Towards Intuitive Modeling of Business Processes: Prospects for Flow- and Natural-Language Orientation

Gramática de usuario final extendida para procesos de negocios

10.35537/10915/111619 ◽

2020 ◽

Author(s):

◽

Enrique Eduardo Aramayo

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Business Processes ◽

End User ◽

El Sistema

El concepto proceso de negocio está estrechamente vinculado a la forma en la que una organización gestiona sus operaciones. Conocer y comprender las operaciones de una organización es un punto clave que se debe tener presente dentro del proceso de desarrollo de software. A su vez, el enfoque de desarrollo dirigido por modelos denominado MockupDD captura requerimientos usando prototipos de interfaz de usuario denominados Mockups. Los usuarios finales pueden comprender fácilmente dichos prototipos y realizar anotaciones sobre los mismos. Este enfoque se basa en ésta característica principal y a partir de la misma genera valiosos modelos conceptuales que luego pueden ser aprovechados por todos los integrantes de un equipo de desarrollo de software. Utilizar el lenguaje natural para realizar anotaciones sobre los Mockups es un aspecto clave que puede ser aprovechado. En éste último aspecto una rama de la inteligencia artificial denominada “Natural Language Processing – Procesamiento del Lenguaje Natural” (NLP) viene realizando importantes aportes vinculados al uso y al aprovechamiento del lenguaje natural de las personas. El presente trabajo de tesis propone una nueva técnica denominada “End User Grammar Extended for Business Processes – Gramática de Usuario Final Extendida para Procesos de Negocios” (EUGEBP). La misma está compuesta por un conjunto de reglas de redacción diseñada para ser aplicada sobre Mockups, y por una serie de pasos que permiten procesar dichas anotaciones con el propósito derivar procesos de negocios desde las mismas. Esto se logra a través de la identificación de los elementos que componen los procesos de negocios y de las relaciones que existen entre ellos. En esencia el presente trabajo propone utilizar las anotaciones de usuario final realizadas sobre los Mockups en lenguaje natural y a partir de las mismas derivar procesos de negocio. Las anotaciones del usuario final tienen como objetivo ayudar a describir las interfaces de usuario, pero también pueden ayudarnos a identificar los procesos de negocio que el sistema debe soportar. Mientras en analista recopila información para el desarrollo de una aplicación, implícitamente también está describiendo los procesos de negocio de la organización.

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AN EXPLANATION FACILITY FRAMEWORK FOR RULE-BASED SYSTEMS

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213012500133 ◽

2012 ◽

Vol 21 (04) ◽

pp. 1250013 ◽

Cited By ~ 1

Author(s):

BOJAN TOMIĆ ◽

BORIS HORVAT ◽

NEMANJA JOVANOVIĆ

Keyword(s):

Natural Language ◽

Business Processes ◽

Management Systems ◽

Business Rules ◽

Business Rule ◽

End User ◽

Inference Process ◽

Rule Based ◽

Rule Management ◽

Rule Based Systems

Rule engines, business rule management systems and other rule-based systems used today widely utilize methods, techniques and technologies from the era of expert systems. Unfortunately, this doesn't seem to be the case when it comes to explanation facilities. Nowadays, the use of explanation facilities seems more important than ever. Business rule management systems control or constrain the behavior of business processes through business rules and an explanation of the inference process intended for the end user would be more than welcome. An explanation facility framework which was created in order to remedy this situation is presented in this paper. It is written in Java and is supposed to be a generic solution for modern rule-based systems. Besides being free and open-source, it is simple to use and can generate explanations in the form of natural language like sentences. Internationalization is also supported and explanations can be saved as textual, XML or PDF reports.

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Natural Language Processing and Fuzzy Tools for Business Processes in a Geolocation Context

Advances in Artificial Intelligence ◽

10.1155/2017/9462457 ◽

2017 ◽

Vol 2017 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Isis Truck ◽

Mohammed-Amine Abchir

Keyword(s):

Natural Language Processing ◽

User Interface ◽

Natural Language ◽

Language Processing ◽

Business Processes ◽

Software Agent ◽

Natural Language Interfaces ◽

Low Level ◽

High Level ◽

Fuzzy Linguistic

In the geolocation field where high-level programs and low-level devices coexist, it is often difficult to find a friendly user interface to configure all the parameters. The challenge addressed in this paper is to propose intuitive and simple, thus natural language interfaces to interact with low-level devices. Such interfaces contain natural language processing (NLP) and fuzzy representations of words that facilitate the elicitation of business-level objectives in our context. A complete methodology is proposed, from the lexicon construction to a dialogue software agent including a fuzzy linguistic representation, based on synonymy.

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Text-Aware Predictive Monitoring of Business Processes

Business Information Systems ◽

10.52825/bis.v1i.62 ◽

2021 ◽

pp. 221-232

Author(s):

Marco Pegoraro ◽

Merih Seran Uysal ◽

David Benedikt Georgi ◽

Wil M.P Van der Aalst

Keyword(s):

Natural Language ◽

Business Processes ◽

Short Term Memory ◽

Control Flow ◽

Language Models ◽

Historical Event ◽

Prediction Methods ◽

Event Data ◽

Text Documents ◽

Process Prediction

The real-time prediction of business processes using historical event data is an important capability of modern business process monitoring systems. Existing process prediction methods are able to also exploit the data perspective of recorded events, in addition to the control-flow perspective. However, while well-structured numerical or categorical attributes are considered in many prediction techniques, almost no technique is able to utilize text documents written in natural language, which can hold information critical to the prediction task. In this paper, we illustrate the design, implementation, and evaluation of a novel text-aware process prediction model based on Long Short-Term Memory (LSTM) neural networks and natural language models. The proposed model can take categorical, numerical and textual attributes in event data into account to predict the activity and timestamp of the next event, the outcome, and the cycle time of a running process instance. Experiments show that the text-aware model is able to outperform state-of-the-art process prediction methods on simulated and real-world event logs containing textual data.

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