Simple System Dynamics and Control System Project Models

This chapter examines the established Systems Dynamics (SD) methods applied to software projects in order to simplify them. These methods are highly non-linear and contain large numbers of variables and built-in decisions. A SIMULINK version of an SD model is used here and conclusions are made with respect to the initial main controlling factors, compared to a NASA project. Control System methods are used to evaluate the critical features of the SD models. The eigenvalues of the linearised system indicate that the important factors are the hiring delay time, the assimilation time, and the employment time. This illustrates how the initial state of the system is at best neutrally stable with control only being achieved with complex non-linear decisions. The purpose is to compare the simplest SD and control models available required for “good” simulation of project behaviour with the Abdel-Hamid software project model. These models give clues to the decision structures that are necessary for good agreement with reality. The final simplified model, with five states, is a good match for the prime states of the Abdel-Hamid model, the NASA data, and compares favourably to the Ruiz model. The linear control system model has a much simpler structure, with the same limitations. Both the simple SD and control models are more suited to preliminary estimates of project performance.

Founding a Field Theory of Work

This chapter traces the origin of the concept of work in five staged sections. The first section examines the question, what is work? Work originally referred to “doing,” that is, work organization, synergy, and energy. The second section develops the Greek word family for work into a dynamic model of doing. The third section shows how nature guides working change through energy exchange. It examines how a work as re-organization model would function in nature's jurisdictional domain of guiding energy exchanges. Nature's laws provide guidance for self-governing latitude to energy jurisdictional domains' evolutionary change. The fourth section examines policymaking as human guidance imitating nature. Policymaking limits individual self-governance to guide a specified social community of people (polis) doing work. Policymaking is explored to see how humans use policymaking to govern themselves and their cultural social groups including governments by using nature's use of laws as guidance. Policymaking is also a form of laying down basic parameters of work as re-organization through energy exchanges in the ambient environment. Policies are human artifacts designed help a social group work well together. Part five presents an issue analysis as an invited Organization Development consultant to help find ways for the Sri Lankan government, the University of Moratuwa, and the apparel and textile industry to work together in their extreme makeover of human resource development of their apparel and textile industry. Action training and research, stakeholder management, and wicked problem issue analysis are the organization development methods used to demonstrate this field theory of work re-organization through energy exchange.

The Role of Standards in the Development of New Informational Infrastructure

In this chapter, the authors inch towards better understanding of the notion of informational infrastructure and the role of standards in the development of infrastructures in the new information age. Specifically, the authors consider the standardization process as pertaining to informational infrastructure development. They focus on two particular aspects of standardization: temporal dynamics and the social organization. Using Bauman's concept of liquid modernity, the authors argue that standards often become hybrids of solid and liquid modernities linking together different scales of time, space, and social organization. To better illustrate theoretical concepts, they draw on practical examples from the development of informational standards, infrastructures, and services, particularly from the domain of Cognitive Radio Systems (CRS), a new generation of “paradigm changing” communication technologies and services. The aim of this chapter is to offer the scholars of standards and innovation a fresh, non-mainstream perspective on the social and temporal dynamics of standardization and infrastructure development processes, to bring forth new understandings of the complexity of relationships between business, technology, and regulatory domains in the formation of informational infrastructure.

Power Quality Improvement using Improved Approximated Fuzzy Logic Controller for Shunt Active Power Filter

This chapter presents the design approach of an Improved Approximated Simplest Fuzzy Logic Controller (IASFLC). A cascade combination of simplest 4-rule Fuzzy Logic Controller (FLC) and an nth degree polynomial is proposed as an IASFLC to approximate the control characteristics of a 49-rule FLC. The approximation scheme is based on minimizing the sum of square errors between the control outputs of a 49-rule FLC and a simplest 4-rule FLC in the entire range of Universe Of Discourse (UOD). The coefficients of compensating polynomial are evaluated by solving instantaneous square error equations at various test points in the entire UOD. This IASFLC maps the output of a 49-rule FLC with absolute deviation of less than 5%. The proposed IASFLC is used to control the dc link voltage of a three-phase shunt Active Power Filter (APF). A detailed analysis is performed during transient and steady state conditions to check Power Quality (PQ) and dynamic performance indices under randomly varying balanced and unbalanced loading conditions. The performance of proposed IASFLC is compared with a 49-rule FLC and Approximated Simplest Fuzzy Logic Controller (ASFLC) based on minimization of the deviation at central values of Membership Functions (MFs). It is found comparatively better for harmonic and reactive compensation with a comparable dynamic response. The memory requirement and computational time of proposed IASFLC are even lesser than the ASFLC.

A Scouting-Based Multi-Agent System Model to Deal with Service Collaboration in Cloud Computing

Cloud computing addresses the use of scalable and often virtualized resources. It is based on service-level agreements that provide external users with requested services. Cloud computing is still evolving. New specific collaboration models among service providers are needed for enabling effective service collaboration, allowing the process of serving consumers to be more efficient. On the other hand, Scout Movement or Scouting has been a very successful youth movement in which the collaboration of its members can be observed. This motivated a previous work aiming to design MAS-Scout, a framework that defines Multi-Agent Systems based on the principles of Scouting. In this chapter, MAS-Scout is used to design a system to deal with service collaboration in a cloud computing environment focusing on the premise that Scouting has been a very successful social movement in the world and that collaboration is part of its principles. The results presented in this chapter show that MAS-Scout, which is based on the Scouting principles, can be satisfactorily used to automate cloud computing needs.

Multidimensional Data Analysis Based on Links

Analytical processing (OLAP) tools typically only deal with relational data. Hence, the analytical processing systems on XML data do not have all the functionality provided by OLAP tools to traditional data (i.e. relational). In addition, current commercial and academic OLAP tools do not process XML data that contains XLink. Therefore, there is a need to develop a solution for OLAP systems in order to assist in the strategic analysis of the organizational data represented in XML format. Aiming at overcoming this issue, this chapter proposes an analytical system composed by LMDQL (Link-Based Multidimensional Query Language), an analytical query language; XLDM (XLink Data Metamodel), a metamodel given to model cubes of XML documents with XLink and to deal with syntactic, semantic, and structural heterogeneities commonly found in XML documents; and XLPath (XLink Path Language), a navigation language for XML documents connected by XLink. As current W3C query languages for navigating in XML documents do not support XLink, XLPath is discussed in this chapter to provide features for the LMDQL query processing and a prototype system enabling OLAP queries over XML documents linked by XLink and XML schema. This prototype includes a driver, named sql2xquery, which performs the mapping of SQL queries into XQuery in a relational OLAP server. In order to validate the proposed system, a case study and its performance evaluation are presented to analyze the impact of analytical processing over XML/XLink documents.

Modeling a Simple Self-Organizing System

This chapter presents a System Dynamics (SD) simulation model that not only replicates self-organizing system uncertainty results but also looks at self-organization causally. The SD simulation and model analysis results show exactly how distributed control leads positive feedback to explosive growth, which ends when all dynamics have been absorbed into an attractor, leaving the system in a stable, negative feedback state. The chapter's SD model analysis helps explain why phenomena of interest emerge in agent-based models, a topic crucial in understanding and designing Complex Adaptive Self-Organizing Systems (CASOS).

Context Inference Engine (CiE)

Context Awareness is the ability of systems and applications to sense the environment and infer the activity going on in the environment. Context encompasses all knowledge bounded within an environment and includes attributes of both machines and users. A context-aware system is composed of context gathering and context inference modules. This chapter proposes a Context Inference Engine (CiE) that classifies the current context as one of several known context activities. This engine follows a Minkowski distance-based classification approach with standard deviation-based ranks to identify likeliness of classified activity of the current context. Empirical results on different data sets show that the proposed algorithm performs closer to Support Vector Machines (SVM) while it is better than probabilistic reasoning methods where the performance is quantified as success in classification.

Semantically Integrated Conceptual Modeling Method and Modeling Patterns

Managing evolutionary changes, identification of discontinuities, and separation of concerns is not an easy task in the area conceptual modeling in information system development. One of the fundamental problems is that most conventional conceptual modeling techniques deal with the collection of loosely linked meta-models, which are defined by different types of diagrams. Typically, system development methods project interactive, behavioral, and structural aspects of information systems' conceptual representations into disparate views. Therefore, the semantic integrity of various architecture dimensions is difficult to achieve. In this chapter, the authors present a semantically integrated conceptual modeling method. The advantage of this method is stability and flexibility of the diagrams to manage the constant changes of system requirements. This method provides the possibility to visualize the interplay among structural, interactive, and behavioral aspects. This is very important for the control of semantic integrity and to maintain a holistic representation where external and internal views of service conceptualizations are visualized together. Such visualization is also important for separation of concerns, which provides foundation for creation of modeling patterns. Modeling patterns are important for several reasons. First, they can be used for demonstration of the interplay of fundamental constructs that are used for system analysis and design. Secondly, modeling patterns are important for the evaluation of the expressive power of semantic modeling languages. It is demonstrated by case studies that sequential, underlying, enclosing, overriding, and overlaying interaction loops between actors provide the foundation for the composition of complex scenarios, which span across organizational and technical system boundaries.

Neural Network-Based Spatial Modeling of Natural Phenomena and Events

Artificial Neural Networks (ANN) are used for statistical modeling of spatial events in geosciences. The advantage of this method is the ability of neural networks to represent complex interrelations and to be “able to learn” from known (spatial) events. The software advangeo® was developed to enable GIS users to apply neural network methods on raster geodata. The statistic modeling results can be developed and displayed in a user-friendly way within the Esri ArcGIS environment. The complete workflow is documented by the software. This chapter presents five case studies to illustrate the current possibilities and limitations of spatial predictions with the use of artificial neural networks, which describe influencing factors and the selection of known events of the phenomenon to be modeled. These applications include: (1) the prognosis of soil erosion patterns, (2) the country-wide prediction of mineral resources, (3) the vulnerability analysis for forest pests, (4) the spatial distribution of bird species, and (5) the spatial prediction of manganese nodules on the sea bottom.