prudsys XELOPES library

Category Intelligent Software>Data Mining Systems/Tools

Abstract The prudsys XELOPES library (eXtEnded Library fOr Prudsys Embedded Solutions) is an open platform and data source independent business intelligence (BI) library with a focus on embedded data mining.

XELOPES is Common Warehouse Metamodel (CWM) compatible, supports the major BI standards, and can be combined with all prudsys products.

Areas of application --

Integration of prudsys models into user applications - All prudsys data mining products (as well as products from other providers) can export their data mining models as Extensible Markup Language (XML) files in Predictive Model Markup Language (PMML) format.

The XELOPES library, as a part of your application, allows you to import PMML models to be used for new data as scoring or ‘recommendation engines’.

Integration of data mining methods into user applications - The XELOPES library makes advanced data mining algorithms available for you to easily integrate into your application.

The comprehensive design enables the automation of parameter selection of methods, making completely automatic usage possible.

Integration of your own data mining methods - The XELOPES library enables quick integration of new data mining methods, which can access the complete framework of the library, including standards.

Universality -- The XELOPES library expands the "emerging" OMG Common Warehouse Metamodel (CWM) Standard and at the same time represents one of its first implementations.

Like the CWM it is specified completely in the Unified Modeling Language (UML) and thus platform independent.

Implementations for Java, C++ and C# as well as CORBA and web service interfaces are presently available.

A universal mining input stream concept allows the library to be applied to various data sources - from the main memory to files to databases.

It is easy to program your own data access classes. That makes XELOPES completely independent of both programming language and data source.

Standards supported -- By nature of its construction the XELOPES library is completely compatible with the CWM standard.

The PMML data exchange format is extensively supported. Other supported BI standards are the Java Metadata Interface (JMI) and Java Online Analytical Processing (JOLAP).

There are connectors for Object Linking and Embedding - Database (OLE DB) for data mining as well as to different popular data mining libraries.

Architecture --

The XELOPES library conforms completely with the OMG Model Driven Architecture (MDA) standard.

The XELOPES core was defined via UML as a CWM expansion and comprehensively documented. This core forms the platform independent model (PIM) in accordance with MDA specifications.

Various platform-specific models (PSM) were derived and implemented using mappings. There are currently PSMs for Java, C++, C# as well as CORBA and web services. The PSMs are also comprehensively documented.

The XELOPES library features a modular system and contains algorithms from different areas of business intelligence, the focus being on data mining.

The algorithms are arranged in packages and the packages can be used to put together flexible BI applications.

Data import -- Data sources for data mining accesses are uniformly modeled using the abstract class MiningInputStream.

There are ready-to-use access classes for memories, databases and files including special formats like Comma Separated Values (CSV), Excel and logs.

The user can use add-ons to the MiningInputStream class to develop your own data access classes, specifically tailored to your own applications.

Analytical functions -- The analytical functions of the XELOPES library are divided into three (3) large packages: multidimensional, data mining and reinforcement learning.

The multidimensional package contains multidimensional selections, groupings and a complete Online Analytical Processing (OLAP) engine. It thus represents an extremely lean implementation for database functions and OLAP.

The data mining package contains statistics with multidimensional grouping, decision and regression trees, neural networks (NNs), support vector machines (SVM), sparse grids, cluster methods, shopping basket analysis with taxonomies and sequence analysis.

prudsys offers one of the world's fastest data mining methods in the areas of 'non-linear regression', shopping basket and sequence analysis and sequential shopping basket analysis.

The reinforcement learning package contains various methods from the areas of 'dynamic programming' and online learning.

It also uses models from the data mining package for approximations.

Results and export -- XELOPES stores its models in the CWM-class MiningModel, which can be serialized in various ways.

In addition, the models can be exported to other data mining standards like PMML.

System Requirements

Contact manufacturer.


Manufacturer Web Site prudsys XELOPES library

Price Contact manufacturer.

G6G Abstract Number 20348

G6G Manufacturer Number 102237