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Advanced Library to Remove Object Relational Impedance. DBvolution translates all database concepts into Object Oriented concepts, allowing you to spend all your time writing Java rather than fixing broken and awkward SQL. DBvolution transforms your schema into classes, reduces the database configuration to sparse annotations on the classes, and allows querying directly from the classes. Queries are created inside your java code and takes as little as one line for a multi-table outer join. Retrieving the rows from the query is only one more method call. Dozens of SQL functions are available without leaving your Java code and use chaining to build complex expressions easily. Transactions are encapsulated into a thread-like API, allowing you to write complex database interactions in complete safety. The queries performed by DBvolution are always available for debugging and checking by DBAs before release. There are examples in nz.co.gregs.dbvolution.examples, and documentation at http://dbvolution.gregs.co.nz.

Advanced Library to Remove Object Relational Impedance. DBvolution translates all database concepts into Object Oriented concepts, allowing you to spend all your time writing Java rather than fixing broken and awkward SQL. DBvolution transforms your schema into classes, reduces the database configuration to sparse annotations on the classes, and allows querying directly from the classes. Queries are created inside your java code and takes as little as one line for a multi-table outer join. Retrieving the rows from the query is only one more method call. Dozens of SQL functions are available without leaving your Java code and use chaining to build complex expressions easily. Transactions are encapsulated into a thread-like API, allowing you to write complex database interactions in complete safety. The queries performed by DBvolution are always available for debugging and checking by DBAs before release. There are examples in nz.co.gregs.dbvolution.examples, and documentation at http://dbvolution.gregs.co.nz.

Advanced Library to Remove Object Relational Impedance. DBvolution translates all database concepts into Object Oriented concepts, allowing you to spend all your time writing Java rather than fixing broken and awkward SQL. DBvolution transforms your schema into classes, reduces the database configuration to sparse annotations on the classes, and allows querying directly from the classes. Queries are created inside your java code and takes as little as one line for a multi-table outer join. Retrieving the rows from the query is only one more method call. Dozens of SQL functions are available without leaving your Java code and use chaining to build complex expressions easily. Transactions are encapsulated into a thread-like API, allowing you to write complex database interactions in complete safety. The actions performed by DBvolution are always available for debugging and checking by DBAs before release. There are examples in nz.co.gregs.dbvolution.examples, and documentation at http://dbvolution.gregs.co.nz and http://sourceforge.net/p/dbvolution/blog/

The IntelligentGraph SAIL offers an extended capability for embedded calculation support within any RDF graph. When enabled as an RDF4J SAIL, it offers calculation functionality as part of the RDF4J engine, on top of any RDF4J repository, using a variety of script engines including JavaScript, Jython, and Groovy. It preserves the SPARQL capability of RDF4J, but with additional capabilities for calculation debugging and tracing. IntelligentGraph includes the PathQL query language. Just as a spreadsheet cell calculation needs to access other cells, an IntelligentGraph calculation needs to access other nodes within the graph. Although full access to the underlying graph is available to any of the scripts, PathQL provides a succinct, and efficient method to access directly or indirectly related nodes. PathQL can either return just the contents of the referenced nodes, or the contents and the path to the referenced nodes. PathQL can also be used standalone to query the IntelligentGraph-enabled RDF database. This supplements, rather than replaces, SPARQL and GraphQL, as it provides graph-path querying rather than graph-pattern querying capabilities to any IntelligentGraph-enabled RDF database. The principles of IntelligentGraph are described here: https://inova8.com/bg_inova8.com/intelligent-graph-knowledge-graph-embedded-analysis/ The full PathQL syntax is described here: https://inova8.com/bg_inova8.com/pathpatternql-intelligently-finding-knowledge-as-a-path-through-a-maze-of-facts/ Using Jupyter as an IDE to IntelligentGraph and RDF4J, shown here: https://inova8.com/bg_inova8.com/intelligentgraph-getting-started/ IntelligentGraph source is here in GitHub: https://github.com/peterjohnlawrence/com.inova8.intelligentgraph IntelligentGraph Docker containers are available here: https://hub.docker.com/repository/docker/inova8/intelligentgraph

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.

The OODT grid services (product and profile services) use CORBA or RMI as their underlying network transport. However, limitations of CORBA and RMI make them inappropriate for large-scale deployments. For one, both are procedural mechanisms, providing a remote interface that resembles a method call. This makes streaming of data from a service impossible, because there are limitations to the sizes of data structures that can be passed over a remote method call. Instead, repeated calls must be made to retrieve each block of a product, making transfer speeds horribly slow compared to HTTP or FTP. (Block-based retrieval of profiles was never implemented, resulting in out of memory conditions for large profile results, which is another problem.) Second, both CORBA and RMI rely on a central name registry. The registry makes an object independent of its network location, enabling a client to call it by name (looking up its last known location in the registry). However, this requires that server objects be able to make outbound network calls to the registry (through any outbound firewall), and that the registry accept those registrations (through any inbound firewall). This required administrative action at institutions hosting server objects and at the institution hosting the registry. Often, these firewall exceptions would change without notice as system adminstrators changed at each location (apparently firewall exceptions are poorly documented everywhere). Further, in the two major deployments of OODT (PDS and EDRN), server objects have almost never moved, nullifying any benefit of the registry. This project, OODT Web Grid Services, avoids the prolems of CORBA and RMI by using HTTP as the transport mechanism for products and profiles. Further, it provides a password-protected mechanism to add new sets of product and profile query handlers, enabling seamless activation of additional capabilities.