International Business Machines Corporation

Tanveer Afzal Faruquie

L Venkata Subramaniam

Mukesh Kumar Mohania

Hima Prasad Karanam

Snigdha Chaturvedi

Marvin Mendelssohn

Raghuram Krishnapuram

Girish Venkatachaliah

Sachindra Joshi

Vivek Tyagi

Nandita J Mahajan

L Venkat Subramaniam

Sameep Mehta

Nitendra Rajput

Sriram Kolinjivadi Padmanabhan

Ashish Verma

Charles Daniel Wolfson

Shantanu Ravindra Godbole

Chalapathy Venkata Neti

Indrajit Bhattacharya

Mohan Nagraj Dani

Ullas Balan Nambiar

Sandeep Jindal

Angel Marie Smith

Abhishek Verma

Ganesh Ramakrishnan

Computer program products and systems are provided for mining for sub-patterns within a text data set. The embodiments facilitate finding a set of N frequently occurring sub-patterns within the data set, extracting the N sub-patterns from the data set, and clustering the extracted sub-patterns into K groups, where each extracted sub-pattern is placed within the same group with other extracted sub-patterns based upon a distance value D that determines a degree of similarity between the sub-pattern and every other sub-pattern within the same group.

Automatically mining patterns for rule based data standardization systems

The present invention relates to data cleansing, and in particular performing the semantic standardization process within a database before the transform portion of the extract-transform-load (ETL) process. Provided are a method, system and computer program product for standardizing data within a database engine, configuring the standardization function to determine at least one standardized value for at least one data value by applying the standardization table in a context of at least one data value, receiving a database query identifying the standardization function, at least one database value and the context of the data, and invoking the standardization function.

In-querying data cleansing with semantic standardization

Systems and associated methods for address standardization and applications related thereto are described. Embodiments exploit a common context in a taxonomy and a given address to detect and correct deviations in the address. Embodiments establish a possible path from a root of the taxonomy to a leaf in the taxonomy that can possibly generate a given address. Given a new address, embodiments use complete addresses, and/or segments or elements thereof, to compute the representations of the elements and find a closest matching leaf in the taxonomy. Embodiments then traverse the path to a root node to detect the agreement and disagreement between the path and the address entry. Taxonomical structured is thus used to detect, segregate and standardize the expected fields.

Systems and methods for standardization and de-duplication of addresses using taxonomy

Techniques, systems, and articles of manufacture for determining related data points from multi-modal inputs. A method includes collecting multiple items of multi-modal data comprising at least one dimension from multiple data sources, wherein said at least one dimension comprises a geographic dimension, a temporal dimension and/or an event-related dimension, determining a window of relevance for each of the multiple items of multi-modal data with respect to the at least one dimension, and identifying two or more of the multiple items of multi-modal data as related, by determining an overlap of the window of relevance corresponding to each of the two or more items of multi-modal data with respect to the at least one dimension.

Determining related data points from multi-modal inputs

A method, system and a computer program product for determining resources allocation in a distributed computing environment. An embodiment may include identifying resources in a distributed computing environment, computing provisioning parameters, computing configuration parameters and quantifying service parameters in response to a set of service level agreements (SLA). The embodiment may further include iteratively computing a completion time required for completion of the assigned task and a cost. Embodiments may further include computing an optimal resources configuration and computing at least one of an optimal completion time and an optimal cost corresponding to the optimal resources configuration. Embodiments may further include dynamically modifying the optimal resources configuration in response to at least one change in at least one of provisioning parameters, computing parameters and quantifying service parameters.

Resources management in distributed computing environment

According to one embodiment of the present invention, a system controls cleansing of data within a database system, and comprises a computer system including at least one processor. The system receives a data set from the database system, and one or more features of the data set are selected for determining values for one or more characteristics of the selected features. The determined values are applied to a data quality estimation model to determine data quality estimates for the data set. Problematic data within the data set are identified based on the data quality estimates, where the cleansing is adjusted to accommodate the identified problematic data. Embodiments of the present invention further include a method and computer program product for controlling cleansing of data within a database system in substantially the same manner described above.

Cleansing a database system to improve data quality

Methods, computer program products and systems are provided for mining for sub-patterns within a text data set. The embodiments facilitate finding a set of N frequently occurring sub-patterns within the data set, extracting the N sub-patterns from the data set, and clustering the extracted sub-patterns into K groups, where each extracted sub-pattern is placed within the same group with other extracted sub-patterns based upon a distance value D that determines a degree of similarity between the sub-pattern and every other sub-pattern within the same group.

Described herein are methods, systems, apparatuses and products for efficient development of a rule-based system. An aspect provides a method including accessing data records; converting said data records to an intermediate form; utilizing intermediate forms to compute similarity scores for said data records; and selecting as an example to be provided for rule making at least one record of said data records having a maximum dissimilarity score indicative of dissimilarity to already considered examples.

Efficient development of a rule-based system using crowd-sourcing

Systems, methods, and computer products for optimally managing large rule sets are disclosed. Rule dependencies of rules within a set of rules may be determined as a function of rules execution frequency data generated from applying the rules over a data set. The rules within the set of rules may be clustered into rules clusters based on the determined rule dependencies, in which the rules clusters comprise disjoint subsets of the rules within the set of rules. Cluster frequency data for the rules clusters may be used to arrive at an optimal ordering. Each rule within the set of rules may be assigned a unique identification that may capture an execution order of the rules within the set of rules.

Rule set management

A clustering-based approach to data standardization is provided. Certain embodiments take as input a plurality of addresses, identify one or more features of the addresses, cluster the addresses based on the one or more features, utilize the cluster(s) to provide a data-based context useful in identifying one or more synonyms for elements contained in the address(es), and standardize the address(es) to an acceptable format, with one or more synonyms and/or other elements being added to or taken away from the input address(es) as part of the standardization process.

Systems and methods for discovering synonymous elements using context over multiple similar addresses

Systems and methods for efficient development of a rule-based system using crowd-sourcing

Blocking column selection can include determining a first parameter for each column set of a plurality of column sets, wherein the first parameter indicates distribution of blocks in the column set, and determining a second parameter for each column set. The second parameter can indicate block size for the column set. For each column set, a measure of blockability that is dependent upon at least the first parameter and the second parameter can be calculated using a processor. The plurality of column sets can be ranked according to the measures of blockability.

Automatic selection of blocking column for de-duplication

A method of blocking column selection can include determining a first parameter for each column set of a plurality of column sets, wherein the first parameter indicates distribution of blocks in the column set, and determining a second parameter for each column set. The second parameter can indicate block size for the column set. For each column set, a measure of blockability that is dependent upon at least the first parameter and the second parameter can be calculated using a processor. The plurality of column sets can be ranked according to the measures of blockability.

Techniques for identifying one or more inconsistencies between an unstructured document and a back-end fact-base are provided. The techniques include automatically parsing a query document and comparing the document with a back-end fact-base comprising facts relevant to the document, identifying one or more inconsistencies between information mentioned in the document and the facts stored in the back-end fact-base, and providing a response to the query document, wherein the response additionally includes the one or more identified inconsistencies.

Detecting factual inconsistencies between a document and a fact-base

A system () and method are provided for single-pass execution of dynamic pages across multiple request-response cycles. The system () comprises a client () and server () in communication with one another. A container () resides on the server and handles requests made for the result of a dynamic page (). The container controls the processing of the dynamic page. If the dynamic page requires additional information to continue processing, an intermediate request () is transmitted to the client, which responds with an intermediate response () containing the additional information. A notifier servlet () receives the intermediate response and passes the information to the dynamic page so that execution can resume without interruption.

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New Delhi, India

Tanveer Afzal Faruquie