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The Finch HRIS API provides a unified way to connect to a multitide of HRIS systems. The API requires an access token issued by Finch. By default, Organization and Payroll requests use Finch's [Data Syncs](/development-guides/Data-Syncs). If a request is made before the initial sync has completed, Finch will request data live from the provider. The latency on live requests may range from seconds to minutes depending on the provider and batch size. For automated integrations, Deductions requests (both read and write) are always made live to the provider. Latencies may range from seconds to minutes depending on the provider and batch size. Employer products are specified by the product parameter, a space-separated list of products that your application requests from an employer authenticating through Finch Connect. Valid product names are— - `company`: Read basic company data - `directory`: Read company directory and organization structure - `individual`: Read individual data, excluding income and employment data - `employment`: Read individual employment and income data - `payment`: Read payroll and contractor related payments by the company - `pay_statement`: Read detailed pay statements for each individual - `benefits`: Create and manage deductions and contributions and enrollment for an employer [](https://god.gw.postman.com/run-collection/21027137-08db0929-883d-4094-a9ce-dbf5a9bee4a4?action=collection%2Ffork&collection-url=entityId%3D21027137-08db0929-883d-4094-a9ce-dbf5a9bee4a4%26entityType%3Dcollection%26workspaceId%3D1edf19bc-e0a8-41e9-ac55-481a4b50790b)

The Finch HRIS API provides a unified way to connect to a multitide of HRIS systems. The API requires an access token issued by Finch. By default, Organization and Payroll requests use Finch's [Data Syncs](/development-guides/Data-Syncs). If a request is made before the initial sync has completed, Finch will request data live from the provider. The latency on live requests may range from seconds to minutes depending on the provider and batch size. For automated integrations, Deductions requests (both read and write) are always made live to the provider. Latencies may range from seconds to minutes depending on the provider and batch size. Employer products are specified by the product parameter, a space-separated list of products that your application requests from an employer authenticating through Finch Connect. Valid product names are— - `company`: Read basic company data - `directory`: Read company directory and organization structure - `individual`: Read individual data, excluding income and employment data - `employment`: Read individual employment and income data - `payment`: Read payroll and contractor related payments by the company - `pay_statement`: Read detailed pay statements for each individual - `benefits`: Create and manage deductions and contributions and enrollment for an employer [](https://god.gw.postman.com/run-collection/21027137-08db0929-883d-4094-a9ce-dbf5a9bee4a4?action=collection%2Ffork&collection-url=entityId%3D21027137-08db0929-883d-4094-a9ce-dbf5a9bee4a4%26entityType%3Dcollection%26workspaceId%3D1edf19bc-e0a8-41e9-ac55-481a4b50790b)

Java Prolog Interface (JPI) is an Application Provider Interface (API) for interaction between Java and Prolog programming languages. Is a bidirectional interface that communicate Java applications with Prolog program or database and Prolog procedures with Java class and methods. JPI is an abstraction layer over concrete prolog drivers over Prolog Engines. This API define all mechanism to interact with any Prolog Engine and maintain the application independent to a specific underlying engine. JPI have several connectors to open source prolog engines like SWI, YAP, XSB native engines and tuProlog, jTrolog, jLog Java based prolog engines. JPI study all related Java-Prolog integration libraries and take the betters features from each solution with the propose to achieve a common integration interface. The last feature allows switch the under laying Prolog Engine driver and the application code still be the same. JPI run over any Java Virtual Machine that support Java SE 5 or above. The project was tested over HotSpot, Open J9 and JRockit Virtual Machines over Operating Systems like Windows (7,8,10), Linux (Debian, Ubuntu) and Mac OS X. Can be deployed on Servlets Containers like Jetty, Tomcat or Glassfish Application Server. JPI can be include in any Java Project using the commonest Java Integration Development Enviroment (IDE) like Eclipse, Netbeans, IntelijIDEA and so on. JPI is developed and maintained by Prolobjectlink Project an open source initiative for build logic based applications using Prolog like fundamental Logic Programming Language in the persistence layer and application programming.

JUNG the Java Universal Network/Graph Framework--is a software library that provides a common and extensible language for the modeling, analysis, and visualization of data that can be represented as a graph or network. It is written in Java, which allows JUNG-based applications to make use of the extensive built-in capabilities of the Java API, as well as those of other existing third-party Java libraries. The JUNG architecture is designed to support a variety of representations of entities and their relations, such as directed and undirected graphs, multi-modal graphs, graphs with parallel edges, and hypergraphs. It provides a mechanism for annotating graphs, entities, and relations with metadata. This facilitates the creation of analytic tools for complex data sets that can examine the relations between entities as well as the metadata attached to each entity and relation. The current distribution of JUNG includes implementations of a number of algorithms from graph theory, data mining, and social network analysis, such as routines for clustering, decomposition, optimization, random graph generation, statistical analysis, and calculation of network distances, flows, and importance measures (centrality, PageRank, HITS, etc.). JUNG also provides a visualization framework that makes it easy to construct tools for the interactive exploration of network data. Users can use one of the layout algorithms provided, or use the framework to create their own custom layouts. In addition, filtering mechanisms are provided which allow users to focus their attention, or their algorithms, on specific portions of the graph.

JUNG the Java Universal Network/Graph Framework--is a software library that provides a common and extendible language for the modeling, analysis, and visualization of data that can be represented as a graph or network. It is written in Java, which allows JUNG-based applications to make use of the extensive built-in capabilities of the Java API, as well as those of other existing third-party Java libraries. The JUNG architecture is designed to support a variety of representations of entities and their relations, such as directed and undirected graphs, multi-modal graphs, graphs with parallel edges, and hypergraphs. It provides a mechanism for annotating graphs, entities, and relations with metadata. This facilitates the creation of analytic tools for complex data sets that can examine the relations between entities as well as the metadata attached to each entity and relation. The current distribution of JUNG includes implementations of a number of algorithms from graph theory, data mining, and social network analysis, such as routines for clustering, decomposition, optimization, random graph generation, statistical analysis, and calculation of network distances, flows, and importance measures (centrality, PageRank, HITS, etc.). JUNG also provides a visualization framework that makes it easy to construct tools for the interactive exploration of network data. Users can use one of the layout algorithms provided, or use the framework to create their own custom layouts. In addition, filtering mechanisms are provided which allow users to focus their attention, or their algorithms, on specific portions of the graph.

This is an implementation of the Neural Gas algorithm on distance data (Relational Neural Gas) for unsupervised clustering. We recommend that you use the functions provided by the RelationalNeuralGas class for your purposes. All other classes and functions are utilities which are used by this central class. In particular, you can use RelationalNeuralGas.train() to obtain a RNGModel (i.e. a clustering of your data), and subsequently you can use RelationalNeuralGas.getAssignments() to obtain the resulting cluster assignments, and RelationalNeuralGas.classify() to cluster new points which are not part of the training data set. The underlying scientific work is summarized nicely in the dissertation "Topographic Mapping of Dissimilarity Datasets" by Alexander Hasenfuss (2009). The basic properties of an Relational Neural Gas algorithm are the following: 1.) It is relational: The data is represented only in terms of a pairwise distance matrix. 2.) It is a clustering method: The algorithm provides a clustering model, that is: After calculation, each data point should be assigned to a cluster (for this package here we only consider hard clustering, that is: each data point is assigned to exactly one cluster). 3.) It is a vector quantization method: Each cluster corresponds to a prototype, which is in the center of the cluster and data points are assigned to the cluster if and only if they are closest to this particular prototype. 4.) It is rank-based: The updates of the prototypes depend only on the distance ranking, not on the absolute value of the distances.

JUNG the Java Universal Network/Graph Framework--is a software library that provides a common and extendible language for the modeling, analysis, and visualization of data that can be represented as a graph or network. It is written in Java, which allows JUNG-based applications to make use of the extensive built-in capabilities of the Java API, as well as those of other existing third-party Java libraries. The JUNG architecture is designed to support a variety of representations of entities and their relations, such as directed and undirected graphs, multi-modal graphs, graphs with parallel edges, and hypergraphs. It provides a mechanism for annotating graphs, entities, and relations with metadata. This facilitates the creation of analytic tools for complex data sets that can examine the relations between entities as well as the metadata attached to each entity and relation. The current distribution of JUNG includes implementations of a number of algorithms from graph theory, data mining, and social network analysis, such as routines for clustering, decomposition, optimization, random graph generation, statistical analysis, and calculation of network distances, flows, and importance measures (centrality, PageRank, HITS, etc.). JUNG also provides a visualization framework that makes it easy to construct tools for the interactive exploration of network data. Users can use one of the layout algorithms provided, or use the framework to create their own custom layouts. In addition, filtering mechanisms are provided which allow users to focus their attention, or their algorithms, on specific portions of the graph.