Abstract: Provided are methods, systems, and apparatuses for improving supply chain management interfaces and functionality. The supply chain management interfaces may provide visualizations of one or more standardization constructs associated with an organization and locations thereof. A standardization construct may be generated using supply chain records associated with the organization and locations thereof. The visualizations of the one or more standardization constructs may be adjusted at a user interface. The adjusted one or more standardization constructs may be indicative of ways in which the one or more standardization constructs may be improved.

Abstract: A building management system includes one or more memory devices configured to store instructions thereon, that, when executed by one or more processors, cause the one or more processors to receive a publication by an agent on an agent communication channel, the publication comprising timeseries data, identify, based on the publication, an object entity of an entity database associated with the agent, wherein the entity database includes one or more object entities and relationships between the one or more object entities and one or more data entities, identify a data entity related to the object entity based on a relationship of the relationships relating the object entity and the data entity, and ingest the timeseries data into the data entity.

Abstract: Using a natural language (NL) latent presentation in the automated conversion of source code from a base programming language (e.g., C++) to a target programming language (e.g., Python). A base-to-NL model can be used to generate an NL latent representation by processing a base source code snippet in the base programming language. Further, an NL-to-target model can be used to generate a target source code snippet in the target programming language (that is functionally equivalent to the base source code snippet), by processing the NL latent representation. In some implementations, output(s) from the NL-to-target model indicate canonical representation(s) of variables, and in generating the target source code snippet, technique(s) are used to match those canonical representation(s) to variable(s) of the base source code snippet. In some implementations, multiple candidate target source code snippets are generated, and a subset (e.g., one) is selected based on evaluation(s).

Abstract: Methods and computer-readable medium for improved caching are disclosed. The method includes receiving, at a server node, a first data request including an identifier of a requested data entry; performing a lookup in the in-process cache for data corresponding to the identifier; upon determining that data corresponding to the identifier is missing in the in-process cache, invoking a cache loader module configured to: communicate a second data request for the identifier to a shared cache; receive a response from the shared cache; upon determining that data for the identifier was not returned in the response, communicate a third data request for the identifier to a main data store; receive data for the identifier from the main data store; and add the data for the identifier received from the main data store in the in-process cache; and communicating the data for the identifier of the data entry to a requesting system.

Abstract: Systems and methods are described for generation of a query using a non-textual input. For example, the query can be generated using a point and click input. A selection of a data source can be identified and an initial query can be automatically generated based on the selection of the data source. A graphical user interface can be displayed and populated with one or more selectable parameters based on the initial query. A selection of the one or more selectable parameters can be received as a non-textual input and a query can be automatically generated based on the selection. For example, a query for execution by a data intake and query system can be generated based on the selection. The query can be provided to the data intake and query system. The data intake and query system may then execute the query on a set of data.

Abstract: A method, apparatus and system for training an embedding space for content comprehension and response includes, for each layer of a hierarchical taxonomy having at least two layers including respective words resulting in layers of varying complexity, determining a set of words associated with a layer of the hierarchical taxonomy, determining a question answer pair based on a question generated using at least one word of the set of words and at least one content domain, determining a vector representation for the generated question and for content related to the at least one content domain of the question answer pair, and embedding the question vector representation and the content vector representations into a common embedding space where vector representations that are related, are closer in the embedding space than unrelated embedded vector representations. Requests for content can then be fulfilled using the trained, common embedding space.

Abstract: Time and value ordering may be applied for items stored in data backups. A change log that persists changes to a data set may be updated with changes and used to update an in-memory table for the data set, which describes changes to items up to a current time. An event may be detected to seal the in-memory table from subsequent updates and a persistent data object that orders the items in the in-memory according to both keys of the respective items and the respective time values of the items, as stored in the change log, may be generated and stored as part of a backup for the data set.

Abstract: Provided is an aggregation index structure and an aggregation index method for improving aggregation query efficiency. The aggregation index partitions streaming data through two dimensions of grouping and slicing, and then aggregates the partitioned data. The structure includes index metadata, a sliced data list and a detailed data store; the aggregation index method includes three parts: aggregation index definition, aggregation index creation and aggregation index query. The aggregation index structure and method provided by the present disclosure can greatly improve the efficiency of aggregation query, support the ad hoc aggregation query of PB-level data, complete the aggregation query of a large amount of data in seconds, support the insertion of new data at any time, and return the query results in minutes or even seconds latency in the event of changes of the query range conditions.

Abstract: A system and method to generate and manage clusters of search terms. A cluster generation process is executed at a first time to generate a first set of clusters including respective sets of search terms. At least one of a first value of a first parameter associated with the first set of clusters or a first value of a second parameter associated with the first set of clusters is determined. At a second time, the cluster generation process is executed to generate a second set of clusters. At least one of a second value of the first parameter associated with the second set of clusters or a second value of the second parameter associated with the second set of clusters is determined. A determination is made whether either the second value of the first parameter satisfies a first condition or the second value of the second parameter satisfies a second condition.

Abstract: Certain aspects of the disclosure pertain to inferring a candidate entity associated with a transaction with a machine learning model. An organization identifier and description associated with a transaction can be received as input. In response, an entity embedding, comprising a vector for each entity of an organization based on the organization identifier, can be retrieved from storage. A machine learning model can be invoked with the entity embedding and description. The machine learning model can be trained to infer a transaction embedding from the description and compute a similarity score between the transaction embedding and each vector of the entity embedding. A candidate entity with a similarity score satisfying a threshold can be identified and returned. The candidate entity with the highest similarity score can be identified in certain aspects.

Abstract: A system, method, and computer-readable medium for performing a data center asset alert operation. The data center asset alert operation includes: monitoring a plurality of data center assets; determining when a first data center asset of the plurality of data center assets generates a first data center asset alert; determining when a second data center asset of the plurality of data center asserts generates a second data center asset alert; determining when the first data center alert and the second data center alert are substantively similar; and, generating a recommendation when the first data center alert and the second data center alert are substantively similar.

Abstract: A method including receiving a user input for input to a language processing machine learning model (MLM). The method also includes generating modified inputs that are based on, and semantically related to, the user input. The method also includes executing the MLM to generate model outputs of the MLM. The MLM takes as input instances of each of the modified inputs. The method also includes sampling the model outputs using a statistical sampling strategy to generate sampled model outputs. The method also includes clustering the sampled model outputs into clusters. Each cluster of the clusters represents a distinct semantic meaning of the sampled model outputs. The method also includes generating a confidence metric for the user input. The confidence metric includes a predictive entropy of the clusters. The method also includes routing the user input based on whether the confidence metric satisfies or fails to satisfy a threshold value.

Abstract: Mechanisms for the systematic iterative analysis of unstructured data files are disclosed. A first handler is initiated to analyze a plurality of files. Information about each respective file of the plurality of files analyzed by the first handler is stored in a data structure. The information includes a file identifier that identifies the respective file, a handler identifier that identifies the first handler, and results or a reference to results of an analysis of the respective file by the first handler.

Abstract: Various embodiments are generally directed to techniques to provide specific vehicle recommendations to generic user requests. A method for providing the specific vehicle recommendation includes: receiving a generic automobile request from a user, applying a machine learning model (MLM) trained by a corpus of reviews to the received request, and generating, by the MLM, a recommendation for at least one specific automobile feature based on the generic automobile request.

Abstract: A method for privacy preserving vulnerability detection for devices. In an example, an apparatus may include a processor and memory coupled with the processor that effectuates operations. The operations may include receiving a type of a device; receiving a feature for the device; creating a first hash value based on the device type and the feature; receiving an indication that the first hash value matches a second hash value; and based on the indication that the first hash value matches the second hash function, sending an alert that the device has a vulnerability.

Abstract: A system provides an estimated result of an input including a plurality of parameters using a learning model, and the system is configured to provide information necessary to change an estimated result with regard to one parameter among the plurality of parameters, and set a condition for information with regard to at least one parameter among the plurality of parameters, wherein the condition is a condition for not providing some information regarding a parameter which is a target of the set condition as the information necessary to change the estimated result.

Abstract: Aspects described herein may relate to methods, systems, and apparatuses for generating synthetic data based on time series predictions and plural machine learning models. Generating the synthetic data may include receiving one or more data records that include transaction data of a user, categorizing and/or segmenting the one or more data records, determining a first predicted time step based on the categorizing and/or segmenting, and performing an iterative process that determines further predicted time steps. The first predicted time step may be determined using one model of a plurality of machine learning models. The iterative process may determine the further predicted time steps using the plural machine learning models. Based on iterations of the iterative process, potential time series may be determined. The synthetic time series may be determined from the potential time series. The synthetic time series may be used by additional processes, such as additional machine-learning processes.

Abstract: The present disclosure provides systems and methods for deriving a key from a basekey built-in a chip is provided. In an exemplary embodiment, there is provided a method for deriving a key from basekey built-in a chip that may comprise obtaining a basekey built-in the chip and a current version number when the chip starts up and runs read-only boot code, and performing iterations on the basekey via a one-way function to obtain a derived key of the current version. The number of times of the iterations is equal to the difference between a maximum version number and the current version number.

Abstract: An apparatus includes a memory and a processor. The memory stores a dictionary and a machine learning algorithm trained to classify text. The processor receives an image of a page, converts the image into a set of text, and identifies a plurality of tokens within the text. Each token includes one or more contiguous characters that are both preceded and followed by whitespace within the text. The processor identifies invalid tokens by removing tokens of the plurality of tokens that correspond to words of the dictionary. The processor calculates, based on a ratio of a total number of valid tokens to a total number of tokens, a score. In response to determining that the score is greater than a threshold, the processor applies the machine learning algorithm to classify the text into a category and stores the image and/or text in a database according to the category.

Abstract: A system for extracting key phrase candidates from a corpus of documents, including a processor, a memory, and a program executing on the processor. The system is configured to run a key phrase model to extract one or more key phrase candidates from each document in the corpus and convert each extracted key phrase candidate into a feature vector. The key phrase model also filters the feature vectors to remove duplicates using a classifier that was trained on a set of key phrase pairs with manual labels indicating whether two key phrases are duplicates of each other, to produce remaining key phrase candidates. The system uses the remaining key phrase candidates in a computer-implemented application.

Abstract: The present teaching relates to analyzing user behavior associated with web contents. Information related to user interactions associated with a content item placed on a reference property is first obtained. A measurement associated with each user interaction of the content item is determined based on the obtained information. An analyzing model for the content item which characterizes statistics of the measurements associated with the content item is further constructed. A measurement threshold to be used to determine a cost of placing the content item on a target property is further determined using the constructed analyzing model.

Abstract: A system to quantify aggregate alignment of segmented text with an evaluator population, with a data processing system comprising memory and one or more processors, can segment a first extended text object into one or more evaluation text objects associated with a population reference, identify one or more text frame objects corresponding to the evaluation text objects, the text frame objects being associated with a second extended text object, generate, based on the text frame objects, one or more context identifier objects corresponding to the evaluation text objects, and generate a condensed text object including one or more of the evaluation text objects, the evaluation text objects being positioned in the condensed text object in response to output of a first machine learning model trained with input including at least one of the first extended text objects, the evaluation text objects, the context identifier objects, and the text frame objects.

Abstract: Methods and apparatus for providing query suggestions to a user based on one or more past queries submitted by the user. Candidate query suggestions responsive to a current query may be identified. A candidate query similarity measure may be determined for a given candidate query suggestion based on matching entities related to the given candidate query suggestion and the one or more past queries. In some implementations, the similarity measure of the given candidate query suggestion may be based on a comparison of current entities of the given candidate query suggestion that match entities of one or more past queries, to a group of the current entities that includes entities that do not match the entities of one or more past queries. In some implementations a ranking of the candidate query suggestions may be determined based on the similarity measure.

Abstract: In some examples, data-driven social media analytics application synthesis may include generating, for each social media analytics application of a plurality of social media analytics applications, a corpus, performing term normalization, and generating a normalized corpus. An actor, an action and an object may be generated for each social media analytics application, which may be mapped into an embedding space. A semantic cohesion network may be generated for each social media analytics application, and a pair-wise semantic cohesion may be determined to identify semantically cohesive groups. A new social media analytics application may be synthesized based on the identified semantically cohesive groups.

Abstract: Systems and methods for providing automated data governance are disclosed. The system may include a plurality of data environments, a metadata repository storing data attributes and classification requirements, a policy repository, one or more processors, and a memory in communication with the one or more processors storing instructions to execute steps of a method. The system may receive a first dataset from a first data environment having a first dataset ID. The system may transmit the dataset ID to the metadata repository and the metadata repository may return an indication that the first dataset includes at least one data attribute and at least one associated classification requirement. The system may transmit the classification requirement to the policy repository and receive classification code associated with the classification requirement. The system may modify the first dataset by transmitting instructions to the first data environment to execute the classification code.

Abstract: A system, method, and computer readable medium perform a method of query relaxation. A query output from a conversational system is received. At least one search term in the query is identified. Instance data is output to the conversational system in response to determining that the instance data in a data store matches the at least one search term in the query. The computer device outputs the received query to an external domain-specific knowledge source in response to determining that the at least one search term does not the match instance data in the data store. The computer device receives the relaxed data matches from the external domain-specific knowledge source being semantically-related to at least one search term in the query based on a plurality of criteria associated with the query. The computing device generates a response to the query based on contextual information and structural information.

Abstract: A computer-implemented method, a computer program product, and a computer system for keyword extraction. A computer vectorizes word sets by splitting text in a corpus. The computer trains a Gaussian mixture distribution model for the word sets and obtains an inverse document frequency (IDF) value of a word set from the learned Gaussian mixture distribution model. The computer trains a Gaussian mixture distribution model for a cluster and obtains a term frequency (TF) value of the word set in the cluster from the learned Gaussian mixture distribution model. The computer calculates a term frequency-inverse document frequency (TF-IDF) value of the word set in the cluster, based on the TF value and the IDF value. The computer calculates TF-IDF values of the word sets in clusters and rearranges the word sets in a descending order of the TF-IDF values to obtain extracted keywords.

Abstract: Embodiments for a data protection method of grouping assets for protection policy assignment based on asset metadata by defining a set of metrics characterizing each asset in the system and comparing each metric of an asset with corresponding metrics of other asset groups each containing one or more other assets. A unique protection policy is assigned to each group for application to each asset within a respective group. An overall affinity percentage of the metrics of asset with the corresponding metrics of each group is determined, and the asset is automatically grouped into the group based with the highest overall affinity percentage. The user is prompted to confirm the automatic grouping or to select a different group for assigning to the asset.

Abstract: In one embodiment, present disclosure discloses a method for partitioning a temporal graph is described. Embodiments of the method comprises creating a plurality of storage blocks for each type of the different types of graph elements based on predefined label groups, each of the plurality of storage blocks configured to store the telemetry information generated in a corresponding predefined time-range, recreating each of the plurality of storage blocks upon expiry of a configurable rollover time, and sharding each of the plurality of storage blocks into a plurality of shards based on a configurable sharding count.

Abstract: Some embodiments provide a novel method for deploying different virtual networks over several public cloud datacenters for different entities. For each entity, the method (1) identifies a set of public cloud datacenters of one or more public cloud providers to connect a set of machines of the entity, (2) deploys managed forwarding nodes (MFNs) for the entity in the identified set of public cloud datacenters, and then (3) configures the MFNs to implement a virtual network that connects the entity's set of machines across its identified set of public cloud datacenters. In some embodiments, the method identifies the set of public cloud datacenters for an entity by receiving input from the entity's network administrator. In some embodiments, this input specifies the public cloud providers to use and/or the public cloud regions in which the virtual network should be defined. Conjunctively, or alternatively, this input in some embodiments specifies actual public cloud datacenters to use.

Abstract: An information management security health monitoring system is provided herein that can monitor the security health of one or more information management systems, identify any security issues that are occurring within an information management system, and automatically, or in response to a user input, transmit an instruction to the information management system to execute a workflow to resolve the security issue(s). For example, the information management cell health monitoring system receives security data and security policies, and/or similar data from an information management cell(s) via a network. The information management security health monitoring system analyzes the received information to identify any issues. If an issue is detected, the information management security health monitoring system retrieves workflows and determines whether any of the workflows can be used to resolve the detected issue.

Abstract: A large collection of sample genomes containing misclassified k-mers and metadata errors from a reference taxonomy was converted to a self-consistent k-mer database comprising a self-consistent taxonomy. The self-consistent taxonomy was based on genetic distances calculated using the MinHash method or the Meier-Koltoff method. An agglomerative clustering algorithm was used to calculate the self-consistent taxonomy. Each k-mer of the sample genomes was assigned to only one node of the self-consistent taxonomy. In another step, each node of the self-consistent taxonomy was mapped to the reference taxonomy, thereby preserving in the self-consistent taxonomy links to the reference taxonomy while correcting for the misclassification errors therein. The self-consistent k-mer database can be used to taxonomically profile sequenced nucleic acids with greater specificity compared to systems relying on the reference taxonomy.

Abstract: System and method for processing time-related geospatial data from one or more data sources. For example, a system includes an application server; and a storage. The application server is configured to: receive data including temporal information and geospatial information for each data object of one or more data objects, send the data to a client device to display the data on a map, and generate one or more first multi-dimensional tiles based at least in part on the temporal information and the geospatial information. The one or more first multi-dimensional tiles correspond to a temporal dimension associated with a first temporal width. The application server is further configured to send the one or more first multi-dimensional tiles to store in the storage for retrieval by the client device.

Abstract: A method performed by a computing system comprises receiving, by a hierarchical document classification system (HDCS), a document that includes image data and textual data, wherein the document is associated with a document type. The HDCS determines a first machine learning (ML) classifier of the HDCS associated with the document type. The first ML classifier corresponds to one of an image-based ML classifier of the HDCS and a text-based ML classifier of the HDCS. The determined first ML classifier classifies the document as being associated with a particular class of document of a plurality of classes of documents. A second ML classifier associated with the class of document classifies the document as being associated with a particular subclass of a plurality of subclasses of the class of document. The HDCS communicates information that specifies the class and the subclass associated with the document to a terminal.

Abstract: The technology disclosed relates to manipulating and visualizing database objects that represent layers and parts of layers in insurance or other risk transfer programs. Disclosed is graphically creating placement objects, maintaining a database of objects that can be linked in trees, with policy objects linked to respective proposal objects, and coverage objects linked to the proposal objects and the policy objects. A GUI represents a placement using objects, with metadata that describes objects' statuses, with groups of objects properly nested and linked in the database of objects. User selection of a displayed first object and mode control initiates creation of second objects linked to the first object and, opening of a panel that scales the selected first object to fit the panel and allows the user to add nested second objects, by drawing an added object or entering metadata for the added object, with consistent metadata constrained with the nesting.

Abstract: Provided is a sensor network capable of acquiring, from a data center, learning data composed of a pair of sensor data (input) and a classification label (output) necessary for adding/changing a classification label for updating a classifier while reducing the volume of communication between a sensor and the center. One aspect of the present invention relates to a sensor network, including one or more sensor nodes and a data center. The sensor node includes: an encoding unit for encoding sensor data by an encoder part of an autoencoder; and a transmission unit for transmitting the encoded data. The data center includes: a reception unit for receiving data encoded from sensor data by the encoder part of the autoencoder; a decoding unit for decoding the encoded data by a decoder part of the autoencoder; and a storage unit for storing the decoded data therein.

Abstract: Systems, devices, and methods are provided for efficient query execution on distributed data sets, such as in the context of data lakes. In at least one embodiment, indexing information is used to identify candidate and non-candidate portions of a data set. Non-candidate portions may be irrelevant to the query. Indexing information can be encoded using Bloom filters.

Abstract: A computer-implemented method includes a first module of a computing system receiving application-specific data for determining utilization attributes of multiple application programs. A second module of the system receives data for determining patient archetypes. A third module of the system determines a clinical benefit of each application program based on analysis of the application-specific data. The system matches an application program with a particular patient archetype based on analysis of each patient archetype and the determined clinical benefit of the application program. The system selects a particular application program for use by a patient of a first patient group based on: i) a characteristic of the first patient group that is included in a first patient archetype; and ii) the clinical benefit of the particular application exceeding a threshold clinical benefit.

Abstract: A dynamically generated search query is generated based on rarity scores associated with raw-level computer events. Event data is pre-processed using historical information about the frequency, or rarity, of instances of individual events. Each event is assigned one or more labels that identify the event based on the historical information. The rarity scores represent probabilities of events occurring with the same labels. The rarity scores are associated with n-grams of the labels (e.g., a combination of two labels, three labels, etc.). A label n-gram score is calculated based on newly observed events and the rarity scores corresponding to the label n-grams. The search query is generated based on the label n-gram score. The search query is executed against a database to retrieve information, such as diagnostics, used to alert an administrator to events that are potentially anomalous.

Abstract: An artificial intelligence (AI) search system can build or include one or more knowledge graphs and databases of indexes. The knowledge graphs can encode information and relationships from a variety of domains. The databases of indexes can store pointers to public and/or private data and raw content in those domains. An example of a domain includes fields requiring or handling geospatial data. The AI search system can receive a user query and process the query with AI models, including language models and knowledge graphs to identify data matching the intention and context of the user query. The AI search system can present relevant results in an assortment of user interfaces.

Abstract: A method for issuing control signals in response to sentiment. In some embodiments, the method includes: assigning, to each of a plurality of comments, a respective topic vector of length k; determining whether a largest element of the topic vector of a first comment of the plurality of comments exceeds a weight threshold; in response to the determining that the largest element exceeds the weight threshold, classifying the first comment into a first topic, of k topics, the first topic corresponding to the position, in the topic vector, of the largest element of the topic vector; calculating a first sentiment score; calculating an average sentiment score, based in part on the first sentiment score; determining whether the average sentiment score meets a criterion; and in response to the determining that the criterion is met, generating a control signal, the control signal including a message related to the first topic.

Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform obtaining a set of items that have been grouped together as matching items in a group; performing an ensemble mismatch detection; performing multiple detection models on the set of items to generate respective outputs regarding mismatches; combining the respective outputs to determine whether a quantity of detected mismatches is at least a predetermined threshold; when the quantity of detected mismatches is at least the predetermined threshold, the acts also can include separating at least one of the set of items from the group; and when the quantity of detected mismatches is not at least the predetermined threshold, the acts additionally can include maintaining each item of the set of items in the group. Other embodiments are disclosed.

Abstract: One or more computing devices, systems, and/or methods for constructing and implementing a network management plan are provided. Baseline classifications are assigned to a set of locations. Sub-locations at the set of locations are evaluated to determine whether to override any of the baseline classifications. A model is used to evaluate images depicting the set of locations to generate predicted classifications for the set of locations. Classifications are assigned to the set of locations by implementing conflict resolution rules to selectively retain or replace baseline classifications with predicted classifications. The classifications are used to construct and/or implement a network management plan.

Abstract: Systems and methods for managing access to data stored in a distributed manner across different zones of a multi-zone computing platform involve maintaining a global representation of all data stored within the multi-zone computing platform. After receiving a request on behalf of a user to access given data, a first zone of the multi-zone computing platform (i) obtains, from the global representation, information indicating a particular zone where the given data is stored, (ii) validates the request, and (iii) if the validation is successful, processes the request. If the given data is stored at the first zone, the first zone retrieves the given data for provision to the user. If the given data is stored at a second zone, the first zone issues a request to the second zone to validate the request, and if the validation is successful, retrieve the given data for provision to the user.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for providing filters from resource content. In one aspect, a system receives data identifying a set of resources that are determined to be responsive to a search query and extracts a set of keywords from the contents of the resources and related queries. The keywords are processed according to candidate selection criteria, and a set of candidate query filters are determined. The candidate filters may be used to filter the resources that are responsive to the query.

Abstract: An information processing device includes: a memory; and a processor coupled to the memory, the processor being configured to: sort stream data buffered in units of wraps of a sequential recording medium, in a column order and a time order of the stream data, as primary data to be written into a primary wrap of the sequential recording medium; and control writing of the sorted primary data into the primary wrap, wherein the sorting of the stream data is configured to sort secondary data to be written into a secondary wrap that follows the primary wrap, in a reverse order of the column order and in the time order, and wherein the controlling of the primary data is configured to control writing of the sorted secondary data into the secondary wrap.

Abstract: Embodiments described herein provide natural language processing (NLP) systems and methods that provide a customized summarization of scientific or technical articles, which disentangles background information from new contributions, and summarizes the background information or the new information (or both) based on a user's preference. Specifically, the systems and methods utilize machine learning classifiers to classify portions of sentences within the article as containing background information or as containing a new contribution attributable to the article. The systems and methods then incorporate the background information in the summary or incorporate the new contribution in the summary and output the summary. In this way, the systems and methods can provide summaries of scientific literatures, which largely accelerates literature review in scientific fields.

Abstract: An improved hash table structure compatible with in-memory processing for increasing cache efficiency during hash join processing of a small and large table in a relational database system. The hash table, residing in processor memory, includes a first partition containing a join condition column providing best selectivity for joining the small table with the large table, at least one additional partition containing additional join condition columns for joining the small table with the large table; and an array of hash values, the array of hash values providing an index into the hash table partitions.

Abstract: In a method for managing data in a partition table, a server, and a storage medium, a management node obtains a creation duration range of data of a target partition in the partition table, further obtains a geographical area to which the target partition belongs, historical access information of the target partition, or a service type of the target partition; and then, when the creation duration range of the data of the target partition meets a predetermined time range of a first storage stage and a preset condition is met, the management node alters the data that is of the target partition and that has been stored based on a storage policy corresponding to a second storage stage to be stored based on a storage policy corresponding to the first storage stage.

Abstract: A method of persisting and performing a clustering analysis through use of a large data electronic file system includes generating a job identifier and linking the job identifier with a configuration identifier, a plurality of model identifiers and a plurality of data regularization identifiers. Each of the configuration identifier, model identifiers and data regularization identifiers are stored in respective management tables of the file system along with meta-data indicating a physical location of an analysis configuration, a physical location of a data regularizer and a physical location of a clustering model, respectively. The method further includes specifying the job identifier to a clustering analysis application causing the analysis configuration, the clustering models and the data regularizers to load into the clustering analysis application and receiving a plurality of scores resulting from a cluster analysis performed by the clustering analysis application based on the job identifier.