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

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COEN 250: Acceptable use policy Acceptable Use Policy Author Date 1) Overview (Purpose) HotPot provides its employees computer devices like desktop systems, laptops, and mobile devices such as iPad, networks to achieve its vision, missions and initiatives. The purpose of this policy is to establish employess' acceptable and unacceptable use of the devices and network resources while maintaining its confidentiality, integrity, and availability in conjunction with HotPot's established ethical and lawful behavior. 2) Scope The policy is solely applicable to Information assets belonging to, or leased by, or connect to Hotpot's network or reside at its premise. All employees, consultants, contractors, vendors, visitors and customers at HotPot must follow to this policy. 3) Policy Statement 3.1 General use and ownership 1) In order to maintain reasonable level of privacy and protection, employees should be aware that the data they create on HotPot's systems remains the property of the company and confidentiality of the information stored on the sytems is not gurenteed. 2) It is employees' responsibilities to make decision about personal use of HotPot's resources. It should not affect on individuals' productivity. Individual department should provide guidelines regarding personal use of the company's systems. In absence of such policies, employee should take advice from their supervisor or manager. 3) It is advisible to users to encrypt any information which seems to be sensitive or vulnerable. 4) As per HotPot's audit policy for security concerns, HotPot's authorized personnels can monitor the company's resources and network traffic at any time and it should be checked on periodic basis in order to ensure compliance with thi... ... - Some of the applications such as Facebook, weather, Twitter, which can be benificial to the company are allowed to use, while few applications which involve downloading music, games are not allowed to use during work hours. - Storing illicit information, storing proprietary information belonging to other company, harrasing or threating others or involvement in outside business activities are strictly prohibited on your device. Cloud Based services - Cloud based services must comply with HotPot's acceptable use policy and strictly follow all laws and regulations related to personal identifiable information, HotPot's financial and proproetary information. - Some types of datas may not be stored in cloud. For example, HotPot's confidential or proprietary data or communications must not be stored, manipulate or exchange on your personal cloud based service accounts.

The Team Leader Assembly Department

This evaluation will focus on the job of team leader assembler for the can manufacturing firm. The major components, tasks and responsibilities required for this position include: good manual dexterity, the ability to assemble components, the ability to stand for extended periods of time, ability to operate various plant equipment including conveyers and counting machines, good manual dexterity, attention to detail, ability to engage in repetitive motions. Independent judgment is required to inspect components and visual acuity is necessary to ensure that only the best quality products are passed through the assembly line. The team leader of the assembly unit is also responsible for coordinating communication and working relations with all team members. The team leader is also responsible for ensuring the safety of all members of the team, for tracking time cards, for ensuring that all team members are cross-trained in job functions and to ensure that productivity goals are met in a timely fashion. Basic job description includes assembling and performing all steps vital to product production in accordance with specifications for product design. This position can cultivate a sense of intrinsic motivation by allowing the team leader a certain level of autonomy while supervising the work functions of other responsibilities. Team leaders are also responsible for scheduling employees, addressing minor disputes among employees and for the quality of work produced by their team. For many the ability to lead and represent a unit of employees is in an of itself enough to encourage intrinsic motivation, depending on what factors motivate the individual team leader. The team leader position also offers more financial incentives than other positions, which contributes to motivating the employee in this role. Company wide rewards offered all employees include a comprehensive profit sharing plan that allows all employees to enjoy the rewards the company reaps when the company is doing well. This type of award however, many not prove as motivating for a team leader, as profit sharing awards generally appeal to higher ups in the company who have more capital to invest and are often afforded more profit sharing opportunities within the company (Greider, Logue & Yates, 2001). Management for example, often enjoys many of the benefits associated with profit sharing in the company. Real employee ownership may come in other forms including allowing employees to participate in important decision-making processes within the organization (Greider, Logue & Yates, 2001; Schneier & Shaw, 1995). Praise recognition does exist within the company, and is currently part of the performance review system. The current performance review system is provided employees once per annum to provide employees a critique of their performance during the year. The team leader clearly would receive much praise and encouragement for meeting the goals and expectations outlined by his or her supervisor and for ensuring that his or her team succeeds during the year. The performance appraisal system currently reflects the accomplishments and achievements of the individual team leader, rather than reflect on the accomplishments of the team unit however. This may provide some level of motivation for the team leader, but ultimately does not provide as comprehensive a review as might a group performance review that reflects on the achievements of the team. Such a review might provide the team leader with more insight into how their actions affect the success and ability of the team, and the team's contributions to the company as a whole. It might also serve to improve communication more among team members. Goals are used in the company for this position in many ways. The team leader meets with his or her supervisor during the annual performance review, at which time goals are set for the year. These may include for example, ensuring that all parts and products are assembled in a timely fashion, ensuring that all team members come to work on time and that absenteeism is limited, and ensuring that group communication is amply facilitated within the organization. The team leader also meets with team members once per month to discuss their team goals. This may include ensuring that all products assembled meet stringent quality guidelines or ensuring that zero defects are realized within the scope of products assembled by the team. Generally goals are used in the company as a motivator and as an educational tool, allowing each member of the organization to realize what the company's aims and objectives are for the year, and helping individual employees realize what their place is in relation to the company's goals and objectives. The goal system is relatively effective for this position, though it may benefit with some targeted changes. The job redesign for the position of team leader will entail a strategic job redesign and assessment that includes contributions from employees. Job redesign can serve as a useful tool for increasing a job's motivating potential â€Å"depending on the job categorizations† that result from job redesign (Kulik, 1989). For these job categorizations to be truly motivating and encourage greater intrinsic employee motivation they must encourage participation and feedback from the employee whose job is being redesigned. Much research including that presented by social information processing theorists suggests that employees' evaluation of their jobs motivating potential is influenced by multiple factors including clues provided by their social environment (Kulik, 1989). This suggests that an environment that supports a job as worthwhile and beneficial is more likely to encourage employees to remain motivated an interested. Thus a job redesign should consider factors that lead to social evaluation of the job, such as job title. In this case the designation â€Å"team leader† suggests that the job incumbent has some level of authority, lending itself to a certain amount of respect and authority, and likely serving to increase employees intrinsic motivation. Other motivating factors are based solely on job content. Hence it may be important to evaluate the job's content and determine whether additional responsibilities would add to motivation or decrease employee motivation. Thus the content and responsibilities of the team leader must also be assessed as part of this redesign. Schippmann (1999) suggests redesign that focuses on the concept of â€Å"strategic job modeling† a job redesign process that focuses more on people working in jobs and encourages employers to collect information about the people working in their jobs to help guide efforts â€Å"to select, build or modify the components of a human resources system to achieve an organizationally relevant outcome† (3). This theory suggests that more accurate information to help guide decisions regarding job redesign may be gathered when individuals working within a position are consulted about the job redesign process. Cronshaw (1999) along similar lings suggests that it is important to consult with employees as much as it is management to ensure that job redesign occurs in a functional manner and works to enhance employee motivation. One important component of job redesign in the manufacturing environment includes providing a performance measurement and rewards system that supports the use of teams (Schneier & Shaw, 1995). The current performance review system adopted by the company still works too diligently to review the individual performance of the team leader rather than address the collaborative efforts of the team. There is much to be said however of measuring the performance of teams (Frohman, 1995). For the position of team leader, the following recommendations are necessary to help promote intrinsic motivation and boost the productivity of the team leader and his or her underlings: (1) the performance review process for team leader must be modified to reflect the contributions not only of the team leader but also of the team (2) the job should include cross training for the team leader with assemblers but also supervisors and managers within the assembly department to promote greater knowledge sharing and understanding of how other job roles influence the assembly line (3) the team leader should be provided an opportunity to participate in a rewards based program that promotes bonuses for achieving goals established at the annual performance review (4) the team leader should be provided the opportunity to engage team members more fully and participate more in their performance review processes and (5) the team leaders job should be benchmarked with other team leader or supervisory positions within other companies to ensure that the job content matches similar job descriptions, titles and pay within other industries. Let's examine each of these components more thoroughly. First, it is vital in a team-oriented situation that the performance review process reflects not only the achievements and accomplishments of the person assessed, but also the rest of the team. This will encourage the team leader to actively engage team members and participate more fully in communication efforts, knowledge sharing and strategic planning at the team level. It also encourages the team leader to be more accountable for the actions of the team as a whole. If the team for example, performs poorly during the year despite good attendance and performance on the team leaders part, it is still important that the team's performance is reflected in the performance appraisal process so recommendations for improvement may be made. Second, team leaders should be provided the opportunity to learn more about the inner operations and workings of the company as a whole. The best way to facilitate this process is through cross training, allowing the team leader a birds eye view of what other supervisors and front line employees do in the organization, how their work affects the assembly line, and remind the team leader of the importance of interpersonal communication and knowledge sharing among all levels of the organization. The team leader should also be provided more rewards incentives for work well done. While a profit sharing program is beneficial to higher ups as discussed earlier, it provides little intrinsic motivation many times for front line employees (Frohman, 1995; Greider, Logue & Yates, 2001). A more appropriate rewards or incentives program may focus on providing the team leader with annual performance based bonuses. This can be achieved by establishing a set of goals or expectations that provide opportunities for bonuses when the team leader meets or exceeds expectations. Bonuses do not have to come in the way of financial compensation to be effective either (Cronshaw & Fine, 1999). The company may opt for example, to provide bonuses that include extra vacation days or paid time off to team leaders for meeting or exceeding their goal expectations. Presently the team leader provides a brief summary or dialogue as part of the review process for team members. The team leader may realize more motivation and have more desire to participate in performance reviews if afforded the opportunity to actually sit in on performance appraisals or reviews with team members. This will allow team members more feedback from their lead and help them realize the authority and status as well as the common interests the team leader has with them. Lastly, it is vital the job content of team leader matches that of other jobs in similar industries. At minimum annually the company should reevaluate the job content so that it accurately reflects similar jobs in the industry. On the same token it is important that the company elicit feedback from the incumbent so they can provide more detail regarding the job's functions and responsibilities, and so that the job can be modified to reflect actual responsibilities more fully (Cronshaw & Fine, 1999). This type of analysis will allow greater participation from the team leader in the redesign process and will therefore serve to increase motivation and enthusiasm for the job (Kulic, 1989; Frohman, 1995; Schippmann, 1999). This helps promote employee ownership in job functions and encourages more intrinsic motivation because the employee recognizes that they are an active participant in the job redesign process. It also helps stimulate interest in the job redesign process and ensures that the company is redesigning the job in a way that meets the employees as well as the company's needs, wants, goals and expectations.

Using the World Wide Web - Free Essay Example

Sample details Pages: 4 Words: 1182 Downloads: 8 Date added: 2017/06/26 Category Information Systems Essay Type Narrative essay Did you like this example? USING THE WORLD WIDE WEB Introduction The internet has pervaded every aspect of life, and nowadays it is hard to picture our society without it. The technological advancements that have been made since the invention of the World Wide Web have surpassed the progress made in the last two centuries. Today, the web has come to serve countless purposes. Don’t waste time! Our writers will create an original "Using the World Wide Web" essay for you Create order There are so many things that revolve around the internet, like staying in touch with friends, entertaining, and doing research. The World Wide Web has revolutionized how we live and will continue to dominate our lives in the future. History of the World Wide Web Birth of the Internet. In 1989, the World Wide Web started as a project of the CERN, or the European Organization for Nuclear Research. The project was entitled â€Å"Enquire.† The project was intended to address the needs of universities and other institutions in the sharing of information. The first website developed by CERN described how to access other individuals’ documents and how to create a server. Because CERN is a physics-based organization, this application was first utilized by the physics community. Tim Berners-Lee. Tim Berners-Lee, a British physicist at CERN, proposed the Enquire program that eventually developed into the World Wide Web. He came from a family of scientists with a great k nowledge about computers and grew up fascinated by technology. Berners-Lee’s goal for this project was to create a network that could be accessed worldwide. He once saidAll the bits of information in every computer at CERN, and on the planet, would be available to me and to anyone else. There would be a single, global information space.† By late 1990, Berners-Lee had created a draft of the first web page. AOL Instant Messaging. Also in 1989, in the United States, AOL, or America Online, developed the first instant messaging system. The United States was using Berners-Lee’s technology to facilitate this project. This sparked the use of the World Wide Web in offices and homes around the country. It became increasingly more accessible to the world through these applications. Uses of the World Wide Web Telemedicine. In recent years, the Web has begun to serve a purpose in healthcare, both domestically and globally. The American Telemedicine Association descr ibes telemedicine as â€Å"the use of medical information exchanged from one site to another via electronic communications to improve a patient’s clinical health status.† It makes healthcare more easily accessible to patients even in remote areas in our own country and throughout the world by providing medical advice and care online. Through telemedicine, clinical care is provided via technology. In rural areas of the world where healthcare is not nearby, patients can be diagnosed and in some cases treated by a doctor on the other side of the world. It is just one of the many purposes of the Web. Social Media. One of the most recent uses of the Web is social media. This popular application of technology is utilized by businesses and individuals alike. These are the most popular social media platforms: Facebook Twitter Polyvore Etsy Businesses use social media for marketing purposes because it is a more immediate way for them to connect with their customers. Consumers can hear about the latest products from companies, and more easily purchase them. On the other hand, individuals can use social media to share their own personal media and thoughts. Education. Education is another major purpose that the World Wide Web serves. In recent years, online schools have popped up to give more people the opportunity to complete their education. The Web has made education more accessible than ever. Interactive learning, which allows for students to learn outside of a classroom setting, is becoming more popular. Students are now able to take classes without the teacher being near due to the World Wide Web. The Future of the World Wide Web The Deep Web. One of the growing threats to the World Wide Web is the Deep Web. The Deep Web is a matrix of encrypted websites that are not recognized by search engines, and thus are hidden from the public eye. The Deep Web serves as a hideout for criminal activity websites. Some of the more popular websites on the Deep Web are: The Silk Road 4chan Freedom Hosting The purchase of drugs and hitmen can be made through the Deep Web and the police are unable to crack down on most transactions. However, some black market websites have recently been busted, and there is a constant fight to stop the Deep Web. Net neutrality. Tim Berners-Lee, the inventor of the World Wide Web, has promoted net neutrality, which is simply the free use of the Web. Major corporations like Verizon want to charge customers for preferential access and faster speed for downloading from and uploading to the Web. Berners-Lee argues that the Web needs to remain accessible, and said that â€Å"It’s important for the open markets, for the economy and for democracy.† Currently, this issue is being extensively debated in Congress. Privacy. Through tracking software and cookies, companies are able to track individuals’ computer usage and location. Companies use this information to market specific products and services to individuals. This has recently b ecome an issue because many people believe it is an invasion of privacy. The government, particularly the NSA, or National Security Agency, has monitored personal use of the World Wide Web for potential threats and criminal activity. WikiLeaks founder and activist Julian Assange criticized the governments for invading the privacy of citizens, and then later released classified information on the World Wide Web. Privacy on the World Wide Web will continue to be an issue as long as individuals rely more and more on the internet in their daily lives. Conclusion The World Wide Web is a revolutionary innovation that has made information more easily accessible. It has allowed for global connectivity, which was the intention when the founder Tim Berners-Lee invented the World Wide Web. The Web serves to entertain, educate, and make our lives more convenient. While some companies have attempted to charge for Web use, there is a huge fight to keep the internet free. The Web is a techno logical advancement that will continue to have a strong presence in the world for the foreseeable future. WORKS CITED Bilton, Nick. As the Web Turns 25, Its Creator Talks About Its Future. The New York Times, 11 Mar. 2014. Web. 15 May 2015. CERN Accelerating Science.The Birth of the Web. N.p., n.d. Web. 15 May 2015. 8 Types of Social Media and How Each Can Benefit Your Business.Hootsuite. N.p., 12 Mar. 2015. Web. 15 May 2015. Global Telemedicine.Global Med. N.p., n.d. Web. 15 May 2015. The Mind Behind the Web.Scientific American Global RSS. N.p., n.d. Web. 15 May 2015. Reporter, Daily Mail. The Disturbing World of the Deep Web, Where Contract Killers and Drug Dealers Ply Their Trade on the Internet.Mail Online. Associated Newspapers, 11 Oct. 2013. Web. 15 May 2015. What Is Telemedicine?American Telemedication Association. N.p., n.d. Web. 15 May 2015. World Wide Web Timeline.Pew Research Centers Internet American Life Project RSS. N.p., 11 Mar. 2014. Web. 1 5 May 2015.