Abstract: Methods and systems are provided for searching information in a distributed data processing system. A system for processing a semantic search query where the system may include a memory and a processor coupled to the memory being configured to, receive a structured search query, process the structured search query to deconstruct into query elements, identify a set of connected elements that define a data source associated with the received structured search query based on a processed query element, process the query elements to determine one or more command data element types associated with the received structured search query, and process data associated with the defined data source according to a command data element type to develop a semantic search query resultant data set.

Abstract: Techniques for detecting an anomaly in queries of a relational database are disclosed. The techniques include identifying a set of attribute values from a query for accessing data within a database. Based on previously received queries, at least one of a joint probability for the set of attribute values or individual probabilities for the set of attribute values is determined. When at least one of the joint probability for the set of attribute values or an individual probability for one or more attribute values in the set of attribute values does not satisfy a probability cutoff, an indication that the query is anomalous is outputted.

Abstract: Methods, systems, and computer-readable storage media for receiving, by a semantic context service, a semantic context associated with a report generated by a first analytical tool, storing, by the semantic context service, the semantic context, receiving, by the semantic context service, a semantic context request from a second analytical tool, the semantic context request including a set of parameters, determining, by the semantic context service, a response to the semantic context request based on the set of parameters, the response including the semantic context, and returning, by the semantic context service, the response to the second analytical tool, the second analytical tool applying the semantic context to a report generated by the second analytical tool.

Abstract: Embodiments include methods, systems and computer program products method for editing and correcting structural metadata associated a binary large object (BLOB). The computer-implemented method includes obtaining, using a processor, at least a portion of structural metadata associated with the BLOB. The processor converts one or more fields associated with the at least a portion of structural metadata and determines that the one or more fields generated one or more errors or null values. The processor provides an interface, wherein the interface is used to cause a first movement or edit the one or more fields. The processor determines that the first movement or edit of the one or more fields fixes the one or more errors or null values and provides an indication that the first movement or edit of the one or more fields has or has not fixed the one or more errors or null values.

Abstract: A digital magazine server displays content items from various sources to users of client devices. Each source of a content item is identified by a domain, and content items for different sources have different domain-level quality. To differentiate sources of content items, the domains identifying the sources are ranked based on domain scores of the domains generated by an aggregate of multiple trained domain classifiers. A domain score of a domain indicates a domain-level quality of content items provided by a source identified by the domain. Each of the trained domain classifiers (e.g., a naïve Bayes classifier, a random forest classifier, and a logistic regression classifier) generates a prediction of whether a domain is a spam domain based on the domain features and domains with known labels. Based on the domain scores of domains, the domain ranking module may adaptively select content items from the sources with corresponding domains scores.

Abstract: In some examples, one or more computing devices may perform deduplication of data. For instance, a first device may receive, from a second device, a first data-portion identifier corresponding to a first deduplication data portion. The first device may include a first index portion of a deduplication index and the second device may include a second index portion of the deduplication index. Further, the first data-portion identifier may be received based on a first data-portion identifier portion being in a range of values assigned to the first index portion. The first device may locate, in the first index portion of the deduplication index, a second data-portion identifier that matches the first data-portion identifier. The first device may associate the first reference information for the first deduplication data portion with a second deduplication data portion referenced by reference information associated with the second data-portion identifier.

Abstract: A method of selecting and presenting content based on learned user preferences is provided. The method includes providing a content System including a set of content items organized by genre characterizing the content items, and wherein the set of content items contains microgenre metadata further characterizing the content items. The method also includes receiving search input from the user for identifying desired content items and, in response, presenting a subset of content items to the user. The method further includes receiving content item selection actions from the user and analyzing the microgenre metadata within the selected content items to learn the preferred microgenres of the user. The method includes, in response to receiving subsequent user search input, selecting and presenting content items in an order that portrays as relatively more relevant those content items containing microgenre metadata that more closely match the learned microgenre preferences of the user.

Abstract: One or a soft correlation of a database can be adjusted (e.g., modified, replaced, overwritten) for use with respect to one or more record(s) of the database associated with the soft correlation, by considering at least one or more violations of the soft correlations in the one or more of records database records associated with the soft correlation. In addition, an adjusted soft correlation can be stored and used for optimizations of database queries pertaining to one or more records associated with the adjusted soft correlation. Typically, the adjusted soft correlation is adjusted by at least considering the violations of an original soft correlation in the one or more records relating to the database queries.

Abstract: Systems and methods of the present disclosure facilitate synchronizing data between a device management system and ticketing systems. In some embodiments, the system includes an update module, a mapping module, and a service board selection module. The update module may be configured to update ticketing information about a ticketing ticket on the ticketing system to match device management information about a device management ticket on the device management system. The mapping module may be configured to select a ticket category for a device management ticket on the device management system responsive to the device management information about the device management ticket. The service board selection module may be configured to select a service board for a ticketing ticket on the ticketing system.

Abstract: Search results can include, not only responsive content, typically in the form of documents, but can also include information regarding responsive entities, including tasks relevant to those entities, and can include identifications of individuals from whom additional information can be obtained, or who are otherwise relevant to the search. An association between entities and documents referencing those entities is identified and maintained. Documents responsive to a user's search query are identified utilizing existing algorithmic mechanisms and such identified responsive documents are referenced to determine whether they are associated with one or more entities. Additionally, a people/query mapping is maintained associating individual people with specific queries. User actions directed to one or more people in response to a query are utilized as a feedback loop.

Abstract: A method for garbage collecting for non-volatile memories are disclosed. The method includes steps: a) providing a SSD, connected to a host, containing a plurality of TLC blocks and SLC blocks; b) reading 3M TLC pages in a TLC block having data; c) moving valid data in the TLC blocks to at least one clean TLC block; d) sending a host program command of 1 page to the host; e) repeating step b) to step d) until valid data in 8 TLC blocks are moved; f) reading 1 SLC page in a SLC block having data; g) moving valid data in the SLC block to the at least one clean TLC block; h) sending a host program command of ? page to the host; and i) repeating step f) to step h) until valid data in the SLC block having data are moved.

Abstract: A data storage device that includes a memory operable to store a data table and an indexing engine implemented by a processor. The indexing engine is configured to receive data and to store the data in the data table. The indexing engine is further configured to determine an index key and data location information for the stored data and to determine a set of index table references based on the index key. Each index table reference identifies an index table that links index keys with data location information. The indexing engine is further configured to identify a set of index tables corresponding with the set of index table references and to store the index key and the data location information for the stored data in one or more index tables from the set of index tables.

Abstract: Methods, systems, and computer-readable storage media for receiving, by a query manager service (QMS) executed within a first data center, a query definition provided from a browser of an analytics platform executed within a second data center, providing, by the QMS, a calculation scenario as an object model based on the query definition, executing, by a calculation engine within the first data center, the calculation scenario to provide an analytical artifact including a query-level calculation view on transactional data stored in the first data center, and transmitting, by the first data center, the analytical artifact to the second data center to perform analytics on at least a portion of the transactional data stored in the first data center.

Abstract: A search engine is modified to perform increasingly precise image searching using iterative Natural Language (NL) interactions. From an NL search input, the modification extracts a set of input features, which includes a set of response features corresponding to an NL statement in the NL search input and a set of image features from a seed image in the NL search input. The modification performs image analysis on an image result in a result set of a query including at least some of the input features. In a next iteration of NL interactions, at least some of the result set is provided. An NL response in the iteration is added to a cumulative NL basis, and a revised result set is provided, which includes a new image result corresponding to a new response feature extracted from the cumulative NL basis.

Abstract: According to one embodiment, a communication device includes one or more processors. The processors decide on a first-type parameter representing a conversion operation for converting input data into first-type conversion data. The processors calculate first-type predicted distributions based on second-type parameters, each representing one of the plurality of clusters. The processors update the first-type parameter and the second-type parameters so as to achieve optimization of first-type differences representing differences between the first-type predicted distributions and a target distribution, and second-type differences representing differences between the first-type predicted distributions and second-type predicted distributions that indicate probability at which second-type converted data. The second-type converted data is obtained by converting data, which is formed by augmentation of the input data, using the first-type parameter belongs to the clusters.

Abstract: Methods and systems for document classification are provided. One method includes generating by a processor, a plurality of topics using content of a plurality of electronic documents, where each topic includes a plurality of words associated with the plurality of electronic documents; reducing by the processor, the plurality of topics to a subset of topics to represent the plurality of electronic documents based on a parameter indicating a property of each subset topic and separation between the subset topics; automatically generating by the processor, a tag for each subset topic, based on the tag's position within the subset topic; wherein each tag is an attribute of each subset topic; storing by the processor, the subset of topics with corresponding tags in a model data structure; and updating the model data structure by the processor based on one of a new topic and a new tag associated with an electronic document.

Abstract: A computer-implemented method for content-agnostic referencing of a binary data file, the method comprising: determining a length of the binary data file, the length comprising the number of bits of the binary data file; for the determined length, generating all permutations of data of the determined length; locating an index within the generated permutations, wherein the index is the starting position of the binary data file within the generated permutations; and using the length and the index to indicate the binary data file.

Abstract: In an example embodiment, a machine learned model is used to determine whether to send a notification for a feed object to a user. This machine learned model is optimized not just based on the likelihood that the notification will cause the user to interact with the feed object, but also the likely short-term and long-term impacts of the user interacting with the feed object. This machine learned model factors in not only the viewer's probability of immediate action, such as clicking on a feed object, but also the probability of long-term impact, such as the display causing the viewer to contribute content to the network or the viewer's response encouraging more people to contribute content to the network. As such, the machine learned model is optimized not just on notification interactivity but also on feed objects interactivity.

Abstract: Example embodiments for classification are described. In an example embodiment, a request including a text term to be classified is received from a source system via a communication network at a computer system. A rule associated with the text term is accessed, in which the rule indicates at least one classifier of a plurality of classifiers installed at the computer system. Classification information for the text term is generated at the computer system using the at least one classifier indicated by the rule. The generated classification information includes a classification selected from a taxonomy by the at least one classifier. The generated classification information is transmitted via the communication network to the source system.

Abstract: A query is executed in a massively parallel processing data storage system comprising a master node communicating with a cluster of multiple segments that access data in distributed storage by producing a self-described query plan at the master node that incorporates changeable metadata and information needed to execute the self-described query plan on the segments, and that incorporates references to obtain static metadata and information for functions and operators of the query plan from metadata stores on the segments. The distributed storage may be the Hadoop distributed file system, and the query plan may be a full function SQL query plan.