نقش داده كاوي توسط الگوريتم شبكه عصبي و ژنتيك در مديريت دانش شركت ساخت و ساز

Role of knowledge mining by artificial neural network and genetic algorithm in construction industry

امروزه با گسترش سيستم هاي پايگاهي و حجم بالاي داده هاي ذخيره شده در اين سيستم ها، نياز به ابزاري است تا بتوان داده هاي ذخيره شده را پردازش كرد و اطلاعات حاصل از اين پردازش را در اختيار كاربران قرار داد. با استفاده از پرسش هاي ساده و ابزارهاي گوناگون گزارش گيري معمولي، مي توان اطلاعاتي را در اختيار كاربران قرار داد تا بتوانند به نتيجه گيري در مورد داده ها و روابط منطقي ميان آنها بپردازند اما وقتي كه حجم داده ها بالا باشد، كاربران هر چند زبر دست و با تجربه باشند نمي توانند الگوهاي مفيد را در ميان حجم انبوه داده ها تشخيص دهند و يا اگر قادر به اين كار هم باشند، هزينه عمليات از نظر نيروي انساني و مادي بسيار بالا است. داده كاوي توسط الگوريتم هاي شبكه عصبي و ژنتيك يكي از مهمترين اين روشها است كه به وسيله آن الگوهاي مفيد در داده ها با حداقل دخالت كاربران شناخته مي شوند و اطلاعاتي را در اختيار كاربران و تحليل گران قرار مي دهند تا براساس آنها تصميمات مهم و حياتي در سازمانها اتخاذ شوند. در اين مقاله سعي شده ابعاد متفاوتي از مديريت دانش و نقش داده كاوي توسط الگوريتم شبكه عصبي و ژنتيك در بازيابي اطلاعات از ميان انبوه داده هاي غير مرتبط مورد بحث قرار گيرد و به نقش داده كاوي در بهبود و بهينه سازي اطلاعات بپردازيم.

Nowadays, with the development of paigahi systems and high volume of data stored in these systems, the need for a tool so as to process stored data and information derived from this process to the users. Using a variety of toolsin a simple and conventional reporting, information can be used to put the users so that they can to conclusion about the data and the relationships between them, but it is reasonable to pay when a high volume of data, users experienced rough hands though and can`t be useful patterns among the massof data and diagnosis or if unable to work together, the cost of these operations from the viewpoint of energy The high human and material. Data miningby neural network and genetic algorithms is one of the most important of these methods is by it`s useful data patterns with minimum involvement of users are identified and information to users and analysts are supposed to according to the decisions they adopt are important and vital organizations. In this article have been trying different dimensions of knowledge management and the role of data mining by neural networks and genetic algorithm in the recovery of information from among the mass of non-related data and to discuss the role of data mining in the improvement and optimization of information.Nowadays, with the development of paigahi systems and high volume of data stored in these systems, the need for a tool so as to process stored data and information derived from this process to the users. Using a variety of toolsin a simple and conventional reporting, information can be used to put the users so that they can to conclusion about the data and the relationships between them, but it is reasonable to pay when a high volume of data, users experienced rough hands though and can`t be useful patterns among the massof data and diagnosis or if unable to work together, the cost of these operations from the viewpoint of energy The high human and material. Data miningby neural network and genetic algorithms is one of the most important of these methods is by it`s useful data patterns with minimum involvement of users are identified and information to users and analysts are supposed to according to the decisions they adopt are important and vital organizations. In this article have been trying different dimensions of knowledge management and the role of data mining by neural networks and genetic algorithm in the recovery of information from among the mass of non-related data and to discuss the role of data mining in the improvement and optimization of information.