Tag: Processes

  • What is Distributed Data Processing (DDP)?

    What is Distributed Data Processing (DDP)?

    What is Distributed Data Processing (DDP)?


    An arrangement of networked computers in which data processing capabilities are spread across the network. In DDP, specific jobs are performed by specialized computers which may be far removed from the user and/or from other such computers. This arrangement is in contrast to ‘centralized’ computing in which several client computers share the same server (usually a mini or mainframe computer) or a cluster of servers. DDP provides greater scalability, but also requires more network administration resources.

    Understanding of Distributed Data Processing (DDP)


    Distributed database system technology is the union of what appear to be two diametrically opposed approaches to data processing: database system and computer network technologies. The database system has taken us from a paradigm of data processing in which each application defined and maintained its own data to one in which the data is defined and administered centrally. This new orientation results in data independence, whereby the application programs are immune to changes in the logical or physical organization of the data. One of the major motivations behind the use of database systems is the desire to integrate the operation data of an enterprise and to provide centralized, thus controlled access to that data. The technology of computer networks, on the other hand, promotes a mode of that work that goes against all centralization efforts. At first glance, it might be difficult to understand how these two contrasting approaches can possibly be synthesized to produce a technology that is more powerful and more promising than either one alone. The key to this understanding is the realization that the most important objective of the database technology is integration, not centralization. It is important to realize that either one of these terms does not necessarily imply the other. It is possible to achieve integration with centralization and that is exactly what at distributed database technology attempts to achieve.

    The term distributed processing is probably the most used term in computer science for the last couple of years. It has been used to refer to such diverse system as multiprocessing systems, distributed data processing, and computer networks. Here are some of the other term that has been synonymous with distributed processing distributed/multi-computers, satellite processing /satellite computers, back-end processing, dedicated/special-purpose computers, time-shared systems and functionally modular system.

    Obviously, some degree of the distributed processing goes on in any computer system, ever on single-processor computers, starting with the second-generation computers, the central processing. However, it should be quite clear that what we would like to refer to as distributed processing, or distributed computing has nothing to do with this form of distribution of the function of function in a single-processor computer system. Web Developer’s Workflow Become Much Easier with this Innovative Gadgets.

    A term that has caused so much confusion is obviously quite difficult to define precisely. The working definition we use for a distributed computing systems states that it is a number of autonomous processing elements that are interconnected by a computer network and that cooperate in performing their assigned tasks. The processing elements referred to in this definition is a computing device that can execute a program on its own.

    One fundamental question that needs to be asked is: Distributed is one thing that might be distributed is that processing logic. In fact, the definition of a distributed computing computer system give above implicitly assumes that the processing logic or processing elements are distributed. Another possible distribution is according to function. Various functions of a computer system could be delegated to various pieces of hardware sites. Finally, control can be distributed. The control of execution of various task might be distributed instead of being performed by one computer systems, from the view of distributed instead of being the system, these modes of distribution are all necessary and important. Strategic Role of e-HR (Electronic Human Resource).

     

    A distributed computing system can be classified with respect to a number of criteria. Some of these criteria are as follows: degree of coupling, an interconnection structure, the interdependence of components, and synchronization between components. The degree of coupling refers to a measure that determines closely the processing elements are connected together. This can be measured as the ratio of the amount of data exchanged to the amount of local processing performed in executing a task. If the communication is done a computer network, there exits weak coupling among the processing elements. However, if components are shared we talk about strong coupling. Shared components can be both primary memory or secondary storage devices. As for the interconnection structure, one can talk about those case that has a point to point interconnection channel. The processing elements might depend on each other quite strongly in the execution of a task, or this interdependence might be as minimal as passing message at the beginning of execution and reporting results at the end. Synchronization between processing elements might be maintained by synchronous or by asynchronous means. Note that some of these criteria are not entirely independent of the processing elements to be strongly interdependent and possibly to work in a strongly coupled fashion.

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  • What is Phases of the Data Mining Process?

    What is Phases of the Data Mining Process?

    What is Phases of the Data Mining Process?


    The Cross-Industry Standard Process for Data Mining (CRISP-DM) is the dominant data-mining process framework. It’s an open standard; anyone may use it. The following list describes the various phases of the process.

    Phases-of-the-Data-Mining-Process
    The Cross-Industry Standard Process for Data Mining

    Business understanding

    In the business understanding phase:

    First, it is required to understand business objectives clearly and find out what are the business’s needs.

    Next, we have to assess the current situation by finding of the resources, assumptions, constraints and other important factors which should be considered.

    Then, from the business objectives and current situations, we need to create data mining goals to achieve the business objectives within the current situation.

    Finally, a good data mining plan has to be established to achieve both business and data mining goals. The plan should be as detailed as possible.

    Data understanding

    First, the data understanding phase starts with initial data collection, which we collect from available data sources, to help us get familiar with the data. Some important activities must be performed including data load and data integration in order to make the data collection successfully.

    Next, the “gross” or “surface” properties of acquired data need to be examined carefully and reported.

    Then, the data needs to be explored by tackling the data mining questions, which can be addressed using querying, reporting, and visualization.

    Finally, the data quality must be examined by answering some important questions such as “Is the acquired data complete?”, “Is there any missing values in the acquired data?”

    Data preparation

    The data preparation typically consumes about 90% of the time of the project. The outcome of the data preparation phase is the final data set. Once available data sources are identified, they need to be selected, cleaned, constructed and formatted into the desired form. The data exploration task at a greater depth may be carried during this phase to notice the patterns based on business understanding.

    Modeling

    First, modeling techniques have to be selected to be used for the prepared dataset.

    Next, the test scenario must be generated to validate the quality and validity of the model.

    Then, one or more models are created by running the modeling tool on the prepared dataset.

    Finally, models need to be assessed carefully involving stakeholders to make sure that created models are met business initiatives.

    Evaluation

    In the evaluation phase, the model results must be evaluated in the context of business objectives in the first phase. In this phase, new business requirements may be raised due to the new patterns that have been discovered in the model results or from other factors. Gaining business understanding is an iterative process in data mining. The go or no-go decision must be made in this step to move to the deployment phase.

    Deployment

    The knowledge or information, which we gain through data mining process, needs to be presented in such a way that stakeholders can use it when they want it. Based on the business requirements, the deployment phase could be as simple as creating a report or as complex as a repeatable data mining process across the organization. In the deployment phase, the plans for deployment, maintenance, and monitoring have to be created for implementation and also future supports. From the project point of view, the final report of the project needs to summary the project experiences and review the project to see what need to improved created learned lessons.

    The CRISP-DM offers a uniform framework for experience documentation and guidelines. In addition, the CRISP-DM can apply in various industries with different types of data.

    In this article, you have learned about the data mining processes and examined the cross-industry standard process for data mining.

    Something is not Forgetting What? Data mining is a promising and relatively new technology. Data mining is defined as a process of discovering hidden valuable knowledge by analyzing large amounts of data, which is stored in databases or data warehouse, using various data mining techniques such as machine learning, artificial intelligence(AI) and statistical.

    Many organizations in various industries are taking advantages of data mining including manufacturing, marketing, chemical, aerospace… etc, to increase their business efficiency. Therefore, the needs for a standard data mining process increased dramatically. A data mining process must be reliable and it must be repeatable by business people with little or no knowledge of data mining background. As the result, in 1990, a cross-industry standard process for data mining (CRISP-DM) first published after going through a lot of workshops, and contributions from over 300 organizations.

    What-is-Phases-of-the-Data-Mining-Process


  • Process of The Data Mining

    Process of The Data Mining

    Process of The Data Mining


    Data mining is a promising and relatively new technology. Data mining is defined as a process of discovering hidden valuable knowledge by analyzing large amounts of data, which is stored in databases or data warehouse, using various data mining techniques such as machine learning, artificial intelligence(AI) and statistical.

    Many organizations in various industries are taking advantages of data mining including manufacturing, marketing, chemical, aerospace… etc, to increase their business efficiency. Therefore, the needs for a standard data mining process increased dramatically. A data mining process must be reliable and it must be repeatable by business people with little or no knowledge of data mining background. As the result, in 1990, a cross-industry standard process for data mining (CRISP-DM) first published after going through a lot of workshops, and contributions from over 300 organizations.

    The data mining process involves much hard work, including perhaps building data warehouse if the enterprise does not have one. A typical data mining process is likely to include the following steps:

    Requirements analysis: The enterprise decision makers need to formulate goals that the data mining process is expected to achieve. The business problem must be clearly defined. One cannot use data mining without a good idea of what kind of outcomes the enterprise is looking for, since the technique to be used and the data that is required are likely to be different for different goals. Furthermore, if the objectives have been clearly defined, it is easier to evaluate the results of the project. Once the goals have been agreed upon, the following further steps are needed.

    Data selection and collection: This step may include finding the best source databases for the data that is required. If the enterprise has implemented a data warehouse, then most of the data could be available there. If the data is not available in the warehouse or the enterprise does not have a warehouse, the source OLTP (On-line Transaction Processing) systems need to be identified and the required information extracted and stored in some temporary system. In some cases, only a sample of the data available may be required.

    Cleaning and preparing data: This may not be an onerous task if a data warehouse containing the required . data exists, since most of this must have already been done when data was loaded in the warehouse. Otherwise this task can be very resource intensive and sometimes more than 50% of effort in a data mining project is spent on this step. Essentially a data store that integrates data from a number of databases may need to be created. When integrating data, one often encounters problems like identifying data, dealing with missing data, data conflicts and ambiguity. An ETL (extraction, transformation and loading) tool may be used to overcome these problems.

    Data mining exploration and validation: Once appropriate data has been collected and cleaned, it is possible to start data mining exploration. Assuming that the user has access to one or more data mining tools, a data mining model may be constructed based on the enterprise’s needs. It may be possible to take a sample of data and apply a number of relevant techniques. For each technique the results should be evaluated and their significance interpreted. This is likely to be an iterative process which should lead to selection of one or more techniques that are suitable for further exploration, testing, and validation.

    Implementing, evaluating, and monitoring: Once a model has been selected and validated, the model can be implemented for use by the decision makers. This may involve software development for generating reports, or for results visualization and explanation for managers. It may be that more than one technique is available for the given data mining task. It is then important to evaluate the results and choose the best technique. Evaluation may involve checking the accuracy and effectiveness of the technique. Furthermore, there is a need for regular monitoring of the performance of the techniques that have been implemented. It is essential that use of the tools by the managers be monitored and results evaluated regularly. Every enterprise evolves with time and so must the data mining system. Therefore, monitoring is likely to lead from time to time to refinement of tools and techniques that have been implemented.

    Results visualization: Explaining the results of data mining to the decision makers is an important step of the data mining process. Most commercial data mining tools include data visualization modules. These tools are often vital in communicating the data mining results to the managers, although a problem dealing with a number of dimensions must be visualized using a two dimensional computer screen or printout. Clever data visualization tools are being developed to display results that deal with more than two dimensions. The visualization tools available should be tried and used if found effective for the given problem.

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  • What is Efficacy of Activated Processes?

    What is Efficacy of Activated Processes?


    Much research has been conducted on the four major psychological processes through which self-beliefs of efficacy affect human functioning.

    Cognitive Processes


    The effects of self-efficacy beliefs on cognitive processes take a variety of forms. Much human behavior, being purposive, is regulated by forethought embodying valued goals. Personal goal setting is influenced by self-appraisal of capabilities. The stronger the perceived self-efficacy, the higher the goal challenges people set for themselves and the firmer is their commitment to them.

    Most courses of action are initially organized in thought. People’s beliefs in their efficacy shape the types of anticipatory scenarios they construct and rehearse. Those who have a high sense of efficacy, visualize success scenarios that provide positive guides and supports for performance. Those who doubt their efficacy, visualize failure scenarios and dwell on the many things that can go wrong. It is difficult to achieve much while fighting self-doubt. A major function of thought is to enable people to predict events and to develop ways to control those that affect their lives. Such skills require effective cognitive processing of information that contains many ambiguities and uncertainties. In learning predictive and regulative rules people must draw on their knowledge to construct options, to weight and integrate predictive factors, to test and revise their judgments against the immediate and distal results of their actions, and to remember which factors they had tested and how well they had worked.

    It requires a strong sense of efficacy to remain task oriented in the face of pressing situational demands, failures and setbacks that have significant repercussions. Indeed, when people are faced with the tasks of managing difficult environmental demands under taxing circumstances, those who are beset by self-doubts about their efficacy become more and more erratic in their analytic thinking, lower their aspirations and the quality of their performance deteriorates. In contrast, those who maintain a resilient sense of efficacy set themselves challenging goals and use good analytic thinking which pays off in performance accomplishments.

    Motivational Processes


    Self-beliefs of efficacy play a key role in the self-regulation of motivation. Most human motivation is cognitively generated. People motivate themselves and guide their actions anticipatorily by the exercise of forethought. They form beliefs about what they can do. They anticipate likely outcomes of prospective actions. They set goals for themselves and plan courses of action designed to realize valued futures.

    There are three different forms of cognitive motivators around which different theories have been built. They include causal attributions, outcome expectancies, and cognized goals. The corresponding theories are attribution theory, expectancy-value theory and goal theory, respectively. Self-efficacy beliefs operate in each of these types of cognitive motivation. Self-efficacy beliefs influence causal attributions. People who regard themselves as highly efficacious attribute their failures to insufficient effort, those who regard themselves as inefficacious attribute their failures to low ability. Causal attributions affect motivation, performance and affective reactions mainly through beliefs of self-efficacy.

    In expectancy-value theory, motivation is regulated by the expectation that a given course of behavior will produce certain outcomes and the value of those outcomes. But people act on their beliefs about what they can do, as well as on their beliefs about the likely outcomes of performance. The motivating influence of outcome expectancies is thus partly governed by self-beliefs of efficacy. There are countless attractive options people do not pursue because they judge they lack the capabilities for them. The predictiveness of expectancy-value theory is enhanced by including the influence of perceived self- efficacy.

    The capacity to exercise self-influence by goal challenges and evaluative reaction to one’s own attainments provides a major cognitive mechanism of motivation. A large body of evidence shows that explicit, challenging goals enhance and sustain motivation. Goals operate largely through self-influence processes rather than regulate motivation and action directly. Motivation based on goal setting involves a cognitive comparison process. By making self-satisfaction conditional on matching adopted goals, people give direction to their behavior and create incentives to persist in their efforts until they fulfill their goals. They seek self-satisfaction from fulfilling valued goals and are prompted to intensify their efforts by discontent with substandard performances.

    Motivation based on goals or personal standards is governed by three types of self-influences. They include self-satisfying and self-dissatisfying reactions to one’s performance, perceived self-efficacy for goal attainment, and readjustment of personal goals based on one’s progress. Self-efficacy beliefs contribute to motivation in several ways: They determine the goals people set for themselves; how much effort they expend; how long they persevere in the face of difficulties; and their resilience to failures. When faced with obstacles and failures people who harbor self-doubts about their capabilities slacken their efforts or give up quickly. Those who have a strong belief in their capabilities exert greater effort when they fail to master the challenge. Strong perseverance contributes to performance accomplishments.

    Affective Processes


    People’s beliefs in their coping capabilities affect how much stress and depression they experience in threatening or difficult situations, as well as their level of motivation. Perceived self-efficacy to exercise control over stressors plays a central role in anxiety arousal. People who believe they can exercise control over threats do not conjure up disturbing thought patterns. But those who believe they cannot manage threats experience high anxiety arousal. They dwell on their coping deficiencies. They view many aspects of their environment as fraught with danger. They magnify the severity of possible threats and worry about things that rarely happen. Through such inefficacious thinking they distress themselves and impair their level of functioning. Perceived coping self-efficacy regulates avoidance behavior as well as anxiety arousal. The stronger the sense of self-efficacy the bolder people are in taking on taxing and threatening activities.

    Anxiety arousal is affected not only by perceived coping efficacy but by perceived efficacy to control disturbing thoughts. The exercise of control over one’s own consciousness is summed up well in the proverb: “You cannot prevent the birds of worry and care from flying over your head. But you can stop them from building a nest in your head.” Perceived self-efficacy to control thought processes is a key factor in regulating thought produced stress and depression. It is not the sheer frequency of disturbing thoughts but the perceived inability to turn them off that is the major source of distress. Both perceived coping self-efficacy and thought control efficacy operate jointly to reduce anxiety and avoidant behavior.

    Social cognitive theory prescribes mastery experiences as the principal means of personality change. Guided mastery is a powerful vehicle for instilling a robust sense of coping efficacy in people whose functioning is seriously impaired by intense apprehension and phobic self-protective reactions. Mastery experiences are structured in ways to build coping skills and instill beliefs that one can exercise control over potential threats. Intractable phobics, of course, are not about to do what they dread. One must, therefore, create an environment so that incapacitated phobics can perform successfully despite themselves. This is achieved by enlisting a variety of performance mastery aids. Feared activities are first modeled to show people how to cope with threats and to disconfirm their worst fears. Coping tasks are broken down into subtasks of easily mastered steps. Performing feared activities together with the therapist further enables phobics to do things they would resist doing by themselves. Another way of overcoming resistance is to use graduated time. Phobics will refuse threatening tasks if they will have to endure stress for a long time. But they will risk them for a short period. As their coping efficacy increases the time they perform the activity is extended. Protective aids and dosing the severity of threats also help to restore and develop a sense of coping efficacy.

    After functioning is fully restored, the mastery aids are withdrawn to verify that coping successes stem from personal efficacy rather than from mastery aids. Self-directed mastery experiences, designed to provide varied confirmatory tests of coping capabilities, are then arranged to strengthen and generalize the sense of coping efficacy. Once people develop a resilient sense of efficacy they can withstand difficulties and adversities without adverse effects.

    Guided mastery treatment achieves widespread psychological changes in a relatively short time. It eliminates phobic behavior and anxiety and biological stress reactions, creates positive attitudes and eradicates phobic ruminations and nightmares. Evidence that achievement of coping efficacy profoundly affects dream activity is a particularly striking generalized impact.

    A low sense of efficacy to exercise control produces depression as well as anxiety. It does so in several different ways. One route to depression is through unfulfilled aspiration. People who impose on themselves standards of self-worth they judge they cannot attain drive themselves to bouts of depression. A second efficacy route to depression is through a low sense of social efficacy. People who judge themselves to be socially efficacious seek out and cultivate social relationships that provide models on how to manage difficult situations, cushion the adverse effects of chronic stressors and bring satisfaction to people’s lives. Perceived social inefficacy to develop satisfying and supportive relationships increases vulnerability to depression through social isolation. Much human depression is cognitively generated by dejecting ruminative thought. A low sense of efficacy to exercise control over ruminative thought also contributes to the occurrence, duration and recurrence of depressive episodes.

    Other efficacy-activated processes in the affective domain concern the impact of perceived coping self-efficacy on biological systems that affect health functioning. Stress has been implicated as an important contributing factor to many physical dysfunctions. Controllability appears to be a key organizing principle regarding the nature of these stress effects. It is not stressful life conditions per se, but the perceived inability to manage them that is debilitating. Thus, exposure to stressors with ability to control them has no adverse biological effects. But exposure to the same stressors without the ability to control them impairs the immune system. The impairment of immune function increases susceptibility to infection, contributes to the development of physical disorders and accelerates the progression of disease.

    Biological systems are highly interdependent. A weak sense of efficacy to exercise control over stressors activates autonomic reactions, catecholamine secretion and release of endogenous opioids. These biological systems are involved in the regulation of the immune system. Stress activated in the process of acquiring coping capabilities may have different effects than stress experienced in aversive situations with no prospect in sight of ever gaining any self-protective efficacy. There are substantial evolutionary benefits to experiencing enhanced immune function during development of coping capabilities vital for effective adaptation. It would not be evolutionarily advantageous if acute stressors invariably impaired immune function, because of their prevalence in everyday life. If this were the case, people would experience high vulnerability to infective agents that would quickly do them in. There is some evidence that providing people with effective means for managing stressors may have a positive effect on immune function. Moreover, stress aroused while gaining coping mastery over stressors can enhance different components of the immune system.

    There are other ways in which perceived self-efficacy serves to promote health. Lifestyle habits can enhance or impair health. This enables people to exert behavioral influence over their vitality and quality of health. Perceived self-efficacy affects every phase of personal change–whether people even consider changing their health habits; whether they enlist the motivation and perseverance needed to succeed should they choose to do so; and how well they maintain the habit changes they have achieved. The stronger the perceived self-regulatory efficacy the more successful people are in reducing health-impairing habits and adopting and integrating health-promoting habits into their regular lifestyle. Comprehensive community programs designed to prevent cardiovascular disease by altering risk-related habits reduce the rate of morbidity and mortality.

    Selection Processes


    The discussion so far has centered on efficacy-activated processes that enable people to create beneficial environments and to exercise some control over those they encounter day in and day out. People are partly the product of their environment. Therefore, beliefs of personal efficacy can shape the course lives take by influencing they types of activities and environments people choose. People avoid activities and situations they believe exceed their coping capabilities. But they readily undertake challenging activities and select situations they judge themselves capable of handling. By the choices they make, people cultivate different competencies, interests and social networks that determine life courses. Any factor that influences choice behavior can profoundly affect the direction of personal development. This is because the social influences operating in selected environments continue to promote certain competencies, values, and interests long after the efficacy decisional determinant has rendered its inaugurating effect.

    Career choice and development is but one example of the power of self-efficacy beliefs to affect the course of life paths through choice-related processes. The higher the level of people’s perceived self-efficacy the wider the range of career options they seriously consider, the greater their interest in them, and the better they prepare themselves educationally for the occupational pursuits they choose and the greater is their success. Occupations structure a good part of people’s lives and provide them with a major source of personal growth.