Abstract: Data elements from data sources and having a data value set are linked by using hash functions to determine a dimensionally reduced instance signature for each data element based on all data values associated with that data element to yield a plurality of dimensionally reduced instance signatures of equivalent fixed size such that similarities among the data values in the data value sets across all data elements is maintained among the plurality of instance signatures. Candidate pairs of data elements to link are identified using the plurality of instance signatures in locality sensitive hash functions, and a similarity index is generated for each candidate pair using a pre-determined measure of similarity. Candidate pairs of data elements having a similarity index above a given threshold are linked.

Abstract: Data elements from data sources and having a data value set are linked by using hash functions to determine a dimensionally reduced instance signature for each data element based on all data values associated with that data element to yield a plurality of dimensionally reduced instance signatures of equivalent fixed size such that similarities among the data values in the data value sets across all data elements is maintained among the plurality of instance signatures. Candidate pairs of data elements to link are identified using the plurality of instance signatures in locality sensitive hash functions, and a similarity index is generated for each candidate pair using a pre-determined measure of similarity. Candidate pairs of data elements having a similarity index above a given threshold are linked.

Abstract: A method for predictive analytics in a semi-structured process including receiving traces of the semi-structured process, at least one of the traces including a document content value, determining a process model from the traces, the process model comprising tasks at nodes of the semi-structured process and embodies all possible execution sequences in the process, determining a probabilistic graph including a probability at each of the tasks of the semi-structured process advancing from one task to another task, and combining the process model and probabilistic graph to determine a probabilistic process model including probabilities and strengths of transitions between tasks.

Abstract: A “Real-Time-Ready Analyzer” combines a data stream management system (DSMS) with a map-reduce (M-R) framework to construct a streaming map-reduce framework that is suitable for real-time Behavioral Targeting (BT) (or other temporal queries). The Real-Time-Ready Analyzer allows users to write “dual-intent” temporal analysis queries for BT. These queries are succinct and easy to express, scale well on large-scale offline data, and can also work over real-time data. Further, the Real-Time-Ready Analyzer uses the aforementioned streaming map-reduce framework to provide dual-intent algorithms for end-to-end BT phases. Experiments using real data from an advertisement system show that the Real-Time-Ready Analyzer is very efficient and incurs orders-of-magnitude lower development effort than conventional systems.

Abstract: Computer-implemented methods for mapping an element of a source information model to an element of a target information model, forming a cluster of elements for mapping across information models, and evaluating a mapping of elements across information models, and a system and computer program product thereof. The method of mapping an element of a source information model to an element of a target information model includes: receiving information for mapping a first element in a source cluster to an element in the target information model; mapping the first element to the target element using the received information for mapping the first element to the target element; and mapping all other elements in the source cluster to the target element.

Abstract: According to an aspect of the present principles, a method is provided for generating resource description framework benchmarks. The method includes deriving a resultant benchmark dataset with a user specified size and a user specified coherence from and with respect to an input dataset of a given size and a given coherence by determining which triples of subject-property-object to add to the input dataset or remove from the input dataset to derive the resultant benchmark dataset.

Abstract: Systems and methods are provided for the automatic detection of different types of changes in a business process. A system includes a transformer for performing a transformation on data derived from process traces or models extracted from the processes traces to generate transformed data. The process traces are for a business process corresponding to a set of related tasks for a specified goal. Each of the models has at least a transition matrix of dimension N×N, where N is a total number of the related tasks. The system further includes a change detector for performing change detection on the transformed data to identify at least one of when a change occurs in the business process and a degree of the change.

Abstract: Methods and systems for aggregating search query results include receiving search query results and schema information for the query results from multiple heterogeneous sources, determining types for elements of the query results based on the schema information, determining potential aggregations for the query results based on the types, which are based on accumulated information from the plurality of heterogeneous resources, and aggregating the query results according to one or more of the potential aggregations.

Abstract: Methods and systems for determining schema element types are shown that include pooling potential annotations for an element of an unlabeled schema from a plurality of heterogeneous sources, scoring the pool of potential annotations according to relevancy using information using instance information from the plurality of heterogeneous sources to produce a relevancy score, and annotating the element of the unlabeled schema using the most relevant potential annotations.

Abstract: According to an aspect of the present principles, a method is provided for generating resource description framework benchmarks. The method includes deriving a resultant benchmark dataset with a user specified size and a user specified coherence from and with respect to an input dataset of a given size and a given coherence by determining which triples of subject-property-object to add to the input dataset or remove from the input dataset to derive the resultant benchmark dataset.

Abstract: A method for predictive analytics in a semi-structured process including receiving traces of the semi-structured process, at least one of the traces including a document content value, determining a process model from the traces, the process model comprising tasks at nodes of the semi-structured process and embodies all possible execution sequences in the process, determining a probabilistic graph including a probability at each of the tasks of the semi-structured process advancing from one task to another task, and combining the process model and probabilistic graph to determine a probabilistic process model including probabilities and strengths of transitions between tasks.

Abstract: Computer-implemented methods for mapping an element of a source information model to an element of a target information model, forming a cluster of elements for mapping across information models, and evaluating a mapping of elements across information models, and a system and computer program product thereof. The method of mapping an element of a source information model to an element of a target information model includes: receiving information for mapping a first element in a source cluster to an element in the target information model; mapping the first element to the target element using the received information for mapping the first element to the target element; and mapping all other elements in the source cluster to the target element.

Abstract: A method for predictive analytics for a process includes receiving at least one trace of the process, building a probabilistic graph modeling the at least one trace, determining content at each node of the probabilistic graph, wherein a node represents an activity of the process and at least one node is a decision node, modeling each decision node as a respective decision tree, and predicting, for an execution of the process, a path in the probabilistic graph from any decision node to a prediction target node of a plurality of prediction target nodes given the content.

Abstract: A method of performing a graph query issued by a user is provided. The method includes performing on a processor, receiving a user graph query; rewriting the user graph query as a new query based on a query policy expressed in a graph query language; and performing the new query on graph data to obtain a result.

Abstract: A method of processing a query is provided. The method includes performing on a processor: receiving a database query that includes a plurality of predicates that associate a subject with an object, where one or more of the predicates is a variable predicate; generating at least one new query by selectively replacing the at least one variable predicate in the database query with a non-variable predicate; and performing the at least one new database query on a database to obtain a query result.

Abstract: A “Real-Time-Ready Analyzer” combines a data stream management system (DSMS) with a map-reduce (M-R) framework to construct a streaming map-reduce framework that is suitable for real-time Behavioral Targeting (BT) (or other temporal queries). The Real-Time-Ready Analyzer allows users to write “dual-intent” temporal analysis queries for BT. These queries are succinct and easy to express, scale well on large-scale offline data, and can also work over real-time data. Further, the Real-Time-Ready Analyzer uses the aforementioned streaming map-reduce framework to provide dual-intent algorithms for end-to-end BT phases. Experiments using real data from an advertisement system show that the Real-Time-Ready Analyzer is very efficient and incurs orders-of-magnitude lower development effort than conventional systems.