知識經濟世代的來臨,企業用於克敵致勝的生產因素不再只是資本、土地或是勞力,而是知識。知識不同於其他傳統企業資產的地方,在於它是無形資產,散落在企業各個角落,獲得與失去都難以察覺與掌握。由於財務金融資料的時間序列特性,所導致的相關管理問題,更致使證券投資分析業無法有效率的進行知識管理與探索。因此本研究針對證券投資分析業的時間序列資料特性提出一「財務資料倉儲上知識探索與管理」的架構,此架構依據資料流順序分為五個層級,外部資源層級、資料轉置層級、資料儲存與管理層級、知識/趨勢/樣版層級以及使用者處理層級,藉由此研究架構可作為後續證券投資分析業建置財務資料倉儲之發展藍圖,以解決因時間序列特性所衍生的資料建立、管理與查詢等問題,並利於知識探索模組之建立。
We propose a knowledge discovery/knowledge management process for equity management institutions. We implement the process on a financial decision support system, that is able to convert data from various sources into data warehouse, to retrieve data cubes based on different power users’ commands using OLAP tool to support data mining modules. We focus on defining the characteristics of data, including data types, meta data, and data operations, in each of the following aspects: the resource layer, the data process layer, the data storage & management layer, the knowledge/trend/pattern layer, and finally, the user process layer. The system is designed to be object-oriented, COM-based, scalable, portable and user friendly. With this system, the users can easily convert their data source into predefined data warehouse, to define various periodical reports, corporate Web pages to serve business clients, as well as managing thousands of files generated by analysts.