Krzysztof Kaczor

FORMALIZATION AND MODELING OF RULES USING THE XTT2 METHOD

The paper discusses a new knowledge representation for rule-based systems called XTT2. It combines decision trees and decision tables forming a transparent and hierarchical visual representation of the decision units linked into a workflow-like structure. There are two levels of abstraction in the XTT2 model: the lower level, where a single knowledge component defined by a set of rules working in the same context is represented by a single decision table, and the higher level, where the structure of the whole knowledge base is considered. This model has a concise formalization which opens up possibility of well-defined, structured design and verification of formal characteristics. Based on the visual XTT2 model, a textual representation of the rule base is generated. A dedicated engine provides a unified run-time environment for the XTT2 rule bases. The focus of the paper is on the formalization of the presented approach. It is based on the so-called ALSV(FD) logic that provides an expressive calculus for rules.

HalVA - rule analysis framework for XTT2 rules

Quality and reliability issues are important in development and exploration of rule-based systems. In the paper a formalized knowledge representation for rules called XTT2 is considered. It is a rule language based on an expressive attribute logic called ALSV(FD). A custom runtime and verification framework for XTT2 called HalVA is proposed. It allows for verification of certain formal properties of rules, including determinism, subsumption or completeness.

Algorithms for rule inference in modularized rule bases

In the paper an extended knowledge representation for rules is considered. It is called Extended Tabular Trees (XTT2) and it provides a network of decision units grouping rules working in the same context. The units are linked into an inference network, where a number of inference options are considered. The original contribution of the paper is the proposal and formalization of several different inference algorithms working on the same rule base. Such an approach allows for a more flexible rule design and deployment, since the same knowledge base may be used in different ways, depending on the application.

Enriching business processes with rules using the oryx BPMN editor

BPMN is a leading visual notation for modeling Business Processes. Although it can be efficiently used for modeling workflow structures, it is not suitable for modeling the low-level logic of particular tasks in the process. Recently, Business Rules have been used for this purpose. Such rules are often specified in natural language and in an informal way. In this paper, we consider an approach to the integration of Business Processes with Business Rules. As a proof of concept, we propose a framework based on the Oryx BPMN editor integrated with rule-based tools. The goal of the integration of the BPMN editor with the XTT2 rule framework is to provide an environment for visual modeling processes with formally described business rules. We also discuss execution options of the integrated model. In the future, this opens up a possibility to execute such models using the HeaRT rule engine.

Proposal of an Inference Engine Architecture for Business Rules and Processes

In this paper, we discuss a new architecture for integrating and executing business process models with rules. It is based on a workflow engine that runs a BPMN-based business process model. On the lower level, rules are used to express the specific parts of the business logic. Rules working in the same context are grouped into a single task in the process model. Such a rule task is modeled by a formally defined decision table, which is designed in a visual way and its quality can be formally verified. In the runtime environment, tables are executed by a dedicated rule inference engine controlled by a workflow engine.

Overview of knowledge formalization with XTT2 rules

The paper discusses a new formalized knowledge representation for rule-based systems called XTT2. This hybrid knowledge representation combines decision diagrams with extended decision tables. A single decision table contains a set of rules of similar structure operating within a common context. The structure of XTT2 constitutes a hierarchical knowledge representation consisting of lower level knowledge components, where specification is provided by a set of rules working in the same context, and at the higher level, where the decision diagram defines the overall structure of the knowledge base. This model has a concise formalization which opens up possibility for rigorous design and verification. The focus of the paper is on the presentation of the formal aspects of the approach starting from an initial logical specification.

How to Reason by HeaRT in a Semantic Knowledge-Based Wiki

Semantic wikis constitute an increasingly popular class of systems for collaborative knowledge engineering. We developed Loki, a semantic wiki that uses a logic-based knowledge representation. It is compatible with semantic annotations mechanism as well as Semantic Web languages. We integrated the system with a rule engine called Heart that supports inference with production rules. Several modes for modularized rule bases, suitable for the distributed rule bases present in a wiki, are considered. Embedding the rule engine enables strong reasoning and allows to run production rules over semantic knowledge bases. In the paper, we demonstrate the system concepts and functionality using an illustrative example.

Visual Design of Drools Rule Bases Using the XTT2 Method

Drools is one of the most popular expert system frameworks. It uses rules in several formats as knowledge representation. However, Drools has several limitations. It does not support visual modeling of rules. Moreover, it does not assure quality of the knowledge base. In this paper an alternative way of knowledge base designing for Drools is proposed. The method extends the Drools design process by using the XTT2 rule representation and the HQEd visual rule editor. To deal with the differences between the XTT2 and Drools representations, the Drools Export Plugin for HQEd has been implemented. The proposed approach overcomes limitations of the Drools design methodology. Furthermore, it provides a visual rule design editor for Drools and supports formal verification of the model using an optional module.

Mobile context-based framework for threat monitoring in urban environment with social threat monitor

Engaging users in threat reporting is important in order to improve threat monitoring in urban environments. Today, mobile applications are mostly used to provide basic reporting interfaces. With a rapid evolution of mobile devices, the idea of context awareness has gained a remarkable popularity in recent years. Modern smartphones and tablets are equipped with a variety of sensors including accelerometers, gyroscopes, pressure gauges, light and GPS sensors. Additionally, the devices become computationally powerful which allows for real-time processing of data gathered by their sensors. Universal access to the Internet via WiFi hot-spots and GSM network makes mobile devices perfect platforms for ubiquitous computing. Although there exist numerous frameworks for context-aware systems, they are usually dedicated to static, centralized, client-server architectures. There is still space for research in the field of context modeling and reasoning for mobile devices. In this paper, we propose a lightweight context-aware framework for mobile devices that uses data gathered by mobile device sensors and performs on-line reasoning about possible threats based on the information provided by the Social Threat Monitor system developed in the INDECT project.

Towards Rule Interoperability: Design of Drools Rule Bases Using the XTT2 Method

Despite the maturity of rule-based technologies and number of rule formalisms, the practical rule interoperability is still challenging. In a distributed environment where many knowledge engineers work in a collective way, this causes severe problems. This is a methodological paper, which introduces an approach that can be considered such an interoperability method. Its aim is to provide ai?źunified and formalized method for knowledge interchange for the most common rule languages. Our approach involves three levels of interoperability abstraction: semantic, model and environment level. On each level different problems are addressed. Ini?źorder to assess the appropriateness of such decomposition we provide a proof of concept solution concerning the interoperability between the Drools and XTT2 rule bases.