VLEPPO is an integrated system for modeling and solving planning problems. It offers a convenient, intuitive and easy-to-use graphical interface, which allows design, comprehension and maintenance of planning domains and corresponding problems. VLEPPO accommodates for compatibility with standards, as most visual elements present in the system correspond to PDDL elements. Compliance with the PDDL standard is also achieved through the import and export features. VLEPPO provides increased flexibility in integration of external planning systems by employing the current technology of web services. The system was implemented in Java for portability and interoperability purposes.

VDR-Device is a visual integrated environment for developing (creating, editing, running, testing and deploying) defeasible rule bases for the Semantic Web.

O-DEVICE is a deductive object-oriented knowledge base system for reasoning over OWL documents. O-DEVICE exploits the rule language of an existing production rule system, called CLIPS and transforms OWL ontologies into an object-oriented schema of the CLIPS Object-Oriented Language (COOL). It is an extension of a previous system called R-DEVICE. O-DEVICE exploits the advantages of the object-oriented programming model by transforming OWL ontologies into classes, properties and objects of the OO programming language provided within CLIPS, called COOL. A complete list of all the transformations can be found here. The system also features a powerful deductive rule language which supports inferencing over the transformed OWL descriptions. Users can either use this deductive language to express queries or a RuleML-like syntax.

DR-DEVICE is capable of reasoning about RDF metadata over multiple Web sources using defeasible logic rules. The system is implemented on top of CLIPS production rule system and builds upon R-DEVICE, an earlier deductive rule system over RDF metadata that also supports derived attribute and aggregate attribute rules. Rules can be expressed either in a native CLIPS-like language, or in an extension of the OO-RuleML syntax. The operational semantics of defeasible logic are implemented through compilation into the generic rule language of R-DEVICE. The most important features of DR-DEVICE are the following: * Support for multiple rule types of defeasible logic, such as strict rules, defeasible rules, and defeaters. * Support for both classical (strong) negation and negation-as-failure. * Support for conflicting literals, i.e. derived objects that exclude each other. * Direct import from the Web of RDF ontologies and data as input facts to the defeasible logic program. * Direct import from the Web of defeasible logic programs in an XML compliant rule syntax (RuleML). * Direct export to the Web of the results (conclusions) of the logic program as an RDF document.

R-DEVICE is a deductive object-oriented knowledge base system for querying and reasoning about RDF metadata. R-DEVICE, transforms RDF triples into objects and uses a deductive rule language for querying and reasoning about them. More specifically, R-DEVICE imports RDF data into the CLIPS production rule system as COOL objects. The main difference between the RDF and our object model is that properties are treated both as first-class objects and as attributes of resource objects. In this way resource properties are gathered together in one object, resulting in superior query performance than the performance of a triple-based query model. Most other RDF storage and querying systems that are based on a triple model scatter resource properties across several triples and they require several joins to query the properties of a single resource. The descriptive semantics of RDF data may call for dynamic redefinitions of resource classes and objects, which are handled by R-DEVICE. R-DEVICE features a powerful deductive rule language which is able to express arbitrary queries both on the RDF schema and data, including generalized path expressions, stratified negation, aggregate, grouping, and sorting, functions, mainly due to the second-order syntax of the rule language, i.e. variables ranging over class and slot names, which is efficiently translated into sets of first-order logic rules using metadata. Furthermore, R-DEVICE rules define views which are materialized and incrementally maintained. Finally, users can use CLIPS functions or can define their own arbitrary functions using the CLIPS host language.

Device is a system that integrates production rules in an active Object-Oriented Database (OODB) system that supports event-driven rules. Production rules are useful for several tasks of active database systems, such as integrity constraint checking, derived data maintenance, database state monitoring, etc. Furthermore production rules can express knowledge in a high-level form for problem solving in Knowledge Base Systems (KBS). Present active OODB systems traditionally provide event-driven rules, which are triggered by events, i.e. database modifications

X-DEVICE is a deductive object-oriented database for managing XML data. X-DEVICE is an extension of the active object-oriented knowledge base system DEVICE. X-DEVICE extends DEVICE by incorporating XML data into the OODB by automatically mapping XML document DTDs to object schemata, without loosing the document’s original order of elements. XML elements are represented either as first-class objects or as attributes based on their complexity. Furthermore, X-DEVICE extends the deductive rule language of DEVICE with new operators that are used for specifying complex queries and materialized views over the stored semi-structured data. Most of the new operators have a second-order syntax (i.e. variables range over class and attribute names), but they are implemented by translating them into first-order DEVICE rules (i.e. variables can range over class instances and attribute values), so that they can be efficiently executed against the underlying deductive object-oriented database.

WebDisC is a knowledge-based Web information system for the fusion of classifiers induced at geographically distributed databases. The main features of WebDisC are: 1. A declarative rule language for classifier selection that allows the combination of syntactically heterogeneous distributed classifiers, 2. A variety of standard methods for fusing the output of distributed classifiers, 3. A new approach for clustering classifiers in order to deal with the semantic heterogeneity of distributed classifiers, detect their interesting similarities and differences and enhance their fusion and 4. An architecture based on the Web services paradigm that utilizes the open and scalable standards of XML and SOAP

PRACTIC is a parallel Object-Oriented Database system that is based on a concurrent object data model. PRACTIC means PaRallel ACTIve Classes and is based on the vertical partitioning and concurrent management of the database schema classes and meta-classes, which are collectively called active objects. Active objects are permanent processes in memory that encapsulate their definitions, methods and management procedures. Semi-active and passive objects exist to realise abstract classes and instances (the actual data), respectively. The object model gives rise to a query/method execution model that provides parallelism on all levels of the instantiation hierarchy. The abstract PRACTIC machine directly maps the model to a MIMD machine, providing a hierarchical architecture and a hierarchical de-clustering scheme

L-Base is a tightly coupled Prolog and Relational Database System. It integrates Arity Prolog and DBase III+. The advantages of L-Base can be grouped into two main categories: data organisation and data semantics. Prolog’s structure flexibility offers space saving because of variable record length. Records are organised in lists allowing both tuple- and attribute- oriented queries. Data dependencies are organised in list-fields within records. Prolog offers fast data retrieval due to efficient indexing through b-trees. Hash tables and buckets reduce the size of data to be searched offering a powerful data modelling tool. Data semantic advantages of Prolog arise from the use of facts, which behave as actual data, and rules, which compute data at the querying time. The result is the ability to extract more meaning from the data without distracting the data file’s structure. Prolog offers a sophisticated inference engine that allows complicated logic queries.

CoLan is a high-level declarative Constraint Description Language, for use with an Object-Oriented Database (OODB). CoLan has features of both first-order logic and functional programming and is based on Daplex. CoLan expressions are translated into Prolog code that implements the operational semantics of the constraint. Pieces of generated code are cached inside the class descriptor of the ‘host’ class attached to appropriate slots. The pieces of code are retrieved along an inheritance path when an update on the database is attempted. If the update violates any of the retrieved constraints then it is rejected with an informative message. Thus constraints are expressed declaratively and they can even be retracted individually. However, they are implemented efficiently as code-generated methods, triggered selectively by an update.