112336
On integrating catalogs
We address the problem of integrating documents from different sources into a master catalog. This problem is pervasive in web marketplaces and portals. Current technology for automating this process consists of building a classifier that uses the categorization of documents in the master catalog to construct a model for predicting the category of unknown documents. Our key insight is that many of the data sources have their own categorization, and classification accuracy can be improved by factoring in the implicit information in these source categorizations. We show how a Naive Bayes classification can be enhanced to incorporate the similarity information present in source catalogs. Our analysis and empirical evaluation show substantial improvement in the accuracy of catalog integration.
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.
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
52857598
Privacy preserving mining of association rules
2251924
Automating layout of relational databases
The choice of database layout, i.e., how database objects such as tables and indexes are assigned to disk drives can significantly impact the I/O performance of the system. Today, DBAs typically rely on fully striping objects across all available disk drives as the basic mechanism for optimizing I/O performance. While full striping maximizes I/O parallelism, when query execution involves coaccess of two or more large objects, e.g., a merge join of two tables, the above strategy may be suboptimal due to the increased number of random I/O accesses on each disk drive. Here, we propose a framework for automating the choice of database layout for a given database that also takes into account the effects of coaccessed objects in the workload faced by the system. We formulate the above as an optimization problem and present an efficient solution to the problem that judiciously takes into account the trade-off between I/O parallelism and random I/O accesses. Our experiments on Microsoft SQL server show the superior I/O performance of our techniques compared to the traditional approach of fully striping each database object across all disk drives.