Systems as described herein may include generating an aggregated service status report for a real-time service delivery platform. A plurality of services running in a service domain may be determined. A request for a status of system behavior corresponding to a particular service may be received. Service connection details of the particular service may be discovered and metric data of real-time data movement may be tracked. Real-time snapshot aggregation of the particular service may be provided. In a variety of embodiments, a real-time system behavior report for the service across availability zones may be presented.

Systems and methods include determination, for each of a plurality of discrete features, of statistics based on a number of occurrences of each discrete value of the discrete feature in the data, determination of first summary statistics based on the determined statistics, determine of a dissimilarity for each discrete feature based on the first summary statistics and on the statistics determined for the discrete feature, determination of candidate discrete features based on the determined dissimilarities, determination, for each of the candidate discrete features, of second summary statistics based on values of a continuous feature associated with each discrete value of the candidate discrete feature, determination of a deviation score for each of the candidate discrete features based on the second summary statistics, and transmission of the candidate discrete features for display in association with the continuous feature based on the determined deviation scores.

Various methods, apparatuses/systems, and media for automatically mapping physical data models or objects to logical data models which in turn are automatically mapped to business terms are disclosed. A database stores a raw physical data model of an application. A processor extracts the raw physical data model of the application from the database. The processor also converts physical object names associated with the raw physical data model into English terms based on a taxonomy expansion list; applies a plurality of standardization and contextualization rules to the English terms generated from converting the physical object names; outputs names based on applying the plurality of standardization and contextualization rules to the English terms; applies fuzzy logic and machine learning routines and matching algorithms for matching the names to predefined logical terms; and automatically generates a mapping of physical objects or elements in the application with logical attributes and related business terms.

A method of obtaining and imputing missing data and a measurement system having the same are disclosed. The method. includes obtaining measurement values of measurement variables, among z variables corresponding to z components of a measurement object, wherein z is a natural number greater than 1, and the z variables of the measurement object include measurement variables and missing variables which are not measured, and the measurement variables are of an amount less than z; generating missing data having the measurement variables with the measurement values and the missing variables with missing values in the z components, wherein each of the missing values is predetermined value indicating that a missing variable has not been measured; generating k pieces of final imputation data having k final imputation values, by using the missing data, wherein k is a natural number greater than 1, each of the k final imputation values are in the z components, sing the missing data includes performing multiple imputations on the missing data; and generating average data having average component values in the z components, wherein each of the average component values in a component is an average value of the k final imputation values of the k pieces of final imputation data in the component, and selecting, in each of the z components, a next measurement variable, wherein a difference value between a final imputation values and an average component value, of the next measurement variable, is larger than a difference value of the missing variables.

Some embodiments provide a non-transitory machine-readable medium that stores a program. The program identifies a first data structure having a first type. The first data structure is configured to store a set of geometries. The program further identifies a second data structure associated with the first data structure. The second data structure is configured to store modifications to the set of geometries. The program also perform a merge operation on the first data structure and the second data structure to form a third data structure.

Techniques are disclosed relating to malware clustering based on function call graph similarity. In some embodiments, a computer system may access information corresponding to a plurality of malware samples and, based on the information, generate a function call graph for each of the malware samples. In some embodiments, generating the function call graph for a given malware sample includes identifying a plurality of function calls included in the information, assigning a label to each of the function calls, identifying relationships between the function calls, and generating the function call graph based on the relationships and the labels. Based on the function call graphs, the computer system may assign each of the plurality of malware samples into one of a plurality of clusters of related malware samples.

Various systems and methods of aggregating and analyzing geographically indexed data are disclosed. The system can include a server database hosting an application that a client computer may access via a web browser according to a SaaS architecture. The server database can store a variety of geographically indexed data, which may include economic data, demographic data, social data, and various other data types. The server database can be programmed to cause the client to display a map for receiving a selected geographic area defined on the map and then retrieve selected data corresponding to user-selected criteria for the selected geographic area. The server database can then transmit the selected data to the client for display on the map.

Techniques for synthesizing and analyzing data are disclosed. A ML model anonymizes microdata to generate synthesized data. This anonymizing is performed by reproducing attributes identified within microdata and by applying constraints to prevent rare attribute combinations from being reproduced in the synthesized data. User input selects attributes to filter the synthesized data, thereby generating a subset of records. A UI displays a synthesized aggregate count representing how many records are in the subset. Pre-computed aggregate counts are accessed to indicate how many records in the microdata embody certain attributes. Based on the user input, there is an attempt to identify a particular count from the pre-computed aggregate counts. This count reflects how many records of the microdata would remain if the selected attributes were used to filter the microdata. That count is displayed along with the synthesized aggregate count. The two counts are juxtaposed next to one another.

Example implementations relate to generating a virtual co-parent key for a document information tree (DIT) including a plurality of child indexes each associated with a parent index. A virtual co-parent key for a first child index is generated. The first child index may be mapped to a child entry and may have a unique identification key including a child key and associated parent key. Generation of the co-parent key may include the computation of a co-parent seed key from the parent key or child key of the child index. Generation of the co-parent key may further include pre-pending the unique identification key with the computed co-parent seed key.

Various embodiments relate generally to data science and data analysis, computer software and systems, and network communications to interface among repositories of disparate datasets and computing machine-based entities configured to access datasets, and, more specifically, to a computing and data storage platform configured to provide one or more computerized tools to deploy predictive data models based on in-situ auxiliary query commands implemented in a query, and configured to facilitate development and management of data projects by providing an interactive, project-centric workspace interface coupled to collaborative computing devices and user accounts. For example, a method may include activating a query engine, implementing a subset of auxiliary instructions, at least one auxiliary instruction being configured to access model data, receiving a query that causes the query engine to access the model data, receiving serialized model data, performing a function associated with the serialized model data, and generating resultant data.