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Titlebook: Business Intelligence Techniques; A Perspective from A Murugan Anandarajan,Asokan Anandarajan,Cadambi A. Book 2004 Springer-Verlag Berlin

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發(fā)表于 2025-3-21 18:05:12 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱(chēng)Business Intelligence Techniques
期刊簡(jiǎn)稱(chēng)A Perspective from A
影響因子2023Murugan Anandarajan,Asokan Anandarajan,Cadambi A.
視頻videohttp://file.papertrans.cn/193/192216/192216.mp4
發(fā)行地址Combines a rather established area (accounting) with the fast developing field of business intelligence.Shows how to use information technology to improve traditional accounting methods, giving manage
圖書(shū)封面Titlebook: Business Intelligence Techniques; A Perspective from A Murugan Anandarajan,Asokan Anandarajan,Cadambi A.  Book 2004 Springer-Verlag Berlin
影響因子.Modern businesses generate huge volumes of accounting data on a daily basis. The recent advancements in information technology have given organizations the ability to capture and store these data in an efficient and effective manner. However, there is a widening gap between this data storage and usage of the data. Business intelligence techniques can help an organization obtain and process relevant accounting data quickly and cost efficiently. Such techniques include, query and reporting tools, online analytical processing (OLAP), statistical analysis, text mining, data mining, and visualization. .Business Intelligence?Techniques .is a compilation of chapters written by experts in the various areas. While these chapters stand of their own, taken together they provide a comprehensive overview of how to exploit accounting data in the business environment..
Pindex Book 2004
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Online Analytical Processing in Accounting,ef history of OLAP. Then we define OLAP. We move on to analyze various types of OLAP- the Relational OLAP (ROLAP), the Multi-dimensional OLAP (MOLAP), the Hybrid OLAP (HOLAP) and the Desktop OLAP (DOLAP). We discuss some applications of OLAP tools in various areas of accounting. Finally, through a s
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Bankruptcy Prediction Using Neural Networks,y is threefold. First, we use only financially stressed firms in our control sample. This sampling enables the models to more closely approximate the actual decision processes of auditors and other interested parties. Second, we develop a more parsimonious model using qualitative “bad news” variable
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Visualization of Patterns in Accounting Data with Self-organizing Maps,s have already been applied in many different business areas; and they can be used for prediction, classifying, and clustering. They can learn, remember, and compare complex patterns. This chapter shows how a neural network, especially Kohonen’s self-organizing map (SOM), can be used in visualizatio
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Alignment of AIS with Business Intelligence Requirements,nization’s needs for conducting business intelligence activities. The present research identifies sources of requirements for business intelligence activities that are contingent on the degree of organizational formalization, information interdependence among functional areas, and dependence in inte
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