Abstract:
A text mining system for collecting business intelligence about a client, as well as for identifying prospective customers of the client, for use in a lead generation system accessible by the client via the Internet. The text mining system has various components, including a data acquisition process that extracts textual data from Internet web sites, including their logs, content, processes, and transactions. The system compares log data to content and process data, and relates the results of the comparison to transaction data. This permits the system to provide aggregate cluster data representing statistics useful for customer lead generation.

Description:
RELATED PATENT APPLICATIONS 
     This application claims the benefit of U.S. Provisional Application No. 60/238,094, filed Oct. 4, 2000 and entitled “Server Log File System Utilizing Text mining Methodologies and Technologies”. The present patent application and additionally the following patent application is a conversion from the foregoing provisional filing: U.S. Pat. No. 7,043,531 entitled “Web-Based Customer Lead. Generator System with Pre-Emptive Profiling” and filed Oct. 4, 2001. 
     This patent application is related to the following pending applications: patent application Ser. No. 09/862,832 entitled “Web-Based Customer Lead Generator System” and filed May 21, 2001; patent application Ser. No. 09/865,802 entitled “Database Server System for Web-Based Business Intelligence” and filed May 24, 2001; patent application Ser. No. 09/865,804 entitled “Data Mining System for Web-Based Business Intelligence” and filed May 24, 2001; patent application Ser. No. 09/865,735 entitled “Text Mining System for Web-Based Business Intelligence” and filed May 24, 2001; patent application Ser. No. 09/862,814 entitled “Web-Based Customer Prospects Harvester System” and filed May 21, 2001; patent application Ser. No. 09/865,805 entitled “Text Indexing System for Web-Based Business Intelligence” and filed May 24, 2001. 
    
    
     TECHNICAL FIELD OF THE INVENTION 
     This invention relates to electronic commerce, and more particularly to business intelligence software tools for acquiring leads for prospective customers, using Internet data sources. 
     BACKGROUND OF THE INVENTION 
     Most small and medium sized companies face similar challenges in developing successful marketing and sales campaigns. These challenges include locating qualified prospects who are making immediate buying decisions. It is desirable to personalize marketing and sales information to match those prospects, and to deliver the marketing and sales information in a timely and compelling manner. Other challenges are to assess current customers to determine which customer profile produces the highest net revenue, then to use those profiles to maximize prospecting results. Further challenges are to monitor the sales cycle for opportunities and inefficiencies, and to relate those findings to net revenue numbers. 
     Today&#39;s corporations are experiencing exponential growth to the extent that the volume and variety of business information collected and accumulated is overwhelming. Further, this information is found in disparate locations and formats. Finally, even if the individual data bases and information sources are successfully tapped, the output and reports may be little more than spreadsheets, pie charts and bar charts that do not directly relate the exposed business intelligence to the companies&#39; processes, expenses, and to its net revenues. 
     With the growth of the Internet, one trend in developing marketing and sales campaigns is to gather customer information by accessing Internet data sources. Internet data intelligence and data mining products face specific challenges. First, they tend to be designed for use by technicians, and are not flexible or intuitive in their operation; secondly, the technologies behind the various engines are changing rapidly to take advantage of advances in hardware and software, and finally, the results of their harvesting and mining are not typically related to a specific department goals and objectives. 
     SUMMARY OF THE INVENTION 
     One aspect of the invention is a text mining system for collecting business intelligence about a client, as well as for identifying prospective customers of the client. The text mining system may be used in a lead generation system accessible by the client via the Internet. 
     The text mining system has various components, including a data acquisition process that extracts textual data from Internet web sites, including their logs, content, processes, and transactions. The system compares log data to content and process data, and relates the results of the comparison to transaction data. This permits the system to provide aggregate cluster data representing statistics useful for customer lead generation. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  illustrates the operating environment for a web based lead generator system in accordance with the invention. 
         FIG. 2  illustrates the various functional elements of the lead generator system. 
         FIG. 3  illustrates the various data sources and a first embodiment of the prospects harvester. 
         FIGS. 4 and 5  illustrate a database server system, which may be used within the lead generation system of  FIGS. 1 and 2 . 
         FIGS. 6 and 7  illustrate a data mining system, which may be used within the lead generation system of  FIGS. 1 and 2 . 
         FIGS. 8 and 9  illustrate a text mining system, which may be used within the lead generation system of  FIGS. 1 and 2 . 
         FIG. 10  illustrates a text mining system, similar to that of  FIG. 8 , applied to web site server logs. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     Lead Generator System Overview 
       FIG. 1  illustrates the operating environment for a web-based customer lead generation system  10  in accordance with the invention. System  10  is in communication, via the Internet, with unstructured data sources  11 , an administrator  12 , client systems  13 , reverse look-up sources  14 , and client applications  15 . 
     The users of system  10  may be any business entity that desires to conduct more effective marketing campaigns. These users may be direct marketers who wish to maximizing the effectiveness of direct sales calls, or e-commerce web site who wish to build audiences. 
     In general, system  10  may be described as a web-based Application Service Provider (ASP) data collection tool. The general purpose of system  10  is to analyze a client&#39;s marketing and sales cycle in order to reveal inefficiencies and opportunities, then to relate those discoveries to net revenue estimates. Part of the latter process is proactively harvesting prequalified leads from external and internal data sources. As explained below, system  10  implements an automated process of vertical industry intelligence building that involves automated reverse lookup of contact information using an email address and key phrase highlighting based on business rules and search criteria. 
     More specifically, system  10  performs the following tasks:
         Uses client-provided criteria to search Internet postings for prospects who are discussing products or services that are related to the client&#39;s business offerings   Selects those prospects matching the client&#39;s criteria   Pushes the harvested prospect contact information to the client, with a link to the original document that verifies the prospects interest   Automatically opens or generates personalized sales scripts and direct marketing materials that appeal to the prospects&#39; stated or implied interests   Examines internal sales and marketing materials, and by applying data and text mining analytical tools, generates profiles of the client&#39;s most profitable customers   Cross-references and matches the customer profiles with harvested leads to facilitate more efficient harvesting and sales presentations   In the audience building environment, requests permission to contact the prospect to offer discounts on services or products that are directly or indirectly related to the conversation topic, or to direct the prospect to a commerce source.       

     System  10  provides open access to its web site. A firewall (not shown) is used to prevent access to client records and the entire database server. Further details of system security are discussed below in connection with  FIG. 5 . 
     Consistent with the ASP architecture of system  10 , interactions between client system  13  and system  10  will typically be by means of Internet access, such as by a web portal. Authorized client personnel will be able to create and modify profiles that will be used to search designated web sites and other selected sources for relevant prospects. 
     Client system  11  may be any computer station or network of computers having data communication to lead generator system  10 . Each client system  11  is programmed such that each client has the following capabilities: a master user account and multiple sub user accounts, a user activity log in the system database, the ability to customize and personalize the workspace; configurable, tiered user access; online signup, configuration and modification, sales territory configuration and representation, goals and target establishment, and online reporting comparing goals to target (e.g., expense/revenue; budget/actual). 
     Administration system  14  performs such tasks as account activation, security administration, performance monitoring and reporting, assignment of master user id and licensing limits (user seats, access, etc.), billing limits and profile, account termination and lockout, and a help system and client communication. 
     System  10  interfaces with various client applications  15 . For example, system  10  may interface with commercially available enterprise resource planning (ERP), sales force automation (SFA), call center, e-commerce, data warehousing, and custom and legacy applications. 
     Lead Generator System Architecture 
       FIG. 2  illustrates the various functional elements of lead generator system  10 . In the embodiment of  FIG. 2 , the above described functions of system  10  are partitioned between two distinct processes. 
     A prospects harvester process  21  uses a combination of external data sources, client internal data sources and user-parameter extraction interfaces, in conjunction with a search, recognition and retrieval system, to harvest contact information from the web and return it to a staging data base  22 . In general, process  21  collects business intelligence data from both inside the client&#39;s organization and outside the organization. The information collected can be either structured data as in corporate databases/spreadsheet files or unstructured data as in textual files. 
     Process  21  may be further programmed to validate and enhance the data, utilizing a system of lookup, reverse lookup and comparative methodologies that maximize the value of the contact information. Process  21  may be used to elicit the prospect&#39;s permission to be contacted. The prospect&#39;s name and email address are linked to and delivered with ancillary information to facilitate both a more efficient sales call and a tailored e-commerce sales process. The related information may include the prospect&#39;s email address, Web site address and other contact information. In addition, prospects are linked to timely documents on the Internet that verify and highlight the reason(s) that they are in fact a viable prospect. For example, process  21  may link the contact data, via the Internet, to a related document wherein the contact&#39;s comments and questions verify the high level value of the contact to the user of this system (the client). 
     A profiles generation process  25  analyzes the user&#39;s in-house files and records related to the user&#39;s existing customers to identify and group those customers into profile categories based on the customer&#39;s buying patterns and purchasing volumes. The patterns and purchasing volumes of the existing customers are overlaid on the salient contact information previously harvested to allow the aggregation of the revenue-based leads into prioritized demand generation sets. Process  25  uses an analysis engine and both data and text mining engines to mine a company&#39;s internal client records, digital voice records, accounting records, contact management information and other internal files. It creates a profile of the most profitable customers, reveals additional prospecting opportunities, and enables sales cycle improvements. Profiles include items such as purchasing criteria, buying cycles and trends, cross-selling and up-selling opportunities, and effort to expense/revenue correlations. The resulting profiles are then overlaid on the data obtained by process  21  to facilitate more accurate revenue projections and to enhance the sales and marketing process. The client may add certain value judgments (rankings) in a table that is linked to a unique lead id that can subsequently be analyzed by data mining or OLAP analytical tools. The results are stored in the deliverable database  24 . 
     Profiles generation process  25  can be used to create a user (client) profiles database  26 , which stores profiles of the client and its customers. As explained below, this database  26  may be accessed during various data and text mining processes to better identify prospective customers of the client. 
     Web server  29  provides the interface between the client systems  13  and the lead generation system  10 . As explained below, it may route different types of requests to different sub processes within system  10 . The various web servers described below in connection with  FIGS. 4-11  may be implemented as separate servers in communication with a front end server  29 . Alternatively, the server functions could be integrated or partitioned in other ways. 
     Data Sources 
       FIG. 3  provides additional detail of the data sources of  FIGS. 1 and 2 . Access to data sources may be provided by various text mining tools, such as by the crawler process  31  or  41  of  FIGS. 3 and 4 . 
     One data source is newsgroups, such as USENET. To access discussion documents from USENET newsgroups such as “news.giganews.com”, NNTP protocol is used by the crawler process to talk to USENET news server such as “news.giganews.com.” Most of the news servers only archive news articles for a limited period (giganews.com archives news articles for two weeks), it is necessary for the iNet Crawler to incrementally download and archive these newsgroups periodically in a scheduled sequence. This aspect of crawler process  31  is controlled by user-specified parameters such as news server name, IP address, newsgroup name and download frequency, etc. 
     Another data source is web-Based discussion forums. The crawler process follows the hyper links on a web-based discussion forum, traverse these links to user or design specified depths and subsequently access and retrieve discussion documents. Unless the discussion documents are archived historically on the web site, the crawler process will download and archive a copy for each of the individual documents in a file repository. If the discussion forum is membership-based, the crawler process will act on behalf of the authorized user to logon to the site automatically in order to retrieve documents. This function of the crawler process is controlled by user specified parameters such as a discussion forum&#39;s URL, starting page, the number of traversal levels and crawling frequency. 
     A third data source is Internet-based or facilitated mailing lists wherein individuals send to a centralized location emails that are then viewed and/or responded to by members of a particular group. Once a suitable list has been identified a subscription request is initiated. Once approved, these emails are sent to a mail server where they are downloaded, stored in system  10  and then processed in a fashion similar to documents harvested from other sources. The system stores in a database the filters, original URL and approval information to ensure only authorized messages are actually processed by system  10 . 
     A fourth data source is corporations&#39; internal documents. These internal documents may include sales notes, customer support notes and knowledge base. The crawler process accesses corporations&#39; internal documents from their Intranet through Unix/Windows file system or alternately be able to access their internal documents by riding in the databases through an ODBC connection. If internal documents are password-protected, crawler process  31  acts on behalf of the authorized user to logon to the file systems or databases and be able to subsequently retrieve documents. This function of the crawler process is controlled by user-specified parameters such as directory path and database ODBC path, starting file id and ending file id, and access frequency. Other internal sources are customer information, sales records, accounting records, and digitally recorded correspondence such as e-mail files or digital voice records. 
     A fifth data source is web pages from Internet web sites. This function of the crawler process is similar to the functionality associated with web-discussion-forums. Searches are controlled by user-specified parameters such as web site URL, starting page, the number of traversal levels and crawling frequency. 
     Database Server System 
       FIGS. 4 and 5  illustrate a database server system  41 , which may be used within system  10  of  FIGS. 1 and 2 .  FIG. 4  illustrates the elements of system  41  and  FIG. 5  is a data flow diagram. Specifically, system  41  could be used to implement the profiles generation process  25 , which collects profile data about the client. 
     The input data  42  can be the client&#39;s sales data, customer-contact data, customer purchase data and account data etc. Various data sources for customer data can be contact management software packages such as ACT, MarketForce, Goldmine, and Remedy. Various data sources for accounting data are Great Plains, Solomon and other accounting packages typically found in small and medium-sized businesses. If the client has ERP (enterprise resource planning) systems (such as JD Edwards, PeopleSoft and SAP) installed, the data sources for customer and accounting data will be extracted from ERP customer and accounting modules. This data is typically structured and stored in flat files or relational databases. System  41  is typically an OLAP (On-line analytic processing) type server-based system. It has five major components. A data acquisition component  41   a  collects and extracts data from different data sources, applying appropriate transformation, aggregation and cleansing to the data collected. This component consists of predefined data conversions to accomplish most commonly used data transformations, for as many different types of data sources as possible. For data sources not covered by these predefined conversions, custom conversions need to be developed. The tools for data acquisition may be commercially available tools, such as Data Junction, ETI*EXTRACT, or equivalents. Open standards and APIs will permit employing the tool that affords the most efficient data acquisition and migration based on the organizational architecture. 
     Data mart  41   b  captures and stores an enterprise&#39;s sales information. The sales data collected from data acquisition component  41   a  are “sliced and diced” into multidimensional tables by time dimension, region dimension, product dimension and customer dimension, etc. The general design of the data mart follows data warehouse/data mart Star-Schema methodology. The total number of dimension tables and fact tables will vary from customer to customer, but data mart  41   b  is designed to accommodate the data collected from the majority of commonly used software packages such as PeopleSoft or Great Plains. 
     Various commercially available software packages, such as Cognos, Brio, Informatica, may be used to design and deploy data mart  41   b . The Data Mart can reside in DB2, Oracle, Sybase, MS SQL server, P.SQL or similar database application. Data mart  41   b  stores sales and accounting fact and dimension tables that will accommodate the data extracted from the majority of industry accounting and customer contact software packages. 
     A Predefined Query Repository Component  41   c  is the central storage for predefined queries. These predefined queries are parameterized macros/business rules that extract information from fact tables or dimension tables in the data mart  41   b . The results of these queries are delivered as business charts (such as bar charts or pie charts) in a web browser environment to the end users. Charts in the same category are bounded with the same predefined query using different parameters. (i.e. quarterly revenue charts are all associated with the same predefined quarterly revenue query, the parameters passed are the specific region, the specific year and the specific quarter). These queries are stored in either flat file format or as a text field in a relational database. 
     A Business Intelligence Charts Repository Component  41   d  serves two purposes in the database server system  41 . A first purpose is to improve the performance of chart retrieval process. The chart repository  41   d  captures and stores the most frequently visited charts in a central location. When an end user requests a chart, system  41  first queries the chart repository  41   d  to see if there is an existing chart. If there is a preexisting chart, server  41   e  pulls that chart directly from the repository. If there is no preexisting chart, server  41   e  runs the corresponding predefined query from the query repository  41   c  in order to extract data from data mart  41   b  and subsequently feed the data to the requested chart. A second purpose is to allow chart sharing, collaboration and distribution among the end users. Because charts are treated as objects in the chart repository, users can bookmark a chart just like bookmarking a regular URL in a web browser. They can also send and receive charts as an email attachment. In addition, users may logon to system  41  to collaboratively make decisions from different physical locations. These users can also place the comments on an existing chart for collaboration. 
     Another component of system  41  is the Web Server component  41   e , which has a number of subcomponents. A web server subcomponent (such as Microsoft IIS or Apache server or any other commercially available web servers) serves HTTP requests. A database server subcomponent (such as Tango, Cold Fusion or PHP) provides database drill-down functionality. An application server subcomponent routes different information requests to different other servers. For example, sales revenue chart requests will be routed to the database system  41 ; customer profile requests will be routed to a Data Mining server, and competition information requests will be routed to a Text Mining server. The latter two systems are discussed below. Another subcomponent of server  41   e  is the chart server, which receives requests from the application server. It either runs queries against data mart  41   b , using query repository  41   c , or retrieves charts from chart repository  41   c.    
     As output  43 , database server system  41  delivers business intelligence about an organization&#39;s sales performance as charts over the Internet or corporate Intranet. Users can pick and choose charts by regions, by quarters, by products, by companies and even by different chart styles. Users can drill-down on these charts to reveal the underlying data sources, get detailed information charts or detailed raw data. All charts are drill-down enabled allowing users to navigate and explore information either vertically or horizontally. Pie charts, bar charts, map views and data views are delivered via the Internet or Intranet. 
     As an example of operation of system  41 , gross revenue analysis of worldwide sales may be contained in predefined queries that are stored in the query repository  41   c . Gross revenue queries accept region and/or time period as parameters and extract data from the Data Mart  41   b  and send them to the web server  41   e . Web server  41   e  transforms the raw data into charts and publishes them on the web. 
     Data Mining System 
       FIGS. 6 and 7  illustrate a data mining system  61 , which may be used within system  10  of  FIGS. 1 and 2 . 
       FIG. 6  illustrates the elements of system  61  and  FIG. 7  is a data flow diagram. Specifically, system  61  could be used to implement the profiles process  25 , which collects profile data about the client. 
     Data sources  62  for system  61  are the Data Mart  41   b , e.g., data from the tables that reside in Data Mart  41   b , as well as data collected from marketing campaigns or sales promotions. 
     For data coming from the Data Mart  41   b , data acquisition process  61   a  between Mining Base  61   b  and Data Mart  41   b  extract/transfer and format/transform data from tables in the Data Mart  41   b  into Data Mining base  61   b . For data collected from sales and marketing events, data acquisition process  61   a  may be used to extract and transform this kind of data and store it in the Data Mining base  61   b.    
     Data Mining base  61   b  is the central data store for the data for data mining system  61 . The data it stores is specifically prepared and formatted for data mining purposes. The Data Mining base  61   b  is a separate data repository from the Data Mart  41   b , even though some of the data it stores is extracted from Data Mart&#39;s tables. The Data Mining base  61   b  can reside in DB2, Oracle, Sybase, MS SQL server, P.SQL or similar database application. 
     Chart repository  61   d  contains data mining outputs. The most frequently used decision tree charts are stored in the chart repository  61   d  for rapid retrieval. 
     Customer purchasing behavior analysis is accomplished by using predefined Data Mining models that are stored in a model repository  61   e . Unlike the predefined queries of system  41 , these predefined models are industry-specific and business-specific models that address a particular business problem. Third party data mining tools such as IBM Intelligent Miner and Clementine, and various integrated development environments (IDEs) may be used to explore and develop these data mining models until the results are satisfactory. Then the models are exported from the IDE into standalone modules (in C or C++) and integrated into model repository  61   e  by using data mining APIs. 
     Data mining server  61   c  supplies data for the models, using data from database  61   c .  FIG. 7  illustrates the data paths and functions associated with server  61   c . Various tools and applications that may be used to implement server  61   c  include VDI, EspressChart, and a data mining GUI. 
     The outputs of server  61   e  may include various options, such as decision trees, Rule Sets, and charts. 
     By default, all the outputs have drill-down capability to allow users to interactively navigate and explore information in either a vertical or horizontal direction. Views may also be varied, such as by influencing factor. For example, in bar charts, bars may represent factors that influence customer purchasing (decision-making) or purchasing behavior. The height of the bars may represent the impact on the actual customer purchase amount, so that the higher the bar is the more important the influencing factor is on customers, purchasing behavior. Decision trees offer a unique way to deliver business intelligence on customers&#39; purchasing behavior. A decision tree consists of tree nodes, paths and node notations. Each individual node in a decision tree represents an influencing. A path is the route from root node (upper most level) to any other node in the tree. Each path represents a unique purchasing behavior that leads to a particular group of customers with an average purchase amount. This provides a quick and easy way for on-line users to identify where the valued customers are and what the most important factors are when customer are making purchase decisions. This also facilitates tailored marketing campaigns and delivery of sales presentations that focus on the product features or functions that matter most to a particular customer group. Rules Sets are plain-English descriptions of the decision tree. A single rule in the RuleSet is associated with a particular path in the decision tree. Rules that lead to the same destination node are grouped into a RuleSet. RuleSet views allow users to look at the same information presented in a decision tree from a different angle. When users drill down deep enough on any chart, they will reach the last drill-down level that is data view. A data view is a table view of the underlying data that supports the data mining results. Data Views are dynamically linked with Data Mining base  61   b  and Data Mart  41   b  through web server  61   f.    
     Web server  61   f , which may be the same as database server  41   e , provides Internet access to the output of mining server  61   c . Existing outputs may be directly accessed from storage in charts repository  61   d . Or requests may be directed to models repository  61   e . Consistent with the application service architecture of lead generation system  10 , access by the client to web server  61   f  is via the Internet and the client&#39;s web browser. 
     Text Mining System 
       FIGS. 8 and 9  illustrate a text mining system  81 , which may be used within system  10  of  FIGS. 1 and 2 .  FIG. 8  illustrates the elements of system  81  and  FIG. 9  is a data flow diagram. As indicated in  FIG. 8 , the source data  82  for system  81  may be either external and internal data sources. Thus, system  81  may be used to implement both the prospects system and profiles system of  FIG. 2 . 
     The source data  82  for text mining system  81  falls into two main categories, which can be mined to provide business intelligence. Internal documents contain business information about sales, marketing, and human resources. External sources consist primarily of the public domain in the Internet. Newsgroups, discussion forums, mailing lists and general web sites provide information on technology trends, competitive information, and customer concerns. 
     More specifically, the source data  82  for text mining system  81  is from five major sources. Web Sites: on-line discussion groups, forums and general web sites. Internet News Group: Internet newsgroups for special interests such as alt.ecommerce and microsoft.software.interdev. For some of the active newsgroups, hundreds of news articles may be harvested on a weekly basis. Internet Mailing Lists: mailing lists for special interests, such as e-commerce mailing list, company product support mailing list or Internet marketing mailing list. For some of the active mailing lists, hundreds of news articles will be harvested on a weekly basis. Corporate textual files: internal documents such as emails, customer support notes sales notes, and digital voice records. 
     For data acquisition  81   a  from web sites, user-interactive web crawlers are used to collect textual information. Users can specify the URLs, the depth and the frequency of web crawling. The information gathered by the web crawlers is stored in a central repository, the text archive  81   b . For data acquisition from newsgroups, a news collector contacts the news server to download and transform news articles in an html format and deposit them in text archive  81   b . Users can specify the newsgroups names, the frequency of downloads and the display format of the news articles to news collector. For data acquisition from Internet mailing lists, a mailing list collector automatically receives, sorts and formats email messages from the subscribed mailing lists and deposit them into text archive  81   b . Users can specify the mailing list names and address and the display format of the mail messages. For data acquisition from client text files, internal documents are sorted, collected and stored in the Text Archive  81   b . The files stored in Text Archive  81   b  can be either physical copies or dynamic pointers to the original files. 
     The Text Archive  81   b  is the central data store for all the textual information for mining. The textual information it stores is specially formatted and indexed for text mining purpose. The Text Archive  81   b  supports a wide variety of file formats, such plain text, html, MS Word and Acrobat. 
     Text Mining Server  81   c  operates on the Text Archive  81   b . Tools and applications used by server  81   c  may include ThemeScape and a Text Mining GUI  81   c . A repository  81   d  stores text mining outputs. Web server  81   e  is the front end interface to the client system  13 , permitting the client to access database  81   b , using an on-line search executed by server  81   c  or server  81   e.    
     The outputs of system  81  may include various options. Map views and simple query views may be delivered over the Internet or Intranet. By default, all the outputs have drill-down capability to allow users to reach the original documents. HTML links will be retained to permit further lateral or horizontal navigation. Keywords will be highlighted or otherwise pointed to in order to facilitate rapid location of the relevant areas of text when a document is located through a keyword search. For example, Map Views are the outputs produced by ThemeScape. Textual information is presented on a topological map on which similar “themes” are grouped together to form “mountains.” On-line users can search or drill down on the map to get the original files. Simple query views are similar to the interfaces of most of the Internet search engines offered (such as Yahoo, Excite and HotBot). It allows on-line users to query the Text Archive  81   b  for keywords or key phrases or search on different groups of textual information collected over time. 
     A typical user session using text-mining system  81  might follow the following steps. It is assumed that the user is connected to server  81   e  via the Internet and a web browser, as illustrated in  FIG. 1 . In the example of this description, server  81   e  is in communication with server  81   c , which is implemented using ThemeScape software.
         1. Compile list of data sources (Newsgroups, Discussion Groups, etc).   2. Start ThemeScape Publisher or comparable application.   3. Select “File”.   4. Select “Map Manager” or comparable function.   5. Verify that server and email blocks are correctly set. If not, insert proper information.   6. Enter password.   7. Press “Connect” button   8. Select “New”.   9. Enter a name for the new map.   10. If duplicating another maps settings, use drop down box to select the map name.   11. Select “Next”.   12. Select “Add Source”.   13. Enter a Source Description.   14. Source Type remains “World Wide Web (WWW)”.   15. Enter the URL to the site to be mined.   16. Add additional URLs, if desired.   17. Set “Harvest Depth.” Parameters range from 1 level to 20 levels.   18. Set “Filters” if appropriate. These include Extensions, Inclusions, Exclusions, Document Length and Rations.   19. Set Advanced Settings, if appropriate. These include Parsing Settings, Harvest Paths, Domains, and Security and their sub-settings.   20. Repeat steps 14 through 20 for each additional URL to be mined.   21. Select “Advanced Settings” if desired. These include Summarization Settings, Stopwords, and Punctuation.   22. Select “Finish” once ready to harvest the sites.   23. The software downloads and mines (collectively known as harvesting) the documents and creates a topographical map.   24. Once the map has been created, it can be opened and searched.       

     Text Mining Applied to Web Site Server Logs 
     The text mining concepts discussed above in connection with text mining system  81  can be applied to web site server logs. 
       FIG. 10  illustrates a text mining system  101  applied to web site server logs. Text mining system  101  is programmed to aggregate unstructured factual and contextual log entries for comparison to related content pages and processes occurring at the moment indicated by the log entry. This aggregated intelligence is then related to consummated and incomplete purchase transactions. Various predictive statistics are then extracted. These statistics include the most profitable aggregation clusters, the least profitable, the mean aggregation clusters, and dropped transaction aggregation clusters. The various aggregation clusters may be overlaid on transaction, survey, and user-entered demographics and preferences. 
     OTHER EMBODIMENTS 
     Although the present invention has been described in detail, it should be understood that various changes, substitutions, and alterations can be made hereto without departing from the spirit and scope of the invention as defined by the appended claims.