초록 ▼

The system of the invention is a data server that can provide access to data, such as mainframe data, by open systems platforms. The system includes a shared storage interface coupling a first computing system to a shared storage device in which the shared data is maintained by a second computing sy

The system of the invention is a data server that can provide access to data, such as mainframe data, by open systems platforms. The system includes a shared storage interface coupling a first computing system to a shared storage device in which the shared data is maintained by a second computing system in a manner that is not natively compatible to the first computing system. The computing system further includes a data access server which executes on the processor in the first computing system. When executing, the data access server receives, via the network interface, a client message to access data on the shared storage device and in response to receiving the client message, retrieves, via the network interface, data storage information provided from the second computing system coupled to the first computing system. The data storage information is stored in the memory system and allows the data access server on the first computing system to access the data in the shared storage device in a manner that is compatible with the first computing system. The data access server can also provide a virtual file system to allow networked clients that implement file or other data sharing protocols such as NFS and CIFS.

대표청구항 ▼

The system of the invention is a data server that can provide access to data, such as mainframe data, by open systems platforms. The system includes a shared storage interface coupling a first computing system to a shared storage device in which the shared data is maintained by a second computing sy

The system of the invention is a data server that can provide access to data, such as mainframe data, by open systems platforms. The system includes a shared storage interface coupling a first computing system to a shared storage device in which the shared data is maintained by a second computing system in a manner that is not natively compatible to the first computing system. The computing system further includes a data access server which executes on the processor in the first computing system. When executing, the data access server receives, via the network interface, a client message to access data on the shared storage device and in response to receiving the client message, retrieves, via the network interface, data storage information provided from the second computing system coupled to the first computing system. The data storage information is stored in the memory system and allows the data access server on the first computing system to access the data in the shared storage device in a manner that is compatible with the first computing system. The data access server can also provide a virtual file system to allow networked clients that implement file or other data sharing protocols such as NFS and CIFS. of claim 1, further comprising an electronic mail transmission means. 6. The system for filtering and modeling electronic text messages of claim 5 wherein said electronic mail transmission means includes a means for transmitting electronic mail using the Simple Mail Transfer Protocol. 7. The system for filtering and modeling electronic text messages of claim 5 wherein said electronic mail transmission means includes a means for transmitting electronic mail using the protocol of the International Telecommunications Union Recommendation X.400. 8. The system for filtering and modeling electronic text messages of claim 1 wherein said message reception means further comprises an interface means to an asynchronous data network. 9. The system for filtering and modeling electronic text messages of claim 8 wherein said asynchronous data network further comprises an Ethernet local area network. 10. The system for filtering and modeling electronic text messages of claim 8 wherein said asynchronous data network further comprises a protocol handler with Transfer Control Protocol/Internet Protocol Internet capabilities. 11. The system for filtering and modeling electronic text messages of claim 1, further comprising an interface to an asynchronous data network. 12. The system for filtering and modeling electronic text messages of claim 11 wherein said asynchronous data network further comprises Ethernet local area network. 13. The system for filtering and modeling electronic text messages of claim 11 wherein said asynchronous data network further comprises a protocol handler with Transfer Control Protocol/Internet Protocol Internet capabilities. 14. The system for filtering and modeling electronic text messages of claim 1 wherein said message reception means further comprises a database interface means. 15. The system for filtering and modeling electronic text messages of claim 14 wherein said database means is a database with an open database interface. 16. The system for filtering and modeling electronic text messages of claim 1, further comprising an interface to a database means. 17. The system for filtering and modeling electronic text messages of claim 16 wherein said database means comprises an open database interface. 18. The system for filtering and modeling electronic text messages of claim 1 wherein said feature extraction means further comprises a keyword frequency analysis means which outputs a multi-dimensional keyword frequency signal. 19. The system for filtering and modeling electronic text messages of claim 1 wherein said feature extraction means further comprises a morphological process means for increasing the probability of pattern recognition. 20. The system for filtering and modeling electronic text messages of claim 1 wherein said feature extraction means further comprises a natural language processing means. 21. The system for filtering and modeling electronic text messages of claim 1 wherein said feature extraction means further comprises a dimensional reduction means which employs thesauri. 22. The system for filtering and modeling electronic text messages of claim 1 wherein said feature extraction means further comprises a word co-occurrence analysis means, which outputs a word co-occurrence statistics signal. 23. The system for filtering and modeling electronic text messages of claim 1 wherein said feature extraction means further comprises a syllabic analysis means which outputs a phoneme frequency signal. 24. The system for filtering and modeling electronic text messages of claim 1 wherein said feature extraction means further comprises word-level sentence, paragraph and page structure analysis means which outputs a structural pattern signal. 25. The system for filtering and modeling electronic text messages of claim 1 wherein said feature extraction means further comprises an author profile estimation means, out pulling an author profile signal. 26. The system for filtering and modeling electronic text messag es of claim 1 wherein said feature extraction means further comprises an author education level estimation means, outputting an author education level signal. 27. The system for filtering and modeling electronic text messages of claim 1 wherein said clustering means further comprises a k-means means for producing message tags in the message tag set. 28. The system for filtering and modeling electronic text messages of claim 1 wherein said clustering means further comprises a isodata means for producing message tags in the message tag set. 29. The system for filtering and modeling electronic text messages of claim 1 wherein said clustering means further comprises a backpropagation learning analysis means for producing message tags in the message tag set. 30. The system for filtering and modeling electronic text messages of claim 1 wherein said message tag set further comprises an author's attitude tag. 31. The system for filtering and modeling electronic text messages of claim 1 wherein said message tag set further comprises an issue-problem tag. 32. The system for filtering and modeling electronic text messages of claim 1 wherein said message tag set further comprises a request tag. 33. The system for filtering and modeling electronic text messages of claim 1 wherein said message tag set further comprises an author's profile tag. 34. The system for filtering and modeling electronic text messages of claim 1 wherein said message tag set further comprises an author's education level tag. 35. The system for filtering and modeling electronic text messages of claim 1 further comprises a learning means which includes: a tagged message reception means for receiving said tagged messages from said clustering means; a network update means which is capable of modifying parameters, thresholds, and coefficients within said feature extraction means and within said clustering means; and a user interface means for presenting the received electronic text message and said message tag set, receiving operator input modifying said message tag set, and providing network updates to the system via said network update means. 36. A process for filtering and modeling electronic text messages of asynchronous communications systems comprising the steps of: receiving an electronic text-based message via a reception media, said text message having a header and a body, said body containing a natural language text message from an author; performing feature extraction by performing natural language analysis of the text message to produce one or more output signals relating to any of keyword frequencies, word co-occurrence statistics, a dimensionally-reduced representation of the keyword frequencies, phoneme frequencies, structural pattern statistics for any of sentences, paragraphs, and pages, estimated education level of the author, and customer type; performing clustering according to said feature extraction output signals to produce a set of assigned properties based upon the content of the body of the electronic message, said assigned properties including an attitude, one or more issues presented, on or more requests, an author type, and an author's education level; and performing a learning process by receiving said assigned properties, executing relevance ranking and query by example, and learning changes to said assigned properties submitted via a user interface such that rules and thresholds used in said feature extraction means and/or clustering means are updated automatically in real time without operator intervention. 37. A process for filtering and modeling electronic text messages of asynchronous communications systems of claim 36 wherein said step of feature extraction further comprises performing keyword analysis on said text message. 38. A process for filtering and modeling electronic text messages of asynchronous communications systems of claim 36 wherein said step of feature extraction further comprises performing morphology on said text message. 39. A process for filtering and modeling electronic text messages of asynchronous communications systems of claim 36 wherein said step of feature extraction further comprises performing natural language processing on said text message. 40. A process for filtering and modeling electronic text messages of asynchronous communications systems of claim 36 wherein said step of feature extraction further comprises performing dimensional reduction of said signals using thesauri. 41. A process for filtering and modeling electronic text messages of asynchronous communications systems of claim 36 wherein said step of feature extraction further comprises performing co-occurrence statistical analysis. 42. A process for filtering and modeling electronic text messages of asynchronous communications systems of claim 36 wherein said step of feature extraction further comprises performing syllabic analysis. 43. A process for filtering and modeling electronic text messages of a sychronous communications systems of claim 36 wherein said step of feature extraction further comprises performing word analysis. 44. A process for filtering and modeling electronic text messages of asynchronous communications systems of claim 36 wherein said step of clustering further comprises performing k-means techniques. 45. A process for filtering and modeling electronic text messages of asynchronous communications systems of claim 36 wherein said step of clustering further comprises performing isodata techniques. 46. A process for filtering and modeling electronic text messages of asynchronous communications systems of claim 36 wherein said step of clustering further comprises performing auto-indexing techniques. 47. A process for filtering and modeling electronic text messages of a sychronous communications systems of claim 36 wherein said step of clustering further comprises performing backpropagation learning algorithm techniques. 48. A process for filtering and modeling electronic text messages of asynchronous communications systems of claim 36 further comprising the steps: presentation of the electronic text message and the message tags to a user via a user interface; receiving corrections to said message tags via said user interface from said user; and automatically modifying logic within said determination of inherent factor within said text message. 49. A computer-readable medium containing a data structure for storing property tags for electronic text-based messages comprising. an identifier link to a received electronic text-based message, an entry for an author's apparent attitude; an entry for an issue raised by the message, an entry for a request made in the message; an entry for a demographic profile indication for the author; and an entry for an estimated education level of the author.