The present disclosure provides a method, a device, an equipment and a storage medium for mining a topic concept. The method includes: acquiring a plurality of candidate topic concepts based on a query; performing word segmentation on the plurality of candidate topic concepts and performing part-of-speech tagging on words obtained after performing the word segmentation, to obtain a part-of-speech sequence of each of the plurality of candidate topic concepts; and filtering the plurality of candidate topic concepts based on the part-of-speech sequence, to filter out a topic concept corresponding to a target part-of-speech sequence among the plurality of candidate topic concepts, in which a proportion of accurate topic concepts in the target part-of-speech sequence is lower than or equal to a first preset threshold, or a proportion of inaccurate topic concepts in the target part-of-speech sequence is higher than or equal to a second preset threshold.

Blockchain environments may mix-and-match different encryption, difficulty, and/or proof-of-work schemes when mining blockchain transactions. Each encryption, difficulty, and/or proof-of-work scheme may be separate, stand-alone programs, files, or third-party services. Blockchain miners may be agnostic to a particular coin's or network's encryption, difficulty, and/or proof-of-work schemes, thus allowing any blockchain miner to process or mine data in multiple blockchains. GPUs, ASICs, and other specialized processing hardware components may be deterred by forcing cache misses, cache latencies, and processor stalls. Hashing, difficulty, and/or proof-of-work schemes require less programming code, consume less storage space/usage in bytes, and execute faster. Blockchain mining schemes may further randomize byte or memory block access, further improve cryptographic security.

Techniques facilitating automatic feature extraction from a relational database are provided. In an embodiment, a method can include generating an entity graph based on a relational database, wherein the entity graph comprises a first node associated with a first table in the relational database and a second node associated with a second table in the relational database. In another embodiment, the method can include joining the first table and the second table based on an edge between the first table and the second table defined by the entity graph, wherein a resulting joined table is connected by a column of data. In another embodiment, the method can include extracting a feature from the column of data using a data mining algorithm selected from a set of data mining algorithms based on a type of data in the column of data.

In an embodiment a method is provided. The method includes receiving forecast information in a computer from a first group of users. The method also includes integrating the forecast information into a database and corresponding OLAP cube accessible by the computer. The method further includes receiving comments on specific entries of the forecast information in the computer from a second group of users. The method also includes receiving changes of the forecast information in the computer from a second group of users The method further includes integrating comments and changes into the database and OLAP cube. Moreover, the method includes providing a display of the comments and the changes to the first group of users in an interface to the computer.

Facilitating separation of intended and non-intended browsing traffic in browsing history advanced networks (e.g., 4G, 5G, and beyond) is provided herein. Operations of a system can comprise determining respective contradiction values for second-level domains of a group of second-level domains in observed browsing history traffic. The operations can also comprise separating intended network traffic from non-intended network traffic based on the respective contradiction values. The respective contradiction values can indicate levels of inconsistency between the observed browsing history traffic and a determined popularity ranking.

Embodiments are directed to a system, computer program product, and method for text mining, and dynamic facet and facet value management and application to a document collection. Two or more words from a first document collection are extracted, with the extracted words being associated with an applied annotation. At least one word is selected from the extracted words, designated as a facet, and a value is selectively added to the facet. An analysis of the added value is dynamically performed, and a dictionary with the annotation, facet, and values is constructed and the dictionary is applied to the document collection. A targeted list of documents is returned from the dictionary application to the document collection.

Strategic competitive intelligence data generation systems and methods are provided. The system stores internal intellectual property data of an entity, receives external technology development data from predefined sources and intellectual property data from the internal portfolio database of the entity, stores a set of relevant rules, executes them, filters the technology development data and the intellectual property data, based on the rules executed by the rule engine. It stores in one of an entity aligned cluster database, the filtered technology development data and the intellectual property data. The entity aligned cluster databases are created against predefined entity aligned clusters. The system executes an input query, determines one relevant entity aligned cluster database for executing the search query, and populates relevancy scores in two matrices, aggregates the relevancy scores through multiplication of the matrices. The system then analyses the resultant matrix based on set of rules and displays results of the analytics.

In accordance with an embodiment, described herein is a system and method for providing a mobile device application and rendering engine, for use with a data analytics environment in generating interactive visualizations of large amounts of data, with high frame rates. The mobile device can receive, from a data analytics server, a data payload describing data points associated with data analytics visualizations, and employs shader objects or routines (shaders) in combination with a graphics processing unit (GPU), to render data visualizations with high resolution, including support for features such as drill-downs. User interaction with a visualization can be interpreted by the rendering engine as transformations or deltas of an original presentation, and communicated to a shader to cause the GPU to render an updated visualization. A hierarchical data model enables data visualizations to be layered and brought forward or backward within the overall presentation in a multi-dimensional exploratory manner.

Systems and methods for analyzing a plurality of data records to provide a comprehensive understanding of the data. For example, one or more public or private databases may be searched based on a user's search term(s). The results from the search may be analyzed to determine values, categories, core trends, concepts, and/or clusters present within the search results. The search results may be grouped or organized based on the values, categories, core trends, clusters, and/or concepts, and may be presented to a user via a user interface. Additionally, systems and methods of the present disclosure relate to analyzing public or private company data, and tracking or monitoring the data over time to provide real time analysis. For example, customer reviews, complaints, social media posts, or other data related to a company or product may be analyzed to determine values, categories, core trends, concepts, and/or clusters.

A method for intent mining that includes: receiving conversation data; using an intent mining algorithm to automatically mine intents from the conversation data; and uploading the mined intents into the conversational bot. The intent mining algorithm may include: analyzing utterances of the conversation data to identify intent-bearing utterances; analyzing the identified intent-bearing utterances to identify candidate intents; selecting salient intents from the candidate intents; grouping the selected salient intents into salient intent groups in accordance with a degree of semantic similarity; for each of the salient intent groups, selecting one of the salient intents as the intent label and designating the others as the intent alternatives; and associating the intent-bearing utterances with the salient intent groups via determining a degree of semantic similarity between the candidate intents present in the intent-bearing utterance and the intent alternatives within each group.