Abstract: A system includes a machine learning (ML) engine in which a data store is coupled to a processing system. The processing system executes code to receive a dataset from the data store, and produces three models each comprising three tests. Each of the tests seeks to detect one of three anomaly types corresponding to each of the models. The processing system performs at least two of the three tests relating to each of the three anomaly types. Separately for each anomaly type, the processing system detects an anomaly when two-out-of-three (2oo3) tests conclude that the anomaly is present in the dataset. The dataset including the flagged anomalies is stored in a data repository. The anomaly is filtered from the dataset. The processing system is configured to use data from the dataset to retrain an existing trained ML model.

Abstract: Semantic queries are expressed and executed within a relational database. This can be done by defining semantic rules applied to execute the semantic queries using table valued functions and common table expressions, and then simply calling the defined table valued functions to execute the queries.

Abstract: A conveyor system includes an elongated vacuum plenum having an interior defined between upwardly and downwardly facing surfaces between opposing ends. A slot defining a portion of a conveyor path is formed in the plenum and extends from one end of the plenum at the upwardly facing surface toward the other end and the downwardly facing surface. A plurality of conveyor modules forming part of an endless loop are received in the slot and move along the conveyor path. At least some of the modules have at least one opening in fluid communication with the interior of the plenum. An object disposed on at least one of the modules is held against the at least one module by a vacuum in the interior of the plenum as the at least one module moves along the conveyor path from the upwardly facing surface toward the downwardly facing surface.

Abstract: In one embodiment, a method includes receiving a query from a first user of an online social network and identifying a set of objects associated with the online social network that substantially match the query. The method also includes calculating, for each identified object, multiple scores corresponding to multiple scoring axes, respectively, each scoring axis having a threshold score that is statistically determined for the scoring axis. The method further includes filtering one or more of the identified objects from the set of objects based on the calculated scores, where each filtered object is associated with one or more scores for one or more scoring axes, respectively, below the threshold score of the respective scoring axis. The method also includes generating one or more search results corresponding to one or more of the identified objects remaining in the set of objects, respectively.

Abstract: A conveyor system includes an elongated vacuum plenum having an interior defined between upwardly and downwardly facing surfaces between opposing ends. A slot defining a portion of a conveyor path is formed in the plenum and extends from one end of the plenum at the upwardly facing surface toward the other end and the downwardly facing surface. A plurality of conveyor modules forming part of an endless loop are received in the slot and move along the conveyor path. At least some of the modules have at least one opening in fluid communication with the interior of the plenum. An object disposed on at least one of the modules is held against the at least one module by a vacuum in the interior of the plenum as the at least one module moves along the conveyor path from the upwardly facing surface toward the downwardly facing surface.

Abstract: A semantic reasoning engine is described for performing probabilistic reasoning over a semantic graph in a time-efficient and viable manner. The semantic reasoning engine includes a data store that provides the semantic graph, where the semantic graph is formed by a plurality of concepts connected together via probabilistic assertions. The semantic reasoning engine operates by providing an answer to a query by recursively collapsing the semantic graph based on at least one collapsing rule.

Abstract: The described method/system/apparatus uses intelligence to better allocate tasks/work items among the processors and computers in the cloud. A priority score may be calculated for each task/work unit for each specific processor. The priority score may indicate how well suited a task/work item is for a processor. The result is that tasks/work items may be more efficiently executed by being assigned to processors in the cloud that are better prepared to execute the tasks/work items.

Abstract: Semantic queries are expressed and executed within a relational database. This can be done by defining semantic rules applied to execute the semantic queries using table valued functions and common table expressions, and then simply calling the defined table valued functions to execute the queries.

Abstract: The described method/system/apparatus uses intelligence to better allocate tasks/work items among the processors and computers in the cloud. A priority score may be calculated for each task/work unit for each specific processor. The priority score may indicate how well suited a task/work item is for a processor. The result is that tasks/work items may be more efficiently executed by being assigned to processors in the cloud that are better prepared to execute the tasks/work items.

Abstract: A method and system of using a forward chaining application on a computing device to monitor a semantic storage system and invoke computations on scientific data according to declarative rules, while capturing operational provenance data stored alongside the scientific data where all data is stored in a semantic graph is disclosed and described. As the provenance data is stored with the data as nodes in the semantic graph, it will stay with the data and may be searched and queried using the same methods as searching the underlying data.

Abstract: Described are techniques to facilitate temporal features in a semantic data store. Information about lifetimes of facts in a semantic store is maintained. Even when a fact is logically deleted, a physical record is kept available. The record of a logically deleted or invalid fact has associated lifetime information. For example, valid-from and valid-to time values. The record of a fact not yet deleted may have a valid-from time value indicating when it was created, became valid, etc. Queries against the semantic store may specify a timeslice (a point in time or a time range). The lifetime information can be used to satisfy such time-specific queries. Because records are maintained after they are logically deleted, it is also possible to accurately query a past state of the semantic store. Even if such a query is run at different times, same results may be obtained.

Abstract: A semantic reasoning engine is described for performing probabilistic reasoning over a semantic graph in a time-efficient and viable manner. The semantic reasoning engine includes a data store that provides the semantic graph, where the semantic graph is formed by a plurality of concepts connected together via probabilistic assertions. The semantic reasoning engine operates by providing an answer to a query by recursively collapsing the semantic graph based on at least one collapsing rule.

Abstract: Semantic queries are expressed and executed within a relational database. This can be done by defining semantic rules applied to execute the semantic queries using table valued functions and common table expressions, and then simply calling the defined table valued functions to execute the queries.

Abstract: Described herein is using type information with a graph of nodes and predicates, in which the type information may be used to determine validity of (type check) a query to be executed against the graph. In one aspect, each node has a type, and each predicate indicates a valid relationship between two types of nodes. A type checking mechanism uses the type information to determine whether a query is valid, which may be the entire query prior to query processing/compilation time, or as the query is being composed by a user. One or more valid predicates for a given node may be discovered based upon the node type, such as discovered to assist the user during query composition. Also described is using the type information to optimize the query.

Abstract: Semantic queries are expressed and executed within a relational database. This can be done by defining semantic rules applied to execute the semantic queries using table valued functions and common table expressions, and then simply calling the defined table valued functions to execute the queries.

Abstract: In one example, information may be stored in a relational database. The information in the database may define a graph, in the sense that the information may define a set of entities and relations between the entities. A user may want to query the information using a graph-based query language. A graph query engine may receive the query, and may convert the query into a relational query language, for execution by the relational database. The relational database may calculate views of the underlying tables. Each view corresponds to a particular relation, and the rows in each view are pairs of entities to which the relation applies. Since the views correspond very closely to the specification of a graph, the graph-based query may be translated into a relational query that performs relational algebraic operations on the views in order to answer the graph-based query.

Abstract: A dolly device for loading and unloading a container which comprises a control frame that pivots among a plurality of operational positions such that the control frame facilitates the loading or unloading of the container and supports the container while being transported from location to location.

Abstract: A roof truss incorporates both metallic and non-metallic webs, in special locations to optimize truss performance.

Abstract: A roof truss incorporates both metallic and non-metallic webs, in special locations to optimize truss performance.

Abstract: The invention discloses substituted 2H,3H-2,1-benzisothiazole-S-oxides having pharmacological activity in animals and useful as CNS depressant agents. The compounds may be prepared by reacting a substituted 2H,3H-2,1-benzisothiazole with an oxidizing agent. The substituted 2H,3H-2,1-benzisothiazole may be prepared by reacting a substituted .alpha.-methylthio-o-toluidine with one equivalent of an oxidizing agent, followed by treatment with base.