Abstract: A first set of subconditions is obtained. Approximate matching of the first set of subconditions against a plurality of sets of subconditions is performed. A second set of subconditions among the plurality of sets of subconditions is accessed, wherein a representation of a subcondition in the first set and/or the second set of subconditions comprises a value having one of a plurality of states, and wherein the plurality of states comprises a state of “don't care”. The first set of subconditions is approximately matched against the second set of subconditions. The first set of subconditions is compared against the second set of subconditions. A matching criterion is determined to be met based on a result of the matching. Information indicating that the second set of subconditions is at least an approximate match for the first set of subconditions is output.

Abstract: System for ontological evaluation and filtering of digital content evaluates metadata associated with content available from an original content server. The metadata is filtered and evaluated by a processing cluster to develop correlation among content for the formation of content “channels”.

Abstract: Compressing files is disclosed. An input file to be compressed is first aligned. When the file has multiple axes or dimensions, the file is aligned along each of the axes. Aligning the file includes splitting the file into sequences that can be aligned along each of the axes or dimensions. Aligning the file generates a compression tensor, where each row or dimensional space of the compression tensor corresponds to part of the file. A consensus tensor is determined from the compression tensor. Using the consensus tensor, pointer lists are generated. Each pointer lists identifies a subsequence or portion of the consensus tensor. The compressed file includes the pointer lists and the consensus tensor.

Abstract: Certain example embodiments relate to meta-learning based error detection. Base classifiers are provided for historical attributes in historical datasets. Each is trained to indicate dirtiness of a value for the associated historical attribute. Clusters and a clustering model are generated using historical clustering features determined for each historical attribute, which are then associated with the clusters. For each dirty attribute in a dirty dataset, corresponding dirty clustering features are determined. The dirty attributes are assigned to the clusters using the corresponding determined dirty clustering features and the clustering model. The base classifiers associated with the clusters to which the dirty attributes were assigned are retrieved. Dirty features are extracted from the dirty dataset, and selectively modified. The extracted dirty features are applied to the retrieved the base classifiers to determine meta-features. A meta-classifier is trained using labeled meta-features.

Abstract: A method and system for operation objects discovery from operation data includes performing pattern matching of operation data with patterns in a database. Fields in the operation data are identified as having matching patterns with the database as first potential objects. Data profiling is performed on unmatched fields of the operation data to generate data profiles. The data profiles are field classified and second potential objects are generated. The first potential objects and the second potential objects are de-duplicated, and operation objects are generated.

Abstract: A system for retrieving multimodal assets using domain-specific knowledge includes receiving a search query for searching for multimodal assets; encoding the search query into a first query representation via a first trained query representation machine-learning (ML) model and a second query representation via a second trained query representation ML model; comparing the first query representation to a plurality of multimodal representations to calculate a first similarity score, each of the plurality of multimodal representations being a representation of one of the plurality of candidate multimodal assets; comparing the second query representation to a plurality of domain-specific representations to calculate a second similarity score, the domain-specific representations being representations of domain-specific data associated with one or more of the plurality of the multimodal representations; calculating a third similarity score based on keyword matching between the domain-specific data and the one or mor

Abstract: A system and method for generating structured data objects from unstructured data is provided. The unstructured data is received and parsed by comparing substrings with entries from an entities data structure and a listing of curated patterns to identify and classify substrings. The identified substrings are then ranked and filtered to identify one or more of the most relevant substrings. One or more of the ranked and filtered substrings are used to generate a structured data object.

Abstract: Systems and methods for ranking search results according to a knowledge level of a user are provided. A computer-implemented method includes: receiving, by a computing device, a search query from a user via a user computer device; determining, by the computing device, a plurality of results based on the search query; determining, by the computing device, a respective expertise level for each of the plurality of results; ranking, by the computing device, the plurality of results based on a knowledge level of the user and the respective expertise level for each of the plurality of results; and outputting, by the computing device, the ranked plurality of results to the user computer device.

Abstract: A system and method and for retrieving one or more one or more multimodal assets includes receiving a search query for searching for one or more multimodal assets from among a plurality of candidate multimodal assets, encoding the search query into one or more query embedding representations via a trained query representation machine-learning (ML) model, comparing, via a matching unit, the one or more query embedding representations to a plurality of multimodal tensor representations, each of the plurality of multimodal tensor representations being a representation of one of the plurality of candidate multimodal assets, and identifying, based on the comparison, at least one of the plurality of the candidate multimodal assets as a search result for the search query, and providing the at least one of the plurality of the candidate multimodal assets for display as the search result.

Abstract: A system and a method are disclosed for receiving, via a user interface, user input of a first parameter and a second parameter. The system identifies aggregations corresponding to the parameters, the aggregations updated based on input from respective sets of machines, the aggregations being siloed with respect to one another. The system transmits a first query to the first aggregation corresponding to the first parameter, and receives a first response to the first query comprising first data, and transmits a second query to the second aggregation corresponding to the second parameter, and receives a second response to the second query comprising second data. The system integrates the first data and the second data into integrated data, and provides for display, via the user interface, a representation of the integrated data.

Abstract: Systems and methods provide a system that gathers information about data as it progresses through data processing pipelines of data analysis projects. The data analytics system derives value indicators and implicit metadata from the data processing pipelines. For example, the data analytics system may derive value indicators and implicit metadata from data-related products themselves, semantic analysis of the code/processing steps used to process the data-related products, the structure of data processing pipelines, and human behavior related to production and usage of data-related products. Once a new data analysis project is initiated, the data analytics system gathers parameters and characteristics about the new data analysis project and references the value indicators and implicit metadata to recommend useful processing steps, datasets, and/or other data-related products for the new data analysis project.

Abstract: Embodiments for providing rare topic detection using hierarchical topic modeling by a processor. A hierarchical topic model may be learned from one or more data sources. One or more dominant words in a selected cluster may be iteratively removed using the hierarchical topic model. The dominant words may relate to one or more primary topics of the cluster. The learned hierarchical topic model may be seeded with one or more words, n-grams, phrases, text snippets, or a combination thereof to evolve the hierarchical topic model and the removed dominant words are reinstated upon completion of the seeding.

Abstract: Systems and methods provide a system that gathers information about data as it progresses through data processing pipelines of data analysis projects. The data analytics system derives value indicators and implicit metadata from the data processing pipelines. For example, the data analytics system may derive value indicators and implicit metadata from data-related products themselves, semantic analysis of the code/processing steps used to process the data-related products, the structure of data processing pipelines, and human behavior related to production and usage of data-related products. Once a new data analysis project is initiated, the data analytics system gathers parameters and characteristics about the new data analysis project and references the value indicators and implicit metadata to recommend useful processing steps, datasets, and/or other data-related products for the new data analysis project.

Abstract: A system that enables searching for representing listings for accommodation reservations on a map is described. The system receives, by a network site of a listing network platform, input comprising search criteria associated with a geographical region and identifies a plurality of listings matching the search criteria. The system identifies a first subset listings of the plurality of listings that is each associated with a respective location that is within a boundary associated with the geographical region and a second subset listings of the plurality of listings that is each associated with a respective location that is outside the boundary associated with the geographical region. The system visually distinguishes the first subset of listings from the second subset of listings on a map-based graphical user interface (GUI) that represents the plurality of listings matching the search criteria.

Abstract: Methods, computer-readable media and apparatuses are disclosed for graph data write. In an example, in response to receiving a first graph data write request, row lock indexes of corresponding row locks of target write objects are determined based on object identification information of the target write objects in the first graph data write request, where each target write object corresponds to a row lock. The target write objects are locked based on the row lock indexes of the target write objects; graph data write is performed for the first graph data write request after the target write objects are locked; the row locks held by the first graph data write request are unlocked after graph data of the target write objects is written; and a graph data write result is provided to a user after all the row locks held by the first graph data write request are unlocked.

Abstract: Methods and systems are provided for inferred information propagation for aircraft prognostics. The method includes receiving, by a processor, an original time-series of data points for a component as an input; preprocessing the input to divide the original time-series of data into subsets of data by applying a time-window over the original time-series of data points; and computing, by the processor, a Mutual Information (MI) value for each pair of variables within each subset of data. The method also includes constructing, by the processor, a sequence of relationship graphs using the computed MI values; clustering, by the processor, each relationship graph; and analyzing, by the processor, the time-ordered sequence of clustered relationship graphs to identify features in the component.

Abstract: Examples of the present disclosure describe systems and methods for migrating mailbox identities. In aspects, a system may receive one or more requests to perform tasks for migrating identities of a plurality of mailboxes included in a security group created at a source tenant to a plurality of mail users created at a target tenant that correspond to the plurality of mailboxes. Based on the one or more requests, the system may copy the identities to a data store associated with the target tenant, upload mapping data to the data store, and use that mapping data to map the identities copied to the data store to the corresponding plurality of mail users in the target tenant. For each of the identities mapped, the system may copy at least one or more attributes of the respective identity from the data store to the respective corresponding mail user in the target tenant.

Abstract: Aspects of the present invention disclose a method, computer program product, and system for query execution in a multi-tenant cloud service. The method includes one or more processors determining category classes for service queries. The method further includes sending for execution, a selected number of service queries from one of the determined category classes to a shadow query engine. Respective service queries of the categorically classified service queries comprise a different set of configuration parameter values for the shadow query engine. The method further includes recording metadata for the selected number of service queries of the one category class executed on said shadow query engine. The method further includes determining correlations between the recorded metadata.

Abstract: A method includes receiving an electronic feed inventory file; creating a feed message including a batch of inventory messages based on the feed inventory file; placing the feed message in a message queue; performing validation of each of the inventory messages; generating a response to each invalid inventory messages; generating a first batch of response message including the responses to the invalid inventory messages; publishing the first batch of response message to the message queue; updating a first database based on validated inventory messages; generating responses to each of the validated inventory messages; generating a second batch of response message including the responses to the validated inventory messages, the first and second batch of response message having the batch identification; publishing the second batch of response message to the message queue; and updating a second database based on the first and second batch of response messages.

Abstract: Enabling data portability between systems, including: submitting, to a cloud-based metadata store storing metadata describing data stored in a plurality of storage systems each coupled for data communications with a corresponding on-premises cloud infrastructure of a plurality of on-premises cloud infrastructures, a query; selecting a subset of the data responsive to the query and stored in at least one first storage system coupled to at least one first on-premises cloud infrastructure; and storing, from the at least one first storage system to a second storage system coupled to a second on-premises cloud infrastructure, the subset of the data responsive to the query.