Download JAR files tagged by event with all dependencies

Protempa supports the construction of software systems for querying clinical databases, summarizing patient data, detecting clinically significant data patterns in groups of patients or individual patients, and extract, transform and load (ETL). Its special feature, temporal abstraction, supports queries and summarization of data in terms of temporal patterns. Temporal patterns enable robust querying and extraction of clinical history. These patterns include frequencies (at least two, three, etc. of some event or observation); sequences (chemotherapy followed by surgery within 180 days, a hospital encounter followed by another hospital encounter within 30 days); and overlaps (falling platelet counts in the setting of HELLP syndrome).

Mbassador is a fast and flexible event bus system following the publish subscribe pattern. It is designed for ease of use and aims to be feature rich and extensible while preserving resource efficiency and performance. It provides non-blocking iterators and minimal write contention with low memory footprint. Some features: declarative handler definition via annotations, sync and/or async event delivery, weak or strong references, configurable event filters,

FunctionExecutors 1. GeoWithinFunctionExecutor Input : (longitude double, latitude double, geoJSONGeometryFence string) OR (geoJSONGeometry string, geoJSONGeometryFence string) Output : true if (longitude, latitude) or geoJSONGeometry is within the geoJSONGeometryFence 2. GeoIntersectsFunctionExecutor Input : (longitude double, latitude double, geoJSONGeometryFence string) OR (geoJSONGeometry string, geoJSONGeometryFence string) Output : true if (longitude, latitude) or geoJSONGeometry intersects the geoJSONGeometryFence 3. GeoWithinDistanceFunctionExecutor Input : (longitude double, latitude double, geoJSONGeometryFence string, distance double) OR (geoJSONGeometry string, geoJSONGeometryFence string, distance double) Output : true if (longitude, latitude) or geoJSONGeometry is within distance of the geoJSONGeometryFence StreamProcessors 1. GeoCrossesStreamProcessor Input : (id string, longitude double, latitude double, geoJSONGeometryFence string) OR (id string, geoJSONGeometry string, geoJSONGeometryFence string) Output : an event with `crosses` additional attribute set to true when the object ((longitude, latitude) or geoJSONGeometry) crosses into geoJSONGeometryFence and an event with `crosses` additional attribute set to false when the object crosses out of the geoJSONGeometryFence 2. GeoStationaryStreamProcessor Input : (id string, longitude double, latitude double, geoJSONGeometryFence string, radius double) OR (id string, geoJSONGeometry string, geoJSONGeometryFence string, radius double) Output : when the object ((longitude, latitude) or geoJSONGeometry) starts being stationary within the radius an event with `stationary` additional attribute set to true. When the object starts to move out of the radius an event with `stationary` additional attribute set to false. 3. GeoProximityStreamProcessor Input : (id string, longitude double, latitude double, geoJSONGeometryFence string, radius double) OR (id string, geoJSONGeometry string, geoJSONGeometryFence string, radius double) Output : when two objects ((longitude, latitude) or geoJSONGeometry) starts being in close proximity within the radius an event with `inCloseProximity` additional attribute set to true. When the object starts to move out of the radius an event with `inCloseProximity` additional. attribute set to false. On each event, additional attributes `proximityWith` gives the id of the object that this object is in close proximity and `proximityId` is an id unique to the pair of objects

The rooster crows immediately before sunrise, the rooster causes the sun to rise. Using GraphX to detect possible causes and effects between time related events. We observe a true causation signal by generating random correlations over same events at different time and back propagate these scores to their most connected events. Finally, we extract the most probable causes and effects together with a score of aggressiveness (how likely an event could explain downstream effects) and sensitivity (how likely an event results from an upstream cause).

The TraceView Java instrumentation agent. Contains the required jar, configuration file and native libraries for Windows and Linux. TraceView provides application performance monitoring for Java web apps through this agent. The files contained here allow for event tracking within your application to identify latency timings between your application layers. More options can be found here: http://docs.traceview.solarwinds.com/Instrumentation/java.html#configuring-instrumentation. For other information on our Java agent please visit http://docs.traceview.solarwinds.com/Instrumentation/java.html.

The AppNeta Java instrumentation agent. Contains the required jar, configuration file and native libraries for Windows and Linux. AppNeta provides application performance monitoring for Java web apps through this agent. The files contained here allow for event tracking within your application to identify latency timings between your application layers. More options can be found here: https://docs.appneta.com/java#configuring-instrumentation. For other information on our Java agent please visit https://docs.appneta.com/java.

Information extraction is the process of identifying specified classes of entities, relations, and events in natural language text – creating structured data from unstructured input. JET, the Java Extraction Toolkit, developed at New York University over the past fifteen years, provides a rich set of tools for research and education in information extraction from English text. These include standard language processing tools such as a tokenizer, sentence segmenter, part-of-speech tagger, name tagger, regular-expression pattern matcher, and dependency parser. Also provided are relation and event extractors based on the specifications of the U.S. Government's ACE [Automatic Content Extraction] program. The program is provided under an Apache 2.0 license.

Asynchronous publish-subscribe communication library. Features: 1. Listeners are stored using WeakReferences to prevent memory leaks when they are not unsubscribed. 2. Uses fork-join framework for concurrent event delivery. 3. Publish methods returns collection of Futures that represent event notifications. 4. Supports ordered publishing: guaranteed to notify of the events in order they were published. 5. Provides EventSource and EventService interfaces for better integration with IOC containers and alternative implementations.

SSJ is a Java library for stochastic simulation, developed under the direction of Pierre L'Ecuyer, in the Département d'Informatique et de Recherche Opérationnelle (DIRO), at the Université de Montréal. It provides facilities for generating uniform and nonuniform random variates, computing different measures related to probability distributions, performing goodness-of-fit tests, applying quasi-Monte Carlo methods, collecting (elementary) statistics, and programming discrete-event simulations with both events and processes.

Weighing less than 50KB, Rupy is probably the smallest Java NIO application server in the world. Rupy is inherently non-blocking asynchronous, which makes it the ideal candidate for high concurrency real-time applications pushing dynamic data. Tested with acme, rupy performs on average ~1500 requests per second. To put that figure in perspective; acme doesn't use keep-alive, so that means 1500 unique TCP connections serving dynamic content per second! Thanks to NIO and an event queue to avoid selector trashing, this figure degrades gracefully under high concurrency.