Publisher's Synopsis
The interdisciplinary field of Data Mining (DM) arises from the confluence of statistics and machine learning (artificial intelligence). It provides a technology that helps to analyze and understand the information contained in a database, and it has been used in a large number of fields or applications. Specifically, the concept DM derives from the similarity between the search for valuable information in databases and mining valuable minerals in a mountain. The idea is that the raw material is the data to analyse, and we use a set of learning algorithms acting as diggers to search for valuable nuggets of information. In order to make a decision, the managers need knowledge. In case of massive data amounts, issues may occur because of data analysis and necessary knowledge extract. Data is analyzed through an automated process, known as Knowledge Discovery in data mining techniques. Data mining can be defined as a process of exploring and analysis for large amounts of data with a specific target on discovering significantly important patterns and rules. Data mining helps finding knowledge from raw, unprocessed data. Using data mining techniques allows extracting knowledge from the data mart, data warehouse and, in particular cases, even from operational databases. In this context, data mining gets an important role in helping organizations to understand their customers and their behavior, keeping clients, stocks anticipation, sale policies optimization as well as other benefits which bring a considerable competitive advantage to the organization. The main purpose of these techniques is to find patterns and hidden (but relevant) relations that might lead to revenue increase. The essential difference between data mining techniques and the conventional database operation techniques is that, for the second ones, the database becomes passive and is only being used for large amounts of data population, therefore helping in future finding of that specific data. Alternatively, the database is not passive anymore, being able to serve useful information regarding the business plans put in discussion. This book presents and disseminates new concepts and developments in the areas of data warehousing and data mining, in particular on the research trends shaped during the last few year. Developing Decision Support Systems involves time, high-costs and human resources efforts and the success of the system can be affected by many risks like: system design, data quality, and technology obsolescence. The decision support systems objective is to assist the managers and executives to make decision regarding the benefit of investment, budgeting cash flows and financial planning, especially in the case of public funds. Presently, many institutions invest in building organizational data warehouses and data marts in order to increase the performance and the efficiency of the analytical reporting activity. Also, there are several expensive tools and software that can be used to analyze the trends and to predict some future characteristics and evolution of the business. Some of these tools analyze data from the statistic perspective or by using neural networks. In our opinion, in order to build an efficient decision support system there must be combined several techniques and methods that can improve the performance and the accuracy of the analysis from two major perspectives: historical data and forecasts. The interdisciplinary field of Data Mining (DM) arises from the confluence of statistics and machine learning (artificial intelligence). It provides a technology that helps to analyze and understand the information contained in a database, and it has been used in a large number of fields or applications. Specifically, the concept DM derives from the similarity between the search for valuable information in databases and mining valuable minerals in a mountain. The idea is that the raw material is the data to analyse, and we use a set of learning algorithms acting as diggers to search for valuable nuggets of information. In order to make a decision, the managers need knowledge. In case of massive data amounts, issues may occur because of data analysis and necessary knowledge extract. Data is analyzed through an automated process, known as Knowledge Discovery in data mining techniques. Data mining can be defined as a process of exploring and analysis for large amounts of data with a specific target on discovering significantly important patterns and rules. Data mining helps finding knowledge from raw, unprocessed data. Using data mining techniques allows extracting knowledge from the data mart, data warehouse and, in particular cases, even from operational databases. In this context, data mining gets an important role in helping organizations to understand their customers and their behavior, keeping clients, stocks anticipation, sale policies optimization as well as other benefits which bring a considerable competitive advantage to the organization. The main purpose of these techniques is to find patterns and hidden (but relevant) relations that might lead to revenue increase. The essential difference between data mining techniques and the conventional database operation techniques is that, for the second ones, the database becomes passive and is only being used for large amounts of data population, therefore helping in future finding of that specific data. Alternatively, the database is not passive anymore, being able to serve useful information regarding the business plans put in discussion. This book presents and disseminates new concepts and developments in the areas of data warehousing and data mining, in particular on the research trends shaped during the last few year. Developing Decision Support Systems involves time, high-costs and human resources efforts and the success of the system can be affected by many risks like: system design, data quality, and technology obsolescence. The decision support systems objective is to assist the managers and executives to make decision regarding the benefit of investment, budgeting cash flows and financial planning, especially in the case of public funds. Presently, many institutions invest in building organizational data warehouses and data marts in order to increase the performance and the efficiency of the analytical reporting activity. Also, there are several expensive tools and software that can be used to analyze the trends and to predict some future characteristics and evolution of the business. Some of these tools analyze data from the statistic perspective or by using neural networks. In our opinion, in order to build an efficient decision support system there must be combined several techniques and methods that can improve the performance and the accuracy of the analysis from two major perspectives: historical data and forecasts. The interdisciplinary field of Data Mining (DM) arises from the confluence of statistics and machine learning (artificial intelligence). It provides a technology that helps to analyze and understand the information contained in a database, and it has been used in a large number of fields or applications. Specifically, the concept DM derives from the similarity between the search for valuable information in databases and mining valuable minerals in a mountain. The idea is that the raw material is the data to analyse, and we use a set of learning algorithms acting as diggers to search for valuable nuggets of information. In order to make a decision, the managers need knowledge. In case of massive data amounts, issues may occur because of data analysis and necessary knowledge extract. Data is analyzed through an automated process, known as Knowledge Discovery in data mining techniques. Data mining can be defined as a process of exploring and analysis for large amounts of data with a specific target on discovering significantly important patterns and rules. Data mining helps finding knowledge from raw, unprocessed data. Using data mining techniques allows extracting knowledge from the data mart, data warehouse and, in particular cases, even from operational databases. In this context, data mining gets an important role in helping organizations to understand their customers and their behavior, keeping clients, stocks anticipation, sale policies optimization as well as other benefits which bring a considerable competitive advantage to the organization. The main purpose of these techniques is to find patterns and hidden (but relevant) relations that might lead to revenue increase. The essential difference between data mining techniques and the conventional database operation techniques is that, for the second ones, the database becomes passive and is only being used for large amounts of data population, therefore helping in future finding of that specific data. Alternatively, the database is not passive anymore, being able to serve useful information regarding the business plans put in discussion. This book presents and disseminates new concepts and developments in the areas of data warehousing and data mining, in particular on the research trends shaped during the last few year. Developing Decision Support Systems involves time, high-costs and human resources efforts and the success of the system can be affected by many risks like: system design, data quality, and technology obsolescence. The decision support systems objective is to assist the managers and executives to make decision regarding the benefit of investment, budgeting cash flows and financial planning, especially in the case of public funds. Presently, many institutions invest in building organizational data warehouses and data marts in order to increase the performance and the efficiency of the analytical reporting activity. Also, there are several expensive tools and software that can be used to analyze the trends and to predict some future characteristics and evolution of the business. Some o
