American History Women's Rights Reform Movements from 1877 to 2013 Research Paper

American History Women's Rights Reform Movements from 1877 to 2013 - Research Paper Example The paper therefore seeks to find a stand on whether Women Reform Movements have realized much of their goals over the years. This struggle in pursuit of rights has been turbulent over the years and still continues to date. In the United States, women rights movements have had a long history. As a result of their struggles, various legislative measures have been created over the years to safeguard the rights of women and prevent much of the discriminations seen in a society that is still skeptical about the ability of women. The core of the argument is therefore the determination of how the period spanning between 1877 to present could have seen tremendous positive steps towards the realization of equality with respect to men and women1. The suffrage movements were some of the most dominant women movements in history. For many years in the United States, women were not allowed to vote. In the early years of the women reform movements, the right to vote was therefore one of the centra l issues which the movements fought for. In 1878, Susan B. Anthony proposed and submitted a right-to-vote amendment to the constitution in order to grant women the right to vote in America. The era of women suffrage took much activity in the 1890s and Wyoming was the first state to have an organized women suffrage. The movement was mostly driven by the formation of the National American Women’s Suffrage association in 1890. ... The Anthony amendment which had been written earlier in 1878 was subsequently ratified as the 19th amendment and thereby became law in 19202. It must be emphasized that differences in leadership and other misunderstandings amongst the women led to the formation of several groups. The period following 1920 saw the creation of many splitter women political groups most of which fought for the same rights. The League of Women Voters was created in 1920 and became a very strong voice in championing for the rights of women. In order to address the rights of black women who experienced the worst discrimination than their white counterparts, the National Council of Negro Women was formed in 1935. These groups strongly fought for various forms of liberal reforms in the country. However, it should be remembered that many of the rights they fought for were not always granted easily. For instance, the National Women’s Party which was formed in 1913 proposed an equal rights amendment in 19 23 which actually stayed dormant for the next 50 years3. In the early years of the women rights movement, most of the women activists were concentrated in the North. This was due to the much awareness, industry and education in the North. It was not until 1890s that women began to organize in the south after much inspiration and influence from what was transpiring in the north. In pushing for the right to vote, the National Women Suffrage Association (NWSA) and the American Woman Suffrage Association (AWSA) were working together but later separated on ideological grounds. While NWSA sought to transform the status of women on the basis of ideological foundations in the hitherto patriarchal society, AWSA was more conservative and

OLAP Multidimensional Database Concept

OLAP Multidimensional Database Concept CHAPTER 2 LITERATURE REVIEW 2.1 INTRODUCTION This chapter is designed to provide background information and reviewing the characteristics of data warehouse, OLAP multidimensional database Concept, data mining model and the application of data mining. Within this research, the concept, design and implementation approaches in developing a complete data warehouse technology framework for deploying a successful model with the integration of OLAP Multidimensional Database and data mining model. Section 2.2 discussed about the fundamental of data warehouse, data warehouse model and also the Extract, Transform and Loading (ETL) of raw database to data warehouse. It includes research and study on existing data warehouse models authored by William Inmon, Ralph Kimball and various scholars venturing into data warehouse models. Section 2.3 introduces background information of OLAP. It includes the studies and research on various OLAP models, OLAP architecture and concept on processing multidimensional databases, multidimensional database schemas design and implementation in this research. It includes studies and research on schema design and implementation. Section 2.4 introduces fundamental information of data mining. It includes studies and research on the available techniques, method and process for OLAP Data Mining. Section 2.5 discussed the product comparisons for data warehouse, data mining and OLAP by Mitch Kramer. It includes the reason why Microsoft is used to design and implement the new proposed model. In this literature review, introduction to the relationships between data warehouse, OLAP multidimensional database and data mining model for deploying four experimental applications for benchmarking. This research also proves that the â€Å"new proposed model† data warehouse technology framework is ready to transform any type of raw data into useful information. It will also help us to review the new proposed model of each existing data warehouse OLAP Multidimensional database framework. 2.2 DATA WAREHOUSE According to William Inmon (1999), known as the â€Å"Father of Data Warehousing†, data warehouse is a subject-oriented, integrated, time-variant, and non-volatile collection of data in support of the managements decision-making process. Data warehouse is a database containing data that usually represents the business history of an organization. This historical data is used for analysis that supports business decisions at many levels, from strategic planning to performance evaluation of a discrete organizational unit. Data Warehouse is a type of database system aimed at effective integration of operational databases into an environment that enables strategic use of data (G. Zhou et al., 1995). These technologies include relational and multidimensional database management systems, client/server architecture, meta-data modelling and repositories, graphical user interface and much more (J. Hammer et al., 1995; V. Harinarayan et al., 1996). Data warehouse currently are much a subject of researched is not only commonly used in business or finance sector but can be applied appropriately in various sectors. Data warehouse are designed for analyzing or processing of data into useful information using data mining tools for critical decision-making. Data warehouse provides access to difficult environments of an enterprise data In these literature studies, two important authors are identified as the main contributors and co-founder in the area of Data Warehouse, William Inmon (1999; 2005) and Ralph Kimball (1996, 2000). Both author perceptions on data warehouse design and architecture differ from one another. According to Inmon (1996), data warehouse is a dependent data mart structure, whereas Kimball (1999) defined data warehouse as a bus structure which is a combination of data mart populated together as a data warehouse. Table 2.1 discussed the differences in data warehouse ideology between William Inmon and Ralph Kimball. Table 2.1 William Inmon and Ralph Kimball Data Warehouse Differences William Inmon Ralph Kimball Paradigm Inmons Paradigm: An enterprise has one data warehouse, and data marts source their information from the data warehouse. Information is stored in 3rd normal form. Kimballs Paradigm: Data warehouse is the collection of heterogeneous data marts within the enterprise. Information is always stored in the dimensional model. Architecture Architecture: Using TOP-DOWN approach Architecture: Using Bottom-up approach Concept Datas integration from various systems to centralized repository Concept of dimensional modelling (Bridging between Relational and multidimensional DB) Design The design pattern dependent on 3rd normalization form, purpose is for data granularity. Datas marts are connected in a bus structure. Datas marts are the union of data warehouse. This approach is known also as Virtual Data Warehouse. ETL Methods Datas extraction from operational data sources. Data are feed in staging database area. Data are then transformed, integrate, and consolidate and transfer to Operational Data Store database. Data are then load to data mart. Data extracted from legacy system and then consolidated and verified in staging database. Data feed into ODS and more data us added/updated. Operational Data Store contains fresh copy data that is integrated and transformed to the data mart structure. Data mart Data Marts are available as a subset of the data warehouse. Data Marts can be placed at different at different servers or in geographical locations. Based on this Data Warehouse literature, both Inmon (2005) and Kimball (2000) have different philosophies, but they do have similar agreement on a successful design and implementation of data warehouse and data marts are mainly depending on the effective collection of operational data and validation of data mart. Both approaches having the same database staging concepts and ETL process of data from a database source. They also have a common understanding that independent data marts or data warehouses cannot fulfil the requirements of end users on an enterprise level for precise, timed and relevant data. 2.2.1 DATA WAREHOUSE ARCHITECTURE Data warehouse architecture is a wide research area. It has many different sub-areas and it can be treated with different approaches in terms or analysis, design and implementation by different enterprise. In this research studies, the aim is to provide a complete view on data warehouse architecture. Two important scholars Thilini (2005) and Eckerson (2003) from TDWI will discussed in more details on the topic on data warehouse architecture. According to Eckerson (2003), before implementing a successful business intelligence systems where users can use programs like specialized reporting tools, OLAP tools and data mining tools upfront, a data warehouse architecture model mainly concentrate on the database staging process from different integrated OLTP systems is responsible for the ETL to the whole process workable. Thilini (2005) conducted a two phase study survey on investigating which factors may influence the selection of data warehouse architecture. In Thilini literature study, there are five data warehouse architectures that are practice today as shown in Table 2.2. Table 2.2 Data Warehouse Architectures (Adapted from Thilini, 2005) Data Warehouse Architecture Independent Data Marts Independent data marts also known as localized and small sized data warehouses. It is mainly used by departments, divisions or regions of company to provide own operational databases. The data marts are different as the structures are different from different location with inconsistent database design which makes it difficult to analyze across the data marts. Thilini (2005) cited the work of Winsberg (1996) and Hoss (2002) that It is common for organizational units to develop their own data marts. Data marts are best used as a prototype for adhoc data warehouse and as for evaluation before building a real data warehouse. Data Mart Bus Architecture Kimball (1996) pioneered the designed and architecture of data warehouse with unions of data marts which are known as the bus architecture. Bus architecture Data Warehouse is derived from the unions of the data marts which are also known as Virtual Data Warehouse. Bus architecture allows data marts not only located in one server but it can be also being located on different server. This allows the data warehouse to functions more as virtual reality mode and gathered all data marts and process as one data warehouse. Hub-and-spoke architecture Inmon (2005) developed Hub and Spoke architecture. The hub is the central server taking care of information exchange and the spoke handle data transformation for all regional operation data stores. Hub and Spoke mainly focused on building a scalable and maintainable infrastructure for data warehouse. Centralized Data Warehouse Architecture Central data warehouse architecture almost similar to hub-and-spoke architecture without the dependent data marts. This architecture copies and stores heterogeneous operational and external data to a single and consistent data warehouse. This architecture has only one data model which are consistent and complete from all data sources. According to Inmon (1999) and Kimball (2000), central data warehouse should have Database staging or known as Operational Data Store as an intermediate stage for operational processing of data integration before transform into the data warehouse. Federated Architecture According to Hackney (2000), Federated Data Warehouse is a integration of multiple heterogeneous data marts, database staging or Operational data store, combination of analytical application and reporting systems. The concept of federated focus on framework of integration to make data warehouse as greatest as possible. Jindal (2004) conclude that federated data warehouse approach are a practical approach for a data warehouse architecture as it is focus on higher reliability and provide excellent value if it is well defined, documented and integrated business rules. Thilini (2005) conclude that hub and spoke and centralized data warehouse architectures are similar and the survey scores are almost the same. Hub and spoke is faster and easier to implement because no data mart are required. For centralized data warehouse architecture scored higher than hub and spoke as for urgency needs for relatively fast implementation approach. A data warehouse is a read-only data source where end-users are not allow to change the values or data elements. Inmons (1999) data warehouse architecture strategy are different from Kimballs (1996). Inmons data warehouse model splits data marts as a copy and distributed as an interface between data warehouse and end users. Kimballs views data warehouse as a unions of data marts. The data warehouse is the collections of data marts combine into one central repository. Diagram 2.1 illustrates the differences between Inmons and Kimballs data warehouse architecture adapted from Mailvaganam, H. (2007) Diagram 2.1 Inmons and Kimballs Data Warehouse Architecture (adapted from Mailvaganam, 2007) In this work, it is very important to identify which data warehouse architecture that is robust and scalable in terms of building and deploying enterprise wide systems. According to Laney (2000) and Watson, H. (2005), it is important to understand and select the appropriate data warehouse architecture and â€Å"the success of the various architectures† acclaimed by Watson. Analysis of this research proved that the most popular data warehouse architecture is hub-and-spoke proposed by Inmon as it is a centralized data warehouse with dependant data marts and second is the data mart bus architecture with dimensional data marts proposed by Kimball. The selection of the new proposed model will use the combination data warehouse architecture of hub-and-spoke and data mart bus architecture as the new proposed model data warehouse architecture are designed with centralized data warehouse and with data marts that can are used for multidimensional database modelling. 2.2.2 DATA WAREHOUSE EXTRACT, TRANSFORM, LOADING Data warehouse architecture begins with extract, transform, loading (ETL) process to ensure the data passes the quality threshold. According to Evin (2001), it is essential that right data are important and critical for the success on an enterprise. ETL are an important tool in data warehouse environment to ensure data in the data warehouse are cleansed from various systems and locations. ETLs are also responsible for running scheduled tasks that extract data from OLTPs. Typically, a data warehouse is populated with historical information from within a particular organization (Bunger, C. J et al., 2001). The complete process descriptions of ETL are discussed in table 2.3. Table 2.3 Extract, Transform, and Load Process in Data Warehouse architecture Process Descriptions Extract Extract are the first process which involve in moving data from operational databases into database staging area or operational data store before populating into the data warehouse. In this stage, operational databases data need to be examined by extracting into the staging area for handling exceptions and fix all errors before it enters into data warehouse as this will save lots of time when loading into the data warehouse. Transform In completion of data extraction in database staging area, it is then transform to ensure data integrity within the data warehouse. Transformation of data can be done in several methods such as filed mapping and algorithm comparisons. Load After extract and transform of data, it is finally loaded into data warehouse (in Inmons model) or into data marts (in Kimballs model). Data loaded into data warehouse are quality data after the process of extraction where erroneous data are removed and data are transform to ensure integrity of the data. Calvanese, D. et al. (2001) highlight an enterprise data warehouse database tables may be populated with a wide variety of data sources from different locations and often including data providing information concerning a competitor business. Collecting all the different data and storing it in one central location is an extremely challenging task where ETL can make it possible. ETL process as depicts in Diagram 2.2 begins with data extract from operational databases where data cleansing and scrubbing are done, to ensure all datas are validated. Then it is transformed to meet the data warehouse standards before it is loaded into data warehouse. Diagram 2.2Extract, Transport, Load Process G. Zhou et al.(1995) emphasise on data integration in data warehousing stress that ETL can assist in import and export of operational data between heterogeneous data sources using OLE-DB (Object linking and embedding database) based architecture where the data are transform to populate all quality data into data warehouse. This is important to ensure that there are no restrictions on the size of the data warehouse with this approach. In Kimball (2000) data warehouse architecture model depict in Diagram 2.3, the model focus in two important modules, â€Å"the back room† â€Å"presentation server† and â€Å"the front room†. In the back room process, where the data staging services in charge of gathering all source systems operational databases to perform extraction of data from source systems from different file format from different systems and platforms. Second step is to run the transformation process to ensure all inconsistency are removed to ensure data integrity. Finally, it is loaded into data marts. The ETL processes are commonly executed from a job control via scheduling task. The presentation server is the data warehouse where data marts are stored and process here. Data stored in star schema consist of dimension and fact tables. This is where data are then process of in the front room where it is access by query services such as reporting tools, desktop tools, OLAP and data mining to ols. Diagram 2.3 Data Warehouse Architecture (adapted from Kimball, 2000) Nicola, M (2000) explains the process of retrieving data from the warehouse can vary greatly depending on the desired results. There are many form of possible retrieval from a data warehouses and it is flexibility that will drive how this retrieving process can be implemented. There are many tools for retrieving the data warehouse, such as building simple query and reporting through SQL statements. The tools may expand to OLAP and data mining, where the structure includes many more third party tools. There are many inherent problems associated with data, which includes the limited amount of portability, and the often-vast amount of data that must be sifted through for each query. Essentially, ETL are mandatory for data warehouse to ensure data integrity. There are many factors to be considered such as complexity and scalability are among the two major issues that most enterprise faces by integrating information from different sources in order to have a clean and reliable source of data for mission critical business decisions. One way to achieve a scalable, non-complex solution is to adopt a â€Å"hub-and-spoke† architecture for the ETL process. According to Evin (2001), ETL best operates in hub-and-spoke architecture because of its flexibility and efficiency. Because of its centralized data warehouse design, it can influence the maintaining full access control of ETL processes. Also, empowers the usage of analytical and data mining tools by knowledge workers. In this study on ETL for effective data warehouse architecture, it is known that Hub-and-spoke is best for data integration as it has the similarity in Inmon and Kimball architecture. The hub is the data warehouse after processing data from operational database to staging database and the spoke(s) are the data marts for distributing data. Inmon and Kimball also recommend same ETL processes to enable hub-and-spoke architecture. Sherman, R (2005) state that hub-and-spoke approach uses one-to-many interfaces from Data warehouse to many data marts. One-to-many are simpler to implement, cost effective in a long run and ensure consistent dimensions. Compared to many-to-many approach it is more complicated and costly. In this work on the new proposed model, hub-and-spoke architecture are use as â€Å"Central repository service†, as many scholars including Inmon, Kimball, Evin, Sherman and Nicola adopt to this data warehouse architecture. This approach allows locating the hub (data warehouse) and spokes (data marts) centrally and can be distributed across local or wide area network depending on business requirement. In designing the new proposed model, the hub-and-spoke architecture clearly identifies six important data warehouse components that a data warehouse should have, which includes ETL, Staging Database or operational database store, Data marts, multidimensional database, OLAP and data mining end users applications such as Data query, reporting, analysis, statistical tools. However, this process may differ from organization to organization. Depending on the ETL setup, some data warehouse may overwrite old data with new data and in some data warehouse may only maintain history and aud it trial of all changes of the data. Diagram 2.4 depicts the concept of the new proposed model data warehouse architecture. Diagram 2.4 New Proposed Model Data Warehouse Architecture 2.2.3 DATA WAREHOUSE FAILURE AND SUCCESS FACTORS Building a data warehouse is indeed challenging as data warehouse project inheriting a unique characteristic that may impact the overall setup if the analysis, design and implementation phase are not properly done. In this research effort, it discusses the studies on failure and success factors in data warehouse project. Section 2.2.3.1 focuses on the investigation on data warehouse project failure and section 2.2.3.2 discuss and investigate mainly on the success factors by implementing the correct model to support a successful data warehouse project implementation. 2.2.3.1 DATA WAREHOUSE FAILURE FACTORS Hayen, R.L. (2007) studies shows that implementing a data warehouse project is costly and risky as a data warehouse project can cost over $1 million in the first year. It is estimated that one-half ad two-thirds of the effort of setting up the data warehouse projects attempt will fail eventually. Hayen R.L. (2007) citied on the work of Briggs (2002) and noticed three factors for the failure of data warehouse project that is Environment, Project and Technical factors as shown in Diagram 2.5 and table 2.4 discussed the factors in more details. Diagram 2.5 Factors for Data Warehouse Failures (adapted from Briggs, 2002) Table 2.4 Factors for Data Warehouse Failures (adapted from Briggs, 2002) Factors Descriptions Environment This leads to organization changes in business, politics, mergers, takeovers and lack of top management support. Also, including human error, corporate culture, decision making and change management. Technical Technical factors of a data warehouse project complexity and workload are taken too lightly where high expenses involving in hardware/software and people. Problems occurred when assigning a Project manager with lack of knowledge and project experience in data warehouse costing may lead to impediment of quantifying the return on investment (ROI). Also, failure of managing a data warehouse projects also includes:  · Challenge in setting up a competent operational and development team plus not having a data warehouse manager or expert that is politically sound.  · Having an extended timeframe for development and delivery of data warehouse system may due to lack of experience and knowledge for selection of data warehouse products and end-user tools. * Failure to manage the scope of data warehouse project. Project Poor knowledge on the requirements of data definitions and data quality on different organization business departments. Also, Running a data warehouse projects with incompetent and insufficient knowledge in what technology to use may lead into problems later on data integration, data warehouse model and data warehouse applications. Vassiliadis (2004) studies shows that data warehouse project failures are an enormous threat and threatened by factors such as design, technical, procedural and socio-technical as illustrated in Diagram 2.6. These factors of failures are vital in finding any unwanted action for success. Each factor group is described in table 2.5. Diagram 2.6 Factors for Data Warehouse Failures (adapted from Vassiliadis, 2007) Table 2.5 Factors for Data Warehouse Failures (adapted from Vassiliadis, 2007) Factors Descriptions Design Design factors in data warehouse project can put up with No Standard techniques or design methodologies. A data warehouse project when doing the analysis and design phase may accept ideas on metadata techniques or languages and data engineering techniques. Also, a proprietary solutions and also recommendations from vendors or in-house experts may define the design of the data warehouse blueprint landscape. Technical Technical factors associate to the lack of know-how experience in evaluation and choices of hardware setup for data warehouse systems Procedural Procedural factors concerning on the imperfection of data warehouse deployment. This factor focuses on training the end-users extensively on the new technology and the design of data warehouse which are completely different than the conventional IT solutions. users communities plays a vital role and are crucial in this factor. Socio-Technical Socio-technical factors in a data warehouse project may lead into problems on violation of organization modus operandi where the data warehouse systems will lead into restructuring or reorganization on the way organization operates by introducing changes to the user community. According to Vassiliadis (2007) also, another potential factors for the failure of data warehouse projects are the â€Å"data ownership and access†. This is considered vulnerable within the organization and one mustnt share nor acquire someone else data as this is comparable with losing authority on the data ownership and access. Also, restrict any departmental declaration or request to own a total ownership of pure clean and error free data as this might cause potential problem on ownership data rights. Watson (2004) stress that the general factors for the failures in data warehouse project success comprises of â€Å"weak sponsorship† and top management support, inadequate funding and users participation and organizational politic. 2.2.3.2 DATA WAREHOUSE SUCCESS FACTORS Data Warehouse Failures can lead into disastrous implementation if careful factors or measures are not taken into serious considerations as discussed in section 2.2.3.1 based on Briggs (2002) and Vassiliadis (2004) studies that may lead into data warehouse failures. According to Hwang M.I. (2007), data warehouse implementations are an important area of research and industrial practices but only few researches made an assessment in the critical success factors for data warehouse implementations. No doubt there is procedure for data warehouse design and implementation but only certain guidelines are subjected for experimental testing. So, it is best to decide and choose the proper data warehouse model for implementation success. In this study on identifying and filling the gap analysis of the data warehouse success factors, a number of success factors are gathered from data warehouse scholars and professionals (Watson Haley, 1997; Chen et al., 2000; Wixom Watson, 2001; Watson et al., 2001; Hwang Cappel, 2002; Shin, 2003) to validate their experimental work and research strength individually on various characteristics of data warehouse success. This study beneficial in planning and implementing data warehouse projects and direct into the success of designing and implementing the new proposed model in this research. There are several success factors in designing and implementing data warehouse solutions and the most important success factors depend on the data warehouse model selection, as different organization may have different scope and road maps in the development of data warehouse. The results of building a successful data warehouse are then used to quantify the factors that are used and also prioritize those factors that are beneficial for continued research purpose to improve and enhanced the data warehouse model success. According to Hayen, R.L. (2007), data warehouse is a complex system which can complicate business procedures. The complexity of data warehouse prevents companies from changing data or transaction which are necessary. It is important then to analyze on which data warehouse model to be used for such complex systems that are sound critical to an organization. Hwang M.I. (2007) conducted a study on data warehousing model and success factors as a critical area of practice and research but only a few studies have been accomplish to measure the data warehouse projects and success factors. Many scholars had conducted a profound research in the area of data warehouse and may have succeeded or failed due to possible reasons based on each scholars outcomes on the research. It is useful inspect a few case studies on a selected companied data warehouse implementation and to experiment the failure and success factors through survey. (Winter, 2001; Watson et al., 2004) Hwang M. I. (2007) conducted a survey study on six data warehouse scholars (Watson Haley, 1997; Chen et al., 2000; Wixom Watson, 2001; Watson et al., 2001; Hwang Cappel, 2002; Shin, 2003) on the success factors in a data warehouse project. Each scholar has different success factors that are measures in a project. Table 2.6 shows the mentioned six scholars survey study on data warehouse, Watson (1997) measures data warehouse success factors, Chen et al. (2000), Watson et al. (2001) and Shin (2003) measures data warehouse implementation factors and Hwang (2002) measures through development and management practices. Only Wixom (2001) as shown in diagram 2.7 measures both Data warehouse implementation and success factors which can be used as a model for a successful data warehouse implementation. Study shown in all 6 scholars review, without having data warehouse implementation and success factors, the consequences of any factors on a data warehouse success cannot be validated. Table 2.6 Factors for Data Warehouse Success (adapted from Hwang M.I., 2007) Study Data Warehouse Success Factors Data Warehouse Implementation Factors Results Reported Watson Haley (1997) Focus on user involvement and support by having a clear and understandable business needs. Using methodology and modelling methods in data warehouse by targeting on clean data. Thus, support from upper management to contribute on the success. N/A Ordered list of success Chen et al. (2000) N/A Focused on exactness and preciseness of User satisfaction by Support and realization of end users needs. Support for end users affects user satisfaction Wixom Watson (2001) Implementation factors include management support, resources, User participation, team skills, Source systems aand development technology which contribute to the implementatio

The International Whaling Regime Essay -- Argumentative Persuasive Ess

The International Whaling Regime In his article, â€Å"Whale Mining, Whale Saving,† Sidney Holt states, â€Å"saving the whales is for millions of people a crucial test of their political ability to halt environmental destruction†(Holt 1985). In a world where environmental issues are often so vast that solving them seems impossible, it is rare to encounter a regime which successfully addresses these problems. If we judge a regime’s effectiveness by its ability to change the behavior of its members and possibly even encourage others to join, then the whaling regime was in fact quite effective. The significant decrease in commercial whaling brought about by the International Whaling Commission (IWC)’s 1982 moratorium is proof in itself of the whaling regime’s effectiveness. That being said, the history of the regime has not been without imperfections, and these shortcomings will continue to shape the successes and failures of the whaling regime in the future. While the peak of whaling in recorded history occurred in the 1930s where close to 55,000 whales were caught each year, whaling has been practiced by people for centuries and was unregulated for most of that time period (Andresen 2000). However, in 1946, the Convention for the Regulation of Whaling (composed of 15 nations including the U.S.) met and created the International Whaling Commission in order to address the problem of declining whale stocks. An increase in commercial whaling as well as introduction of â€Å"factory ships† which allowed whalers to travel far out to sea, catch whales (pelagic fishing), and process them on the boat without going back to shore had begun to put a strain on population numbers of certain whale species. (Fletcher 2001). Thus, the IWC was primarily c... ... Aron, William, W. Burke, M. Freeman. 2000. â€Å"The Whaling Issue.† Marine Policy. 24: 179-191. Fletcher, Kristen M. â€Å"The International Whaling Regime and U.S. Foreign Policy.† In The Environment, International Relations, and U.S. Foreign Policy. Washington, D.C.: Georgetown University Press, 2001. Greenpeace: Whaling. 2000. http://whales.greenpeace.org/whaling. Accessed 5/9/04. Institute of Cetacean Research. 2002. http://www.icrwhale.org/eng-index.htm. Accessed 5/9/04. Holt, Sidney J.. 2003. â€Å"Is the IWC Finished as an Instrument for the Conservation of Whales?† Marine Pollution Bulletin 46: 924-926. Holt, Sidney J.. 2000. â€Å"The Whaling Controversy.† Fisheries Research 54: 145-151. Holt, Sidney J.. 1985. â€Å"Whale Mining, Whale Saving.† Marine Policy 4: 192-214. O’Connell, Kate. July 2002. â€Å"The 2002 IWC Annual Meeting.† Whales Alive! 11(3).

Macbeth Essay

The first recurrent image is the dark or darkness. Dark represents evil and hell. All of our fears rise in the dark. We can see that most of the mains scenes happen in a dark place or during the night. In fact, all the murders and treasons are done in darkness as if the dark could cover and hide the horrible deeds. For example, in act I scene V l. 53 to 56, Lady Macbeth says: â€Å"Come thick night, And pall thee in the dunnest smoke of hell, that my keen knife see not the wound it makes, Nor heaven peep through the blanket of the dark, To cry, Hold! Hold! In this passage, Lady Macbeth is thinking about Duncan’s murder, and she wants to act in darkness so she will not see the murder. In that way darkness blinds out all of the terrible things that could be done. Then, the scene of Macbeth’s vision of the dagger happens in the complete darkness so the vision of his future murder comes to Macbeth only at night when no light can bring him back to goodness. Banquo’s murder also happens in the dark. Such evil deeds could only be done in the dark. Then during Lady Macbeth’s sleep walking, the only source of light comes from the candle that she keeps by her at night. In fact, Lady Macbeth is very afraid of darkness because it makes her remember of all the deeds that happened during the night. (Here, light has a positive reassuring role. ) In this scene, she reveals all the crimes that her husband committed with her support. In conclusion, darkness intensifies the horrible deeds and murders and brings a very fearful ambiance to the play. The second image is the one of the sleep that is kind of related to the one of the dark because dark and sleep comes together. Firstly, we can see that Duncan is killed during his sleep. This fact is even mentioned by Lady Macbeth in act II scene 2 l. 15 to 16: â€Å"Had he not resembled My father as he slept, I had done’t. † Then, in the same scene, Macbeth says l. 46 to 47: â€Å"Sleep no more! Macbeth does murder sleep! † Ironically, he’s going to lose his sleep as well as Lady Macbeth who will become a sleep walker. Then, Lady Macbeth relates sleep with death, when she says in act II scene 2 l. 67 to 70: â€Å"The sleeping and the dead Are but as pictures: ‘tis the eye of childhood That fears a painted evil. † The second reference to sleep in relation to death is present in act II scene 3 l. 9 to 80: â€Å"Shake off this downy sleep, death’s counterfeilt, And look on death itself! † The third image that appears in the book is the image of the light. Light in opposite of the theme of darkness is representative of purity, God, goodness, heaven etc. Light is for Macbeth a disadvantage because all his actions consist on killing people, committing deeds and crimes and he doesn’t want any light to lighten his awful actions. For example, in act I, he says: â€Å"Stars, hide your fires; Let not loight see my black and deep desires: The eye wink at the hand; yet let that be, Which the eye fears, when it done, to see. Here, we understand that his desires are so terrible that he can’t even stand the thin light of the stars that shine on them; he doesn’t even want to look at them himself probably because he feels ashamed. We can also say that through his words, Macbeth constructs a bridge between light and morality. Within the whole drama, the sun seems to shine only twice. First, in the beautiful but ironical passage in which Duncan sees the swallows flirting around the castle of death (it’s Macbeth’s ca stle when he’s going to be murdered). The second time, when at the close of the army (who wants to take revenge) gathers to rid the earth of its shame. Therefore, the reader can conclude that Shakespeare portrays darkness to establish the evil parts of the play, we can say that he employs daylight to define victory or goodness (as it said before) in the play. The fourth and last theme is the animal. We found a lot of comparaison between the characters and the animals in this play, for example: â€Å"Raven himself is hoarse† said by Lady Macbeth in act I scene V. Raven represents death. Looks like the innocent flower but be the serpent under it. † Also said by Lady Macbeth in act I scene V. She says that his husband must look nice and calm on the outside but evil inside. â€Å"We have scorched the snake not killed it. † Said by Macbeth in act III scene II, this quote means that snake represents everything that prevents Macbeth from enjoying his kingship. â€Å"And Duncan’s horses, beauteous and swift, the minions of their race, turned wild in nature, broke their stalls, flung out, contending ‘gainst obedience, as they would make war with mankind† said by Ross in act II scene IV. Here, Ross says that Duncan’s horses were acting strange. They broke out of their stalls and started to attack anyone who came in their way. Another image appear in act III scene IV, said by Macbeth : â€Å"Approach thou like the rugged Russian bear. † Here, Macbeth is describing how the ghost of Banquo is hauting him by coming closer to him like a bear. These imageries of animals which symbolizes the different character of the play, helps to make the play’s atmosphere from supernatural nature. Shakespeare uses animal imagery to characterize, to show emotions and also to foreshadow.

Uncontrolled Ambition in Macbeth

It’s good to have ambition, as it’s the foundation of a successful life. Ambition means to have strong desire towards achieving something. Because of this, it’s true that one without ambition will struggle, however sometimes, our own ambitions and desires can change us for the negative. Ambition in its nature can tempt obsessive behaviour, which has a destructive nature of its own. When an ambition purely of passion turns into obsession, it ultimately forces one to only focus on that and do anything to achieve that goal.Shakespeare’s Macbeth is the perfect example of where the theme of obsessive ambition is prevalent. Shakespeare through the protagonist Macbeth conveys how our own desires, if obsessive has a both corrupting and blinding power of its own, ultimately changing things for the worse and destroying everything. Macbeth, as a result of his obsessive quest for power, corrupts his own judgement and motivates him towards immoral actions. It also blin ds him because he becomes very self-centered and begins to ignore Lady Macbeth, destroying his own marriage.Macbeth follows the tragic life of a soldier who is very dedicated and loyal, but does the wrong things when he meets 3 witches that prophesize that he will become a powerful king one day. Macbeth in the play kills so many people because his obsessive ambitions is so corruptive, it takes control of his actions, fueling his many immoral actions. To begin with, when Macbeth hears the prophecies, he is introduced to the idea that he will become king one day. Stunned and baffled, he tells his wife about the prophecy. She tells Macbeth that in order for this prophecy to come true, Duncan, the king must be killed.Initially, Macbeth is very reluctant and hesitant to consider because he isn’t inclined to committing immoral deeds and being violent for a selfish reason. In his mind, he is thinking, I have no spur, to prick the sides of my intent, but only vaulting ambition, which overleaps itself. He explains how it’s just his own ambition that makes him want to do this, but that isn’t a reason to commit treason and defy. Despite this belief, Macbeth ends up agreeing to kill Duncan. From the following, we learn that Macbeth himself strongly desires power on the border of the obsessive as it impairs his own judgement and corrupts it.It essentially motivates him to towards something immoral and treasonous, taking control of his own actions. After the killing of Duncan, he stews in paranoia and lives in constant fear because of his defiance, proving that it does not offer anything pleasantry. Next, after the murder of Duncan, Macbeth realizes that Banquo is a possible threat and contemplates killing because he wants to stay in power. He thinks to himself, But to be safely thus: our fears in Banquo, Stick deep; and in his royalty of nature, Reigns that which would be feared. Whose being I do fear, and under him, my genius is rebuked.Macbeth fears Banquo’s honesty and if Banquo suspects him, he will have to surrender everything. From the following, we learn that Macbeth will go to any limit if it means sacrificing him being in power and eliminating his own doubts and fears, even if it requires deceiving his innocent friend. He now is so powerfully corrupted, he does not realize he has turned into a tyrant who seeks nothing but violence for his own satisfaction, showing how it has taken over him. Afterwards, he wallows in so much guilt for his deception, he has nothing to feel accomplished for.In conclusion, his obsessive ambition drives him to such terrible atrocities that ultimately does not gain him anything. Near the end, he descends into a kind of frantic, boastful madness as a result of this, changing his life for the worse. Macbeth however also ruins his own marriage in his ultimate quest for power. When Macbeth rises in power, Lady Macbeth descends in importance. His quest for power corrupts him so much, he beco mes very self-centered and loses his feelings for his wife. To begin with, Macbeth is unable to give importance to his wife because he obsesses over his enemies and thinks they are out to get him: .Ere we will eat our meal in fear, and sleep In the affliction of these terrible dreams That shake us nightly. Better be with the dead, Whom we, to gain our peace, have sent to peace From this, Macbeth explains that he cannot sleep and has nightmares of people turning against him. He says he feels endless mental torture and cannot live in peace. He has so many fearful thoughts of people plotting against him, he cannot concentrate on Lady Macbeth. His wife descends in importance and all the martial affection and emotional bonding is lost.In fact, Lady Macbeth feels, â€Å"naught's had, all's spent when our desire is got without content. † This proves how she feels ignored and how him being king doesn’t truly benefit their marriage and slowly begins to destroy it. .In conclusio n, Shakespeare’s Macbeth suggests that when our own ambitions go out of control, it has a corrupting power of its own and can change things for the negative, ultimately destroying everything. Man should always strive with ambition, but not to the obsessive where one becomes overambitious. (Elaborate further)