Overview of Business Intelligence through Data Mining

Business Intelligence (BI) systems are designed to provide information to support the decision making process in companies and governmental institutions. In this scenario, future events depend on the decisions and on the previous events. Therefore, the mathematical analysis of past data can be an important tool for the decision making process and to detect anomalies. Depending on the amount and the type of data to be analyzed, techniques from statistics, Machine Learning (ML), data mining and signal processing can be used to automate all or part of the system. In this paper, we propose to incorporate Gaussian Process for Regression (GPR) in BI systems in order to predict the data. As presented in this work, fraud detection is one important application of BI systems. We show that such application is possible with the use of GPR in the predictive stage, considering that GPR natively returns a full statistical description of the estimated variable, which can be used as a trigger measure to classify trusted and untrusted data. We validate our proposal with real world BI data provided by the Brazilian Federal Patrimony Department (SPU), regarding the monthly collection of federal taxes. In order to take into account the multidimensional structure of this specific data, we propose a pre-processing stage for reshaping the original time series into a bidimensional structure. The resulting algorithm, with GPR at its core, outperforms classical predictive schemes such as Artificial Neural Network (ANN).

Over the last decade data warehousing and data mining tools have evolved from research into a unique and popular applications, ranging from data warehousing and data mining for decision support to business intelligence and other kind of applications. The chapter presents and discusses data warehousing methodologies along with the main components of data mining tools and technologies and how they all could be integrated together for knowledge management in a broader sense. Knowledge management refers to the set of processes developed in an organization to create, extract, transfer, store and apply knowledge. The chapter also focuses on how data mining tools and technologies could be used in extracting knowledge from large databases or data warehouses. Knowledge management increases the ability of an organization to learn from its environment and to incorporate knowledge into the business processes by adapting to new tools and technologies. Knowledge management is also about the reusability of the knowledge that is being extracted and stored in the knowledge base. One way to improve the reusability is to use this knowledge base as front-ends to case-based reasoning (CBR) applications. The chapter further focuses on the reusability issues of knowledge management and presents an integrated framework for knowledge management by combining data mining (DM) tools and technologies with CBR methodologies. The purpose of the integrated framework is to discover, validate, retain, reuse and share knowledge in an organization with its internal users as well as its external users. The framework is independent of application domain and would be suitable for uses in areas, such as data mining and knowledge management in e-government.

As a tool and system, business intelligence plays a vital role in the strategic planning process of a business company. Business intelligence provides companies with functions such as collecting, storing, accessing, and analyzing company data. In the traditional business model, most companies use human resources to track these large amounts of data and information. And business intelligence provides an automated data processing method that significantly improves efficiency. During a customer relationship management service system, a large amount of data will be generated and stored. With this natural data advantage, data analysis has more possibilities in customer relationship management service systems. Based on the business intelligence system, this paper analyzes the data of the customer system. It puts forward the K-means clustering method to analyze the customer quality, the fuzzy set clustering algorithm to examine whether the customer is active. The rough set algorithm to judge to improve the user activity decision analysis. The proposed intelligent business system brings new technical solutions to the maintenance of current corporate customer relationships.

The World Wide Web (WWW) has grown quickly in the past two decades from a small research community to the biggest and most popular infrastructure for communication, information dissemination, search, social interaction and commerce. The continuous growth in size and use of the WWW creates a need for methods to process these wicked volumes of data. Web Intelligence (WI), as a research direction, has a broad agenda to deal with the issues that arise around the WWW phenomenon. WI corresponds to research and development to explore the fundamental roles, artificial intelligence and advanced information technology on the web-empowered systems, services, and activities. In this context, WI is concerning of mining in web data and user behaviors. This creates a demand for using mining technologies to search large volume of data for gaining hidden knowledge. This hidden knowledge helps in gaining competitive advantages, better customers’ relationships, and even fraud detection. Achieving the intelligence to the web enhances Business Intelligence (BI) for the enterprises. Web Usage Mining (WUM) and Web Opinion Mining (WOM) are considered a leading mining approaches in mining the user behavior and reviews. Most of previous studies depended in building intelligent web on mining and analyzing user’s profiles or opinions separately. This becomes not fair enough and cause of limitations. This limitations could be narrowed if both of user preferences and opinions are considered in building recommendations. The paper proposes a framework for achieving the intelligence via using WUM and WOM. The proposed framework would contribute in solving the problem. The paper also surveys the background of using the intelligent web as an approach to enhance the Business Intelligence (BI) applications

Data Mining (DM) is being applied with success in Business Intelligence (BI) environments, and several examples of applications can be found. BI and DM have different roots and, as a consequence, have significantly different characteristics. DM came up from scientific environments; thus, it is not business oriented. DM tools still demand heavy work in order to obtain the intended results. On the contrary, BI is rooted in industry and business. As a result, BI tools are user-friendly. This chapter reflects on this difference from a historical perspective. Starting with a separated historical perspective of each one, BI and DM, the author then discusses how they converged into the current situation, when DM is used, and integrated, in BI environments with success.

IT-based decision support is in the heart of business intelligence. It should be based on a successful integration of data analysis techniques and certain system engineering (like system dynamics) concepts. This contribution introduces in the large realm of IT-based decision support and its meaning for a modern business strategy. Central is the relationship to Business Intelligence with its own characteristics and requirements. The relevant data mining techniques are summarized and characterized by its special role within traditional business intelligence approaches.

Business Intelligence (BI) is one emergent area of the Decision Support Systems (DSS) discipline. BI is oriented to business activities. Over the past years, the evolution in this area has been considerable. Similarly, in the past years, there has been a huge growth and consolidation of the Data Mining (DM) field. DM is being used with success in BI systems, but DM models are not directly accessible to business users, thus its power is not completely explored. This paper introduces a new DM language, named as QMBE that is iterative and interactive in nature and is also oriented to business activities. This paper suggests that the effective usage of DM in BI can be achieved by making DM models accessible to business users, through the use of the presented DM language.

The escalating threat of fraud in financial institutions is a global issue, with the Malaysian sector being no exception. This study focuses on the implementation and efficacy of Data Mining and Machine Learning methodologies in identifying and mitigating fraudulent activities within these institutions. The paper critically reviews existing literature, bridging the gap between advanced technology application and fraud management. Fraudulent transactions in the financial sector are dynamic and sophisticated, requiring advanced detection techniques. Traditional approaches often struggle to manage this complexity effectively, demonstrating a need for more advanced and adaptive strategies. This is where Data Mining and Machine Learning techniques, renowned for their predictive and analytical prowess, can significantly contribute. Data Mining, the process of uncovering patterns and correlations within large datasets, is a useful tool for detecting anomalies that may suggest fraud. The study assesses various data mining techniques, such as clustering, classification, and association, and explores their application in detecting fraudulent transactions. Findings indicate that these techniques can substantially enhance fraud detection rates while minimizing false positives. Furthermore, Machine Learning, an artificial intelligence subset, has shown immense potential in fraud detection. Its ability to learn from and make decisions based on data makes it a viable solution for fraud detection. This paper explores both supervised and unsupervised learning algorithms and their efficacy in identifying fraud in the Malaysian financial sector. Results suggest that machine learning models, when correctly implemented, can significantly improve the accuracy of fraud detection. The review underscores the importance of employing advanced technologies like Data Mining and Machine Learning to combat financial fraud effectively. It also suggests future research directions, emphasizing the need for context-specific, localized models considering Malaysia's unique socio-economic environment. Moreover, the development of hybrid models, integrating both data mining and machine learning, could offer improved results. In conclusion, this study sets a precedent for further exploration into the application of advanced analytical tools in fraud detection in the Malaysian financial sector. The potential these technologies offer for improving accuracy and adaptability in fraud detection systems is substantial and warrants thorough investigation. Keywords: Fraud Detection, Malaysian Financial Institutions, Data Mining, Machine Learning, Financial Fraud Management

As a new computational paradigm, cloud computing is attracting a lot of interest from researchers in the field of the business community and information technology sector and it can integrate with several heterogeneous resources makes it distinguished and unique to fulfill the demands of different types of users. The rapid increase in data volume and fixed access to online resources, which is related to all departments need to mine data for the discovery of knowledge. Its principal peculiarities incorporate a versatile asset design and along this line a suitable system for tending to be comprehended in an ideal mode. From the specific situations where cloud computing can be integrated, its use in business information and intelligence also conveys the highest aspirations from data mining to updates. This study gives an outline of the recent condition of the arrangement of Cloud Computing and elaborates, its implications in Business Intelligence and Data Mining. This study defines multiple layers that are expected to create such a framework in distinctive levels of deliberation, from the fundamental equipment stages to the product assets accessible to actualize the applications. At the end of this study, a few cases related to Data mining methodologies have been relocated to the Cloud Computing paradigm.

Foundations of SQL Server 2008 R2 Business Intelligence introduces the exciting gamut of business intelligence tools included with SQL Server 2008. Microsoft has designed SQL Server 2008 to be more than just a database. Its a complete business intelligence (BI) platform. The database is at the core, and surrounding the database are tools for data mining, modeling, reporting, analyzing, charting, and integration with other, enterprise-level software packages. SQL Server 2008 puts an incredible amount of BI functionality at your disposal. But how do you take advantage of it? Thats what this book is all about. Authors Guy Fouch and Lynn Langit show how to implement end-to-end BI solutions using SQL Server Analysis Services (SSAS), SQL Server Integration Services (SSIS), SQL Server Reporting Services (SSRS), and other tools in the Microsoft BI toolkit. Youll learn about all-new features such as PowerPivot and Report Builder 3.0. Also provided are clear examples of predictive analysis made possible through powerful data mining features in SQL Server. If youre an analyst or developer working with SQL Server 2008 who is charged with delivering results that drive business success, you cant afford to be without this book; you cant afford to ignore the powerful BI suite that Microsoft has placed at your disposal. Provides the big picture of Microsofts BI tool suite Covers PowerPivot and other game-changing technologies introduced alongside SQL Server 2008 Release 2 Gives a practical analysis of features based on real-world practices What youll learn Apply dimensional- and fact-based modeling to create OLAP schemas Optimize data extraction and transformation with SQL Server Integration Services Develop OLAP and data-mining solutions using SQL Server Analysis Services Create compelling reports using the new Report Builder 3.0 in SQL Server Reporting Services Deliver self-service business-intelligence via Microsoft PowerPivot Manage and maintain control over your BI environment Who this book is for Foundations of SQL Server 2008 R2 Business Intelligence is for business intelligence analysts and developers wanting to employ the full stack of business intelligence tools that are part of SQL Server. The book also appeals to database administrators managing databases in support of business intelligence efforts. Table of Contents What Is Business Intelligence? OLAP Modeling Concepts Introducing OLAP Modeling with SSAS Intermediate OLAP Modeling with SSAS Advanced OLAP Modeling with SSAS Cube Storage and Aggregation Introducing SSIS Intermediate SSIS Advanced SSIS Reporting Tools Data Mining with Excel Introducing PowerPivot Introduction to MDX Introduction to Data Mining

Enterprises have successfully deployed business intelligence (data warehousing, reporting, data mining, analytics, and information visualization) technologies to enable strategic decision making. Increasingly, there is a realization that the same sort of high quality information and analytics tools are needed by the many knowledge workers who have to make many operational decisions as they engage in the business operations of the enterprise. Most of these business processes are performed by multiple knowledge workers, and require collaborative decision making, sometimes involving business partners such as outsourced vendors, suppliers, and service providers outside the enterprise. In this paper, we introduce the concept of collaborative business intelligence , which is a combination of business intelligence and collaboration technologies targeted at collaborative decision making. We describe the requirements for collaborative BI, and illustrate them via a real-world use case of managing the operations of a large data center. We describe a prototype collaborative BI platform we are developing at HP. We leverage research that we had done earlier on the Business Cockpit, an intelligent business operations management platform, which combined data integration, process modeling, a process data warehouse, and analytics technologies to enable the monitoring and analysis of business processes. For collaborative BI, we add capabilities for collaboration via 3-dimensonal virtual rooms, visual analytics, and multi-modal interaction technologies. We also need richer metadata models: in addition to modeling data, events, and processes, we also have to model the knowledge of human experts. We describe the experience we have gained from this effort, and our ongoing research.

This paper expounds the fundamental theory of data mining and customer segmentation, and studies on the specific implementation of data mining processes and technology. We put forward the customer segmentation application model based on data mining technology, which is applied in the retailing industry to carry out a empirical study.

As a confluence of data mining and World Wide Web technologies, it is now possible to perform data mining on Web log records collected from the Internet Web-page access history. The behaviour of Web page readers is imprinted in the Web server log files. Analyzing and exploring regularities in this behaviour can improve the system performance, enhance the quality and delivery of Internet information services to the end user, and identify populations of potential customers for electronic commerce. Thus, by observing people using collections of data, data mining can bring a considerable contribution to digital library designers. In a joint effort between the TeleLearning-NCE (Networks of Centres of Excellence) project on the Virtual University and the NCE-IRIS project on data mining, we have been developing a knowledge discovery tool, called WebLogMiner, for mining Web server log files. This paper presents the design of WebLogMiner, reports current progress and outlines future work in this direction.

The thesis starts from the basic theory, introduces the method of physical data acquisition and the concept of data mining. Marketing evolves from traditional marketing schemes to data mining, which leads to the relationship between data mining technology and modern market management. It focuses on the method of obtaining user information and data mining technology in "cross-marketing", "integrated marketing", and "integrated marketing". Applications in "real-time marketing", "green marketing", "relationship marketing", and "network marketing". At the same time, combined with case analysis, the comprehensive application of data mining in "shopping basket analysis" is discussed and elucidated from the theoretical and technical level.

In this world, each and every activity is enriched with lot of information. Business and other organization needs information for better decision making. Business Intelligence is a set of methods, process and technologies that transform raw data into meaningful and useful information. Some of the functions of business intelligence technologies are reporting, Online Analytical Processing, Online Transaction processing, data mining, process mining, complex event processing, business performance management, benchmarking and text mining. The applications of business intelligence includes E-commerce recommender system, approval of bank loan, credit/debit card fraud detection etc., In order to obtain business intelligence from large dataset there many techniques are available in data mining such as characterization, discrimination, frequent itemset mining, outlier analysis, cluster analysis and so on. In this proposed algorithm frequent itemset mining and clustering algorithm is used to extract the information from the dataset in order to make the decision making process more efficient and to improve the business intelligence.