. Your corporate wide (or division wide) data warehouse needs to be scalable, secure, openand, above all, suitable for publication. NEED OF DATA WAREHOUSE :- Missing data: Decision support requires historical data which operational DBs do not typically maintain Data Consolidation: DS requires consolidation (aggregation, summarization) of data from heterogeneous sources: operational DBs, external sources Data quality: Different sources typically use inconsistent data representations, codes and formats which have to be reconciled. pic] DATA WAREHOUSE ARCHITECTURE :- [pic] Components :- †¢ OPERATIONAL DATA WAREHOUSE ( for the DW is supplied from mainframe operational data he ld in first generation hierarchical and network databases, departmental data held in proprietary file systems, private data held on workstaions and private serves and external systems such as the Internet, commercially available DB, or DB assoicated with and organization’s suppliers or customers †¢ OPERATIONAL DATABASE( is a repository of current and integrated operational data used for analysis.It is often structured and supplied with data in the same way as the data warehouse, but may in fact simply act as a staging area for data to be moved into the warehouse †¢ LOAD MANAGER ( also called the frontend component, it performance all the operations associated with the extraction and loading of data into the warehouse. These operations include simple transformations of the data to prepare the data for entry into the warehouse WAREHOUSE MANAGER ( performs all the operations associated with the management of the data in the warehouse. The operations performed by this c omponent include analysis of data to ensure consistency, transformation and merging of source data, creation of indexes and views, generation of denormalizations and aggregations, and archiving and backing-up data. †¢ QUERY MANAGER( also called backend component, it performs all the operations associated with the management of user queries.The operations performed by this component include directing queries to the appropriate tables and scheduling the execution of queries. . END-USER ACCESS TOOLS( can be categorized into five main groups: data reporting and query tools, application development tools, executive information system (EIS) tools, online analytical processing (OLAP) tools, and data mining tools. DATA MART :- It is a subset of a data warehouse that supports the requirements of particular department or business function.The characteristics that differentiate data marts and data warehouses include: †¢ a data mart focuses on only the requirements of users associated with one department or business function †¢ as data marts contain less data compared with data warehouses, data marts are more easily understood and navigated †¢ data marts do not normally contain detailed operational data, unlike data warehouse. pic] META DATA:- Metadata is about controlling the quality of data entering the data stream. Batch processes can be run to address data degradation or changes to data policy. Metadata policies are enhance by using metadata repositories. IMPORTANCE OF META DATA :- The integration of meta-data, that is †data about data† †¢ Meta-data is used for a variety of purposes and the management of it is a critical issue in achieving a fully integrated data warehouse †¢ The major purpose of meta-data is to show the pathway back to where the data began, so that the warehouse administrators know the history of any item in the warehouse †¢ The meta-data associated with data transformation and loading must describe the source data and any changes that were made to the data †¢ The meta-data associated with data management describes the data as it is stored in the warehouse †¢ The meta-data is required by the query manager to generate appropriate queries, also is associated with the user of queries †¢ The major integration issue is how to synchronize the various types of meta-data use throughout the data warehouse. The challenge is to synchronize meta-data between different products from different vendors using different meta-data stores †¢ Two major standards for meta-data and modeling in the areas of data warehousing and component-based development-MDC(Meta Data Coalition) and OMG(Object Management Group) †¢ a data warehouse requires tools to support the administration and management of such complex enviroment. †¢ for the various types of meta-data and the day-to-day operations of the data warehouse, the administration and management tools must be capable of supportin g those tasks: †¢ monitoring data loading from multiple sources data quality and integrity checks †¢ managing and updating meta-data †¢ monitoring database performance to ensure efficient query response times and resource utilization. [pic] [pic] DATA WAREHOUSING PROCESSES :- The process of extracting data from source systems and bring it into the data warehouse is commonly called ELT, which stands for extraction, transformation, and loading. In addition, after the data warehouse (detailed data) is created, several data warehousing processes that are relevant to implementing and using the data warehouse are needed, which include data summarization, data warehouse maintenance. Extraction in Data Warehouse :-Extraction is the operation of extracting data from a source system for future use in a data warehouseenvironment. This is the first step of the ETL process. After extraction, data can be transformed and loaded into the data warehouse. Extraction process does not ne ed involve complex algebraic database operations, such as join and aggregate functions. Its focus is determining which data needs to be extracted, and bring the data into the data warehouse, specifically, to the staging area. The data has to be extracted normally not only once, but several times in a periodic manner to supply all changed data to the data warehouse and keep it up-to-date.Thus, data extraction is not only used in the process of building the data warehouse, but also in the process of maintaining the data warehouse. Every often, entire documents or tables from the data sources are extracted to the data warehouse or staging area, and the data completely contain whole information from the data sources. There are two kinds of logic extraction methods in data warehousing. Full Extraction :- The data is extracted completely from the data sources. As this extraction reflects all the data currently available on the data source, there is no need to keep track of changes to the data source since the last successful extraction. The source data will be provided as-is and no additional logic information is necessary on the source site. Incremental Extraction :-At a specific point in time, only the data that has changed since a well-defined event back in history will be extracted. The event may be the last time of extraction or a more complex business event like the last sale day of a fiscal period. This information can be either provided by the source data itself, or a change table where an appropriate additional mechanism keeps track of the changes besides the originating transaction. in most case, using the latter method means adding extraction logic to the data source. For the independence of data sources, many data warehouses do not use any change-capture technique as part of the extraction process, instead, use full extraction logic.After full extracting, the entire extracted data from the data sources can be compared with the previous extracted data to identify the changed data. Unfortunately, for many source systems, identifying the recently modified data may be difficult or intrusive to the operation of the data source. Change Data Capture is typically the most challenging technical issue in data extraction. [pic] DATA MINING :- Data Mining is the process of discovering new correlations, patterns, and trends by digging into (mining) large amounts of data stored in warehouses, using artificial intelligence, statistical and mathematical techniques. Data mining can also be defined as the process of extracting knowledge hidden from large volumes of raw data i. e. he nontrivial extraction of implicit, previously unknown, and potentially useful information from data. The alternative name of Data Mining is Knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, etc. The importance of collecting data thai reflect your business or scientific activities to achieve competitive advantage is widely recog nized now. Powerful systems for collecting data and managing it in large databases are in place in all large and mid-range companies. [pic] How Data Mining Works :- While large-scale information technology has been evolving separate transaction and analytical systems, data mining provides the link between the two.Data mining software analyzes relationships and patterns in stored transaction data based on open-ended user queries. Several types of analytical software are available: statistical, machine learning, and neural networks. Generally, any of four types of relationships are sought: Classes: Stored data is used to locate data in predetermined groups. For example, a restaurant chain could mine customer purchase data to determine when customers visit and what they typically order. This information could be used to increase traffic by having daily specials. Clusters: Data items are grouped according to logical relationships or consumer preferences. For example, data can be mined t o identify market segments or consumer affinities.Associations: Data can be mined to identify associations. The beer-diaper example is an example of associative mining. Sequential patterns: Data is mined to anticipate behavior patterns and trends. For example, an otitdoor equipment retailer could predict the likelihood of a backpack being purchased based on a consumer's purchase of sleeping bags and hiking shoes. DATA MINING MODELS :- 1. Predictive Model Prediction a. determining how certain attributes will behave in the future Regression b. mapping of data item to real valued prediction variable Classification c. categorization of data based on combinations of attributes Time Series analysis xamining values of attributes with respect to time 2. Descriptive Model Clustering most closely data clubbed together into clusters Data Summarization extracting representative information about database Association Rules associativity defined between data items to form relationship Sequence Di scovery it is used to determine sequential patterns in data based on time sequence of action [pic] APPLICATIONS OF DATA WAREHOUSE :- Exploiting Data for Business Decisions The value of a decision support system depends on its ability to provide the decision-maker with relevant information that can be acted upon at an appropriate time. This means that the information needs to be: Applicable.The information must be current, pertinent to the field of interest and at the correct level of detail to highlight any potential issues or benefits. Conclusive. The information must be sufficient for the decision-maker to derive actions that will bring benefit to the organisation. Timely. The information must be available in a time frame that allows decisions to be effective. Decision Support through Data Warehousing One approach to creating a decision support system is to implement a data warehouse, which integrates existing sources of data with accessible data analysis techniques. An organisati on’s data sources are typically departmental or functional databases that have evolved to service specific and localised requirements.Integrating such highly focussed resources for decision support at the enterprise level requires the addition of other functional capabilities: Fast query handling. Data sources are normally optimised for data storage and processing, not for their speed of response to queries. Increased data depth. Many business conclusions are based on the comparison of current data with historical data. Data sources are normally focussed on the present and so lack this depth. Business language support. The decision-maker will typically have a background in business or management, not in database programming. It is important that such a person can request information using words and not syntax. [pic]The proliferation of data warehouses is highlighted by the â€Å"customer loyalty† schemes that are now run by many leading retailers and airlines. These sc hemes illustrate the potential of the data warehouse for â€Å"micromarketing† and profitability calculations, but there are other applications of equal value, such as: Stock control Product category management Basket analysis Fraud analysis All of these applications offer a direct payback to the customer by facilitating the identification of areas that require attention. This payback, especially in the fields of fraud analysis and stock control, can be of high and immediate value. APPLICATIONS OF DATA MINING:- †¢ Banking: loan/credit card approval †¢ predict good customers based on old customers †¢ Customer relationship management: identify those who are likely to leave for a competitor. †¢ Targeted marketing: †¢ identify likely responders to promotions †¢ Fraud detection: telecommunications, financial transactions †¢ from an online stream of event identify fraudulent events †¢ Manufacturing and production: †¢ automatically adjust knobs when process parameter changes †¢ Medicine: disease outcome, effectiveness of treatments †¢ analyze patient disease history: find relationship between diseases †¢ Molecular/Pharmaceutical: †¢ identify new drugs †¢ Scientific data analysis: †¢ identify new galaxies by searching for sub clusters †¢ Web site/store design and promotion: find affinity of visitor to pages and modify layout. [pic] CONCLUSION :- What we are seeing is two-fold depending on the retailer's strategy: 1) Most retailers build data warehouses to target specific markets and customer segments. They're trying to know their customers. It all starts with CDI – customer data integration. By starting with CDI, the retailers can build the DW around the customer. 2) On the other side — there are retailers who have no idea who their customers are, or feel they don’t need to†¦. the world is their customer and low prices will keep the worldloyal. They use the ir data warehouse to control inventory and negotiate with suppliers.The future will bring real time data warehouse updates†¦with the ability to give the retailer an minute to minute view of what is going on in a retail location†¦and take action either manually or through a condition triggered by the data warehouse data†¦ The future belongs to those who: 1) Possess knowledge of the Customer and 2) Effectively use that knowledge†¦ REFERENCES :- 1. Mining interesting knowledge from weblogs: a survey – Federico Michele Facca, Pier Luca lanzi. http://software. techrepublic. com. com/abstract. aspx http://en. wikipedia. org/ http://msdn. microsoft. com/ Google Books Google Images Google Search www. seminarprojects. com Self =========================================================

Typo Examples of Typographical Errors

Challenges in The Odyssey Essay - 742 Words

Challenges in The Odyssey There are many challenges throughout the Odyssey the most significant being his arrogance that Odysseus must overcome in order to reach home. In book 9, beginning with line 113 Odysseus and his men begin a journey in the land of the Kyklops. This set of challenges for Odysseus shows the reader the battle between arrogance and wisdom within Odysseus. The choices that Odysseus makes during this portion of his journey home will help him to realize his arrogance and the downfalls of yielding to it, which he must overcome to regain his kingdom and property from the suitors. When Odysseus first decides to explore the nearby island he makes a wise decision to take only his own ship and company. The reason he†¦show more content†¦Furthermore, Odysseus show his arrogance when he ignores the advice of his men to take the plunder and leave but instead opts to stay and wait for the caveman to return home to test the owners hospitality. Odysseus makes a costly mistake in his decision, a ll of the evidence he sees in the cave points to a savage and uncivil being, but Odysseus, in his arrogance, elects to wait for the cavemans return anyway. A reader could use these instances to support a theory that it is better to quit while youre ahead. Starting in line 273 when the Kyklops sees Odysseus and his men. The one eyed giant is curios at first as to who they are. He begins in line 274 by asking strangers who are you? And where from? Odysseus responds to the questions of the one eyed giant by describing in great detail where they are from and who they are but becomes arrogant in the end of his description by instructing the Kyklops on what is acceptable behavior according to Zeus. Polyphemus, the one-eyed giant, responds by insulting Odysseus by calling him a ninny. This is a sign of arrogance and disrespect because of the brave and heroic things Odysseus did in the Trojan War. 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In the â€Å"Odyssey,† Homer writes an epic that reflects many stages from Joseph Campbell’s monomyth. The author includes many key points involving a true â€Å"Hero’s Journey,† but the main ones that are illustratedRead MoreEssay about Life in Homers Odyssey1222 Words   |  5 Pages The Odyssey:nbsp; Lifenbsp; nbsp; Odyssey a long series of wanderings filled with notable experience and hardships, or in other words the journey of life. Homers The Odyssey is an epic poem telling of one mans journey. Odysseus, the chosen traveler of this Odyssey, represents the will and perseverance of all humanity. Odysseus journey symbolizes the true toils of mankinds development through, agility, doubt, and faith. In life, agility is needed time and time again, to get out ofRead MoreAs I Lay Dying and The Odyssey: Books of Epic Quests with Incredible Odds805 Words   |  4 Pages As I lay Dying and the Odyssey As I lay dying and the Odyssey can both be considered books of epic quests with incredible odds. Faulkner takes the title of As I Lay Dying from a line spoken in the Odyssey by the Greek warrior Agamemnon. Agamemnon tells Odysseus when he travels to the Underworld, â€Å"As I lay dying, that woman with the dog’s eye would not close my eyes as I descended into Hades.† Both books give off extraordinary characters, with what some may call outlandish behavior, to possibly