Download JAR files tagged by friendly with all dependencies

Our first work is to create a set of tools to help in the adoption drools in our local companies. Those tools are here to make drools "enterprise ready "by adopting an industrial approach : we want to put in common a standard library that we shall all use in our drools project. This tools are of course open source and we use a friendly apache licence. Indeed, drools like any other jboss framework is a swiss knife that offers all possibilities. Our framework implements the best practices that we implemented in real projects that goes from how to monitor, report and supervise the use of drools. We hope you will enjoy our work and invite you to contact us in case of trouble. But do not forget we are a community !

The Source Analyst library is a powerful tool designed to streamline and expedite the tracking of traffic sources for mobile applications. This versatile library is aptly named "Source Analyst" and is an invaluable asset for app developers and marketers seeking to gain deeper insights into the performance of their advertising campaigns. With just one simple function call, Source Analyst empowers you to efficiently investigate the effectiveness of various advertising sources. Key Features: Effortless Tracking: Source Analyst simplifies the complex task of tracking the origins of traffic for your mobile app. No need for convoluted setups or extensive coding – one function is all it takes. Comprehensive Insights: Gain a comprehensive understanding of where your app's users are coming from. Whether it's through social media, search engines, referral links, or other channels, Source Analyst provides you with clear data on traffic sources. Performance Evaluation: Evaluate the performance of your advertising campaigns with precision. Discover which sources are driving the most valuable users to your app, helping you optimize your marketing efforts effectively. Time-Saving: Say goodbye to hours spent on manual data collection and analysis. Source Analyst automates the tracking process, freeing up your time to focus on making data-driven decisions. Customization: Tailor Source Analyst to your specific needs. Customize the library to track the metrics that matter most to your app's success. Real-time Data: Access real-time data, ensuring that you always have up-to-date insights into your traffic sources. Integration-Friendly: Seamlessly integrate Source Analyst into your existing mobile app infrastructure, whether you're developing for Android or iOS. User-Friendly: Source Analyst is designed with user-friendliness in mind. Its intuitive interface and straightforward documentation make it accessible to developers of all levels of expertise. How It Works: Using Source Analyst is as easy as calling a single function within your code. Simply integrate the library into your app, and you can begin tracking traffic sources immediately. From there, Source Analyst compiles and presents the data in a clear and organized manner, allowing you to make data-driven decisions with ease. In a world where understanding the origins of your app's traffic is essential for marketing success, Source Analyst is the go-to solution. Say goodbye to the complexity of tracking sources and embrace the simplicity and effectiveness of Source Analyst for your mobile app. Harness the power of Source Analyst and unlock a new level of insight into your app's performance today!

InfoScope Library: Simplifying Privacy Policy Display with WebView The InfoScope Library is a versatile tool designed to enhance the seamless presentation of privacy policies through WebView integration. Privacy policies play a crucial role in maintaining transparency and trust between users and applications, and the InfoScope Library streamlines this process by offering a range of convenient features. At its core, the library provides the SimpleAutoWebView, a WebView component equipped with fundamental settings for optimal privacy policy display. This WebView component is tailored to effortlessly load and present privacy policy content to users, ensuring a smooth and user-friendly experience. To further enhance the functionality and customization options, the InfoScope Library includes two essential components: SimpleAutoWebViewClient and SimpleAutoWebChromeClient. These components enable developers to quickly establish and configure the basic WebView behavior and appearance. The SimpleAutoWebViewClient is designed to facilitate the interaction between the WebView and the application. It streamlines the process of handling various events, such as page loading, error handling, and navigation. With this component, developers can swiftly create a WebViewClient that aligns with their application's requirements, promoting a consistent and intuitive user journey. Complementing the WebView functionality, the SimpleAutoWebChromeClient focuses on managing the visual aspects of WebView content, including alert dialogs, JavaScript dialogs, and UI interactions. This component empowers developers to define the behavior and appearance of these elements, ensuring a polished and integrated presentation of the privacy policy content. In summary, the InfoScope Library offers a comprehensive toolkit for developers to seamlessly integrate privacy policy display using WebView. By providing the SimpleAutoWebView, SimpleAutoWebViewClient, and SimpleAutoWebChromeClient components, the library enables swift development and easy customization, fostering transparency and trust between users and applications. Embrace the power of the InfoScope Library to elevate your privacy policy presentation and enhance your user experience.

Mahout's goal is to build scalable machine learning libraries. With scalable we mean: Scalable to reasonably large data sets. Our core algorithms for clustering, classification and batch based collaborative filtering are implemented on top of Apache Hadoop using the map/reduce paradigm. However we do not restrict contributions to Hadoop based implementations: Contributions that run on a single node or on a non-Hadoop cluster are welcome as well. The core libraries are highly optimized to allow for good performance also for non-distributed algorithms. Scalable to support your business case. Mahout is distributed under a commercially friendly Apache Software license. Scalable community. The goal of Mahout is to build a vibrant, responsive, diverse community to facilitate discussions not only on the project itself but also on potential use cases. Come to the mailing lists to find out more. Currently Mahout supports mainly four use cases: Recommendation mining takes users' behavior and from that tries to find items users might like. Clustering takes e.g. text documents and groups them into groups of topically related documents. Classification learns from existing categorized documents what documents of a specific category look like and is able to assign unlabelled documents to the (hopefully) correct category. Frequent itemset mining takes a set of item groups (terms in a query session, shopping cart content) and identifies, which individual items usually appear together.

Mahout's goal is to build scalable machine learning libraries. With scalable we mean: Scalable to reasonably large data sets. Our core algorithms for clustering, classfication and batch based collaborative filtering are implemented on top of Apache Hadoop using the map/reduce paradigm. However we do not restrict contributions to Hadoop based implementations: Contributions that run on a single node or on a non-Hadoop cluster are welcome as well. The core libraries are highly optimized to allow for good performance also for non-distributed algorithms. Scalable to support your business case. Mahout is distributed under a commercially friendly Apache Software license. Scalable community. The goal of Mahout is to build a vibrant, responsive, diverse community to facilitate discussions not only on the project itself but also on potential use cases. Come to the mailing lists to find out more. Currently Mahout supports mainly four use cases: Recommendation mining takes users' behavior and from that tries to find items users might like. Clustering takes e.g. text documents and groups them into groups of topically related documents. Classification learns from exisiting categorized documents what documents of a specific category look like and is able to assign unlabelled documents to the (hopefully) correct category. Frequent itemset mining takes a set of item groups (terms in a query session, shopping cart content) and identifies, which individual items usually appear together.