Organizational Knowledge Conversion and Creation Processes in a Chaotic Environment

This is an explorative and conceptual paper, based on the analysis and comparison of relevant literature. the purpose of the article is to clarify the differences between knowledge creating processes and knowledge conversion processes, by analysing them when confronted with a chaotic environment. the way the knowledge conversion and creation processes are presented by Ikujiro Nonaka and his co-workers suggests the necessary existence of a Ba in order to generate the spiral of knowledge creation. this implies the acceptance of a relationship between the environment and the knowledge conversion process, in which the environment influences the knowledge creation. the article is based on the hypothesis that a chaotic environment, characterized by unpredictability, non-linearity and crisis, will lead to specific ways of functioning of the knowledge creation and conversion process that highlight the relations between the two different types of processes. Starting from the general concept of resilience, herein one proposes and explains the concept of resilience of the knowledge conversion system. the role of the attractors from the chaotic environment in the creation of new knowledge is identified and explaine

Scalable System for Opinion Mining on Twitter Data. Dynamic Visualization for Data Related to Refugees’ Crisis and to Terrorist Attacks

Social networks such as Twitter or Facebook grew rapidly in popularity, and users use them to share opinions about topics of interest, to be part of the community or to post messages that are available everywhere. This paper presents a system created in order to process streamed data taken from Twitter and classify it into positive, negative or neutral. The results of these processing’s can be visualized in a suggestive manner on Google Maps, users can select the language of the tweets, can group tweets that present the same news and can even display a dynamic evolution of the news in terms of its appearance. With all this amount of information it is very opportune to do some data analysis to detect different types of events (and their locations) that happen worldwide, especially at the time when this data represents information related to refugee crisis or signals terrorist attacks

User Experience Modeling Method for a Vision of Knowledge Graph-based Process Automation

This research proposes a User Experience modelling method which is an early-stage component of a research project’s vision of innovating Robotic Process Automation with the help of Knowledge Graphs and Natural Language Processing. The core idea is to integrate, in RDF graphs, a representation of the user experience and contextual information about the organization and relevant data sources for that experience. Existing RPA tools use workflow repositories that employ XML-based descriptions for both processes and UI elements. They also provide built-in workflow designers that are not tailored for design-time analysis (e.g., model queries, reporting, reasoning) but instead are just raising the abstraction level of traditional scripting – from writing code to visually connecting pre-programmed pieces of functionality. The proposal detailed in this paper makes use of Domain-Specific Modeling Language engineering to repurpose an open source BPMN implementation for describing User Experience. We rely on a metamodeling platform to ensure that the resulting diagrams are also machine-readable and take advantage of an existing plug-in to make them available as RDF graphs that can be used by other components of an automation architecture. The paper focuses on the modelling method and tool as one of the early steps of the project’s vision

From BPMN Models to Labelled Property Graphs

There\u27s a growing interest in leveraging the structured and formal nature of business process modeling languages in order to make them available not only for human analysis but also to machine-readable knowledge representation. Standard serializations of the past were predominantly XML based, with some of them seemingly discontinued, e.g., XPDL after the dissolution of the Workflow Management Coalition. Recent research has been investigating the interplay between knowledge representation and business process modeling, with the focus typically placed on standards such as RDF and OWL. In this paper we introduce a converter that translates the standards-compliant BPMN XML format to Neo4J labelled property graphs (LPG) thus providing an alternative to both traditional XML-based serialization and to more recent experimental RDF solutions, while ensuring conceptual alignment with the standard serialization of BPMN 2.0. A demonstrator was built to highlight the benefits of having such a parser and the completeness of coverage for BPMN models. The proposal facilitates graph-based processing of business process models in a knowledge intensive context, where procedural knowledge available as BPMN diagrams must be exposed to machines and LPG-driven applications

Quantitative Imaging Parameters in the Diagnosis of Endometriomas

The classic imaging diagnosis of endometriomas encounters multiple limitations, including the subjective evaluation of medical examinations and a similar imaging appearance with other adnexal lesions, especially the functional hemorrhagic cysts. For this reason, a definite diagnosis of endometriomas can be made only by pathological analysis, which reveals particular features in terms of cellularity and biochemical components of their fluid content. It is theorized that these histopathological features can also be reflected in medical images, altering the pixel intensity and distribution, but these changes are too subtle to be assessed by the naked eye. New quantitative imaging evaluations and emerging computer-aided diagnosis techniques can provide a detailed description of image contents that can be furtherly processed by algorithms, aiming to provide a more accurate and non-invasive diagnosis for this disease