This disclosure provides systems, methods, and apparatuses, including computer programs encoded on computer storage media, for accessing information associated with an organization hierarchy. In one aspect of the disclosure, a method includes transmitting, from a device to a server in which multiple group models are stored, an access request to access a first group model of the multiple group models. Each group model of the multiple group models is associated with a different organization and includes multiple group data structures, multiple group type data structures, and multiple group member data structures. Each group model is associated with group hierarchy information that indicates a hierarchy associated with the multiple group data structures associated with the group model. The method further includes receiving, at the device and based on the access request, first hierarchy information associated with a first group model. Other aspects and features are also claimed and described.

A method and system for accessing updated data from a database in response to a user query has been developed. First, multiple transaction logs are generated for a database. Each transaction log contains a record of actions executed by a database management system and referenced according to the specified date of the actions. Data updates are received and stored with the database. An incremental database partition is created for each data update. Each incremental database partition is stored with reference to a corresponding transaction log for the date of the data update. The updated data is accessed through the incremental database partition in response to an outdated user query. The outdated user query contains a data access request for a date earlier than the receipt of data updates.

Disclosed herein is a system and method to any two or more MLMs to be merged into a multiline MLM system despite having different commission structures. Each member of the original MLMs is able to maintain their existing downlines without any changes. Further the existing MLM members have full access to the multi-line MLM commission structure, for example, a member of a binary MLM may now add a 3rd, 4th, 5th, etc. line if they choose. The multiline commission plan will be different than the commission plans from any of the original MLMs but this change should not affect the income of a large portion of users, and users that are affected by the changeover can be compensated or made whole on an individual level. In addition to this multiline commission plan, the commission structure of the original MLMs has been broken into several separate ‘types’ which together form an MLM system. These ‘types’ include the income received from downline commission based on position and the income received based on enrolling a member in the MLM, also known as sponsorship. This system is one that may be hosted on the internet or cloud computing services and may provide remote access to the newly formed MLMs. The system will provide essential services to the MLMs such as databasing, commission calculation, and commission structure modification, for free or for a fee. MLMs, their agents, executives, employees, members, and other entities selected by the MLM will be able to access the system using secure credentials.

Machine learning models, semantic networks, adaptive systems, artificial neural networks, convolutional neural networks, and other forms of knowledge processing systems are disclosed. An ensemble machine learning system is coupled to a graph module storing a graph structure, wherein a collection of entities and the relationships between those entities forms nodes and connection arcs between the various nodes. A hotfile module and hotfile propagation engine coordinate with the graph module or may be subsumed within the graph module, and implement the various hot file functionality generated by the machine learning systems.

Systems, methods, and devices are provided for dynamically generating a histogram for viewing via a user interface. Embodiments presented herein provide apparatus and techniques for generating a histogram and adjusting a view of the histogram without the computations being perceived by a user. To do so, histogram indices may be computed for various time intervals (e.g., minutes, hours, days, etc.) of input data. The indices may be used to generate a histogram for a time interval that may be larger than the interval used to compute the histogram indices. At the time period for the histogram displayed via the user interface is changed by the user, a dynamic histogram builder computes an adjusted histogram using histogram indices for the updated and/or changed time period. Embodiments herein provide techniques which reduce the time to compute the histogram and consume less computing resources to do so.

A data management method causes a computer to execute processing including: creating, when a predetermined data processing program performs data processing, based on an access frequency to a data store, high-frequency state item list information obtained by listing high-frequency state items of which the access frequency is high; determining, when state information that includes a value of the high-frequency state item is written to the data store, whether or not the state information corresponds to the high-frequency state item with reference to the high-frequency state item list information; grouping and writing pieces of the state information of a plurality of the high-frequency state item.

Exemplary embodiments provide computer-implemented methods, mediums, and apparatuses configured to import, create, and edit formulas for custom fields in an analytical chemistry system. The custom fields may be represented as a tree structure with nodes representing operators. When the custom field is defined, it may be incorporated into a workflow for the analytical chemistry system. Dependencies within the custom field may be identified and the custom field may be incorporated into the workflow at an appropriate workflow stage. The custom field may be scoped to define which elements of a data acquisition the custom field operates over. Various visualization options are described to allow the custom field to be displayed and/or edited in different ways.

Methods, computer systems, and computer-storage medium are provided for providing closed-loop intelligence. A selection of data is received, at a cloud service, from a database comprising data from a plurality of sources in a Fast Healthcare Interoperability Resources (FHIR) format to build a data model. After a feature vector corresponding to the data model is extracted, a selection of an algorithm for a machine learning model to apply to the data model is received. A portion of the selection of data is utilized for training data and test data and the machine learning model is applied to the training data. Once the model is trained, the trained machine learning model can be saved at the cloud service, where it may be accessed by others.

Techniques and apparatuses to decompress data that has been stack compressed is described. Stack compression refers to compression of data in one or more dimensions. For uncompressed data blocks that are very sparse, i.e., data blocks that contain many zeros, stack compression can be effective. In stack compression, uncompressed data block is compressed into compressed data block by removing one or more zero words from the uncompressed data block. A map metadata that maps the zero words of the uncompressed data block is generated during compression. With the use of the map metadata, the compressed data block can be decompressed to restore the uncompressed data block.

Provided is an information search system by which high-speed search is possible commonly used across a plurality of districts, the system including: a database (12) that stores a plurality of pieces of information that are text-searchable; a query sentence acceptance unit (26) that accepts a query sentence; an inputted search keyword extractor (44) that extracts an inputted search keyword from the query sentence; a retrieval executor (40) that executes retrieval processing from the database using the inputted search keyword; a local management apparatus (100) that stores district material in a local database (104); and an information management apparatus (110) that executes character extraction processing on the material stored in the local database and converts a file format of the material according to a size thereof, stores the material in a temporary memory as stored material, and outputs the stored material to the database.