RaimaDB
RaimaDB, an embedded time series database that can be used for Edge and IoT devices, can run in-memory. It is a lightweight, secure, and extremely powerful RDBMS. It has been field tested by more than 20 000 developers around the world and has been deployed in excess of 25 000 000 times.
RaimaDB is a high-performance, cross-platform embedded database optimized for mission-critical applications in industries such as IoT and edge computing. Its lightweight design makes it ideal for resource-constrained environments, supporting both in-memory and persistent storage options. RaimaDB offers flexible data modeling, including traditional relational models and direct relationships through network model sets. With ACID-compliant transactions and advanced indexing methods like B+Tree, Hash Table, R-Tree, and AVL-Tree, it ensures data reliability and efficiency. Built for real-time processing, it incorporates multi-version concurrency control (MVCC) and snapshot isolation, making it a robust solution for applications demanding speed and reliability.
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Workable
Workable stands as the world's foremost recruitment and human resources management platform. We equip internal recruiters, hiring teams, and HR experts with a plethora of avenues to discover highly qualified candidates, facilitating seamless collaboration in the quest to identify, recruit, onboard, and oversee top-tier talent. Businesses that employ Workable experience accelerated transitions from requisition to offer letter thanks to our array of automated and AI-driven tools that streamline candidate sourcing, simplify decision-making, optimize the hiring workflow, and elevate employee management.
Established in 2012, Workable has made substantial contributions to the hiring efforts of over 27,000 companies, aiding in the selection of 1.5 million candidates. Our global team of over 250 professionals spans locations in Greece, Australia, the United States, and the United Kingdom, serving a diverse clientele, including esteemed organizations like Starling Bank, JOEY Restaurants, RyanAir, among others.
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Eyezag
Eye tracking is a prevalent qualitative research technique utilized in market research, predominantly by larger firms that often operate their own dedicated laboratories. As a result, studies employing this method tend to be both costly and time-intensive, typically limited to a select number of projects and participants. In the context of analyzing user interactions on websites and interfaces, researchers gather visual data alongside mouse movement recordings. Due to the need for specialized and expensive equipment, participants generally must be present in a laboratory environment. Consequently, the high financial burden associated with eye tracking has led to its infrequent use in the UI/UX field, making researchers seek alternative methods that are more accessible and cost-effective. This lack of commonality in eye tracking usage underscores the challenges faced in integrating advanced research techniques into broader design practices.
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Tobii Pro Lab
Pro Lab is an advanced eye tracking software tailored for executing experimental research using Tobii Pro hardware. This versatile platform assists researchers throughout the complete research process, accommodating both straightforward and intricate experiments. When combined with Tobii Pro hardware, Pro Lab forms a comprehensive solution aimed at exploring human behavior. It features an intuitive visual interface and specialized software tools that effectively support researchers at every stage of an eye tracking experiment—ranging from test design and data collection to detailed analysis. By promoting sound experimental practices, Pro Lab enables newcomers to easily embark on their eye tracking journey while also providing substantial flexibility to accommodate evolving research objectives. Furthermore, Pro Lab is specifically crafted to work seamlessly with our screen-based and wearable eye trackers, ensuring optimal and efficient processing of data streams for researchers seeking high-quality insights. This synergy enhances the overall research experience, making it more robust and insightful.
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