Open Source Java Frameworks - Page 15

Almond Java Application Framework is an easy to use Swing based framework for Java applications. Almond is very much inspired by the K Desktop Environment.

This Amazon Mechanical Turk (MTurk) SDK is no longer under active development as of 12/31/2017. The latest MTurk SDKs, with support for most common programming languages, are now a part of the Amazon Web Service (AWS) SDKs at https://aws.amazon.com/tools/. With the AWS SDKs, you can also connect to MTurk using the AWS Command Line Interface (https://aws.amazon.com/cli/), allowing you to perform operations from your Windows, Mac, or Linux command line without having to write code.

Example application to demonstrate the use and the abilities of the Spring RCP framework in conjunction with the Amazon ECS WebServices. The application acts as a frontend to the Amazon Services.

Ambient Composite Engine, a web-based framework for writing composite software, submitted by WebactiveLabs.

Amethyst is a framework intended to help in creation of applications with the MVC or a similar pattern. The project consists of low- and high-level API. Low-level API is responsible for exchanging properties (as Map) between various components. A component can be for example model, view or controller as in the MVC pattern. The main class is a class named Communicator. Communicator class defines the structure of components' system, provides communication channel between them and life-cycle management for each one. It also provides ability to run components in different threads (multitasking). IT CAN SUCCESSFULLY SPLIT MODEL'S THREAD FROM GUI THREAD

Ammentos Persistence Layer is a lightweight persistence framework for Java (JDK5.0 or later). Works with annotations, does not require any configuration/mapping file, runs with any JDBC compliant database (no SQL dialects to configure), supports multi.d

This project manages program (usually web) flow and web page /web component generation. Currently this project is only integrated with Tapestry 4. However, the code is separated out and implementing different web UI frameworks should be possible.

The Amplify MDA project is aimed at delivering a 'model-to-text' generator tool with real-world applicability in less than 1000 lines of Java/Groovy code.

A complete library of high-speed java collection classes to complement existing classes with new and advanced algorithms. Complements standard J2SE classes and Jakarta commons classes

The Pando project intends to deliver an OSGi-based implementation of the FIPA agent platform. The motivation behind Pando is the need for an open platform supporting the integration and interoperation of disparate software systems.

This is to help developer to render complex bangla or bengali font for android.

Android, multidex, splitdex, subpackage plugin. The Gradle script was integrated into the plug-in, so that only one configuration file needs to be maintained. Readers can choose separate configuration or integrated configuration according to their own needs. In this way, we maintain the number of methods of the main Dex at about 15,000, and we no longer have to worry about the number of methods. Through the above official subcontracting, the original Dex has been divided into 1 master Dex and more slave Dex. The main Dex contains all 4 major components, Application, Annotation, multidex, etc. and their necessary direct dependencies. Since the number of our methods has reached 16W, hundreds of activities were all stuffed into the main Dex, and the main Dex was successfully exploded.

Angkor is an easy-to-use component model for producing interactive and reusable J2EE web systems using object oriented methodologies. It is built on a flexible pipeline architecture and very easy to integrate with other technologies.

Anna is lightweight Java framework to build pipeline systems of any kind. It will take care of recurrent needs such as dependency management, data serialisation and synchronization as well as parallelization of execution.

Un sistema formato da server, client, linguaggio di script, controllo versione e distribuzione automatica. Tutto questo per creare applicazioni di tipo enterprise che lavorino principalmente con database.

The Annotated Object Validator provides a mechanism for writing validators for any Object using Java Annotations. This allows for complex, reusable validation code to be constructed and applied to Fields and Classes with a minimum amount of effort.

Ever wanted to create notes for pages on the web and be able to share them? This framework allows to create annotations to XML and HTML documents. Annotations are independent of presentation and can be traced back even if part of the document changed.

Anokha is a MVC web framework based on Java/J2EE technology that simplify the enterprise application development by providing a set of presentation and business tier components being J2EE-API independent and high extensible. Easy to use and learn.

GPL'ed API for the commercial, closed source Another Charts exchange quotation analysis software. http://anothercharts.net

Antidoto is a java technology mashup providing building blocks for modular architectures. Antídoto is the portuguese word for antidote. An antidote is a substance which can counteract a form of poisoning.

Apache Beam is an open source, unified programming model to define both batch and streaming data-parallel processing pipelines, as well as certain language-specific SDKs for constructing pipelines and Runners. These pipelines are executed on one of Beam’s supported distributed processing back-ends, which include Apache Apex, Apache Flink, Apache Spark, and Google Cloud Dataflow. Beam is especially useful for Embarrassingly Parallel data processing tasks, and caters to the different needs and backgrounds of end users, SDK writers and runner writers.

Apache Cocoon 2.2 Beispiele die im Buch "Apache Cocoon" im dPunkt Verlag beschrieben werden. Beispiele können auch unabhängig vom Buch den Einstieg in Cocoon erleichtern.

Apache Flink is a distributed engine for stateful computations over data streams and batches, designed for low-latency processing at scale. Its core runtime executes dataflow graphs with fine-grained backpressure and checkpointing, allowing applications to recover consistently from failures. Flink’s event-time model and watermarks enable accurate out-of-order processing, windowing, and complex time semantics that typical real-time systems struggle with. Developers program against high-level APIs—DataStream and Table/SQL—to express transformations, joins, and stateful patterns, while specialized libraries support CEP, machine learning workflows, and connectors. A rich connector ecosystem integrates with systems like Kafka, Kinesis, filesystems, JDBC sources/sinks, and object stores. Deployments span Kubernetes, YARN, Mesos, and standalone clusters, and operational features such as savepoints, state backends, and metrics make long-running jobs manageable in production.

Apache InLong is a one-stop integration framework for massive data that provides automatic, secure and reliable data transmission capabilities. InLong supports both batch and stream data processing at the same time, which offers great power to build data analysis, modeling and other real-time applications based on streaming data. InLong (应龙) is a divine beast in Chinese mythology who guides the river into the sea, and it is regarded as a metaphor of the InLong system for reporting data streams. InLong was originally built at Tencent, which has served online businesses for more than 8 years, to support massive data (data scale of more than 80 trillion pieces of data per day) reporting services in big data scenarios. The entire platform has integrated 5 modules: Ingestion, Convergence, Caching, Sorting, and Management, so that the business only needs to provide data sources, data service quality, data landing clusters and data landing formats.

Apache Sedona™ is a cluster computing system for processing large-scale spatial data. Sedona extends existing cluster computing systems, such as Apache Spark and Apache Flink, with a set of out-of-the-box distributed Spatial Datasets and Spatial SQL that efficiently load, process, and analyze large-scale spatial data across machines. According to our benchmark and third-party research papers, Sedona runs 2X - 10X faster than other Spark-based geospatial data systems on computation-intensive query workloads. According to our benchmark and third-party research papers, Sedona has 50% less peak memory consumption than other Spark-based geospatial data systems for large-scale in-memory query processing. Sedona offers Scala, Java, Spatial SQL, Python, and R APIs and integrates them into underlying system kernels with care. You can simply create spatial analytics and data mining applications and run them in any cloud environments.