International Business Machines Corporation

Applied Materials, Inc.

Paul Geoffrey Brown

Peter Haas

Jussi Petri Myllymaki

Volker Gerhard Markl

Sam Hyungsam Kim

William Norman Sterling

Jae-Chull Lee

Ihab F Ilyas

A pattern-based data matching system matches pattern-based data. The data matching system generates a regular expression pattern for input datasets and describes similarity measures between the generated patterns. The data matching system analyzes an input dataset in terms of symbol classes, generalizing input values into a general pattern to allow identification or extrapolation of overlap between input datasets, aiding in matching fields in databases that are being merged and in learning a pattern for an input dataset. For each sequence of data values, the present system computes a compact pattern describing the sequence. Embodiments of the data matching system comprise noise reduction and repetitive pattern discovery in the input dataset and calculation of recall and precision of the generated pattern.

System for matching pattern-based data

Embodiments of an apparatus for sealing a substrate transfer passage in a chamber are provided. In one embodiment, an apparatus for sealing a substrate transfer passage in a chamber includes an elongated door member coupled to an actuator by a ball joint. The ball joint is configured to allow movement of the door member relative to the lever arm around a center of the ball joint. In one embodiment, a sealing face of the elongated door is curved. In another embodiment, the chamber is one of a chemical vapor deposition chamber, a load lock chamber, a metrology chamber, a thermal processing chamber, or a physical vapor disposition chamber, a load lock chamber, a substrate transfer chamber or a vacuum chamber.

Valve door with ball coupling

A scheme is used to automatically discover algebraic constraints between pairs of columns in relational data. The constraints may be 'fuzzy' in that they hold for most, but not all, of the records, and the columns may be in the same table or different tables. The scheme first identifies candidate sets of column value pairs that are likely to satisfy an algebraic constraint. For each candidate, the scheme constructs algebraic constraints by applying statistical histogramming, segmentation, or clustering techniques to samples of column values. In query-optimization mode, the scheme automatically partitions the data into normal and exception records. During subsequent query processing, queries can be modified to incorporate the constraints; the optimizer uses the constraints to identify new, more efficient access paths. The results are then combined with the results of executing the original query against the (small) set of exception records.

Method for discovering undeclared and fuzzy rules in databases

A system and method of discovering dependencies between relational database column pairs and application of discoveries to query optimization is provided. For each candidate column pair remaining after simultaneously generating column pairs, pruning pairs not satisfying specified heuristic constraints, and eliminating pairs with trivial instances of correlation, a random sample of data values is collected. A candidate column pair is tested for the existence of a soft functional dependency (FD), and if a dependency is not found, statistically tested for correlation using a robust chi-squared statistic. Column pairs for which either a soft FD or a statistical correlation exists are prioritized for recommendation to a query optimizer, based on any of: strength of dependency, degree of correlation, or adjustment factor; statistics for recommended columns pairs are tracked to improve selectivity estimates. Additionally, a dependency graph representing correlations and dependencies as edges and column pairs as nodes is provided.

Detecting correlation from data

A sampling infrastructure/scheme that supports flexible, efficient, scalable and uniform sampling is disclosed. A sample is maintained in a compact histogram form while the sample footprint stays below a specified upper bound. If, at any point, the sample footprint exceeds the upper bound, then the compact representation is abandoned, the sample purged to obtain a subsample. The histogram of the purged subsample is expanded to a bag of values while sampling remaining data values of the partitioned subset. The expanded purged subsample is converted to a histogram and uniform random samples are yielded. The sampling scheme retains the bounded footprint property and to a partial degree the compact representation of the Concise Sampling scheme, while ensuring statistical uniformity. Samples from at least two partitioned subsets are merged on demand to yield uniform merged samples of combined partitions wherein the merged samples also maintain the histogram representation and bounded footprint property.

Flexible, efficient and scalable sampling

A pattern-based data matching method matches pattern-based data. The data matching method generates a regular expression pattern for input datasets and describes similarity measures between the generated patterns. The data matching method analyzes an input dataset in terms of symbol classes, generalizing input values into a general pattern to allow identification or extrapolation of overlap between input datasets, aiding in matching fields in databases that are being merged and in learning a pattern for an input dataset. For each sequence of data values, the present method computes a compact pattern describing the sequence. Embodiments of the data matching method comprise noise reduction and repetitive pattern discovery in the input dataset and calculation of recall and precision of the generated pattern.

Growing community of inventors

San Jose, CA, United States of America

Paul Geoffrey Brown