International Business Machines Corporation

Microsoft Technology Licensing, LLC

Songyun Duan

Anastasios Kementsietsidis

Kavitha Srinivas

Achille Belly Fokoue-Nkoutche

Wangchao Le

Feifei Li

Michael James Ward

Octavian Udrea

Oktie Hassanzadeh

Mihaela Ancuta Bornea

Geetika Tewari Lakshmanan

Alfio Massimiliano Gliozzo

Balan Sethu Raman

Aditya Anand Kalyanpur

Jonathan David Goldstein

James J Fan

Min Wang

Brian Patrick Byrne

Badrish Chandramouli

Paul Turquand Keyser

Rania Yousef Khalaf

Xin Jin

Davood Shamsi

Brendan O'Sullivan

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.

Linking data elements based on similarity data values and semantic annotations

Multiquery optimization is performed in the context of RDF/SPARQL. Heuristic algorithms partition an input batch of queries into groups such that each group of queries can be optimized together. The optimization incorporates an efficient algorithm to discover the common sub-structures of multiple SPARQL queries and an effective cost model to compare candidate execution plans. No assumptions are made about the underlying SPARQL query engine. This provides portability across different RDF stores.

Scalable multi-query optimization for SPARQL

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.

Annotating schema elements based on associating data instances with knowledge base entities

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.

Creating benchmark graph data

A computer-implemented method, system, and article of manufacture for querying and integrating structured and unstructured data. The method includes: receiving entity information that is extracted from a first set of unstructured data using an open domain information extraction system, wherein the entity in-formation comprises relationship information between a first entity and a second entity of the first set of unstructured data; recognizing a pattern based on the relationship information and creating a schema for the first set of unstructured data based on the pattern; and associating an element of the created schema with (i) an entity of a second set of unstructured data or (ii) a schema element of an existing set of structured data if there is sufficient overall similarity between the created schema element and either the second unstructured data entity or the schema element of the existing structured data.

Querying and integrating structured and unstructured data

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. The method includes rewriting the user graph query as a new query based on a query policy expressed in a graph query language. The method includes performing the new query on graph data to obtain a result.

Enforcing query policies over resource description framework data

Keyword searching is used to explore and search large Resource Description Framework datasets having unknown or constantly changing structures. A succinct and effective summarization is built from the underlying resource description framework data. Given a keyword query, the summarization lends significant pruning powers to exploratory keyword searches and leads to much better efficiency compared to previous work. The summarization returns exact results and can be updated incrementally and efficiently.

Scalable summarization of data graphs

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.

Predicting outcomes of a content driven process instance execution

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.

Real-time-ready behavioral targeting in a large-scale advertisement system

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.

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Pleseantville, NY, United States of America

Songyun Duan