A system for data process comprises an operating platform for storing and reading a data unit. A data processing module signally connected to the operating platform. The data unit is structured or unstructured. The data processing module labeling and processing the data unit, and generating a visualization diagram. The system for data process includes a graphical user interface, which can achieve one of the purposes of this present disclosure of improving the data visualization of structured data and unstructured data.

A method and system for maintaining the order of arrangement of a modified object list is provided. The method includes receiving selection of an object from a first plurality of objects of an object list, wherein the object list has an order of arrangement and is included on a first digital page displayed on a user interface of a device, receiving selection of a category icon from a plurality of category icons corresponding, to a plurality of categories, wherein the plurality of category icons are included on the first digital page, modifying the object list to exclude the object from the first digital page, and maintaining the order of arrangement of the object list that is modified, wherein the maintaining includes displaying a second plurality of objects included in the object list that is modified on the first digital page in accordance with the order of arrangement.

A computer generates and displays a data visualization in a data visualization user interface according to placement of data fields, from a data source. The data visualization includes visual data marks representing data from the data source. The computer detects a user input to select a visual data mark. In response to detecting the user input, the computer obtains a data model encoding the data source as a tree of logical tables. The computer identifies one or more aggregated data values for the visual data mark, each of the aggregated data values corresponding to a respective data field in the data model. For each of the aggregated data values, the computer retrieves a respective disaggregated set of data rows from a respective logical table containing the respective data field. The computer displays a summary grid, with a respective tab corresponding to each of the retrieved disaggregated sets of data rows.

A system and method for visualizing and querying high dimensional data to a user. The system includes a user device, a data-pearls visualization and querying server. The server obtains the high dimensional data from the user device associated with user. The server generates data clusters and sub-divides the data clusters into non-overlapping subsets of data-pearls using a clustering technique. The server selects a shape for each data-pearl by comparing a distance between centroid of a data-pearl and a farthest point from a determined centroid using L.sub.p norm distance measures. The server configures each data-pearl in a three-dimensional plot. The server enables the user to visualize the data-pearls on a screen of the user device. The server queries data based on a query using data dimension technique. The server dimensions data related to the query through determined classifiers based on filtered data after pruning unrelated data to the query.

Systems and methods for automated and explainable machine learning to generate seamlessly actionable insights by generating explainable personas directly from customer relationship management systems are disclosed. The personas are defined as a collection of segments, scored by likelihood to generate good opportunities, accompanied ranked profile attribute importance, with descriptive names and summaries, associated human and database readable queries which have been generated to optimally find cluster candidates in a broader data universe. Such a system would effectively and accurately model the composition of past clients, perform the categorization in an explainable way such that actions can be taken on the information to have predictable results. What is further required are the mean to categorize small text components, trained over dependent and independent model sets, to enable a cleaner and more explicit representation of information rich short-strings, in order to facilitate a more meaningful representation of the user profiles.

Provided are systems and methods for classifying and tagging records in a record management system using information extracted and analyzed from specific areas or regions of records. A specific area or region of the record may be scanned, and the content disposed therein processed against a plurality of classification templates. Based on proximity to the classification templates, the record may be assigned one or more tags corresponding to the classification templates.

Embodiments detect fraud of risk targets that include both customer accounts and cashiers. Embodiments receive historical point of sale (“POS”) data and divide the POS data into store groupings. Embodiments create a first aggregation of the POS data corresponding to the customer accounts and a second aggregation of the POS data corresponding to the cashiers. Embodiments calculate first features corresponding to the customer accounts and second features corresponding to the cashiers. Embodiments filter the risk targets based on rules and separate the filtered risk targets into a plurality of data ranges. For each combination of store groupings and data ranges, embodiments train an unsupervised machine learning model. Embodiments then apply the unsupervised machine learning models after the training to generate first anomaly scores for each of the customer accounts and cashiers.

A method of extracting a table information, an electronic device, and a storage medium are provided, which relate to fields of artificial intelligence and big data, in particular to fields of machine learning, knowledge graph, intelligent search and intelligent recommendation, and may be used for an intelligent extraction of an information in a table and other scenarios. The method includes: performing a clustering based on features of a plurality of rows of cells and/or features of a plurality of columns of cells in a table, so as to determine candidate header cells in the table; and performing an information extraction on the table based on the candidate header cells, so as to extract attribute-attribute value pairs in the table.

In some implementations, a device may receive, from a data stream, a set of data points arranged in a dimensional data space. The device may compare the set of data points to identify one or more clusters using values of a distance parameter for data points included in the set of data points, wherein the values of distance parameter includes different values of the distance parameter for different data points. The device may transmit an indication of the one or more clusters to cause a device to display information associated with the one or more clusters. The device may receive, from the device, feedback information associated with at least one data point, wherein the feedback information indicates that at least one data point is associated with an error. The device may modify a value of the distance parameter associated with the at least one data point to a modified value.

A method, system, and computer program product for visualizing a machine learning model are provided. A confusion matrix and model performance metric data are received from a classification model. For each data point in the confusion matrix, a corresponding pixel is generated. The pixels are grouped into clusters. Each cluster represents a label in the confusion matrix. A centroid is generated for each cluster. Using the model performance metric data, a misclassification indicator arrow is generated for each misclassified data point. The misclassification indicator arrow indicates both the predicted class and the actual class. The clusters, the centroids, and the misclassification indicator arrow are displayed as a graphical visualization of the machine learning model.