Abstract: In a Byzantine computing environment in which a database is sharded or partitioned among multiple clusters of computing nodes, consensus for and execution of data transactions (e.g., transactions that require and/or affect data of one or more shards) are achieved in a resilient manner. Within some clusters, multiple primary replicas concurrently propose transactions for processing in parallel by all replicas. For some multi-shard transactions, shards involved in the transactions may be logically ring-ordered; each shard in turn achieves consensus among its nodes to commit the transactions, and then executes its portion of the operation after consensus is obtained among all shards. For some other multi-shard transactions, involved shards first determine whether local data constraints are satisfied, after which data modifications are made in parallel.

Abstract: A method includes storing an anchor row vector identification for an anchor row to a local memory. It is determined whether the anchor row vector identification is visible based on isolation requirements. The anchor row vector identification is accessed upon a determination that the anchor row vector identification is visible, and the row vector identification is re-read from the local memory. It is determined whether the anchor row vector identification has not changed since a start of the accessing. Upon a determination that the anchor row vector identification has not changed, read anchor row fields are returned. A first check history is performed on an anchor row history tuple sequence number (TSN) for the anchor row.

Abstract: A system, method and computer program product for disambiguating one or more entity mentions in one or more documents. The method facilitates the simultaneous linking entity mentions in a document based on convolution neural networks and recurrent neural networks that model both the local and global features for entity linking. The framework uses the capacity of convolution neural networks to induce the underlying representations for local contexts and the advantage of recurrent neural networks to adaptively compress variable length sequences of predictions for global constraints. The RNN functions to accumulate information about the previous entity mentions and/or target entities, and provide them as the global constraints for the linking process of a current entity mention.

Abstract: In a Byzantine computing environment in which a database is sharded or partitioned among multiple clusters of computing nodes, consensus for and execution of data transactions (e.g., transactions that require and/or affect data of one or more shards) are achieved in a resilient manner. Within some clusters, multiple primary replicas concurrently propose transactions for processing in parallel by all replicas. For some multi-shard transactions, shards involved in the transactions may be logically ring-ordered; each shard in turn achieves consensus among its nodes to commit the transactions, and then executes its portion of the operation after consensus is obtained among all shards. For some other multi-shard transactions, involved shards first determine whether local data constraints are satisfied, after which data modifications are made in parallel.

Abstract: A method, system, and recording medium for knowledge graph augmentation using data based on a statistical analysis of attributes in the data, including a ranking device configured to rank semantically similar input data elements to create a ranked list of attributes to augment an input of structured data and populate with a data string corresponding to the instances, where the ranking device further combines a set of filters to refine the ranked list of attributes, the set of filters including a first filter according to column ranges of columns, a second filter according to a column uniqueness of the columns, a third filter according to a type of data in a column of the columns, and a fourth filter according to a distribution of values in the columns.

Abstract: A semantic embedding model using geometrical set-centric approach to capture both ABox and TBox representational models is disclosed. The model transforms a semantic-rich knowledge graph into a set of overlapping, disjoint, and/or subsumed n-dimensional spheres that captures and represents semantics embedded in the knowledge graph.

Abstract: Techniques are provided for pre-filtering of join execution over multi-column range summaries and other synopses. An exemplary method comprises maintaining a synopsis for a plurality of data tables, wherein a given synopsis summarizes a set of records in a corresponding data table; and, in response to a request for a join operation for a set of the data tables: joining the synopses associated with the set of data tables to generate a joined synopsis; for joined records in the joined synopsis, obtaining corresponding records from the set of data tables as candidate records; and joining the candidate records. Two or more of the set of data tables can be distributed across a plurality of nodes and the synopses can be replicated and/or broadcasted across the plurality of nodes. Incremental updates to broadcasted and/or replicated synopses are optionally provided to at least one node.

Abstract: A computer-implemented method of analyzing text documents, includes identifying a relationship in a text document associated with an entity, building a predictive model from training data, in response to said identifying a relationship, wherein the predictive model includes a prediction error, and determining whether to store the identified relationship in memory, based on the prediction error.

Abstract: Methods, systems, and computer program products for expressive temporal predictions over semantically-driven time windows are provided herein. A computer-implemented method includes identifying, within a knowledge graph pertaining to a given prediction, a subset of the knowledge graph related to one or more predicted training examples, wherein the subset comprises (i) a set of nodes and (ii) one or more relationships among the set of nodes; determining, for the identified subset, one or more snapshots of the knowledge graph relevant to the given prediction; quantifying a validity window for the one or more predicted training examples, wherein the validity window comprises a temporal bound for prediction validity; and computing a validity window for the given prediction based on the quantified validity window for the one or more predicted training examples.

Abstract: Methods and systems for performing database transactions include executing a first transaction request in a preplay mode that locks the requested data with a prefetch-lock and reads one or more requested data items from storage into a main memory buffer; locking the requested data items with a read/write lock after said data items are read into the main memory buffer; and performing the requested transaction on the data items in the main memory buffer using a processor.

Abstract: A system, method and computer program product for disambiguating one or more entity mentions in one or more documents. The method facilitates the simultaneous linking entity mentions in a document based on convolution neural networks and recurrent neural networks that model both the local and global features for entity linking. The framework uses the capacity of convolution neural networks to induce the underlying representations for local contexts and the advantage of recurrent neural networks to adaptively compress variable length sequences of predictions for global constraints. The RNN functions to accumulate information about the previous entity mentions and/or target entities, and provide them as the global constraints for the linking process of a current entity mention.

Abstract: A method includes logically organizing, by an object hierarchy processor, data objects in a first hierarchy. A portion of the data objects in the first hierarchy logically includes groupings of other data objects. The object hierarchy processor physically organizes the data objects across two or more types of memory in a second hierarchy. Another portion of the data objects in the second hierarchy physically includes groupings of other data objects. Groupings of the data objects in the second hierarchy are dynamically moved across the two or more types of memory. Levels of access of the data objects are tracked using a data structure that maps groupings of the data objects in the first hierarchy onto metadata information including combined access frequencies of the data objects, and current number of accessors to the data objects, in each grouping of the data objects.

Abstract: A system, method and computer program product for disambiguating one or more entity mentions in one or more documents. The method facilitates the simultaneous linking entity mentions in a document based on convolution neural networks and recurrent neural networks that model both the local and global features for entity linking. The framework uses the capacity of convolution neural networks to induce the underlying representations for local contexts and the advantage of recurrent neural networks to adaptively compress variable length sequences of predictions for global constraints. The RNN functions to accumulate information about the previous entity mentions and/or target entities, and provide them as the global constraints for the linking process of a current entity mention.

Abstract: Methods, systems, and computer program products for linkage prediction through similarity analysis are provided herein. A computer-implemented method includes extracting multiple features from (i) one or more attributes of a set of source nodes within a knowledge graph and (ii) one or more attributes of a set of target nodes within the knowledge graph, wherein at least one extracted feature satisfies a designated complexity level; performing a similarity analysis across the at least one extracted feature by applying one or more similarity measures to the at least one extracted feature; predicting one or more sets of links between the source nodes and the target nodes based on the similarity analysis, wherein one or more sets of predicted links satisfy a pre-determined accuracy threshold; and outputting the one or more sets of predicted links to a user.

Abstract: Methods, systems, and computer program products for iterative and targeted feature selection are provided herein. A computer-implemented method includes generating a first prediction value for a variable attribute of a set of objects by executing a predictive model that comprises a set of features for the set of objects; evaluating the prediction error of the predictive model based on said first prediction value; generating additional features upon a determination that the prediction error exceeds a threshold; incorporating the additional features into the predictive model, generating an updated predictive model; generating a second prediction value for the variable attribute by executing the updated predictive model; evaluating the prediction error of the updated predictive model based on said second prediction value; and outputting the second prediction value to a user upon a determination that the prediction error of the updated predictive model is below the threshold.

Abstract: A method includes storing an anchor row vector identification for an anchor row to a local memory. It is determined whether the anchor row vector identification is visible based on isolation requirements. The anchor row vector identification is accessed upon a determination that the anchor row vector identification is visible, and the row vector identification is re-read from the local memory. It is determined whether the anchor row vector identification has not changed since a start of the accessing. Upon a determination that the anchor row vector identification has not changed, read anchor row fields are returned. A first check history is performed on an anchor row history tuple sequence number (TSN) for the anchor row.

Abstract: A method includes setting, by an update processor, a write latch in a first data structure associated with an object. The first data structure is copied to a storage structure. A history tuple sequence number (TSN) of the first data structure is set to point to a TSN of the copied first data structure. The version identifier is set to point to a transaction identification for the object. Data portions are updated for the first data structure. The version identifier is read from the first data structure. It is determined whether the version identifier of the first data structure is visible for a transaction including isolation requirements. If version identifier of the first data structure is visible, the first data structure is accessed and it is determined whether the version identifier of the first data structure changed since starting the transaction.

Abstract: Methods and apparatus are provided for incremental frequent subgraph mining on dynamic graphs. An exemplary subgraph mining method comprises maintaining a set of embeddings comprising matching embeddings of a given subgraph in an input graph; maintaining a first fringe set of subgraphs comprising subgraphs substantially on a fringe of frequent subgraphs in the input graph that satisfy a predefined support threshold; maintaining a second fringe set of subgraphs comprising subgraphs substantially on a fringe of infrequent subgraphs in the input graph that do not satisfy the predefined support threshold; for an edge addition, checking a support of the subgraphs in the second fringe set based on the set of the embeddings and searching for new embeddings created by the edge addition; and for an edge deletion, removing obsolete embeddings that comprise the deleted edge from the first fringe set based on the set of embeddings.

Abstract: A method, system, and recording medium for knowledge graph augmentation using data based on a statistical analysis of attributes in the data, including a ranking device configured to rank semantically similar input data elements to create a ranked list of attributes to augment an input of structured data and populate with a data string corresponding to the instances, where the ranking device further combines a set of filters to refine the ranked list of attributes, the set of filters including a first filter according to column ranges of columns, a second filter according to a column uniqueness of the columns, a third filter according to a type of data in a column of the columns, and a fourth filter according to a distribution of values in the columns.

Abstract: A method, system, and recording medium for knowledge graph augmentation using data based on a statistical analysis of attributes in the data, including mapping classes, attributes, and instances of the classes of the data, indexing semantically similar input data elements based on the mapped data using at least one of a label-based analysis, a content-based analysis, and an attribute-based clustering, and ranking the semantically similar input data elements to create a ranked list.