such incidents, buyer and seller agents within the marketplace should not be allowed to negotiate with each other directly. By introducing an intermediary to control and monitor negotiations, this not only reduces the risk of a security breach amongst agents, it also helps to ensure fair practices and non−repudiation of concluded transactions. This helps to increase the trust that parties will have in the marketplace, and it also reduces the possibility that each agent may access the private information of other agents. This means the private information is only available to the controller of the virtual marketplace and is carefully protected against illegal access. Secure Transport and Agent Integrity Due to the fact that this application is based on a mobile agent concept, the agent and its data will be susceptible to "attack" while it transverses the network, especially if this application is deployed over the Internet. Therefore, a secure transport mechanism is required (Guan & Yang, 1999), for example, encryption of the agent before transportation. Agent integrity can also be achieved using a similar mechanism as discussed by Wang, Guan, and Chan (2001). Trusted Client Applications Not only fellow agents, but also the virtual marketplace itself has to be protected from malignant agents. To ensure that only trusted agents are allowed into the marketplace, only agents manufactured from trusted agent factories (Guan, 2000; Guan & Zhu, 2001; Zhu, Guan, & Yang, 2000) are allowed into the server. In this particular implementation, only agents constructed and verified by the provided client applications are granted access to the marketplace. The disadvantage of doing so is that this does not allow clients to custom build their own agents that might have greater intelligence and negotiation capabilities, but this downside is seen as minimal since most users would not bother to go through the complexities to do so anyway. Implementation Discussions Agent Identification Each agent in the marketplace is assigned a unique agent identification. This is accomplished by appending the agents name with a six−digit random number. The agents name is, in the case of the buyer and seller agents, indicative of its respective owner. For example, if a user with user id alff creates an agent, its agent identification will be alff_123456. By adopting this identification scheme, the virtual marketplace can uniquely identify agents belonging to registered users and sellers. What is more significant, this allows the airlines in the marketplace to identify their clients. This is very useful when an airline wants to customize its marketing strategy to each individual user. Event−Driven Model All agents created in the virtual marketplace are Java EventListeners. To achieve this, all agent classes extend the parent class VMAgent. VMAgent, in turn, implements a custom EventListener interface called VMAgentEventListener. Security, Trust, and Privacy 316 Figure 11: Format of a VMAgentMessage object As an EventListener, an agent is able to continuously monitor for any incoming events that are being triggered by fellow agents. Agents in the marketplace use this method to signal an event that requires the attention of the target agent. This alerts the target agent which then processes the incoming event once it awakes. Agent Communication Together with the event object VMAgentEvent that is passed to the target agent during an event trigger is a VMAgentMessage object. The VMAgentMessage object is modeled in a similar format to a KQML message packet. As with KQML, the VMAgentMessage uses performatives to indicate the intention of the sending agent and the actions that it wants the target agent to take. The set of performatives that agents support at the moment are limited, but these can be expanded further to increase the complexity of possible actions that agents may take or respond to. Figure 11 shows the contents of a sample VMAgentMessage. Buyer−Agent Migration Agent migration in this research work is done through serialization of the agent, together with all its associated objects, using object serialization. The object serialization computes the transitive closure of all objects belonging to the agent and creates a system− independent representation of the agent. This serialized version of the agent is then sent to the virtual marketplace through a socket connection, and the agent is reinstantiated over on the server. As object serialization is used, all objects referenced by the buyer agent implement the Serializable interface. Shopping List When a buyer agent is created, the agent creates a shopping list of all the items the user wishes to purchase. Within the list are individual Deal objects (Figure 6) which specify the details of the particular item in question. For air tickets, the Deal object stores such information as specific flight times, preferred airlines, and the number of tickets to purchase. If items of varying categories are to be specified, then the Deal object will have to explicitly state which ontology is being used. This may be applicable to a marketplace that hosts sellers dealing in many different types of products requiring different specifications. Purchasing Strategy For every Deal object that is created, a corresponding BuyStrategy object is also created and is contained within the Deal. This allows the user to customize a specific strategy for each item that the user wishes to Agent Identification 317 purchase. The BuyStrategy object contains the initial price, the maximum permissible price, and the time−based price increment function for that particular item. Selling Strategy The seller agents negotiation strategy is contained in a Strategy object. This object is used by an airline to customize the selling strategy of its representative seller agents. There is a marked difference in the way the buyer and seller agents use their strategies to determine their current offer prices. Because the buyer agents strategy has knowledge of the initial price, maximum price, and the lifespan of the agent, it is able to calculate the exact offer price at each stage of the negotiation given the elapsed time. The Strategy object of the seller agent is unable to do this because, unlike the buyer agent, it has no foreknowledge of the lifespan of the buyer or the length of the negotiation, and therefore the Strategy object can only advise the seller on an appropriate depreciation function. Conclusion and Future Work In this research work, an agent−based virtual marketplace architecture based on a Business−to−Consumer electronic commerce model has been designed and implemented. Its purpose is to provide a conducive environment for self−interested agents from businesses and clients to interact safely and autonomously with one another for the purposes of negotiating agreements on the behalf of their owners. The three fundamental elements of the marketplace architecture are the Control Center, the Business Center, and the Financial Center. This implementation has been concentrated on development of the Control and Business Centers. Of particular interest are two of the design elements that warrant greater attention. These are the negotiation session mechanism and the dynamic pricing strategy management scheme that was implemented. The importance of the negotiation session mechanism within the marketplace architec− ture as a means to increase the trust and security of the overall system can be seen by its ability to combat fraud and misrepresentation. The nature of the negotiation protocol also allows the buyer to arrive at a more informed decision for the product that he/she is purchasing by allowing for simultaneous, non−binding agreements. The marketplace has also provided the opportunity to catch a glimpse into the potential benefits of implementing a dynamic pricing scheme using a just−in−time, individualized analysis of real−time data to maximize profits with greater precision. At present, the pricing strategy of the buyer agents is still limited and based on some simple time−based functions. Future work should therefore try to address this issue and work on enhancing the buyer agents pricing strategy with greater room for customizability by the owner. Also, other than the priority airline settings, users are only able to evaluate an item based on its price. This price−based paradigm is a disservice to both buyers and sellers because it does not allow other value−added services to be brought into the equation. Further work needs to be done in this area to address this limitation. A possible solution would be to set up a rating system similar to the Better Business Bureau currently in use in the Kasbah system (Chavez et al., 1996). This new system should allow buyers to rate the airlines on factors such as punctuality, flight service, food, etc. Users will then be able to evaluate air tickets based on more than just the price, and can include the above criteria listed within the rating system. Finally, in the current implementation, all sellers (and buyers) are assumed to reside within a single marketplace. This does not fully illustrate the migration capability of buyer/ seller agents. Future work should Agent Identification 318 accommodate this aspect. References Chavez, A., Dreilinger, D., Guttman, R., & Maes, P., (1997). A real−life experiment in creating an agent marketplace. Proceedings of the Second International Conference on the Practical Application of Intelligent Agents and Multi−Agent Technology (PAAM97), London, UK. Chavez, A. & Maes, P., (1996). Kasbah: An agent marketplace for buying and selling goods. Proceedings of the First International Conference on the Practical Application of Intelligent Agents and Multi−Agent Technology (PAAM96), 75−90, London, UK. Collins, J., Youngdahl, B., Jamison, S., Mobasher, B., & Gini, M., (1998). A market architecture for multi−agent contracting. Proceedings of the Second International Conference on Autonomous Agents, 285−292. Corradi, A., Montanari, R., & Stefanelli, C., (1999). Mobile agents integrity in e−commerce applications. Proceedings of 19th IEEE International Conference on Distributed Computing Systems, 59−64. Greenberg, M.S., Byington, J.C., & Harper, D.G., (1998). Mobile agents and security. IEEE Communications Magazine, 36(7), 76−85. Guan, S.U., Ng, C.H., & Liu, F., (2002). Virtual marketplace for agent−based electronic commerce, IMSA2002 Conference, Hawaii. Guan, S.U. & Yang, Y., (1999). SAFE: secure−roaming agent for e−commerce. Proceedings of the 26th International Conference on Computers & Industrial Engineering, Melbourne, Australia, 33−37. Guan, S.U. & Zhu, F.M., (2001). Agent fabrication and its implementation for agent−based electronic commerce. To appear in Journal of Applied Systems Studies. Guan, S.U., Zhu, F.M., & Ko, C.C., (2000). Agent fabrication and authorization in agent−based electronic commerce. Proceedings of International ICSC Symposium on Multi−Agents and Mobile Agents in Virtual Organizations and E−Commerce, Wollongong, Austra− lia, 528−534. Hua, F. & Guan, S.U., (2000). Agent and payment systems in e−commerce. In Internet Commerce and Software Agents: Cases, Technologies and Opportunities, S.M. Rahman, S.M. & R.J. Bignall, (eds), 317−330. Hershey, PA: Idea Group Publishing. Maes, P., Guttman, R.H., & Moukas, A.G., (1999). Agents that buy and sell: transforming commerce as we know it. Communications of the ACM, (3). Marques, P.J., Silva, L.M., & Silva, J.G., (1999). Security mechanisms for using mobile agents in electronic commerce. Proceedings of the 18th IEEE Symposium on Reliable Distributed Systems, 378−383. Morris, J. & Maes, P., (2000). Sardine: An agent−facilitated airline ticket bidding system. Proceedings of the Fourth International Conference on Autonomous Agents, Barcelona, Spain. Morris, J. & Maes, P., (2000). Negotiating beyond the bid price. Proceedings of the Conference on Human References 319 Factors in Computing Systems (CHI 2000), Hague, the Netherlands. Tsvetovatyy, M. & Gini, M., (1996). Toward a virtual marketplace: Architectures and strategies. Proceedings of the First International Conference on the Practical Application of Intelligent Agents and Multi−Agent Technology (PAAM96), 597−613, London, UK. Wang, T.H., Guan, S.U., & Chan, T.K., (2001). Integrity protection for code−on−demand mobile agents in e−commerce. To appear in Special Issue of Journal of Systems and Software. Zhu, F.M., Guan, S.U., & Yang, Y. (2000)., SAFER e−commerce: secure agent fabrication, evolution & roaming for e−commerce. In S.M. Rahman, & R.J. Bignall, (eds.), Internet Commerce and Software Agents: Cases, Technologies and Opportunities, 190−206. Hershey, PA: Idea Group Publishing. References 320 Chapter 21: Integrated E−Marketing A Strategy−Driven Technical Analysis Framework Simpson Poon Irfan Altas and Geoff Fellows Charles Sturt University, New South Wales, Australia Copyright © 2003, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. Abstract E−marketing is considered to be one of the key applications in e−business but so far there has been no sure−fire formula for success. One of the problems is that although we can gather visitor information through behaviours online (e.g., cookies and Weblogs), often there is not an integrated approach to link up strategy formulation with empirical data. In this chapter, we propose a framework that addresses the issue of real−time objective−driven e−marketing. We present approaches that combine real−time data packet analysis integrated with data mining techniques to create a responsive e−marketing campaign. Finally, we discuss some of the potential problems facing e−marketers in the future. Introduction E−marketing in this chapter can be broadly defined as carrying out marketing activities using the Web and Internet−based technologies. Since the inception of e−commerce, e− marketing (together with e−advertising) has contributed to the majority of discussions, and was believed to hold huge potential for the new economy. After billions of dollars were spent to support and promote products online, the results were less than encouraging. Although methods and tricks such as using bright colours, posing questions, call to action, etc., (DoubleClick, 2001) had been devised to attract customers and induce decisions, the overall trend is that we were often guessing what customers were thinking and wanting. Technologies are now available to customise e−advertising and e−marketing campaigns. For example, e−customers, Inc., offers a total solution called Enterprise Customer Response Systems that combines the online behaviours of customers, intentions of merchants, and decision rules as input to a data−warehousing application (see Figure 1). In addition, DoubleClick (www.doubleclick.net) offers products such as DART that help to manage online advertising campaigns. 321 Figure 1: Enterprise customer response technology. Source: www.customers.com/tech/index.htm One of the difficulties of marketing online is to align marketing objectives with marketing technology and data mining techniques. This three−stage approach is critical to the success of online marketing because failure to set up key marketing objectives is often the reason for online marketing failure, such as overspending on marketing activities that contribute little to the overall result. Consequently, it is important to formulate clear and tangible marketing objectives before deploying e−marketing solutions and data mining techniques. At the same time, allow empirical data to generate meanings to verify marketing objectives performances. Figure 2 depicts a three−stage model of objective−driven e−marketing with feedback mechanisms. Figure 2: A three−stage model of objective−driven e−marketing with feedback mechanisms Objective−driven e−marketing starts with identifying the objectives of the marketing campaign as the key to successful E−marketing, as well as with a goal (or a strategic goal) based on the organisations mission. For example, a goal can be "to obtain at least 50% of the market among the online interactive game players." This is then factored into a number of objectives. An objective is a management directive of what is to be achieved in an e−marketing campaign. An example of such objective is to "use a cost−effective way to make an impression of Product X on teenagers who play online games over the Internet." In this context, the difference between a goal and an objective is that a goal addresses strategic issues while an objective tactical. Chapter 21: Integrated E−Marketing A Strategy−Driven Technical Analysis Framework 322 Often an e−marketing campaign includes multiple objectives and together constitutes the goal of the campaign. In order to achieve such a goal, it is necessary to deploy e−marketing technology and data mining techniques to provide feedback to measure the achievement of objectives. Not very often, an e−marketing technology is chosen based on close examination of e−marketing objectives. One just hopes that the objectives are somehow satisfied. However, it is increasingly important to have an e−marketing solution that helps to monitor if whether the original objectives are satisfied; if not, there should be sufficient feedback on what additional steps should be taken to ensure this is achieved. In the following sections, we first provide a discussion on the various e−marketing solutions ranging from simple Weblog analysis to real−time packet analysis. We then discuss their strengths and weaknesses together with their suitability in the context of various e− marketing scenarios. Finally, we explain how these solutions can be interfaced with various data mining techniques to provide feedback. The feedback will be analysed to ensure designated marketing objectives are being achieved and if not, what should be done. Technical Analysis Methods for E−Marketers Even though Web designers can make visually appealing Web sites by following the advice of interface designers such as Nielsen (2001), reality has shown that this is insufficient to make a B2C or B2B site successful in terms of financial viability. More importantly, it is how to correctly analyse the data generated on visitors to Web sites. Monitoring and continuously interpreting visitor behaviours can help a site to uncover vital feedback that can help determine if the visitor is likely to purchase. Essentially, there are two guiding principles to extract information out of visitor behaviours: the type (what information) and range (duration and spread) of data left behind as well as the relationship between these data clusters. Compared to the early days of benchmarking the delivery performance of Web servers, the emphasis is on understanding customer satisfaction based on hard data. Analysis of log files can yield extensive informa− tion, but by using Java and JavaScript applets, user behaviours can be sent back to the Web server and provide near real−time analysis. Another alternative is to have separate servers monitoring the raw network transactions, determining the types of interactions, and doing more complex analyses. Log File Analysis The very first Web servers were often implemented on hardware running Unix operating systems. These systems provided text−based log files similar to other system services such as e−mail, FTP, and telnet. Typically, there were two log files: access_log and error_log. The error_log is useful to determine if there are missing pages or graphics, misspelled links and so on. The data in the access_log is a record of items delivered by the server. For example, two lines were taken from the server access_log on a server called farrer.csu.edu.au. is: 203.10.72.216 − − [18/Apr/2001:10:02:52 +1000] GET /ASGAP/banksia.html HTTP/1.0 200 27495 203.10.72.216 − − [18/Apr/2001:10:02:53 +1000] GET /ASGAP/gif/diag1c.gif HTTP/1.0 200 6258. They indicate the transfer of an HTML page and online image on that page. The first segment gives the host name of the client or just IP address to cut down on the workload of the local Domain Name Server (DNS). The second and third segments are optional items and often they are blank. The fourth column is a date stamp indicating when the event occurred. The fifth segment is the HyperText Transport Protocol (HTTP) command given by the client (or Web browser). The sixth segment is the return status number indicating the result of the request, and the seventh segment is the number of bytes transferred. Technical Analysis Methods for E−Marketers 323 With log files like these, it is possible to do some simple analyses. A simple measure would be just to count the lines in the access_log file. This is a measure of the total activity of the server. A better measure would be the lines that have a GET command and an HTML file name that would indicate pages delivered. The Web server on farrer delivered on 18th April 31537 items but only 3048 HTML pages. Another more complex analysis is to sort by client fully−qualified host names (as determined from IP address) in reverse and get an indication where the clients are geographically (for country−based names) or which organisation (.com, .gov, .edu, etc.). This is an important indication for early server operators to see how global their impact was. From a business perspective, it might be important to know if there was interest from customers in a certain region, hence, adjusting the advertising strategy in a more focused manner. One of the most popular, freely available Web server log analysis programs is called Analog (Turner, 2001a). It offers a wide range of reports, including the number of pages requested within a certain time period (hourly, daily, monthly, etc.), breakdown of client operating system and browser type, breakdown of client domain names, among others (University of Cambridge, 2001). Charts can be generated to provide visual information. A useful report is the one that shows the ranking of pages. This helps to decide if changes are required. Popular pages should be easy to download but still compelling. Perhaps the least popular pages should be changed or even deleted by moving their content. Another very useful analysis in marketing is how long a visitor stayed on a page and which pages they went to from that page. A graph of page links, also known as a click−stream, correlating with clients cookie and time sequence would provide further information about the intention of the visitor. However, this only provides the "footprints" of the visitor and further psychological, cognitive, and behavioural analyses are needed. Turner has an excellent description (2001b) of how the Web works and includes his discussion on what can and cant be gleaned from Web site log file data analysis. He gives reasons why the type of analysis that marketers would demand can be difficult to be interpreted from a log file. For example, visitor's identity can only be known if you can tie a cookie (Lavoie & Nielsen 1999; Netscape, 1999) to information entered on a Free to Join form. Once a visitor fills out that form, the server can send a cookie to the clients browser and every time that clients browser asks for a new page the request includes the cookie identification. This can be tied to a visitor database, which includes the details from the online form and past behaviours. Host name cannot be used because often this is in the proxy server cache used by clients ISP, and a different IP address may be assigned each time they connect. Turner reports American On Line may change the IP address of the proxy server used by a clients browser on each request for elements of a Web document. Turner also points out that the click−stream analysis will be muddied by the browsers and the ISPs cache. Web Servers add−ons As well as having server−side scripting for accessing database back−ends and other Common Gateway Interface (CGI) programming, it is possible for server−side scripts to gather click−stream data. Application Program Interfaces (APIs) have been traditionally used to enhance the functionality of a basic Web server. These days Web pages containing VBScript, PERL, Java Servlets, or PHP scripts are used as an alternative to slower CGI scripts. CGI scripts are slower because they are separate child processes and not part of the parent request handling process. The advantage of CGI scripts is that any programming language can be used to build them. Using a script embedded in the HTML which is interpreted by a module that is part of the server is faster because a separate process is not required to be created and later destroyed. Another method is to have a separate back−end server to which the Web server is a client. Other server−side scripts can interact with client−side scripts embedded in Web documents. This arrangement can add an extra channel of interaction between the client and the server programs to overcome some of the limitations of the HyperText Transport Protocol (HTTP) (Fielding et al., 1999). This channel might provide data about mouse movement, which is not normally captured until a link is clicked. Web Servers add−ons 324 Network wire−tap Data Gathering and Analysis Because of the need to maximise Web server response time, the process of tracking visitor behaviours can be off−loaded to another server. The network sniffer is on the local network and captures the raw data packets that make up the interaction between the visitor and the Web server. This separate server could be the server on the other end of the extra channel mentioned in the previous section. It reconstructs and then analyses the visitors behaviour (including that from the extra channel), combines that with previous behaviour from the visitor database, and produces a high−level suggestion to the Web server for remedial actions. Cooley (2000) describes several methods on how this can be achieved. One scenario is that the visitor may decide to make a purchase. However, if a long time lapse occurs since the purchase button was presented and if this lapse time is longer than a predefined waiting period, say, 15 seconds, it suggests that the customer is reviewing his/her decision to purchase. A pop−up window containing further information can be presented for assistance. On the Internet, nobody knows youre a dog. This caption of a classic Steiner cartoon describes the marketers dilemma: you dont know anything about your Web site visitors apart from their behaviours (McClure, 2001). Unless one can convince a visitor to accurately fill out a form using some sort of incentive, one doesnt know who the visitor is beyond the persons click−stream. Once he/she fills out the form, the server can send a cookie to the clients browser. Anonymous click−streams provide useful data for analysing page se− quences but are less effective when trying to close sales. This can be tied to a visitor database that includes the details from the online form and past behaviours. From Analysis to Data Mining Techniques So far the discussion has been focusing on analysis techniques and what to analyse. In this section, the "how to carry out" question is addressed. Nowadays there is a considerable amount of effort to convert a mountain of data collected from Web servers into competitive intelligence that can improve a business performance. "Web data mining" is about extracting previously unknown, actionable intelligence from a Web sites interactions. Similar to a typical data mining exercise, this type of information may be obtained from the analysis of behavioural and transaction data captured at the server level as it is outlined in the previous sections. The data, coupled with a collaborative filtering engine, external demographic, and household information, allow a business to profile its users and discover their preferences, their online behaviours, and purchasing patterns. There are a number of techniques available to gain an insight into the behaviours and features of users to a Web site. There are also different stages of data mining processes within a particular data mining technique (Mena, 1999; Thuraisingham, 1999) as illustrated in Figure 3. Figure 3: Stages in a data mining technique Network wire−tap Data Gathering and Analysis 325 [...]... Berners−Lee, T., ( 199 9) Hypertext Transfer Protocol HTTP/1.1 http://www.w3.org/Protocols/rfc2616/rfc2616.txt [Accessed 7th Sept 2001] Lavoie, B., & Nielsen, H.F., eds, ( 199 9), Web characterization terminology & definitions sheet http://www.w3.org/ 199 9/05/WCA−terms/ [Accessed 7th Sept 2001] Lindley, D., ( 199 6) Interactive classification of dynamic document collections, Ph.D Thesis The University of New South... commerce, to appear in the Journal of Applied Systems Studies Guan, S.U., & Yang, Y., ( 199 9) SAFE: Secure−roaming agent for e−commerce Proceedings of the 26th International Conference on Computers and Industrial Engineering, Australia Guttman, R.H & Maes, P., ( 199 9) Agent−mediated negotiation for retail electronic commerce, Agent In P Noriega & C Sierra, (eds) 70 90 Mediated Electronic Commerce: First International... Shardanand, U., & Maes, P., ( 199 5) Social information filtering: Algorithms for automating Word of Mouth In Proceedings of Human Factors in Computing Systems, 210−217, New York: ACM Press Steiner, P., ( 199 3) Cartoon with caption On the Internet nobody, knows youre a dog The New Yorker, 69 (LXIX) 20: 61, July 5 Archived at http://www.unc.edu/courses/jomc050/idog.html Thuraisingham, B., ( 199 9) Data mining technologies,... Yang ( 199 9) This chapter elaborates the design and implementation of authentication and authorization issues on the basis of the SAFER architecture The remainder of the chapter is organized as follows Section 2 presents background on agent−based e−commerce, mobile agent systems, and PKI Section 3 elaborates the design of agent authentication and authorization Section 4 describes the implementation of the... weighted average of the ratings of those similar users for the product Some major shortcomings of correlation−based approaches are identified in Billsus and Pazzani ( 199 8) Correlation between two user profiles is calculated when both users rate a product via an online evaluation form However, as users might choose any item to rate, given the thousands of items on the many millions of B2C sites, there... purchases are still largely non−automated User presence is still required in all stages of the buying process According to the nomenclature of Maes group in the MIT Media Labs (Maes, 199 4; Guttman & Maes, 199 9), the common commerce behavior can be described with the Consumer Buying Behaviour (CBB) model, which consists of six stages, namely, need identification, product brokering, merchant brokering, negotiation,... the transaction costs The solution to automating electronic purchases could lie in the employment of software agents and relevant AI technologies in e−commerce Software agent technologies can be used to automate several of the most time−consuming stages of the buying process like product information gathering and comparison Unlike traditional software, software agents are personalized, continuously running,... A case for the continued existence of intermediaries in the electronic marketplace and their functionalities were presented in Sarker ( 199 5) Decker et al ( 199 6) examined the agent roles and behaviors required to achieve the intermediary functions of agent matchmaking and brokering in their work Little research has been done in this area, however, there are a number of operations research techniques... Lindley ( 199 6) Similarity measurements can be classified into four classes: distance, probabilistic, correlation, and association coefficient A probabilistic similarity measurement implementation for textual 327 Scalability Issue documents can be found in Lindley, Atlas, & Wilson, 199 8) Pearson correlation coefficient is proposed by Shardanand and Maes ( 199 5) to measure similarities of user profiles All... and Manage− ment, (7), 91 −105 Maes, P., ( 199 4) Agents that reduce work and information overload Communication of the ACM, 37(7), 31−40 Nwana, H.S ( 199 6) Software agents: An overview Knowledge Engineering Review 2(3), 31−40 Poh, T.K., & Guan, S.U., (2000) Internet−enabled smart card agent environment and appli− cations In S.M Rahman & R.J Bignall, (eds) Internet Commerce and Software Agents: Cases, . behaviours and features of users to a Web site. There are also different stages of data mining processes within a particular data mining technique (Mena, 199 9; Thuraisingham, 199 9) as illustrated in. traffic analysis software (online) http://www.businesswire.com/emk/mwave3.htm [Accessed 3rd Sept 2001]. Mena, J., ( 199 9). Data mining your website, Melbourne: Digital Press. Netscape ( 199 9). Persistent. Evaluation of Model 327 documents can be found in Lindley, Atlas, & Wilson, 199 8). Pearson correlation coefficient is proposed by Shardanand and Maes ( 199 5) to measure similarities of user profiles.