Abstract: Techniques facilitating assessment result determination based on predictive analytics and/or machine learning are provided. In one example, a computer-implemented method can comprise matching, by a system operatively coupled to a processor, input data retained in a knowledge source database to an inquiry included in a received questionnaire. The input data can be associated with a target entity. The computer-implemented method can also comprise generating, by the system, a response to the inquiry based on the input data retained in the knowledge source database and a feature value that specifies a defined form of the response. The response can be based on an applicability of the input data to the target entity. Further, generating the response can be based on machine learning applied to information retained in the knowledge source database.

Abstract: A method for query evaluation comprises receiving a query over a set of distributed data sources, decomposing the query into a set of sub-queries of the query, evaluating each sub-query in the set of sub-queries with respect to each data source in the set of distributed data sources, wherein evaluating comprises determining which data sources in the set of distributed data sources are capable of answering each sub-query and at what cost, computing a set of distributed plans by composing one or more of the sub-queries in one or more of the data sources, evaluating each plan in the set of distributed plans, selecting a sub-set of plans from the set of distributed plans to be executed for responding to the query, executing the selected sub-set of plans, and returning results of the query.

Abstract: A method for query evaluation comprises receiving a query over a set of distributed data sources, decomposing the query into a set of sub-queries of the query, evaluating each sub-query in the set of sub-queries with respect to each data source in the set of distributed data sources, wherein evaluating comprises determining which data sources in the set of distributed data sources are capable of answering each sub-query and at what cost, computing a set of distributed plans by composing one or more of the sub-queries in one or more of the data sources, evaluating each plan in the set of distributed plans, selecting a sub-set of plans from the set of distributed plans to be executed for responding to the query, executing the selected sub-set of plans, and returning results of the query.

Abstract: A method for unsupervised transformation of denormalized data into RDF data includes summarizing an existing resource description framework (RDF) file wherein a data summary file is produced, wherein said RDF file includes a plurality of nodes, property labels, and literals and is represented as a directed graph, and said data summary file is a graph that includes a single instance of each node-property-literal triple in the RDF file; analyzing a plurality of denormalized records wherein a record summary is produced, wherein said record summary is represented as a directed graph with a root node corresponding to each record connected to a node for each field in the record; determining one or more transformation patterns that match elements of the record summary to elements of the data summary; and transforming the plurality of denormalized records into a new RDF file by executing one of the one or more transformation patterns.

Abstract: An aspect of collaborative planning includes receiving plan drivers that collectively span multiple knowledge domains, and receiving user profiles, each of which specifies domain-specific data elements attributed to corresponding users who are tasked with carrying out the plan. The domain-specific data elements correspond to the multiple knowledge domains. An aspect further includes mapping each of the drivers to the users based on a relationship between the driver and the corresponding domain-specific data elements, and assigning actions to each mapped driver. For each of the users, an aspect includes creating a customized computer-implemented view of the plan that includes a user-directed corresponding mapped driver and a corresponding assigned action.

Abstract: Accessing data stored on a remote server includes determining a first address of a first server where a block of data identified by a key has been moved from a second server to the first server, the second server having a second address. A first entry of a switch flow table of a datacenter network is updated to indicate that received packets with a destination address including the second address are to be overwritten with the first address. The data center network includes a plurality of physical network switches. A second entry in the switch flow table may be configured to forward packets with the first address to a first port associated with the first server and a first set of the switches. If the second entry is not already present in the table, the second entry may be inserted into the switch flow table.

Abstract: An embodiment of the invention provides a method wherein a database query including a first constraint and additional constraint(s) are received in a first node. Data in the first node that satisfies the first constraint is identified, encoded, and sent to a second node. Encoded data is identified in a mapping table in the second node; and, one or more missing identifiers are identified that include encoded data that is not in the mapping table. The missing identifier is sent to the first node, decoded to retrieve the value of the missing identifier, and mapped to the retrieved value. The mapping of the missing identifier and the retrieved value are sent to the second node. A dictionary in the second node is queried with the retrieved value to identify an identification number for the retrieved value. The missing identifier is mapped to the identification number for the retrieved value.

Abstract: A system for processing a transaction request. A transaction request is received by a server computer that includes a user identification and an associated request token, to execute on an application on the server computer. It is determined by the server computer whether available resources on the server computer to perform the transaction are below respective threshold values. In response to determining, by the server computer, that the available resources on the server computer to perform the transaction are below the threshold value, the server computer determines whether the user identification is allowed access to the application. In response to determining that the user identification is allowed access to the application, the transaction is executed on the server computer. In response to determining that the user identification is not allowed access to the application, the transaction is rejected.

Abstract: A method, system and computer program product for transforming a Linked Data graph into a corresponding anonymous Linked Data graph, in which semantics is preserved and links can be followed to expand the anonymous graph up to r times without breaching anonymity (i.e., anonymity under r-dereferenceability). Anonymizing a Linked Data graph under r-dereferenceability provides privacy guarantees of k-anonymity or l-diversity variants, while taking into account and preserving the rich semantics of the graph.

Abstract: A system and computer program product for transforming a Linked Data graph into a corresponding anonymous Linked Data graph, in which semantics is preserved and links can be followed to expand the anonymous graph up to r times without breaching anonymity (i.e., anonymity under r-dereferenceability). Anonymizing a Linked Data graph under r-dereferenceability provides privacy guarantees of k-anonymity or l-diversity variants, while taking into account and preserving the rich semantics of the graph.

Abstract: A method for query evaluation comprises receiving a query over a set of distributed data sources, decomposing the query into a set of sub-queries of the query, evaluating each sub-query in the set of sub-queries with respect to each data source in the set of distributed data sources, wherein evaluating comprises determining which data sources in the set of distributed data sources are capable of answering each sub-query and at what cost, computing a set of distributed plans by composing one or more of the sub-queries in one or more of the data sources, evaluating each plan in the set of distributed plans, selecting a sub-set of plans from the set of distributed plans to be executed for responding to the query, executing the selected sub-set of plans, and returning results of the query.

Abstract: A method for query evaluation comprises receiving a query over a set of distributed data sources, decomposing the query into a set of sub-queries of the query, evaluating each sub-query in the set of sub-queries with respect to each data source in the set of distributed data sources, wherein evaluating comprises determining which data sources in the set of distributed data sources are capable of answering each sub-query and at what cost, computing a set of distributed plans by composing one or more of the sub-queries in one or more of the data sources, evaluating each plan in the set of distributed plans, selecting a sub-set of plans from the set of distributed plans to be executed for responding to the query, executing the selected sub-set of plans, and returning results of the query.

Abstract: Technique to determine relative relevance of pairs of nodes in a linked data graph, such as an RDF graph, including calculation of raw relevance scores (which are based upon adjacency relations between nodes after edge filtering, the incoming and outgoing degrees of nodes in the graph after the edge filtering, and the distance (or weight) values of the edge(s)) and/or “refined relevance scores,” which take into account “complementary nodes.

Abstract: A method, system and computer program product for preventing sensitive pattern disclosures from Linked Data graphs. The proposed method (i) receives as input a Linked Data graph and a set of query patterns that correspond to sensitive knowledge that needs to be concealed, and (b) minimally distorts the Linked Data graph to generate a sanitized counterpart (graph) in which only the non-sensitive patterns can be discovered. The method supports a variety of utility functions, which it optimizes during the graph sanitization process. The resulting, sanitized graph can be subsequently used for publishing and/or querying purposes.

Abstract: A system for processing a transaction request. A transaction request is received by a server computer that includes a user identification and an associated request token, to execute on an application on the server computer. It is determined by the server computer whether available resources on the server computer to perform the transaction are below respective threshold values. In response to determining, by the server computer, that the available resources on the server computer to perform the transaction are below the threshold value, the server computer determines whether the user identification is allowed access to the application. In response to determining that the user identification is allowed access to the application, the transaction is executed on the server computer. In response to determining that the user identification is not allowed access to the application, the transaction is rejected.

Abstract: A method, system and computer program product for preventing sensitive pattern disclosures from Linked Data graphs. The proposed method (i) receives as input a Linked Data graph and a set of query patterns that correspond to sensitive knowledge that needs to be concealed, and (b) minimally distorts the Linked Data graph to generate a sanitized counterpart (graph) in which only the non-sensitive patterns can be discovered. The method supports a variety of utility functions, which it optimizes during the graph sanitization process. The resulting, sanitized graph can be subsequently used for publishing and/or querying purposes.

Abstract: A system and computer program product for transforming a Linked Data graph into a corresponding anonymous Linked Data graph, in which semantics is preserved and links can be followed to expand the anonymous graph up to r times without breaching anonymity (i.e., anonymity under r-dereferenceability). Anonymizing a Linked Data graph under r-dereferenceability provides privacy guarantees of k-anonymity or l-diversity variants, while taking into account and preserving the rich semantics of the graph.

Abstract: A method, system and computer program product for transforming a Linked Data graph into a corresponding anonymous Linked Data graph, in which semantics is preserved and links can be followed to expand the anonymous graph up to r times without breaching anonymity (i.e., anonymity under r-dereferenceability). Anonymizing a Linked Data graph under r-dereferenceability provides privacy guarantees of k-anonymity or l-diversity variants, while taking into account and preserving the rich semantics of the graph.