Ab Initio Technology LLC

Arlen Anderson

Kamil Trojan

Taro Ikai

A method for clustering data elements stored in a data storage system includes reading data elements from the data storage system. Clusters of data elements are formed with each data element being a member of at least one cluster. At least one data element is associated with two or more clusters. Membership of the data element belonging to respective ones of the two or more clusters is represented by a measure of ambiguity. Information is stored in the data storage system to represent the formed clusters.

Fuzzy data operations

Profiling data includes accessing multiple collections of records to store quantitative information for each particular collection including, for at least one selected field of the records in the particular collection, a corresponding list of value count entries, each including a value appearing in the selected field and a count of the number of records in which the value appears. Processing the quantitative information of two or more collections includes: merging the value count entries of corresponding lists for at least one field from each of a first collection and a second collection to generate a combined list of value count entries, and aggregating value count entries of the combined list of value count entries to generate a list of distinct field value entries identifying a distinct value and including information quantifying a number of records in which the distinct value appears for each of the two or more collections.

Profiling data with source tracking

Received data records, each including one or more values in one or more fields, are processed to identify a matched data cluster. The processing includes: for selected data records, generating a query from one or more values; identifying one or more candidate data records from the received data records using the query; determining whether or not the selected data record satisfies a cluster membership criterion for at least one candidate data cluster of one or more existing data clusters containing the candidate records; and selecting the matched data cluster from among one or more candidate data clusters based at least in part on a growth criterion for the candidate data clusters, or initializing the matched data cluster with the selected data record if the selected data record does not satisfy a cluster membership criterion for any of the existing data clusters or based on a result of the growth criterion.

Data clustering based on candidate queries

A first set of original records is processed by a first processing entity to generate a second set of records that includes the original records and one or more copies of each original record, each original record including one or more fields. The processing of each of at least some of the original records includes: generating at least one copy of the original record, and associating a first segment value with the original record and associating a second segment value with the copy. The method also includes partitioning the second set of records among a plurality of recipient processing entities based on the segment values associated with the records in the second set, and, at each recipient processing entity, performing an operation based on one or more data values of the records received at the recipient processing entity to generate results.

Data clustering, segmentation, and parallelization

Profiling data includes processing an accessed collection of records, including: generating, for a first set of distinct values appearing in a first set of one or more fields, corresponding location information; generating, for the first set of fields, a corresponding list of entries identifying a distinct value from the first set of distinct values and the location information for the distinct value; generating, for a second set of one or more fields, a corresponding list of entries, with each entry identifying a distinct value from a second set of distinct values appearing in the second set of fields; and generating result information, based at least in part on: locating at least one record of the collection using the location information for at least one value appearing in the first set of fields, and determining at least one value appearing in the second set of fields of the located record.

Profiling data with location information

A computing system for representing information, the computing system including at least one processor configured to process information. The processing includes defining a data structure representing a hierarchy of at least one programming attribute for developing an application. The data structure is stored in a file to allow the data structure to be used by other data structures stored in other files. The processing also includes producing a visual diagram including a graphical representation of the data structure and a graphical representation of the file storing the data structure. The visual diagram also includes a graphical representation of a relationship between the data structure and another data structure and a graphical representation of a relationship between the file storing the data structure and another file storing the other data structure. The computing system also including an output device for presenting the visual diagram that includes the graphical representations of the data structure and file and the graphical representations of the relationship of the data structures and the relationship of the files.

Graphically representing programming attributes

A data storage system stores at least one dataset including a plurality of records. A data processing system, coupled to the data storage system, processes the plurality of records to produce codes representing data patterns in the records, the processing including: for each of multiple records in the plurality of records, associating with the record a code encoding one or more elements, wherein each element represents a state or property of a corresponding field or combination of fields as one of a set of element values, and, for at least one element of at least a first code, the number of element values in the set is smaller than the total number of data values that occur in the corresponding field or combination of fields over all of the plurality of records in the dataset.

Generating data pattern information

In one aspect, in general, a method is described for managing an archive for determining approximate matches associated with strings occurring in records. The method includes: processing records to determine a set of string representations that correspond to strings occurring in the records; generating, for each of at least some of the string representations in the set, a plurality of close representations that are each generated from at least some of the same characters in the string; and storing entries in the archive that each represent a potential approximate match between at least two strings based on their respective close representations.

Managing an archive for approximate string matching

Received data records, each including one or more values in one or more fields, are processed to identify one or more data clusters. The processing includes: identifying tokens that each include at least one value or fragment of a value in a field or a combination of fields; generating a network representing the identified tokens, with nodes of the network representing tokens and edges of the network each representing a variant relationship between tokens; and generating a graphical representation of the network with different subsets of nodes distinguished based at least in part on values associated with nodes, where a value associated with a particular node quantifies a count of a number of instances of the token represented by that particular node appearing within the received data records.

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Kidlington, United Kingdom

Arlen Anderson