Literature review about data warehouse

Literature review about data warehouse CHAPTER 2 LITERATURE REVIEW 2.1 INTRODUCTION Chapter 2 provides literature review about data warehouse, OLAP MDDB and data mining concept. We reviewed concept, characteristics, design and implementation approach of each above mentioned technology to identify a suitable data warehouse framework. This framework will support integration of OLAP MDDB and data mining model. Section 2.2 discussed about the fundamental of data warehouse which includes data warehouse models and data processing techniques such as extract, transform and loading (ETL) processes. A comparative study was done on data warehouse models introduced by William Inmons (Inmon, 1999), Ralph Kimball (Kimball, 1996) and Matthias Nicola (Nicola, 2000) to identify suitable model, design and characteristics. Section 2.3 introduces about OLAP model and architecture. We also discussed concept of processing in OLAP based MDDB, MDDB schema design and implementation. Section 2.4 introduces data mining techniques, methods and processes for OLAP mining (OLAM) which is used to mine MDDB. Section 2.5 provides conclusion on literature review especially pointers on our decision to propose a new data warehouse model. Since we propose to use Microsoft  ® product to implement the propose model, we also discussed a product comparison to justify why Microsoft  ® product is selected. 2.2 DATA WAREHOUSE According to William Inmon, data warehouse is a subject-oriented, integrated, time-variant, and non-volatile collection of data in support of the managements decision-making process (Inmon, 1999). 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. It provides an effective integration of operational databases into an environment that enables strategic use of data (Zhou, Hull, King and Franchitti, 1995). These technologies include relational and MDDB management systems, client/server architecture, meta-data modelling and repositories, graphical user interface and much more (Hammer, Garcia-Molina, Labio, Widom, and Zhuge, 1995; Harinarayan, Rajaraman, and Ullman, 1996). The emergence of cross discipline domain such as knowledge management in finance, health and e-commerce have proved that vast amount of data need to be analysed. The evolution of data in data warehouse can provide multiple dataset dimensions to solve various problems. Thus, critical decision making process of this dataset needs suitable data warehouse model (Barquin and Edelstein, 1996). The main proponents of data warehouse are William Inmon (Inmon, 1999) and Ralph Kimball (Kimball, 1996). But they have different perspectives on data warehouse in term of design and architecture. Inmon (Inmon, 1999) defined data warehouse as a dependent data mart structure while Kimball (Kimball, 1996) defined data warehouse as a bus based data mart structure. Table 2.1 discussed the differences in data warehouse structure between William Inmon and Ralph Kimball. A data warehouse is a read-only data source where end-users are not allowed to change the values or data elements. Inmons (Inmon, 1999) data warehouse architecture strategy is different from Kimballs (Kimball, 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. Figure 2.1 illustrates the differences between Inmons and Kimballs data warehouse architecture adopted from (Mailvaganam, 2007). Although Inmon and Kimball have a different design view of data warehouse, they do agree on successful implementation of data warehouse that depends on an effective collection of operational data and validation of data mart. The role of database staging and ETL processes on data are inevitable components in both researchers data warehouse design. Both believed that dependant data warehouse architecture is necessary to fulfil the requirement of enterprise end users in term of preciseness, timing and data relevancy 2.2.1 DATA WAREHOUSE ARCHITECTURE Although data warehouse architecture have wide research scope, and it can be viewed in many perspectives. (Thilini and Hugh, 2005) and (Eckerson, 2003) provide some meaningful way to view and analyse data warehouse architecture. Eckerson states that a successful data warehouse system depends on database staging process which derives data from different integrated Online Transactional Processing (OLTP) system. In this case, ETL process plays a crucial role to make database staging process workable. Survey on factors that influenced selection on data warehouse architecture by (Thilini, 2005) indentifies five data warehouse architecture that are common in use as shown in Table 2.2 Independent Data Marts Independent data marts also known as localized or small scale data warehouse. It is mainly used by departments, divisions of company to provide individual operational databases. This type of data mart is simple yet consists of different form that was derived from multiple design structures from various inconsistent database designs. Thus, it complicates cross data mart analysis. Since every organizational units tend to build their own database which operates as independent data mart (Thilini and Hugh, 2005) cited the work of (Winsberg, 1996) and (Hoss, 2002), it is best used as an ad-hoc data warehouse and also to be use as a prototype before building a real data warehouse. Data Mart Bus Architecture (Kimball, 1996) pioneered the design and architecture of data warehouse with unions of data marts which are known as the bus architecture or 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 in virtual mode and combined all data marts and process as one data warehouse. Hub-and-spoke architecture (Inmon, 1999) 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 build based on hub-and-spoke architecture but without the dependent data mart component. 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, 1996), central data warehouse should consist of 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 an 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 integrated framework to make data warehouse more reliable. (Jindal, 2004) conclude that federated data warehouse are a practical approach as it focus on higher reliability and provide excellent value. (Thilini and Hugh, 2005) conclude that hub and spoke and centralized data warehouse architectures are similar. 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. 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. (Laney, 2000), states that selection of appropriate data warehouse architecture must incorporate successful characteristic of various data warehouse model. It is evident that two data warehouse architecture prove to be popular as shown by (Thilini and Hugh, 2005), (Eckerson, 2003) and (Mailvaganam, 2007). First hub-and-spoke proposed by (Inmon, 1999) as it is a data warehouse with dependant data marts and secondly is the data mart bus architecture with dimensional data marts proposed by (Kimball, 1996). The selection of the new proposed model will use hub-and-spoke data warehouse architecture which can be used for MDDB modelling. 2.2.2 DATA WAREHOUSE EXTRACT, TRANSFORM, LOADING Data warehouse architecture process begins with ETL process to ensure the data passes the quality threshold. According to Evin (2001), it is essential to have right dataset. ETL are an important component in data warehouse environment to ensure dataset in the data warehouse are cleansed from various OLTP systems. ETLs are also responsible for running scheduled tasks that extract data from OLTP systems. Typically, a data warehouse is populated with historical information from within a particular organization (Bunger, Colby, Cole, McKenna, Mulagund, and Wilhite, 2001). The complete process descriptions of ETL are discussed in table 2.3. Data warehouse database can be populated with a wide variety of data sources from different locations, thus collecting all the different dataset and storing it in one central location is an extremely challenging task (Calvanese, Giacomo, Lenzerini, Nardi, and Rosati, , 2001). However, ETL processes eliminate the complexity of data population via simplified process as depicts in figure 2.2. The ETL process 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. (Zhou et al, 1995) states that during data integration process in data warehouse, ETL can assist in import and export of operational data between heterogeneous data sources using Object linking and embedding database (OLE-DB) based architecture where the data are transform to populate all validated data into data warehouse. In (Kimball, 1996) data warehouse architecture as depicted in figure 2.3 focuses on three important modules, which is the back room presentation server and the front room. ETL processes is implemented 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. The second step is to run the transformation process to ensure all inconsistency is 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 tools. Although ETL processes prove to be an essential component to ensure data integrity in data warehouse, the issue of complexity and scalability plays important role in deciding types of data warehouse architecture. One way to achieve a scalable, non-complex solution is to adopt a 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. Centralized data warehouse design can influence the maintenance of full access control of ETL processes. ETL processes in hub and spoke data warehouse architecture is recommended in (Inmon, 1999) and (Kimball, 1996). 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. 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. 2.2.3 DATA WAREHOUSE FAILURE AND SUCCESS FACTORS Building a data warehouse is indeed a challenging task as data warehouse project inheriting a unique characteristics that may influence the overall reliability and robustness of data warehouse. These factors can be applied during the analysis, design and implementation phases which will ensure a successful data warehouse system. Section 2.2.3.1 focus on factors that influence data warehouse project failure. Section 2.2.3.2 discusses on the success factors which implementing the correct model to support a successful data warehouse project. 2.2.3.1 DATA WAREHOUSE FAILURE FACTORS (Hayen, Rutashobya, and Vetter, 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 two-thirds of the effort of setting up the data warehouse projects attempt will fail eventually. (Hayen et al, 2007) cited on the work of (Briggs, 2002) and (Vassiliadis, 2004) noticed three factors for the failure of data warehouse project which is environment, project and technical factors as shown in table 2.4. Environment leads to organization changes in term of business, politics, mergers, takeovers and lack of top management support. These include human error, corporate culture, decision making process and poor change management (Watson, 2004) (Hayen et al, 2007). Poor technical knowledge on the requirements of data definitions and data quality from different organization units may cause data warehouse failure. Incompetent and insufficient knowledge on data integration, poor selection on data warehouse model and data warehouse analysis applications may cause huge failure. In spite of heavy investment on hardware, software and people, poor project management factors may lead data warehouse project failure. For example, assigning a project manager that lacks of knowledge and project experience in data warehouse, may cause impediment of quantifying the return on investment (ROI) and achievement of project triple constraint (cost, scope, time). Data ownership and accessibility is a potential factor that may cause data warehouse project failure. This is considered vulnerable issue within the organization that one must not share or acquire someone else data as this considered losing authority on the data (Vassiliadis, 2004). Thus, it emphasis restriction on any departments to declare total ownership of pure clean and error free data that might cause potential problem on ownership of data rights. 2.2.3.2 DATA WAREHOUSE SUCCESS FACTORS (Hwang M.I., 2007) stress that 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. He conducted a survey on six data warehouse researchers (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. He concluded his survey with a list of successful factors which influenced data warehouse implementation as depicted in figure 2.8. He shows eight implementation factors which will directly affect the six selected success variables The above mentioned data warehouse success factors provide an important guideline for implementing a successful data warehouse projects. (Hwang M.I., 2007) studies shows an integrated selection of various factors such as end user participation, top management support, acquisition of quality source data with profound and well-defined business needs plays crucial role in data warehouse implementation. Beside that, other factors that was highlighted by Hayen R.L. (2007) cited on the work of Briggs (2002) and Vassiliadis (2004), Watson (2004) such as project, environment and technical knowledge also influenced data warehouse implementation. Summary In this work on the new proposed model, hub-and-spoke architecture is use as 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, MDDB, 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 audit trial of all changes of the data. 2.3 ONLINE ANALYTICAL PROCESSING OLAP Council (1997) define OLAP as a group of decision support system that facilitate fast, consistent and interactive access of information that has been reformulate, transformed and summarized from relational dataset mainly from data warehouse into MDDB which allow optimal data retrieval and for performing trend analysis. According to Chaudhuri (1997), Burdick, D. et al. (2006) and Vassiladis, P. (1999), OLAP is important concept for strategic database analysis. OLAP have the ability to analyze large amount of data for the extraction of valuable information. Analytical development can be of business, education or medical sectors. The technologies of data warehouse, OLAP, and analyzing tools support that ability. OLAP enable discovering pattern and relationship contain in business activity by query tons of data from multiple database source systems at one time (Nigel. P., 2008). Processing database information using OLAP required an OLAP server to organize and transformed and builds MDDB. MDDB are then separated by cubes for client OLAP tools to perform data analysis which aim to discover new pattern relationship between the cubes. Some popular OLAP server software programs include Oracle (C), IBM (C) and Microsoft (C). Madeira (2003) supports the fact that OLAP and data warehouse are complementary technology which blends together. Data warehouse stores and manages data while OLAP transforms data warehouse datasets into strategic information. OLAP function ranges from basic navigation and browsing (often known as slice and dice), to calculations and also serious analysis such as time series and complex modelling. As decision-makers implement more advanced OLAP capabilities, they move from basic data access to creation of information and to discovering of new knowledge. 2.3.4 OLAP ARCHITECTURE In comparison to data warehouse which usually based on relational technology, OLAP uses a multidimensional view to aggregate data to provide rapid access to strategic information for analysis. There are three type of OLAP architecture based on the method in which they store multi-dimensional data and perform analysis operations on that dataset (Nigel, P., 2008). The categories are multidimensional OLAP (MOLAP), relational OLAP (ROLAP) and hybrid OLAP (HOLAP). In MOLAP as depicted in Diagram 2.11, datasets are stored and summarized in a multidimensional cube. The MOLAP architecture can perform faster than ROLAP and HOLAP (C). MOLAP cubes designed and build for rapid data retrieval to enhance efficient slicing and dicing operations. MOLAP can perform complex calculations which have been pre-generated after cube creation. MOLAP processing is restricted to initial cube that was created and are not bound to any additional replication of cube. In ROLAP as depict in Diagram 2.12, data and aggregations are stored in relational database tables to provide the OLAP slicing and dicing functionalities. ROLAP are the slowest among the OLAP flavours. ROLAP relies on data manipulating directly in the relational database to give the manifestation of conventional OLAPs slicing and dicing functionality. Basically, each slicing and dicing action is equivalent to adding a WHERE clause in the SQL statement. (C) ROLAP can manage large amounts of data and ROLAP do not have any limitations for data size. ROLAP can influence the intrinsic functionality in a relational database. ROLAP are slow in performance because each ROLAP activity are essentially a SQL query or multiple SQL queries in the relational database. The query time and number of SQL statements executed measures by its complexity of the SQL statements and can be a bottleneck if the underlying dataset size is large. ROLAP essentially depends on SQL statements generation to query the relational database and do not cater all needs which make ROLAP technology conventionally limited by what SQL functionality can offer. (C) HOLAP as depict in Diagram 2.13, combine the technologies of MOLAP and ROLAP. Data are stored in ROLAP relational database tables and the aggregations are stored in MOLAP cube. HOLAP can drill down from multidimensional cube into the underlying relational database data. To acquire summary type of information, HOLAP leverages cube technology for faster performance. Whereas to retrieve detail type of information, HOLAP can drill down from the cube into the underlying relational data. (C) In OLAP architectures (MOLAP, ROLAP and HOLAP), the datasets are stored in a multidimensional format as it involves the creation of multidimensional blocks called data cubes (Harinarayan, 1996). The cube in OLAP architecture may have three axes (dimensions), or more. Each axis (dimension) represents a logical category of data. One axis may for example represent the geographic location of the data, while others may indicate a state of time or a specific school. Each of the categories, which will be described in the following section, can be broken down into successive levels and it is possible to drill up or down between the levels. Cabibo (1997) states that OLAP partitions are normally stored in an OLAP server, with the relational database frequently stored on a separate server from OLAP server. OLAP server must query across the network whenever it needs to access the relational tables to resolve a query. The impact of querying across the network depends on the performance characteristics of the network itself. Even when the relational database is placed on the same server as OLAP server, inter-process calls and the associated context switching are required to retrieve relational data. With a OLAP partition, calls to the relational database, whether local or over the network, do not occur during querying. 2.3.3 OLAP FUNCTIONALITY OLAP functionality offers dynamic multidimensional analysis supporting end users with analytical activities includes calculations and modelling applied across dimensions, trend analysis over time periods, slicing subsets for on-screen viewing, drilling to deeper levels of records (OLAP Council, 1997) OLAP is implemented in a multi-user client/server environment and provide reliably fast response to queries, in spite of database size and complexity. OLAP facilitate the end user integrate enterprise information through relative, customized viewing, analysis of historical and present data in various what-if data model scenario. This is achieved through use of an OLAP Server as depicted in diagram 2.9. OLAP functionality is provided by an OLAP server. OLAP server design and data structure are optimized for fast information retrieval in any course and flexible calculation and transformation of unprocessed data. The OLAP server may either actually carry out the processed multidimensional information to distribute consistent and fast response times to end users, or it may fill its data structures in real time from relational databases, or offer a choice of both. Essentially, OLAP create information in cube form which allows more composite analysis compares to relational database. OLAP analysis techniques employ slice and dice and drilling methods to segregate data into loads of information depending on given parameters. Slice is identifying a single value for one or more variable which is non-subset of multidimensional array. Whereas dice function is application of slice function on more than two dimensions of multidimensional cubes. Drilling function allows end user to traverse between condensed data to most precise data unit as depict in Diagram 2.10. 2.3.5 MULTIDIMENSIONAL DATABASE SCHEMA The base of every data warehouse system is a relational database build using a dimensional model. Dimensional model consists of fact and dimension tables which are described as star schema or snowflake schema (Kimball, 1999). A schema is a collection of database objects, tables, views and indexes (Inmon, 1996). To understand dimensional data modelling, Table 2.10 defines some of the terms commonly used in this type of modelling: In designing data models for data warehouse, the most commonly used schema types are star schema and snowflake schema. In the star schema design, fact table sits in the middle and is connected to other surrounding dimension tables like a star. A star schema can be simple or complex. A simple star consists of one fact table; a complex star can have more than one fact table. Most data warehouses use a star schema to represent the multidimensional data model. The database consists of a single fact table and a single table for each dimension. Each tuple in the fact table consists of a pointer or foreign key to each of the dimensions that provide its multidimensional coordinates, and stores the numeric measures for those coordinates. A tuple consist of a unit of data extracted from cube in a range of member from one or more dimension tables. (C, http://msdn.microsoft.com/en-us/library/aa216769%28SQL.80%29.aspx). Each dimension table consists of columns that correspond to attributes of the dimension. Diagram 2.14 shows an example of a star schema For Medical Informatics System. Star schemas do not explicitly provide support for attribute hierarchies which are not suitable for architecture such as MOLAP which require lots of hierarchies of dimension tables for efficient drilling of datasets. Snowflake schemas provide a refinement of star schemas where the dimensional hierarchy is explicitly represented by normalizing the dimension tables, as shown in Diagram 2.15. The main advantage of the snowflake schema is the improvement in query performance due to minimized disk storage requirements and joining smaller lookup tables. The main disadvantage of the snowflake schema is the additional maintenance efforts needed due to the increase number of lookup tables. (C) Levene. M (2003) stresses that in addition to the fact and dimension tables, data warehouses store selected summary tables containing pre-aggregated data. In the simplest cases, the pre-aggregated data corresponds to aggregating the fact table on one or more selected dimensions. Such pre-aggregated summary data can be represented in the database in at least two ways. Whether to use star or a snowflake mainly depends on business needs. 2.3.2 OLAP Evaluation As OLAP technology taking prominent place in data warehouse industry, there should be a suitable assessment tool to evaluate it. E.F. Codd not only invented OLAP but also provided a set of procedures which are known as the Twelve Rules for OLAP product ability assessment which include data manipulation, unlimited dimensions and aggregation levels and flexible reporting as shown in Table 2.8 (Codd, 1993): Codd twelve rules of OLAP provide us an essential tool to verify the OLAP functions and OLAP models used are able to produce desired result. Berson, A. (2001) stressed that a good OLAP system should also support a complete database management tools as a utility for integrated centralized tool to permit database management to perform distribution of databases within the enterprise. OLAP ability to perform drilling mechanism within the MDDB allows the functionality of drill down right to the source or root of the detail record level. This implies that OLAP tool permit a smooth changeover from the MDDB to the detail record level of the source relational database. OLAP systems also must support incremental database refreshes. This is an important feature as to prevent stability issues on operations and usability problems when the size of the database increases. 2.3.1 OLTP and OLAP The design of OLAP for multidimensional cube is entirely different compare to OLTP for database. OLTP is implemented into relational database to support daily processing in an organization. OLTP system main function is to capture data into computers. OLTP allow effective data manipulation and storage of data for daily operational resulting in huge quantity of transactional data. Organisations build multiple OLTP systems to handle huge quantities of daily operations transactional data can in short period of time. OLAP is designed for data access and analysis to support managerial user strategic decision making process. OLAP technology focuses on aggregating datasets into multidimensional view without hindering the system performance. According to Han, J. (2001), states OLTP systems as Customer oriented and OLAP is a market oriented. He summarized major differences between OLTP and OLAP system based on 17 key criteria as shown in table 2.7. It is complicated to merge OLAP and OLTP into one centralized database system. The dimensional data design model used in OLAP is much more effective for querying than the relational database query used in OLTP system. OLAP may use one central database as data source and OLTP used different data source from different database sites. The dimensional design of OLAP is not suitable for OLTP system, mainly due to redundancy and the loss of referential integrity of the data. Organization chooses to have two separate information systems, one OLTP and one OLAP system (Poe, V., 1997). We can conclude that the purpose of OLTP systems is to get data into computers, whereas the purpose of OLAP is to get data or information out of computers. 2.4 DATA MINING Many data mining scholars (Fayyad, 1998; Freitas, 2002; Han, J. et. al., 1996; Frawley, 1992) have defined data mining as discovering hidden patterns from historical datasets by using pattern recognition as it involves searching for specific, unknown information in a database. Chung, H. (1999) and Fayyad et al (1996) referred data mining as a step of knowledge discovery in database and it is the process of analyzing data and extracts knowledge from a large database also known as data warehouse (Han, J., 2000) and making it into useful information. Freitas (2002) and Fayyad (1996) have recognized the advantageous tool of data mining for extracting knowledge from a da

Ethics in Social Work

In psychology and social work, dual relationships and clinical boundaries are often common. They are often unclear and most times the professional has a difficult time noticing them developing. Ethical dilemmas are found in all professions, but are often different in type and solutions. They are hard to identify and even harder to make a clear decision. Dual relationships and clinical boundaries are one of the biggest ethical dilemmas social workers face because of the difficulties of finding the line between the professional role and the empathetic role a social worker plays.Social work is a profession that helps to solve complex human problems and create a more just and caring society. One of the foundations of social work is the focus on the strengths, as opposed to the shortcomings, of individuals, families and communities so that creative solutions for complex social problems can be found. The profession is characterized by a steadfast commitment to social justice in the service of empowering individuals, families and communities to meet their needs. Few professions offer many different types of employment opportunities.Social workers serve as counselors, in adoption, domestic violence, rehabilitation, hospice, mental health, youth, community development workers, public policy analysts, global rights workers; and in juvenile and adult justice systems, just to name a few. However, the main job of a social worker, however, is to help the client to reach a more stable environment, but to go about it a specific way dependent on the job the social worker held. Each job might come with different ethical problems, but social workers have to follow a strict code of ethics that have guidelines to help them make the correct decisions.The NASW, National Association of Social Work, is the largest group of professional social workers. It is the group that wrote the NASW code of ethics, which are followed by all social workers across the United States (NASW, 2008). Ethi cs are the underlying rules put in place to help society better function. Usually, they are hard to identify and can be interpreted in many different ways. Each person has their own ethical standards, which is why it’s necessary to have ethical codes that make it more general and help each professional make his or her own ethical decision.Ethics play a huge role into social work. Without an ethical background or a code of ethics it could harm not only a client, but also the social worker himself. The biggest struggle that comes along with ethics is the fact that each individual usually interprets them differently. Ethics is two things. First, ethics refers to right and wrong that advise what humans should do, in terms of rights, obligations, benefits to society, fairness, or specific virtues. Ethics can refer to those standards that make humans refrain from rape, stealing, murder, assault, slander, and fraud.Ethical standards also include ideals relating to rights, such as th e right to life, the right to freedom from injury, and the right to privacy. Secondly, ethics refers to the study and development of one's ethical standards. As mentioned above, feelings, laws, and social norms can deviate from what is ethical; therefore it is necessary to constantly examine one's standards to ensure that they are reasonable. The NASW Code of Ethics was written to serve as a guide to the everyday professional conduct of social workers. It includes four sections.The first section, â€Å"Preamble,† summarizes the social work profession's mission and core values. The second section, â€Å"Purpose of the NASW Code of Ethics,†Ã‚  provides an overview of the Code's main functions and a brief guide for dealing with ethical issues or dilemmas in social work practice. The third section,  Ã¢â‚¬Å"Ethical Principles,† presents broad ethical principles, based on social work's core values, that inform social work practice. The final section, â€Å"Ethical S tandards,†Ã‚  includes specific ethical standards to guide social workers' conduct and to provide a basis for adjudication.The Code of Ethics, as used today, was approved  by the 1996 NASW Delegate Assembly and revised by the 1999 NASW Delegate Assembly. (NASW, 2008) The NASW code of ethics is used to help guide social workers when it comes to making ethical decisions in the field. It is used to help give every therapist and client the same treatment and ethical decisions. Usually, each profession has a different code of ethics due to the fact that each profession has a diverse set of ethical issues that come with it.Dual relationships or multiple relationships are interactions in which a client is treating a patient, but is also interacting with them in some other way. It can also be if a therapist is in a professional role with a person and promises to enter into another relationship in the future with that person or someone closely related to the individual. Dual roles re fer to two different roles and multiple roles are when more than two overlapping roles exist. For example if a therapist is treating their child’s teacher, their child’s friend, having sexual relations with the client, or are close to the client in some way.Dual relationships are against the APA ethics code and can cause harm to the patient in some cases. A therapist should never work with people who he or she might have to interact with on a causal level instead of a patient-therapist level, not only for the patient’s confidentiality, but also to help keep the therapist from giving preferential treatment (Barnett, Vasquez, Moorehead-Slaughter, Johnson, 2007) Dual relationships can also allow a therapist to misuse their power and influence. The practitioner is in a position to exploit the client for his or her own personal gain.The problem of the dual relationships and the second relationship, the counselor is now susceptible to other interests (personal, financ ial, or social) that he or she may put before the best interests of the client. Problems that arise usually occur when the professional boundaries are not clear to begin with. Therefore, boundaries should be included as part of the intake paperwork. The wording should be clear and specifically state the therapist's intentions. The therapist-client relationship is one that does not permit contact in a casual manner outside the therapy session.This includes work relationships, social conversations or any type of romantic or sexual contact. † The therapist can state something about not giving personal information to a client, as there is no need for them to know this kind of thing. If the client signs the consent form, a contract is in effect and should not be breached by either party. Not only does the therapist have to gauge the client and the way he or she processes things, but also what the client could take inappropriate.Although it may seem appropriate in a therapistâ€℠¢s eyes it could be inappropriate in the client’s eyes and vice versa. (Syme, 2003) The therapist has to keep a close eye on their actions and make sure their client is not seeing it differently than they are. There are three factors that counselors should consider. First, there is a greater risk of harm when the expectations of client and counselor are mismatched. When clients have one set of assumptions about the ground rules of the relationship, and the professional has a different set of assumptions, there is an increased chance of susceptibility.Another factor is that there is potential for divided loyalties and an associated loss of objectivity. Counselors who have personal, social or business relationships with their clients, are at risk because their self-interest may be involved and thus compromise the client's best interest. Finally, by the very nature of the counselor/client relationship, clients are more dependent, have less authority and are vulnerable. Due to th is power differential, it is the responsibility of the professional to ensure that the client in the relationship is not harmed.One key feature of boundary issues is a conflict of interest that harms clients. Conflicts of interest occur when professionals find themselves in a relationship that could prejudice or give the appearance of prejudicing their decision-making. Thus a counselor who provides services to a client with whom he would like to develop a sexual relationship faces a conflict of interest; the professional’s personal interests collide with his or her professional duty to avoid harming his or her client. Zur, American Psychological Association, 2007) Social workers should be alert to and avoid conflicts of interest that interfere with the exercise of professional discretion and impartial judgment. Social workers should also inform clients when a real or potential conflict of interest arises and take reasonable steps to resolve the issue in a manner that makes th e clients’ interests primary and protects clients’ interests to the greatest extent possible. In some cases, protecting clients’ interests may require termination of the professional relationship with proper referral of the client (standard 1. 6[a]), NASW, 2008). The code goes on to say that â€Å"social workers should not engage in dual or multiple relationships with clients or former clients in which there is a risk of exploitation or potential harm to the client† (standard 1. 06[c], NASW, 2008). While treating someone in therapy, a counselor has to be careful about how their patient is going to interpret their actions and words. When a patient is in therapy, a lot of times they don’t have anyone around to support them and help them overcome their obstacles.That being said, it’s the job of the therapist to be that person for their client and help them to succeed. As a client gets closer to his or her therapist, sometimes the slightest of t hings can be taken in the wrong way. For example, as trust is built the slightest of things can trigger a client to see their therapist in a different light. As a counselor, a pat on the back, hand touch, ect can seem like nothing to you, but to the client can seem like a sexual advance. This an be detrimental to a client and can break all the trust the therapist had built up, putting the patient back to the beginning of the process (Smith, Fitzpatrick, 1995) When the psychologist and the patient develop an extracurricular relationship, this dual relationship can threaten the psychologist's ability to act impartially as a therapist and the patient's ability to receive proper treatment in their vulnerable state. If psychologists are not held accountable to prevent this type of behavior, they can harm the reputation of all clinical psychologists.Personal relationships imply a bias and the private relationship can cross over into therapy and treatment. The term â€Å"conflict of inter est† applies to dual relationships because no matter how objective a psychologist tries to be, their own emotions may taint their trained perceptions. Conflict of interest can be applied to a variety of situations, such as the psychologist should not treat a family member or close friend due to the possibility of favoritism or being non-objective, and could interfere with the treatment being given and received.The psychological ethical codes clearly prohibit the interaction of a personal relationship between the psychologist and the client. Dual relationships and clinical boundaries are one of the biggest ethical dilemmas social workers are faced with; trying to find the line between the professional role and the empathetic role a social worker plays. This being said, as a social worker it is important to distance the client, but also to build trust. It takes time to learn the boundaries and how to avoid crossing them.This is just one of the biggest challenges social workers h ave to overcome in their field. Reference Page: Barnett J, Lazarus A, Vasquez M, Moorehead-Slaughter O, Johnson W (2007) Boundary Issues and Multiple Relationships: Fantasy and Reality; Professional Psychology: Research and Practice, 38 (4) 401-410 doi: 10. 1037/0735-7028. 38. 4. 401 Herlihy, B and Corey G. (1992) Dual Relationships in Counseling. Alexandria, VA: American Association for Counseling Development Reamer, G. F. PhD (2011, October 13). Eye on Ethics Social Work Today, retrieved from http://www. socialworktoday. om/news/eoe_101311. shtml Smith, D. and Fitzpatrick, M. (1995) Patent-Therapist Boundary Issues: An Integrative Review of Theory and Research, Professional Psychology: Research and Practice, 26 (5), 499-506 doi: 10. 1037/0735-7028. 26. 5. 499 Syme, G (2003) Dual Relationships in Counseling and Psychotherapy: Exploring the Limits, London: Sage Publications Zur, O and American Psychological Association (2007) Boundaries in Psychotherapy Ethical and Clinical Explorat ions. Washington, DC : American Psychological Association http://www. socialworkers. org/pubs/code/code. asp

Purgatorius - Facts and Figures

Name: Purgatorius (after Purgatory Hill in Montana); pronounced PER-gah-TORE-ee-us Habitat: Woodlands of North America Historical Period: Late Cretaceous (65 million years ago) Size and Weight: About six inches long and a few ounces Diet: Probably omnivorous Distinguishing Characteristics: Small size; primate-like teeth; ankle bones adapted to climbing trees About Purgatorius Most of the prehistoric mammals of the late Cretaceous period looked pretty much the same--small, quivering, mouse-sized creatures that spent most of their lives high up in trees, the better to avoid rampaging raptors and tyrannosaurs. On closer examination, though, especially of their teeth, its clear that these mammals were each specialized in their own distinct way. What set Purgatorius apart from the the rest of the rat pack is that it possessed distinctly primate-like teeth, leading to speculation that this tiny creature may have been directly ancestral to modern-day chimps, rhesus monkeys, and humans--all of whom had the chance to evolve only after the dinosaurs went extinct and opened up some valuable breathing room for other types of animals. The trouble is, not all paleontologists agree that Purgatorius was a direct (or even distant) precursor of primates; rather, it may have been an early example of the closely related group of mammals known as plesiadapids, after the most famous member of this family, Plesiadapis. What we do know about Purgatorius is that it lived high up in trees (as we can infer from the structure of its ankles), and that it managed to straddle the K/T Extinction Event: fossils of Purgatorius have been discovered dating both to the late Cretaceous period and the early Paleocene epoch, a few million years later. Most likely, this mammals arboreal habits helped rescue it from oblivion, making accessible a new source of food (nuts and seeds) at a time when most non-tree-climbing dinosaurs were starving to death on the ground.

National Health Care Quality and Disparities Report Essay

The National Healthcare Quality and Disparities Report (NHQDR) (2012) identified three key themes. The themes are health care quality and access are suboptimal, especially for minority and low income groups; overall quality is improving, access is getting worse and disparities are not changing; and urgent attention is warranted to ensure continued improvements in: quality diabetic care, maternal and child health, adverse events, disparities in cancer care and quality care among the states in the south. The NHQDR 2012 is a comprehensive report that implies there are changes that need to occur at multiple levels within the health care system and public policy. The report implies that the health care system needs to become more accessible†¦show more content†¦APRNs are able to become politically involved by writing letter to the senators and congressman in support of bills related to access and quality of care, and support preventative health care. Other means of action include voting on matters of health, attending political meetings to learn what health policies are being discussed, and supporting wellness. APRNs need to be an advocate for the population they serve by referring clients to agencies for health care insurance, medication services, visiting nurses and other social service supports that will enhance the client’s quality and access to health care. Education is paramount in order for quality of care to improve. APRNs need to provide culturally relevant education to the population they treat. Education needs to be comprehensive and include an explanation of the disease, how the disease may impact the client’s life, medications its purpose and benefits. The education also needs to include negotiating with the client in order to allow for cultural integration. . Education should be given verbally in conjunction with instructions written in the primary language and include a follow up appointment. 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