Abstract: A data interpretation apparatus including a platform configured to automatically execute type conversion of an object. The platform is provided in a control unit of the data interpretation apparatus, and includes an obtaining unit and an interpretation unit. The obtaining unit obtains input data as the object of the platform. The interpretation unit generates an initial graph with respect to the object, and performs interpretation by automatically growing a graph from the initial graph while executing, as necessary, the type conversion of the object associated with each node of the graph toward extension or toward intension.

Abstract: Embodiments of the present disclosure are directed to systems, methods, and computer product programs to identify one or more seminal cases within a database containing legal case data. The disclosed systems and methods provide an approach to identify one or more seminal cases for particular legal issues by mining a text database containing electronic legal documents for the reasons for citing and mining the text within to determine whether the legal issue addressed in the reasons for citing is directed to a seminal case. The data is created through data mining and obtained from the plurality the reasons for citing identifying the seminal cases for a particular legal issue such that the output data corresponding to the seminal cases causes an external device to distinguish the seminal cases when one or more of the seminal cases are returned as the result of a search.

Abstract: A method for processing of a data stream using a graph-based data model includes receiving a data stream including data messages; disassembling the data messages data elements and metadata; generating a structured data model comprising the set of data elements based on the type of the data elements and the pattern of the data messages; instantiating a workflow to process the structured data model; configuring a CPS-G model sub-graph to add to the CPS-G model based on the type of the data elements and the pattern of the data messages; adding the CPS-G model sub-graph to the CPS-G model to form the CPS-G dataset; and storing the CPS-G dataset including the CPS-G model sub-graph in a data store for further processing by streaming computation and machine learning algorithms.

Abstract: A system for dataflow graph processing comprises a communication interface and a processor. The communication interface is configured receive an indication to generate a dataflow graph, wherein the indication includes a set of queries and/or commands. The processor is coupled to the communication interface and configured to: determine dependencies of each query in the set of queries on another query; determine a DAG of nodes based at least in part on the dependencies; determine the dataflow graph by determining in-line expressions for tables of the dataflow graph aggregating calculations associated with a subset of dataflow graph nodes designated as view nodes; and provide the dataflow graph.

Abstract: A computer implemented method for keyword search over a knowledge graph. The knowledge graph comprises a large number of vertices representing entities and a large number of edges representing relations between the entities. The knowledge graph is enhanced with static labels. A static label for each vertex includes a list of distances between the vertex and other vertices of the knowledge graph. The method includes receiving a set of keywords, constructing dynamic labels based on the set of keywords and determining a subgraph of the knowledge graph for the set of keywords based on the static labels and based on the dynamic labels. The constructing of the dynamic labels includes obtaining keyword vertices by mapping keywords of the set of keywords to vertices of the knowledge graph and obtaining for the keyword vertices distances between the keyword vertices and predecessors of the keyword vertices from the static labels.

Abstract: The present disclosure describes methods, systems, and computer program products for enabling advanced analytics with large datasets.

Abstract: One embodiment of the present disclosure provides a system and method for facilitating a search for a hybrid-manufacturing process plan for manufacturing an object. During operation, the system can obtain a set of partial order constraints constraining the order in which a set of at least two manufacturing actions, corresponding to addition or removal of predefined regions of space, appear in a process plan. The system can constrain, based on the set of partial order constraints, a search space. The search space can correspond to a tree in which the nodes represent the object's state and the edges represent available actions at each node. The system can then determine a set of optimized process plans represented by orderings of the actions, corresponding to paths on the search tree, that produce the desired final state in a cost-effective manner.

Abstract: Embodiments of the present invention provide for a method for duplicate determination in a graph. The graph comprises nodes representing entities and edges representing relationships between the entities. Embodiments of the present invention identify two target nodes in the graph. A neighborhood subgraph may be determined for each of the two nodes. The neighborhood subgraph includes the respective node. Embodiments of the present invention can then determine whether the two nodes are duplicates with respect to each other, based on a result of a comparison between the two subgraphs and based on a comparison of the two target nodes of the subgraphs.

Abstract: A system and method for information storage using blockchain and pointer databases, comprising a computer with a blockchain manager and datastore manager, and blockchain data input, which connects over a network to a distributed blockchain ledger containing information such as personally-identifying data and a datastore system containing searchable information such as a DNS system on the persons entered into the blockchain, the datastore system also containing reference numbers for each searchable block in the blockchain, such that verification or identification can both be accomplished swiftly and securely of data in the blockchain such as for data verification to verify or identify persons submitting data to such a system.

Abstract: Embodiments include systems, methods, articles of manufacture, and computer-readable media configured process data in a structured format and an unstructured format and applying one or more algorithms to detect elements and links between the elements in the data. Embodiments are further configured to generate a graph model comprising nodes comprising the elements and edges comprising the links.

Abstract: A method for anonymizing and combining personal information in big data, by which big data is anonymized to be freely distributed to an external system without fear of personal information leakage and distributed data can be combined with each other is proposed.

Abstract: A method for anomaly detection of cloud services based on mining time-evolving graphs includes steps of receiving tracing data for a plurality of micro-services of the deployed cloud service, wherein the tracing data defines relationships between the plurality of micro-services of the deployed cloud service at a plurality of different time intervals, computing a functional graph based on the tracing data for each of the plurality of different time intervals, wherein nodes of each functional graph include the plurality of micro-services and wherein links between the nodes represent relationships between the plurality of micro-services, comparing the functional graphs for each of the plurality of time intervals to determine an anomaly score for each of the functional graphs, and detecting a presence of one or more anomalies based on the anomaly scores.

Abstract: The present teaching relates to method and system for generating a stream of content items. A plurality of entities associated with a time-window are obtained, wherein each entity of the plurality of entities is associated with at least one content item. For each entity, a first parameter with respect to the time-window, a second parameter with respect to previous time-windows, and a trendiness score based on a function of the first parameter and the second parameter are respectively calculated. A graph based on one or more entity-pairs is generated, wherein each entity-pair of the one or more entity-pairs satisfies a first criterion. A stream of content items is generated based on the graph, wherein each content item in the stream of content items corresponds to at least one of the one or more entity pairs.

Abstract: The present disclosure discloses a query method, an apparatus, an electronic device, and a storage medium, and relates to the technical field of spatio-temporal big data in big data technologies. The specific implementation scheme is as follows: receiving a query request sent by a terminal device, where the query request is used to request to query dynamic association relationships of a target entity and includes an identifier of the target entity and query starting and ending time; determining at least one time bucket to be queried in a query database according to the query starting and ending time, where each time bucket stores the dynamic association relationships of the target entity in a time period corresponding to the time bucket; and querying the dynamic association relationships of the target entity in the at least one time bucket according to the identifier of the target entity.

Abstract: Computer-implemented systems and methods forecast network resource and/or infrastructure needs for an enterprise computer system that employs network servers to host resources that are requested by network users. Based on the forecasts, the network resources can be scaled or provisioned accordingly. The state of the networkservers can be dynamically adjusted to meet the request needs of the users while reducing excess capacity. The forecasting techniques are also applicable to cloud computing environments. Based on the forecasts, the cloud server pool can be scaled dynamically, so that the system's scale satisfies the changing requests and avoids wasting resources when the system is under low load.

Abstract: A method for standardizing data input, data output and data manipulation at a data lake is provided. Methods include receiving a data transfer instruction comprising a seed file. Methods include parsing the seed file. Methods include validating the seed file. Methods include retrieving one or more data elements from one or more data sources as specified in the seed file. Methods include saving the retrieved data elements to a data lake. Methods include archiving the data elements at the data lake. Methods include receiving a schema from a network data mover client at an edge node at the data lake. Methods include creating a table to match the schema and validating the data elements using the schema. Methods include pushing the data elements into the table using the schema. Methods include saving the table that comprises the data elements in a shoreline edge node within the data lake.

Abstract: Various embodiments relate generally to data science and data analysis, and computer software and systems to provide an interface between repositories of disparate datasets and computing machine-based entities that seek access to the datasets, and, more specifically, to a computing and data storage platform that facilitates consolidation of one or more datasets, whereby data ingestion is performed to form data representing layered data files and data arrangements to facilitate, for example, interrelations among a system of networked collaborative datasets. In some examples, a method may include forming a first layer data file and a second layer data file, assigning addressable identifiers to uniquely identify units of data and data units to facilitate the linking of data, and implementing selectively one or more of a unit of data and a data unit as a function of a context of a data access request for a collaborative dataset.

Abstract: A computer-implemented method and system for searching over queries, writing and performing a data query. The computer-implemented method includes analyzing the query to understand elements described in the query. Further, the computer-implemented method includes extracting aliases for expressions to identify alternate names. Furthermore, the computer-implemented method includes allowing a user to annotate the elements. Moreover, the computer-implemented method includes establishing whether the query contains content for defining a new query, and if so, then enables writing a query according to a shorthand system.

Abstract: The present teaching relates to online user profiling. In one example, content associated with a first user of a social media network is obtained. From the content associated with the first user, a first link to a first piece of content is identified. A second user of the social media network associated with the first user is determined in the context of the social media network. From content associated with the second user of the social media network, a second link to a second piece of content is identified. The first and second pieces of content are retrieved based on the first and second links, respectively. User profile of the first user is generated based, at least in part, on the first and second pieces of content.

Abstract: A system and method for transforming input data in a data graph is structured in such a way that it does not destroy embedded contextual data yet also keeps the number of edges in the data graph sufficiently small in number that computation with respect to the data in the data graph is feasible with existing computational resources on extremely large graph sets. Incoming data is represented as a collection of “cliques” rather than placing each data object into its own node in the graph database. Maintaining the clique structure though the graph build pipeline dramatically reduces the exponential increase in the number of edges in the graph, while also maintaining all of the contextual data presented on the input record.