Abstract: Data records are linked across a plurality of datasets. Each dataset contains at least one data record, and each data record is associated with an entity and includes one or more attributes of that entity and a value for each attribute. Values associated with attributes are compared across datasets, and matching attributes having values that satisfy a predetermined similarity threshold are identified. In addition, linkage points between pairs of datasets are identified. Each linkage point links one or more pairs of data records. Each data record in the pair of data records is contained in one of a given pair of datasets, and each pair of data records is associated with a common entity having matching attributes in the given pair of datasets. Data records associated with the common entities are linked across datasets using the identified linkage points.

Abstract: Data records are linked across a plurality of datasets. Each dataset contains at least one data record, and each data record is associated with an entity and includes one or more attributes of that entity and a value for each attribute. Values associated with attributes are compared across datasets, and matching attributes having values that satisfy a predetermined similarity threshold are identified. In addition, linkage points between pairs of datasets are identified. Each linkage point links one or more pairs of data records. Each data record in the pair of data records is contained in one of a given pair of datasets, and each pair of data records is associated with a common entity having matching attributes in the given pair of datasets. Data records associated with the common entities are linked across datasets using the identified linkage points.

Abstract: Data records are linked across a plurality of datasets. Each dataset contains at least one data record, and each data record is associated with an entity and includes one or more attributes of that entity and a value for each attribute. Values associated with attributes are compared across datasets, and matching attributes having values that satisfy a predetermined similarity threshold are identified. In addition, linkage points between pairs of datasets are identified. Each linkage point links one or more pairs of data records. Each data record in the pair of data records is contained in one of a given pair of datasets, and each pair of data records is associated with a common entity having matching attributes in the given pair of datasets. Data records associated with the common entities are linked across datasets using the identified linkage points.

Abstract: A method includes receiving one or more natural language dependency parse trees as input. A hardware processor is used for processing the dependency parse trees by creating a mapping from nodes of the one or more dependency parse trees into actions, roles and contextual predicates. The mapping is used for information extraction. The actions include the verbs along with attributes of the verbs. The roles include arguments for the verbs. The contextual predicates include modifiers for the verbs.

Abstract: Various embodiments of a method, system and article of manufacture to discover relationships among a first set of elements and a second set of elements are provided. At least one metric algorithm is identified based on a metric selection parameter. A raw result is determined based on the at least one metric algorithm, a first specified structural description of the first set of elements and a second specified structural description of the second set of elements. The raw result comprises a plurality of relationship measurements and the raw result is ordered. In some embodiments, a balanced result is produced based on the raw result and a matching strategy algorithm. In other embodiments, the matching strategy algorithm is identified based on a matching strategy selection parameter.

Abstract: Data records are linked across a plurality of datasets. Each dataset contains at least one data record, and each data record is associated with an entity and includes one or more attributes of that entity and a value for each attribute. Values associated with attributes are compared across datasets, and matching attributes having values that satisfy a predetermined similarity threshold are identified. In addition, linkage points between pairs of datasets are identified. Each linkage point links one or more pairs of data records. Each data record in the pair of data records is contained in one of a given pair of datasets, and each pair of data records is associated with a common entity having matching attributes in the given pair of datasets. Data records associated with the common entities are linked across datasets using the identified linkage points.

Abstract: Data records are linked across a plurality of datasets. Each dataset contains at least one data record, and each data record is associated with an entity and includes one or more attributes of that entity and a value for each attribute. Values associated with attributes are compared across datasets, and matching attributes having values that satisfy a predetermined similarity threshold are identified. In addition, linkage points between pairs of datasets are identified. Each linkage point links one or more pairs of data records. Each data record in the pair of data records is contained in one of a given pair of datasets, and each pair of data records is associated with a common entity having matching attributes in the given pair of datasets. Data records associated with the common entities are linked across datasets using the identified linkage points.

Abstract: A method includes receiving one or more natural language dependency parse trees as input. A hardware processor is used for processing the dependency parse trees by creating a mapping from nodes of the one or more dependency parse trees into actions, roles and contextual predicates. The mapping is used for information extraction. The actions include the verbs along with attributes of the verbs. The roles include arguments for the verbs. The contextual predicates include modifiers for the verbs.

Abstract: Data records are linked across a plurality of datasets. Each dataset contains at least one data record, and each data record is associated with an entity and includes one or more attributes of that entity and a value for each attribute. Values associated with attributes are compared across datasets, and matching attributes having values that satisfy a predetermined similarity threshold are identified. In addition, linkage points between pairs of datasets are identified. Each linkage point links one or more pairs of data records. Each data record in the pair of data records is contained in one of a given pair of datasets, and each pair of data records is associated with a common entity having matching attributes in the given pair of datasets. Data records associated with the common entities are linked across datasets using the identified linkage points.

Abstract: A computer-implemented method for use with an extensible markup language (XML) document includes inputting a high-level mapping specification for a schema mapping; and generating a target XML document based on the mapping. The method may perform schema mapping-based XML transformation as a three-phase process comprising tuple extraction, XML-fragment generation, and data merging. The tuple extraction phase may be adapted to handle streamed XML data (as well as stored/indexed XML data). The data merging phase may use a hybrid method that can dynamically switch between main memory-based and disk-based algorithms based on the size of the XML data to be merged.

Abstract: Described herein are methods, systems, apparatuses and products for automatically discovering patterns in a text corpus. An aspect provides extracting at least one context string related to at least one annotator from the at least one text corpus; analyzing the at least one context string for at least one sequence, the at least one sequence comprised of at least one subsequence; determining at least one sequence signature for each at least one sequence by applying applicable rules to the at least one sequence; and grouping the at least one sequence signature into at least one group.

Abstract: Described herein are methods, systems, apparatuses and products for automatically discovering patterns in a text corpus. An aspect provides extracting at least one context string related to at least one annotator from the at least one text corpus; analyzing the at least one context string for at least one sequence, the at least one sequence comprised of at least one subsequence; determining at least one sequence signature for each at least one sequence by applying applicable rules to the at least one sequence; and grouping the at least one sequence signature into at least one group.

Abstract: A method and system for generating a query implementing a schema mapping. A mapping M is provided from a schema S to a schema T, where M relates S to T, and M includes a plurality of constraints. Schemas S and T each include one or more elements, and T includes at least one set type element. Mapping M is expressed in terms of at least one nested tuple-generating dependency. A query Q is generated where Q is capable of applying M to an input instance I to result in an output instance J, where I conforms to S, J conforms to T, and I and J satisfy the plurality of constraints. Instance J is in partitioned normal form (i.e., satisfies minimal union semantics) and includes no duplicate element instances.

Abstract: A method and apparatus are disclosed for querying streaming extensible markup language (XML) data comprising: routing elements to query nodes, the elements derived from the streaming extensible markup language data; filtering out elements not conforming to one or more predetermined path query patterns; adding remaining elements to one or more dynamic element lists; accessing a decision table to select and return a query node related to a cursor element from the dynamic element lists; and processing the cursor element related to the returned query node to produce an extracted tuple output.

Abstract: A method and apparatus are disclosed for querying streaming extensible markup language (XML) data comprising: routing elements to query nodes, the elements derived from the streaming extensible markup language data; filtering out elements not conforming to one or more predetermined path query patterns; adding remaining elements to one or more dynamic element lists; accessing a decision table to select and return a query node related to a cursor element from the dynamic element lists; and processing the cursor element related to the returned query node to produce an extracted tuple output.

Abstract: A computer-implemented method for use with an extensible markup language (XML) document includes inputting a high-level mapping specification for a schema mapping; and generating a target XML document based on the mapping. The method may perform schema mapping-based XML transformation as a three-phase process comprising tuple extraction, XML-fragment generation, and data merging. The tuple extraction phase may be adapted to handle streamed XML data (as well as stored/indexed XML data). The data merging phase may use a hybrid method that can dynamically switch between main memory-based and disk-based algorithms based on the size of the XML data to be merged.

Abstract: A method for generating nested mapping specifications and transformation queries based thereon. Basic mappings are generated based on source and target schemas and correspondences between elements of the schemas. A directed acyclic graph (DAG) is constructed whose edges represent ways in which each basic mapping is nestable under any of the other basic mappings. Any transitively implied edges are removed from the DAG. Root mappings of the DAG are identified. Trees of mappings are automatically extracted from the DAG, where each tree of mappings is rooted at a root mapping and expresses a nested mapping specification.

Abstract: A method and system for generating nested mapping specifications and transformation queries based thereon. Basic mappings are generated based on source and target schemas and correspondences between elements of the schemas. A directed acyclic graph (DAG) is constructed whose edges represent ways in which each basic mapping is nestable under any of the other basic mappings. Any transitively implied edges are removed from the DAG. Root mappings of the DAG are identified. Trees of mappings are automatically extracted from the DAG, where each tree of mappings is rooted at a root mapping and expresses a nested mapping specification. A transformation query is generated from the nested mapping specification by generating a first query for transforming source data into flat views of the target and a second query for nesting flat view data according to the target format. Generating the first query includes applying default Skolemization to the specification.

Abstract: A computer-implemented method for use with an extensible markup language (XML) document includes inputting a high-level mapping specification for a schema mapping; and generating a target XML document based on the mapping. The method may perform schema mapping-based XML transformation as a three-phase process comprising tuple extraction, XML-fragment generation, and data merging. The tuple extraction phase may be adapted to handle streamed XML data (as well as stored/indexed XML data). The data merging phase may use a hybrid method that can dynamically switch between main memory-based and disk-based algorithms based on the size of the XML data to be merged.

Abstract: A method and system for generating a query implementing a schema mapping. A mapping M is provided from a schema S to a schema T, where M relates S to T, and M includes a plurality of constraints. Schemas S and T each include one or more elements, and T includes at least one set type element. Mapping M is expressed in terms of at least one nested tuple-generating dependency. A query Q is generated where Q is capable of applying M to an input instance I to result in an output instance J, where I conforms to S, J conforms to T, and I and J satisfy the plurality of constraints. Instance J is in partitioned normal form (i.e., satisfies minimal union semantics) and includes no duplicate element instances.