Measuring Inconsistency over Sequences of Business Rule Cases

We investigate inconsistency and culpability measures for multisets of business rule bases. As companies might encounter thousands of rule bases daily, studying not only individual rule bases separately, but rather also their interrelations, becomes necessary. As current works on inconsistency measurement focus on assessing individual rule bases, we therefore present an extension of those works in the domain of business rules management. We show how arbitrary culpability measures (for single rule bases) can be automatically transformed for multisets, propose new rationality postulates for this setting, and investigate the complexity of central aspects regarding multi-rule base inconsistency measurement.

Business Case Mining and E-R Modeling Optimization

Business case mining and business rule discovery are at the center for entity relationship (E-R) modeling and database design to obtain E-R models. How to transform business cases through business rules into E-R models is a fundamental issue for database design. This article addresses this issue by exploring business case mining and E-R modeling optimization. Business case mining is business rule discovery from a business case. This article reviews case-based reasoning, explores business case-based reasoning, and presents a unified approach to business case mining for business rule discovery. The approach includes people-centered entity/business rule discovery and function-centered entity/business rule discovery. E-R modeling optimization aims to improve the E-R modeling process to get a better E-R diagram that reflects the business case properly. This article proposes a unified optimal method for E-R modeling. The unified optimal method includes people-centered E-R modeling, function-centered E-R modeling, and hierarchical E-R modeling. The approach proposed in this research will facilitate the research and development of E-R modeling, database design, data science, and big data analytics.

Ferramentas e recursos disponíveis para reconhecimento de fala em Português Brasileiro

Speech recognition allows natural communication between the humansand machines. With Industry 4.0 there is a great demand forsystems that perform this task, since human-machine integrationsare increasingly attractive. Currently, there are several tools and resourcesthat perform this activity, with some companies providingtheir audio recognition services through the Application ProgrammingInterface, such as Microsoft, Google, IBM and Wit. On theother hand, there are offline libraries and open source that can alsobe explored like Vosk. Each company has its business rule and itsspecificity, in this sense it is difficult to know which is the most interestingfor each situation. Thus, a comparison was made betweenspeech recognition services in terms of usability, limitation andprecision. In the comparison, speech recognition performance metricswere used in a set of audios, using the programming languagePython.

Modeling Business Rule Parallelism by Introducing Inclusive and Complex Gateways in Semantics of Business Vocabulary and Rules

The Semantics of Business Vocabulary and Rules (SBVR) standard was developed by the Object Management Group (OMG) for business purposes. SBVR is used for transformation of business vocabulary and business rules into business processes. Gateways are used for regulating the divergence and convergence of flow objects in the business process. The existing business rules in SVBR do not support all gateways in BPMN, whereas there are conditions where branching situations in business rules occur. This article introduces parallelism rules (OR rules) and complex rules to increase 50.6% usage of the existing AND rules and XOR rules in SBVR. The main contribution of this research is to introduce new formal model of inclusive gateway (OR) and complex gateway that allow parallelism and branching to be modeled using SBVR. Thus, this study increases coverage of the usage gateway in SBVR achieved 66.7%. The authors provide branching cases with various levels of complexity, i.e. nested conditions and non-free choice conditions, using the formal description of SBVR.

A Declarative Approach to Systems Development

The declarative approach specifies what is to be done rather than how to do it. When adopted in information systems development, this implies that the system should be seen as a collection of business rules that can be enacted using a business rules engine. Business rules should be expressed in a form that is as close to the one in which business people perceive the rules. A business rules management system is needed to acquire, store, and allow modification of a business rules database. The rules are then handed over to a rules engine for enactment. The BRMS considered in this chapter uses an antecedent-consequent form for representing rules. These are based in a first order logic. Rules are formed with courses of actions and conditions in rules antecedents and courses of actions in rule consequents. It also introduces notions of state change in the business rule and temporal relation within rule and between different rules. Business rules are structured into atomic, complex, and abstract rules. The business rules are translated into enactment rules and converted to Java.

Probabilistic-Logic Bots for Efficient Evaluation of Business Rules Using Conversational Interfaces

We present an approach for designing conversational interfaces (chatbots) that users interact with to determine whether or not a business rule applies in a context possessing uncertainty (from the point of view of the chatbot) as to the value of input facts. Our approach relies on Bayesian network models that bring together a business rule’s logical, deterministic aspects with its probabilistic components in a common framework. Our probabilistic-logic bots (PL-bots) evaluate business rules by iteratively prompting users to provide the values of unknown facts. The order facts are solicited is dynamic, depends on known facts, and is chosen using mutual information as a heuristic so as to minimize the number of interactions with the user. We have created a web-based content creation and editing tool that quickly enables subject matter experts to create and validate PL-bots with minimal training and without requiring a deep understanding of logic or probability. To date, domain experts at a well-known insurance company have successfully created and deployed over 80 PLbots to help insurance agents determine customer eligibility for policy discounts and endorsements.