The Integration of Agent Technology and Data Warehouse into Executive Banking System (EBIS) Architecture Ismail Faculty of Technology and Communication (FTMK) Technical University of Malaysia Melaka (UTeM),75450 Melaka, Malaysia Milka081283@yahoo.com Nanna Suryana Faculty of Technology and Communication (FTMK) Technical University of Malaysia Melaka (UTeM),75450 Melaka, Malaysia nsuryana@utem.edu.my ABSTRACT It has been identified that existing Executive System faces difficulties to integrate different internal and external data sources. This paper discusses the conceptual development of the proposed system architecture through the integration of data warehouse and agents technology into existing architecture. It is intended to reduce the shortcoming. This paper shows the potential application of agent technology in combination with data ware house processing into finding and filtering information from different external and internal data sources, and at the same time will enable executive to make near real time decision making and make recommendation regarding a particular course of action. Keywords 1) system architecture, 2) external and internal data sources, 3) data warehouse 4) agent technology. 1. INTRODUCTION AND PROBLEM DEFINITION This particular research is related to the development of Executive Systems (EIS). This part is an interim conceptual development report from the on going master research project. In our research EIS has been defined as a computerized system that provides executives with easy access to internal and external information which supports them with the analytical, communicative, and planning needs of executive users that relevant to their business and provide critical success factors via customized presentation formats without the need of any intermediaries. The whole framework of the research adopts Rapid Appraisal Development (RAD) Method with the intention to provide insight into the identification of generic EIS architecture, the characterization, requirements and the integration of agent technology and data warehouse processing into the proposed EIS. Some potentials applications of Executive System (EIS) have been identified as a tool for decision making processes such as to evaluate organizations ability in term of business aspects i.e. how a particular bank is able to maintain their customer s loyalty. To do this an easy of access into external data is an essential to update various sources of internal and external data such as data, information on competitor, product development and government regulations. However, it has been identified that existing EIS has several shortcomings. Existing EIS has the inflexibility in data extraction and the systems are lacks of the functions for continuous scan business environment, filtering irrelevant data and information, and proactively provide signals of potential opportunities or threats [6]. The existing and closed loop EIS architecture also is associated with a high risk for being easily misused by the most powerful people in an organization and it may lead strongly into detrimental effects on the organization. This is simply because executives take information from the system, but do not contribute knowledge to the system. This is being unfortunate, because the executive often have important knowledge and information. In other words, the existing EIS architecture do nothing to facilitate information sharing [3]. In response to this situation as described earlier, this paper is intended to enhance current EIS architecture by incorporating such as intelligent agent and data warehouse processing technology. Hypothetically it will enhance the existing one. 2. BUILDING AND DEVELOPING LOGICAL FRAMEWORK OF EIS ARCHITECTURE To solve the problem as stated in Section 1 of this report as well as referring to the adopted rapid appraisal development (RAD) method all relevant literatures has been studied, reviewed and analyzed. According to the results of an extensive literatures review, the characteristic and features that will have been adopted in this EIS research are (1) Drill down analysis capabilities to underlying detail, (2) Ease to use for executive user, (3) Accessing and integrating a broad range of internal and external data to provide internal and external information of relevance executive, (4) exception reporting, (5) trend analysis capabilities (examination of data across desired time interval), (6) invariably making use of a graphical user interface, and (7) has additional communication system that can share decision-making information from executive to every division such as electronic mail facilities [1],[7]. The 491
adopted RAD methods includes several steps firstly to define the information requirements. The information requirements on banking related business will be collected using survey and interview from various resource persons and will be analyzed using statistical analysis. Secondly to determine data sources and data structure which is highly required and critical step. Thirdly is the integration of different databases. It is considered to be one of important research elements in this study that enable to extend the function of EIS. Further RAD stages is discussed further in Section 2.1 Section 2.4 as belows. 2.1 Requirements As sated earlier, one of the important parts for RAD method is to identify the executive information requirements which determines the successful of EIS. The required information yielded by EIS should give advantages for executive in decision making. To analyse information requirements, a proper approach will have been used in this study is that a critical success factor (CSF) approach. Using CSF approach, data will be collected through, interview, survey and questioner to executive and staff who relate with EIS (IT staff, executive, management staff, etc.). As stated by [9], CSF approach represents an accepted top-down methodology for corporate strategic planning, and while it identifies few success factors, it can highlight the key information requirements of top management. CSF is useful approach for management information requirements because it focuses on areas where things must go right. The focus on CSF is considered able to reduces reliance on a pile of irrelevant data and reduces the area of study only those points that manager of the organization classify as critical success factor. According to [9] has determined one of CSF in the banking industry, which can reflect business goals for the commercial bank manager. Our research identification has been focused on ability of bank marketing as a targeted domain application. The factor of ability of bank marketing holds several items that refer to issues related to business and marketing in bank. They are: 1. Long-term relationships with customers; 2. Deposit acquisition; 3. Realizing the activities of other banks; and 4. Providing sufficient staff incentives. 2.2 Data Sources Data sources consist of two data sources. They are external and internal data sources. External data sources will be obtained from web services or internet that related with information requirements. Internal data sources will be obtained from operational is earned from external environment (internet) in banking such as competitor web site, government regulation in banking, etc. For this study, internal database from operational bank database on ability of bank marketing will be collected and analysed. An external data source is from internet. For example, this paper took the data from www.fdic.gov. This site consists annual reports and offer data in such publication namely; account and deposits in all mutual saving bank, account and deposits of commercial bank, bank operating statistic, etc. 2.3 Data Structure The most external data source from internet consists of HTML documents. HTML document is considered very difficult to be loaded into tabular database. Thus, HTML document has to be changed into other documents. In this case, the HTML will be converted into a unique format XML document by an agent. The XML document is loaded into XML database by using loaded database procedure. The loaded database procedure use PL/SQL language. It will involve several PL/SQL function, namely; creation table of table type XML, delete, insert and exception functions. The exception function is used to consider logging the error and then re-raise the data. Then, the XML database is converted into tabular database. The detail discussion in this technique is given in separate paper. 2.4 Integration database There are many kind of data structures or formats from external data sources. Thus, integration database is needed to make one interoperable database structure. In this case, it has been explained earlier that external data sources are obtained from internet. The most data format from internet is HTML format. Then the HTML is proposed to be converted into XML document by using agent technology. The XML document is input into XML table using load XML engine. Finally, the XML table is converted into tabular table and joined with internal database which is already tabular format. The external and internal database which has been extracted will be integrated intentionally using M-OLAP engine. In the M-OLAP engine, both database (external and internal) and dimension is joined into cube database. Then, the cube database is deployed into application server. The deployment of cube database will provide the information that will be used by executive to make decision. In response to the expected features, the results of information requirements analysis, data sources, database structure as describe earlier, we comes to the temporarily conclusion that the better logical framework of the proposed executive information system that will be implemented in this research is given in Figure 1. It can be seen and considered that the generic EIS is a modification from previous frameworks [5]. Executive database have to relate with operational database. yielded by EIS includes operational activity information at the company including external and internal information. 492
Executive Database Other workstation Companies Database Electronic post box Software colection Agent Technology Personal Computer (Executive) DATABASE TRANSFORMATION Agent Technology Personal Computer (operational ) External Database Other workstation requirement appereance News, external information Web (Intranet) In addition to matter as mentioned above and based on the result of literature review, it becomes apparent that the key characteristic of the agent must be as follows: (1) responsive able to perceive their environment and respond in a timely fashion to changes that occur in it; (2) proactive able to exhibit opportunistic, goal directed behavior and take the initiative where appropriate; (3) social able to interact, when they deem appropriate, with other artificial agent and humans in order to complete their own problem solving. This will be hypothetically appropriate for the purpose of the research to filter out and avoid irrelevant information. The implementation and testing will be treated separately. 2.5 Agent Technology Architecture Based on our early design as presented in Figure 1, the architecture of agent will have been constructed using the combination of wrapper and translation technology. In this regards, the function of wrapper technology is to extract the relevant information from HTML document. The translation technology will be used to translate HTML document into to other document (XML, CSV (text), and spreadsheet). The agent is called Websundew. Figure 1 Proposed Framework for Logical EIS Figure 1 also explains about the possibility data distribution (external and internal data), which yields information to be used by executive in decision-making by using intelligent agent as further discussed in Section 3. Executive database save information that has been extracted through data transformation that consist data warehouse processing, which come from center of company s computer. See further Section 4. Figure 1 also shows that the information can be shared by executive to middle level manager by using certain features using web based services or intranet; lotus notes, e-mail in the EIS. Web based services or intranet software is designed to facilitate communication and data sharing between executives in the company [3]. Although we have to come still to the stages of implementation and various testing and maintenance, this is really promising and hopefully will reduce the shortcoming of the existing EIS. 3. INTELLIGENT AGENT TECHNOLOGY 3.1. Agent Technology Defined and Characteristics According to [10],, agent technology in this research has been defined as software entity that carries out some set of operations on behalf of a user or another program with some degree of independence or autonomy, and in doing so, employ some knowledge or representation of the user s goals or desires. Agents are also often modeled using abstract concept like knowledge, while object on the other side simply encapsulate their inner structure with methods and attribute intelligent agents are useful in automating repetitive tasks, finding and filtering information. Figure 2: Proposed Agent Technology Architecture WebSundew is an alternative solution which allows users to handle web content without using scripts [11]. It was developed for those who wish to use scripts' functionality for web data extraction and not bother for code writing. It provides users with capabilities to extract unstructured HTML data from any web source and save it to a CSV, XML or spreadsheet format. WebSundew allows integrating different web-enabled applications into a single point of reference without modifying them. The users can be easily linked through existing web HTMLbased applications. 4. DATA WAREHOUSE 4.1 Data Warehouse Defined and Characteristics Data warehouse (DW) is an important research and development area in information technology (IT). Data warehouse is used to give decision makers a uniform data access to the large enterprisewide operational data sources [4]. DW is designed to optimize the extraction rather than the input data. In DW, data must be identified, cataloged, and store using structure and organization 493
that ensure users will be able to find the correct information when they need it. According to [12], [13], the definition of data warehouse has been described in by various authors, as set out below: A data warehouse is a collection of integration database designed to support managerial decisionsolving function A data warehouse is a repository of integrated information available for querying and analysis 5. THE INTEGRATED EIS ARCHITECTURE This section discusses the incorporation of agent technology (Section 3) and Data warehousing processing (Section 4) into a proposed proto type and expected EIS architecture as presented in Figure 4. In this research, we adopt the basic idea behind the data warehousing approach is solely addressed to extract, filter, and integrate relevant information in advanced queries. Thus, warehousing can be considered as active approach to information integration Data in the DWH is integrated from various, heterogeneous operational systems (like database systems, flat files, etc.) and further external data sources (like demographic and statistical databases, WWW, etc.). Thus the main characteristic of data warehouse is integration. The next characteristic of data warehouse is historical data. Historical data are necessary for business trend analysis which can be expressed in terms of understanding the differences between several views of the real-time data (e.g. profitability at the end of each month). 4.2 Architecture of data warehouse The data warehouse is to facilitate business analysis and process of decision making. Essentially data warehousing is the warehousing data outside operational system and this has not significantly changed with evolution of data warehousing system [15]. The important feature is the combination of data from more than one operational system to provide the ability of cross referencing. Figure 3: Proposed Data Warehouse Architecture As shown in Figure 3 the centre of a data warehouse system is data warehouse itself. The data import and preparation component is responsible for acquisition. It includes all programs, application, and legacy system interfaces that are responsible for extracting data from internal (operational) and external sources preparing it into the warehouse. Figure 4 The Proposed Final Architecture of EIS These feasibility studies extend the existing and generic EIS with the integration of agent technology and data warehouse into EIS. This architecture is divided into three layers. As also stated earlier, first layer is extraction layer from which the different data sources are extracted. In order to obtained data sources from various sources (external and internal), the external data sources is extracted and converted from HTML into XML format by an agent and continued by converting tabular database into data warehouse. In addition to this, the internal data sources is extracted directly into data warehouse. The second layer is M-OLAP layer. In the M-OLAP layer, data dimension, data cube and mapping data are designed by using M- OLAP engine in order to integrate the various data sources and provide information that support executive in decision making. All data that have been designed by M-OLAP engine will be deployed into application server. The presentation layer provides the EIS application server. The data that has been designed in M- OLAP engine is deployed into application server. The application server is able to present the EIS through internet. It is easier to share information to different staff at different management levels. 494
6. CONCLUDING REMARKS The integration of agent technology and data warehouse into EIS has been proposed in this study. It is till need to come through an intensive and extensive implementation and testing stages. The ability of agent technology is hopefully able to filter and extract data sources and information from internet. Most internet sources use HTML structure. The ability of data warehouse system is to integrate data sources from external and internal, and support decision making to executive. Data warehouse technology comprises a set of new tool which support the knowledge worker (executive) with information material for decision making. EIS application in ability of bank marketing will be developed from application server that will be deployed from data warehouse system (M-OLAP engine). 7. REFERENCES [1] Benford, T.J. Motivating the Organizations Executive System. Proceeding of the IEEE 1991, Aerospace and Electronics Conference, 1991. NAECON 1991. [2] Carlsson, S.A and Widmeyer, G.R., Towards a Theory of Executive System. Proceeding of the Twenty- Third Hawaii International Conference on System Services (IEEE) 1990. [3] King, D. Intelligent Executive System. University of Southern California, 1996. [4] Kurz, A., and Tjoa, A.M. Data Warehousing within Internet: Prototype of a Web-based Executive System. Proceeding of the Eight International Workshops on Database and Expert System Applications (IEEE), 1997. [5] Millet, I and Mawhinney, C.H., EIS versus MIS: A Choice Perspective. Proceeding of the Twenty-Third Hawaii International Conference on System Services (IEEE) 1990. [6] Ong, V Duan, Y., Xu, M., and Mathews, B., Revitalizing Executive System Design and Development. (2005). [7] Pervan, G.P. and Meneely, J., Implementing and Sustaining Executive System: Influencing Factors in Mining Industry Context. Proceeding of the 28 th Annual Hawaii International Conference on System Sciences (IEEE), (1995) [8] Westland, J.C. and Walls, J.G., Communication Bandwidth and the Design of Executive Systems. Proceeding of the Twenty-Five Hawaii International Conference on System Services (IEEE) 1992. [9] Chen, T, Critical Success Factor for Various Strategies in the Banking Industry. International Journal of Bank Marketing, 1999. [10] Ong, V Duan, Y., Xu, M., and Mathews, B., Executive Processing With Intelligent Solution: Insight From Focus Group Research. [11] Websundew product website http://www.sundewsoft.com/ (viewed 10/04/2007). [12] Katic, N., Quirchmayr, G., Scheifer, J., Stolba, M., Tjoa, A.M. A Prototype Model for Data Warehouse Security Based on Metadata. Proceedings of the 9th International Workshop on Database and Expert Systems Applications, 1998. [13] Stevenson, D. Data Warehouse and Executive System Ignoring the Hype. Congress European Co-operation in. Higher Education Systems (EUNIS97), Grenoble, France, 1997. [14] Inmon, W.H (2005) Building The Data Warehouse. Wiley Publishing, inc. [15] Shahjad M.A., Data Warehousing with Oracle. Oracilar, 1999. 495