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This book discusses the effective use of modern ICT solutions for business needs, including the efficient use of IT resources, decision support systems, business intelligence, data mining and advanced data processing algorithms, as well as the processing of large datasets (inter alia social networking such as Twitter and Facebook, etc.). The ability to generate, record and process qualitative and quantitative data, including in the area of big data, the Internet of Things (IoT) and cloud computing offers a real prospect of significant improvements for business, as well as the operation of a company within Industry 4.0. The book presents new ideas, approaches, solutions and algorithms in the area of knowledge representation, management and processing, quantitative and qualitative data processing (including sentiment analysis), problems of simulation performance, and the use of advanced signal processing to increase the speed of computation. The solutions presented are also aimed at the effective use of business process modeling and notation (BPMN), business process semantization and investment project portfolio selection. It is a valuable resource for researchers, data analysts, entrepreneurs and IT professionals alike, and the research findings presented make it possible to reduce costs, increase the accuracy of investment, optimize resources and streamline operations and marketing.

Inhaltsverzeichnis

Frontmatter

Verification of Temporal Knowledge Bases as an Important Aspect of Knowledge Management Processes in Organization

Abstract
The paper deals with the problem of temporal knowledge verification treated as an important process of knowledge management (KM). At the same time authors postulate addition of knowledge verification (KV) to the set of several recognized processes in the theory of V&V. Moreover the motivation for implementing a temporal knowledge base system is presented, the implementation methodology is outlined, and the KV process is described in detail, using the example of the Logos semantic reasoner. The main achievements of the paper are: elaborating a new implementation methodology for a temporal knowledge base system, and elaborating detailed KV steps as well as viewing the KV as a process in KM.
Maria Mach-Król, Krzysztof Michalik

The Role of Simulation Performance in Software-in-the-Loop Simulations

Abstract
The simulation performance is one aspect that becomes important in the practical application of simulations. A number of situations necessitates high-performance simulations: The simulation of large systems, over long time-periods or exploring a large solution space. Starting with these scenarios we discuss the parallelization of a discrete event simulation (DES) model using a synthetic benchmark model. Using the example of the German automatic toll system we explore the performance constraints originating from coupling an abstract simulation model with a real-world system.
Tommy Baumann, Bernd Pfitzinger, Thomas Jestädt, Dragan Macos

Cognitum Ontorion: Knowledge Representation and Reasoning System

Abstract
At any point of human activity, knowledge and expertise are a key factors in understanding and solving any given problem. In present days, computer systems have the ability to support their users in an efficient and reliable way in gathering and processing knowledge. In this chapter we show how to use Cognitum Ontorion system in this areas. In first section, we identify emerging issues focused on how to represent and inference knowledge. Next, we briefly discuss models and methodology of agent-oriented system analysis and design. In the third section, the semantic knowledge management framework of the system is reviewed. Finally, we recapitulate by discussing the usability of Ontorion based on a case study, in which an instance of software process simulation modelling environment is executed and further discussed. In the last section, we provide future work directions and put forward final conclusions.
Paweł Kaplanski, Pawel Weichbroth

Overview of Selected Business Process Semantization Techniques

Abstract
Business Process models help to visualize the processes of an organization. There exist several techniques of semantization of Business Processes. We give an overview of Business Process semantization techniques, focusing on the existing approaches in several Business Process Management tools. We also present the use of the existing techniques in the Prosecco (Processes Semantics Collaboration for Companies) research project.
Krzysztof Kluza, Grzegorz J. Nalepa, Mateusz Ślażyński, Krzysztof Kutt, Edyta Kucharska, Krzysztof Kaczor, Adam Łuszpaj

Selected Approaches Towards Taxonomy of Business Process Anomalies

Abstract
Modeling based on a graphical notation understandable for different specialists has become very popular. Within the area of business processes, the most common one is the Business Process Modeling and Notation (BPMN). BPMN is aimed at all business users who design, analyze, manage and monitor business processes. BPMN specification is relatively precise, but it provides a descriptive form presented at some abstract, graphical level. The main focus of this chapter is an attempt to present an overview of the anomalies which are likely to occur when modeling with use of BPMN.
Anna Suchenia, Tomasz Potempa, Antoni Ligęza, Krystian Jobczyk, Krzysztof Kluza

Hybrid Framework for Investment Project Portfolio Selection

Abstract
Project selection is a complex multi-criteria decision making process with multiple and often conflicting objectives. The complexity of the project selection problems stems primarily from the large number of projects from among which an appropriate collection (an effective portfolio) of investment projects must be selected. This paper presents a new hybrid framework for construction of an effective portfolio of investment projects. The parameters of the considered model are described using both probability distributions and fuzzy numbers (possibility distributions). The proposed framework enables to take into account stochastic dependencies between model parameters and economic dependencies between projects. As a result, a set of Pareto optimal solutions is obtained. The framework is adapted for enterprises with multistage production cycle, i.e., for enterprises of a metallurgical or chemical industry. The performance of the proposed method is illustrated using an example from metallurgical industry.
Bogdan Rȩbiasz, Bartłomiej Gaweł, Iwona Skalna

Towards Predicting Stock Price Moves with Aid of Sentiment Analysis of Twitter Social Network Data and Big Data Processing Environment

Abstract
This chapter illustrates design and evaluation of a sentiment analysis based system that may be used to predict future stock prices. Social media information is processed in order to extract opinions that are associated with Apple Inc. company. The authors took advantage of large datasets available from Twitter micro blogging platform and widely available stock market records. Data was collected during 3 months and processed for further analysis. Machine learning was employed to conduct sentiment classification of data in order to estimate future stock prices. Calculations were performed in distributed environment according to Map Reduce programming model. Evaluation and discussion of predictions results for different time intervals and input datasets is discussed in terms of efficiency and feasibility of the chosen approach.
Andrzej Romanowski, Michał Skuza

On a Property of Phase Correlation and Possibilities to Reduce the Walsh Function System

Abstract
Data processing algorithms are used for business, industry and public sector to filter input data, calculate values, detect abrupt changes, acquire information from data or to ensure signal consistency. It is an important research area for Big Data processing and processing of data received from Internet of Things. Typically, classical algorithms are exploited, i.e. statistical procedures, data mining techniques and computational intelligence algorithms. Referring to the area of signal processing, applications of mathematical transformation (e.g. Fourier Transform, Walsh–Fourier Transform) of input signals from either domain to the other are promising. They enable to perform complementary analyses and to consider additional signal components, in particular cyclic (periodic) ones (sin- and cos-components). The Walsh function system is a multiplicative group of Rademacher and Gray functions. In its structure, it contains discrete-harmonic, sin-components of the Rademacher functions, and cos-components of the Gray function, as well as discrete-irregular components of the Walsh function. In the paper, the phase interdependence property has been defined, in pairs of a complete Walsh function system. Odd (sin-components) and even (cos-components) Walsh function subsystems were extracted as theoretical and numerical processing databases. A perspective concerning the processing efficiency and digital signal processing is outlined.
Lubomyr Petryshyn, Tomasz Pełech-Pilichowski
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