Libraries tagged by data management

Install Magento Store Manager Connector module to fluently connect Store Manager desktop application to Magento database. Increase speed of data management, take advantage of simplicity and reliability with all-in-one Store Manager via Magento Store Manager Bridge Connector.

Go to Download

The IOI City Mall Sales File Generator is a Laravel package that simplifies the creation of daily sales data files for IOI City Mall stores. It seamlessly integrates into Laravel projects, streamlining data generation for retail management.

Go to Download

Functionally complete, flexible TYPO3 integration of Klaro! Consent Management by KIProtect GmbH, a powerful tool that protects your visitors' privacy and data.

Go to Download

Easy2Dev Laravel Tenant Migrate is a Laravel package designed to simplify database migration for multi-tenant applications, particularly those employing PostgreSQL databases. This package streamlines the migration process for Software as a Service (SaaS) web applications where tenant data is stored in separate databases. It eliminates the need to manually adjust database configuration files for each tenant, offering a scalable solution for database management.

Go to Download

Adminer database management tool for your Laravel application.

Go to Download

PHP package to generate schedules for date ranges to use in project or resource planning tools.

Go to Download

API Security: provides authentication, authorization and a role-based access control management via ACL

Go to Download

GenericCollection is a versatile PHP library that provides a type-safe collection class for managing various data types. Simplify your data management with intuitive methods and strong type constraints.

Go to Download

CLI Utility for Validating and Generating CSV files based on custom rules. It ensures your data meets specified criteria, streamlining data management and integrity checks.

Go to Download

Package of data models managements

Go to Download

An advanced PHP serialization tool leveraging attributes for flexible object-to-array and JSON conversion. Supports property aliases, type conversions, and nested object handling. Ideal for APIs, data persistence, and configuration management.

Go to Download

A comprehensive package for seamlessly interacting with Greece’s Ergani system, enabling automated submissions for employee data such as check-ins, check-outs, and other employment-related information. This repository aims to simplify and streamline workforce management and ensure compliance with Greek labor regulations.

Go to Download

# Introduction **INDA (INtelligent Data Analysis)** is an [Intervieweb](https://www.intervieweb.it/hrm/) AI solution provided as a RESTful API. The INDA pricing model is *credits-based*, which means that a certain number of credits is associated to each API request. Hence, users have to purchase a certain amount of credits (established according to their needs) which will be reduced at each API call. INDA accepts and processes a user's request only if their credits quota is grater than - or, at least, equal to - the number of credits required by that request. To obtain further details on the pricing, please visit our [site](https://inda.ai) or contact us. INDA HR embraces a wide range of functionalities to manage the main elements of a recruitment process: + [**candidate**](https://api.inda.ai/hr/docs/v2/#tag/Resume-Management) (hereafter also referred to as **resume** or **applicant**), or rather a person looking for a job; + [**job advertisement**](https://api.inda.ai/hr/docs/v2/#tag/JobAd-Management) (hereafter also referred to as **job ad**), which is a document that collects all the main information and details about a job vacancy; + [**application**](https://api.inda.ai/hr/docs/v2/#tag/Application-Management), that binds candidates to job ads; it is generated whenever a candidate applies for a job. Each of them has a specific set of methods that grants users the ability to create, read, update and delete the relative documents, plus some special features based on AI approaches (such as *document parsing* or *semantic search*). They can be explored in their respective sections. Data about the listed document types can be enriched by connecting them to other INDA supported entities, such as [**companies**](https://api.inda.ai/hr/docs/v2/#tag/Company-Management) and [**universities**](https://api.inda.ai/hr/docs/v2/#tag/Universities), so that recruiters may get a better and more detailed idea on the candidates' experiences and acquired skills. All the functionalities mentioned above are meant to help recruiters during the talent acquisition process, by exploiting the power of AI systems. Among the advantages a recruiter has by using this kind of systems, tackling the bias problem is surely one of the most relevant. Bias in recruitment is a serious issue that affect both recruiters and candidates, since it may cause wrong hiring decisions. As we care a lot about this problem, we are constantly working on reduce the bias in original data so that INDA results may be as fair as possible. As of now, in order to tackle the bias issue, INDA automatically ignores specific fields (such as name, gender, age and nationality) during the initial processing of each candidate data. Furthermore, we decided to let users collect data of various types, including personal or sensitive details, but we do not allow their usage if it is different from statistical purposes; our aim is to discourage recruiters from focusing on candidates' personal information, and to put their attention on the candidate's skills and abilities. We want to help recruiters to prevent any kind of bias while searching for the most valuable candidates they really need. The following documentation is addressed both to developers, in order to provide all technical details for INDA integration, and to managers, to guide them in the exploration of the implementation possibilities. The host of the API is [https://api.inda.ai/hr/v2/](https://api.inda.ai/hr/v2/). We recommend to check the API version and build (displayed near the documentation title). You can contact us at [email protected] in case of problems, suggestions, or particular needs. The search panel on the left can be used to navigate through the documentation and provides an overview of the API structure. On the right, you can find (*i*) the url of the method, (*ii*) an example of request body (if present), and (*iii*) an example of response for each response code. Finally, in the central section of each API method, you can find (*i*) a general description of the purpose of the method, (*ii*) details on parameters and request body schema (if present), and (*iii*) details on response schema, error models, and error codes.

Go to Download

Test data management framework

Go to Download

Tripal Cultivate is a collection of Tripal extension packages focused on providing intuitive data management support for breeding and pre-breeding activities.

Go to Download