1915028
SPRINT: A Scalable Parallel Classifier for Data Mining
Classification is an important data mining problem. Although classification is a wellstudied problem, most of the current classification algorithms require that all or a portion of the the entire dataset remain permanently in memory. This limits their suitability for mining over large databases. We present a new decision-tree-based classification algorithm, called SPRINT that removes all of the memory restrictions, and is fast and scalable. The algorithm has also been designed to be easily parallelized, allowing many processors to work together to build a single consistent model. This parallelization, also presented here, exhibits excellent scalability as well. The combination of these characteristics makes the proposed algorithm an ideal tool for data mining.
566602
Querying Shapes of Histories
We present a shape de nition language, called SDL, for retrieving objects based on shapes contained in the histories associated with these objects. It is a small, yet powerful, language that allows a rich variety of queries about the shapes found in historical time sequences. An interesting feature of SDL is its ability to perform blurry matching. A \blurry" match is one where the user cares about the overall shape but does not care about speci c details. Another important feature of SDL is its e cient implementability. The SDL operators are designed to be greedy to reduce non-determinism, which in turn substantially reduces the amount of back-tracking in the implementation. We give transformation rules for rewriting an SDL expression into a more e cient form as well as an index structure for speeding up the execution of SDL queries. We used SDL to \mine the mined rules" in a data mining application and found that the capability to query the behavior of the mined rules over a period of time led to the discovery of new and interesting information. Current Address: Politecnico di Milano, Italy. Symbol Description lb ub iv fv up slightly increasing transition .05 .19 anyvalue anyvalue Up highly increasing transition .20 1.0 anyvalue anyvalue down slightly decreasing transition -.19 -.05 anyvalue anyvalue Down highly decreasing transition -1.0 -.19 anyvalue anyvalue appears transition from a zero value to a non-zero value 0 1.0 zero nonzero disappears transition from a non-zero value to a zero value -1.0 0 nonzero zero stable the nal value nearly equal to the initial value -.04 .04 anyvalue anyvalue zero both the initial and nal values are zero 0 0 zero zero Table 1: An Illustrative Alphabet A
7027713
Discovery-Driven Exploration of OLAP Data Cubes
Analysts predominantly use OLAP data cubes to identify regions of anomalies that may represent problem areas or new opportunities. The current OLAP systems support hypothesis-driven exploration of data cubes through operations such as drill-down, roll-up, and selection. Using these operations, an analyst navigates unaided through a huge search space looking at large number of values to spot exceptions. We propose a new discovery-driven exploration paradigm that mines the data for such exceptions and summarizes the exceptions at appropriate levels in advance. It then uses these exceptions to lead the analyst to interesting regions of the cube during navigation. We present the statistical foundation underlying our approach. We then discuss the computational issue of finding exceptions in data and making the process efficient on large multidimensional data bases.
15564115
A Linear Method for Deviation Detection in Large Databases
We describe the problem of finding deviations in large data bases. Normally, explicit information outside the data, like integrity constraints or predefined patterns, is used for deviation detection. In contrast, we approach the problem from the inside of the data, using the implicit redundancy of the data. 
 
We give a formal description of the problem and present a linear algorithm for detecting deviations. Our solution simulates a mechanism familiar to human beings: after seeing a series of similar data, an element disturbing the series is considered an exception. We also present experimental results from the application of this algorithm on real-life datasets showing its effectiveness.
14705676
Parallel Mining of Association Rules
We consider the problem of mining association rules on a shared nothing multiprocessor. We present three algorithms that explore a spectrum of trade-offs between computation, communication, memory usage, synchronization, and the use of problem specific information. The best algorithm exhibits near perfect scaleup behavior, yet requires only minimal overhead compared to the current best serial algorithm.
52857598
Privacy preserving mining of association rules
9257184
Mining Process Models from Workflow Logs
Modern enterprises increasingly use the workflow paradigm to prescribe how business processes should be performed. Processes are typically modeled as annotated activity graphs. We present an approach for a system that constructs process models from logs of past, unstructured executions of the given process. The graph so produced conforms to the dependencies and past executions present in the log. By providing models that capture the previous executions of the process, this technique allows easier introduction of a workflow system and evaluation and evolution of existing process models. We also present results from applying the algorithm to synthetic data sets as well as process logs obtained from an IBM Flowmark installation.