Abstract: A document index generating system and method are provided. The system comprises at least one processor and a memory storing a sequence of instructions which when executed by the at least one processor configure the at least one processor to perform the method. The method comprises preprocessing a plurality of pages into a collection of data structures, classifying each preprocessed page into at least one document type, segmenting groups of classified pages into documents, and generating a page and document index for the plurality of pages based on the classified pages and documents. Each data structure comprises a representation of data for a page of the plurality of pages. The representation comprises at least one region on the page.

Abstract: A search engine for searching digital content items has been disclosed herein. The search engine may be utilized by one or more users to perform keywords-based search for retrieving digital content items from the World Wide Web or one or more databases in real time. The search engine includes a search bar for receiving keywords from a user for searching relevant digital content items. The search engine further includes searching modes, such as a value mode, a popularity mode, and an expert mode, that is selectable by the user to perform the search. The search engine further includes privacy modes including a private mode and a public mode. One of the privacy modes is selected by the user to set the user's privacy. The search engine may further execute the search request based on the one or more keywords, the selected searching mode, and the set user's privacy.

Abstract: Adjusting virtual environment using recommendations, including: retrieving character information from a character database, where the character information is associated with a character associated with the virtual environment, and the character information includes character appearance information, character position information, and character action information; retrieving profile information from a profile database, where the profile information indicates one or more profile characteristics and at least one profile characteristic is related to music; comparing at least part of the character information with one or more of the profile characteristics to determine a comparison result; generating adjustment information based on the comparison result; sending the adjustment information to a client system through a network; receiving an adjustment reply from the client system through the network; and adjusting information for the virtual environment based on the adjustment reply.

Abstract: A system and a method are disclosed for automatic content upload and process. The system retrieves a set of files from a source location based on instructions received from a client device of a user. The system then classifies the set of files into a plurality of categories corresponding to a sequence of one or more services configured to process or store files. The system then generates a data structure storing key values, where the key values are derived based on respective processing of subsets of files. Responsive to receiving an input to execute logic relating to the set of files, the system determines that the input is associated with one or more of the key values, retrieves the one or more of the key values, and executing the logic using the one or more retrieved key values.

Abstract: In some examples, an anomaly database system is provided for processing metrics in telemetry data. An example anomaly database system comprises a continuous data management (CDM) node, the CDM node including a metrics library for sending out system metrics in a sparse manner and a statistics relay for receiving streaming metrics from nodes in a node cluster, the node cluster including the CDM node, the statistics relay pushing the received metrics to a metrics collector. A sparse consumers module pulls metrics, from the metrics collector, pushed to the metrics collector by the statistics relay.

Abstract: Compressing files is disclosed. An input file to be compressed is first aligned. During or prior to aligning the input file, hyperparameters are set, determined, or configured. The hyperparameters may be set, determined, or configured to achieve a particular performance characteristic. Aligning the file includes splitting the file into sequences that can be aligned. The result is a compression matrix, where each row of the matrix corresponds to part of the file. A consensus sequence id determined from the compression matrix. Using the consensus sequence, pointer pairs are generated. Each pointer pair identifies a subsequence of the consensus matrix. The compressed file includes the pointer pairs and the consensus sequence.

Abstract: A system for chaining biographic inputs using artificial intelligence includes a computing device, a biographic parameter module operating on the computing device wherein the biographic parameter module is configured to receive a first biographic input wherein the first biographic input identifies a user; parse the first biographic input to retrieve a key element; and retrieve an element of stored data relating to the key element wherein the element of stored data further comprises a plurality of biographic inputs; and a chaining module, the transmission module configured to receive the first biographic input and the element of stored data from the biographic parameter module; evaluate the first biographic input and the element of stored data to determine a relation between the first biographic input and the element of stored data; and chain the first biographic input and the element of stored data.

Abstract: Systems and methods are directed to a compression scheme for stable universally unique identities in a collaborative editing environment. A client receives edits to a document and immediately creates a local short identity for an element of each edit, whereby the local short identity represents an offset from a base unique identity associated with the client from which a unique identifier assigned to the element is derivable. The local short identity information including the local short identity for each element is transmitted to a server. Subsequently, global short identity information is received from the server from which one or more global short identities that each map to a local short identity of a client of a plurality of clients collaborating on the document can be determined by the client. Each global short identity is a compact identity that is universally unique to the plurality of clients including the client.

Abstract: A system may include a processor configured to receive a first set of data indicating a first period of time during which a item has a first status, and a second period of time during which the item has a second status. The processor may generate, based on the first set of data, a second set of data containing a plurality of logical rows, each indicating an encoded value of the first status of the item and an encoded value of the second status of the item during a respective period of time that is smaller than or equal to each of the first period of time and the second period of time. The processor may cause a display to present, based on the first set of data and the second set of data, statistical information of the first status and the second status during a third period of time.

Abstract: Methods, systems, devices, and computer-readable media used by a cloud data management system for collecting data from accounts hosted by a cloud-based data warehousing system on different cloud platforms or in different cloud regions of a cloud platform. Collection of data in the multi-cloud platform and/or multi-cloud region environments may be facilitated by the on-demand creation of one or more data collection accounts. Based on the collected data, one or more recommendations, notifications, or alerts associated with usage of the data warehousing system may be generated.

Abstract: Solutions for optimizing job runtimes via prediction-based token allocation includes receiving training data comprising historical run data, the historical run data comprising job characteristics, runtime results, and a token count for each of a plurality of prior jobs, and the job characteristics comprising an intermediate representation and job graph data; based at least on the training data, training a token estimator, the token estimator comprising a machine learning (ML) model; receiving job characteristics for a user-submitted job; based at least on the received job characteristics, generating, with the token estimator, token prediction data for the user-submitted job; selecting a token count for the user-submitted job, based at least on the token prediction data; identifying the selected token count to an execution environment; and executing, with the execution environment, the user-submitted job in accordance with the selected token count.

Abstract: A method for providing one or more random sample documents from a corpus of documents using a search engine is provided. The providing of each of the random sample documents comprises selecting randomly a time window from a set of time windows. A search query is sent to the search engine defining a search for documents of the corpus with time-stamps within the time window defined by the randomly selected time window. In response to the sending of the search query, a search result is receiving from the search engine. The search result comprises a set of the documents of the corpus with time-stamps within the time window. One of the documents comprised by the received set of documents is then selected randomly.

Abstract: A computer-implemented method is provided for an automated extract, transform, and load process for a target database comprising linked data. During the data transformation phase linked data elements are added as data to a data set.

Abstract: Embodiments described herein are directed to generating a tree-based data structure representative of a data set and the verification thereof. As each data item of a data set is updated, a leaf node is generated that stores a hash value therefor. For every even leaf node generated, a parent node storing a hash value based on the hash values of its child nodes is generated. For each level of the tree, the hash value of the last odd node generated therefor is maintained. The foregoing process is performed recursively at each level of the tree. During verification, a new root hash value is determined for a new tree-based data structure generated for the data set to be verified. The old and new root hash values are compared. If the hash values do not match, a remediation is performed to restore the data set.

Abstract: The disclosure relates to methods and systems of joining data structures based on a composite similarity score (CSS). For example, a computer system may use a plurality of similarity models to generate respective similarity scores. Each similarity score may be a metric that indicates a confidence that a first data value of a first data record is similar to a second data value of a second data record. The computer system may generate the CSS based on the plurality of similarity sub-scores. The CSS may indicate a confidence that the records being compared are similar. Thus, the CSS may be used to detect similar data records across different data structures. The disclosure also relates to a string similarity model that detects similarity among strings without respect to an order of words in each string and in a way that tolerates errors or omissions in one or both strings.

Abstract: The present disclosure provides a computing device and method for providing personal specific information based on semantic queries. The semantic queries may be input in a natural language form, and may include user specific context, such as by referring to prior or future events related to a place the user is searching for. With the user's authorization, data associated with prior or planned activities of the user may be accessed and information from the accessed data may be identified, wherein the information is correlated with the user specific context. One or more query results are determined based on the identified information and provided for output to the user.

Abstract: A document classification method includes: obtaining a document; extracting document keywords and a document abstract from the document; determining, according to the document keywords and the document abstract, a first classification label of the document; receiving a second classification label of the document, the second classification label being determined according to the document keywords, the document abstract, and the first classification label; obtaining a classification efficiency value of the documents, the classification efficiency value indicating a confidence level of the second classification label; and determining the second classification label as a final classification label of the document if the classification efficiency value is greater than or equal to a first threshold. The document classification method described above may improve the speed of document classification by computer, thereby improving the efficiency of document classification.

Abstract: A computer-implemented method includes receiving data records from a plurality of different data sources, in which the data records include information regarding elements or components in one or more systems; consolidating the data records; extracting information from the consolidated data records to identify a plurality of nodes and connections between the plurality of nodes in the one or more systems; storing information identifying the plurality of nodes and the connections; and visually presenting the plurality of nodes and the connections in a user interface.

Abstract: Methods of measuring graph triangle centrality and for ranking the nodes of a graph based upon the graph triangle centrality measure are provided.

Abstract: Implementations described herein relate to methods, systems, and computer-readable media to dynamically customize rankings. In some implementations, a method includes receiving, from a user, a request for a list of items available on an online gaming platform, identifying a particular trained machine learning model based at least in part on user data associated with the user, providing an input feature vector to the particular trained machine learning model, obtaining an output feature vector from the particular trained machine learning model, assigning a rank for each of the plurality of items based on the output feature vector, generating the list of items, wherein items in the list of items are ordered based on respective ranks, and providing a user interface to the user that includes the list of items.