1. As Of March 28 th, 2001 A quick summary of LeNDI / Celware Integration. rbp

2. 1. A place to store traces within Celware. Traces being actions and snapshots in the order in which they are performed by a user. 2. Additions to the Celware feature set. 3. A means to call a routine which partitions (clusters) the snapshots into screens. 4. A routine which partitions snapshots in a way that can be translated into a decision tree based on Celware features (including the additions to the Celware feature set) 5. A means to store these partitions for expert user perusal and analysis. 6. A means to allow the user to provide feedback indicating mistakes in the partitioning. 7. A means to update the decision tree and fingerprint tables in Celware. Progress On Each Point Things to Implement 1. Kelly suggested that the offline files (.scr) could be an appropriate place to store this information, since much of the needed functionality for multiple traces can be achieved with simple extensions of what already exists. To be completed upon his return. 2. Roland is implementing Table and List structure identification in C++ within the Blacksmith DLL. This feature (7-x) and consecutive page numberings (1-5) are yet to be completed. It will take until Monday to complete this task and sufficiently test it. 3. From Visual Basic a Java class can be invoked through a COM bridge. This has been through preliminary tests and would be a faster solution than translating Java to Visual Basic Scripting. 4. A top-down partitioning algorithm is implemented that can create complete decision trees on at least one legacy system (LOCIS, 406 snapshots). Without prior knowledge of the correct partitioning, other than the number of clusters (screen classes) desired, it generates a decision tree based on features and values which performs an initial clustering of the snapshots. With LOCIS, 87% of the snapshots were correctly clustered automatically. Moreover, the incorrectly classified snapshots tended to be grouped together in other clusters, rather than randomly scattered amongst the clusters. After a perfect classification is made manually from this initial clustering, the algorithm extends the decision tree to one that is 100% accurate in classifying snapshots in the training set. Further testing is currently being done (10-fold cross validation), verifying the accuracy of the algorithm when applied to unseen snapshots. Four additional data sets are ready for testing in the next few days. 5. This is thought to be possible within the offline (.scr) files as well. Kelly will be looking at this sometime after his return. 6. Feedback mechanism. We know what feedback we would like from the user (corrections to the partition). Implementation details as to what form this might take have not yet been adequately discussed. This is only a requirement if a perfect partition does not exist for comparisons with our partitioning algorithms. 7. Kelly plans to implement an API similar to the one that exists for the Celware model from within Visual Basic Scripting. The calls would be low level ones such as “Insert Node here in the decision tree” rather than replace the whole tree. The thought being that this may allow for flexibility in the storage of the decision tree in the future. A slightly faster solution would be to allow the Java (called through Visual Basic) to update the database directly. This would be a brittle long term solution, but may be used for testing, depending on the progress of each individual working on this.

3. Celware Model Storage Decision Tree Storage Offline file (.scr) Partition Info. Visual Basic Scripting Celware Model Celware Storage (currently a.mdb) Java Partitioning Algorithms