Adaptive business rules framework for workflow management

Changing scattered and dynamic business rules in Business Workflow Systems has become a growing problem that hinders the use and configuration of workflow-based applications. There is a gap in the existing research studies which currently focus on solutions that are application specific, without accounting for the universal logical dependencies between the business rules and, as a result, do not support adaptation of the business rules in real time. Design/methodology/approach – To tackle the above problems, this paper adopts a bottom-up approach, which puts forward a component model of the business process workflows and business rules based on purely logical specification which allows incremental development of the workflows and indexing of the rules which govern them during the initial acquisition and real-time execution. Results – The paper introduces a component-based event-driven model for development of business workflows which is purely logic based and can be easily implemented using an object-oriented technology together with a formal model for accounting the business rules dependencies together with a new method for incremental indexing of the business rules controlling the workflows. It proposes a two-level inference mechanism as a vehicle for controlling the business process execution and adaptation of the business rules at real time based on propagating the dependencies between the rules. Originality/value –The major achievement of this research is the universal, strictly logic-based event-driven framework for business process modelling and control which allows automatic adaptation of the business rules governing the business workflows based on accounting for their structural dependencies. An additional advantage of the framework is its support for object-oriented technology which can be implemented with enterprise-level quality and efficiency. Although developed primarily for application in construction industry the framework is entirely domain-independent and can be used in other industries, too

Two-level architecture for rule-based business process management

One of the main challenges in Business Process Management (BPM) systems is the need to adapt business rules in real time. A serious obstacle is the lack of adaptable formal models for managing dynamic business rules. This is, due to the inadequacy of the models ability to describe the rule components, meta-rules, relationships and logical dependencies. To overcome this drawback, this paper presents a two-level rule-based approach to control BPM systems. The model accounts for logical representation of rules components and their relationships in Process-based Systems, as well as a method for incremental indexing of the business rules. The incremental indexing mechanism is described as an approach to control process execution and adaptation of business rules in real time based on rules propagation. Therefore this model provides a basis for an efficient and adaptable solution for managing business rules changes

Model-theoretic Characterizations of Existential Rule Languages

Existential rules, a.k.a. dependencies in databases, and Datalog+/- in knowledge representation and reasoning recently, are a family of important logical languages widely used in computer science and artificial intelligence. Towards a deep understanding of these languages in model theory, we establish model-theoretic characterizations for a number of existential rule languages such as (disjunctive) embedded dependencies, tuple-generating dependencies (TGDs), (frontier-)guarded TGDs and linear TGDs. All these characterizations hold for arbitrary structures, and most of them also work on the class of finite structures. As a natural application of these characterizations, complexity bounds for the rewritability of above languages are also identified.Comment: 17 pages, 2 figures, the full version of a paper submitted to IJCAI 202

Order dependency in the relational model

AbstractThe relational model is formally extended to include fixed orderings on attribute domains. A new constraint, called order dependency, is then introduced to incorporate semantic information involving these orderings. It is shown that this constraint can be applied to enhance the efficiency of an implemented database. The thrust of the paper is to study logical implication for order dependency. The main theoretical results consist in (i) introducing a formalism analogous to propositional calculus for analyzing order dependency, (ii) exhibiting a sound and complete set of inference rules for order dependency, and (iii) demonstrating that determining logical implication for order dependency is co-NP-complete. It is also shown that there are sets of order dependencies for which no Armstrong relations exist

Loop Restricted Existential Rules and First-order Rewritability for Query Answering

In ontology-based data access (OBDA), the classical database is enhanced with an ontology in the form of logical assertions generating new intensional knowledge. A powerful form of such logical assertions is the tuple-generating dependencies (TGDs), also called existential rules, where Horn rules are extended by allowing existential quantifiers to appear in the rule heads. In this paper we introduce a new language called loop restricted (LR) TGDs (existential rules), which are TGDs with certain restrictions on the loops embedded in the underlying rule set. We study the complexity of this new language. We show that the conjunctive query answering (CQA) under the LR TGDs is decid- able. In particular, we prove that this language satisfies the so-called bounded derivation-depth prop- erty (BDDP), which implies that the CQA is first-order rewritable, and its data complexity is in AC0 . We also prove that the combined complexity of the CQA is EXPTIME complete, while the language membership is PSPACE complete. Then we extend the LR TGDs language to the generalised loop restricted (GLR) TGDs language, and prove that this class of TGDs still remains to be first-order rewritable and properly contains most of other first-order rewritable TGDs classes discovered in the literature so far.Comment: Full paper version of extended abstrac

FoCaLiZe: Inside an F-IDE

For years, Integrated Development Environments have demonstrated their usefulness in order to ease the development of software. High-level security or safety systems require proofs of compliance to standards, based on analyses such as code review and, increasingly nowadays, formal proofs of conformance to specifications. This implies mixing computational and logical aspects all along the development, which naturally raises the need for a notion of Formal IDE. This paper examines the FoCaLiZe environment and explores the implementation issues raised by the decision to provide a single language to express specification properties, source code and machine-checked proofs while allowing incremental development and code reusability. Such features create strong dependencies between functions, properties and proofs, and impose an particular compilation scheme, which is described here. The compilation results are runnable OCaml code and a checkable Coq term. All these points are illustrated through a running example.Comment: In Proceedings F-IDE 2014, arXiv:1404.578

A Logical Characterization of Constraint-Based Causal Discovery

We present a novel approach to constraint-based causal discovery, that takes the form of straightforward logical inference, applied to a list of simple, logical statements about causal relations that are derived directly from observed (in)dependencies. It is both sound and complete, in the sense that all invariant features of the corresponding partial ancestral graph (PAG) are identified, even in the presence of latent variables and selection bias. The approach shows that every identifiable causal relation corresponds to one of just two fundamental forms. More importantly, as the basic building blocks of the method do not rely on the detailed (graphical) structure of the corresponding PAG, it opens up a range of new opportunities, including more robust inference, detailed accountability, and application to large models