The AnyCorrectiveAction Stable Design Pattern

This paper aims at modeling a system that has to perform maintenance, by employing a Corrective Action and come up with a stable pattern, which can form a part of such a system, focusing at applying corrective action in a holistic way, rather than tying it on only one application specific context, which might cause potential impedance mismatch between process and workflows at a later stage. This ensures high reusability, ensuring that a design once created can be used to model any application, in any domain, thus making the task of designing more efficient, and the modeled system, largely domain independent. The goal of this paper is to design a Stable Pattern for Corrective Action and to model the system on the basis on an Enduring Business Theme (EBT). Here, the ultimate goal or the EBT is Maintenance and AnyCorrective action is what acts as a workhorse to achieve the goal of maintenance. In this paper, we have first designed a model, which defines the relationship of the Enduring Business Theme to Business Objects. We have then gone ahead with modeling an application scenario, which portrays the reusability of the developed stable pattern. A comparison of the Traditional Models and the Stable Model has also been included to describe how the latter overcomes to drawbacks of the former. Different models, such as, use cases, CRC cards, class diagrams and sequence diagrams have been used to give a better insight into the pattern and possible applications of it

Consumer Complaints and Protection: Stable Analysis and Design Patterns

The concept of consumer complaints and protection has numerous applications across various domains. Using traditional methods of modeling design patterns is a tedious and costly task. The Software Stability Mode (SSM) is a more efficient and effective modeling method. In this thesis, the differences between the traditional method and the SSM is addressed. Then, several patterns are developed using the SSM to deal with consumer complaints. Each area, Advice, Appraisal, Commitment, Complaint, Compliance, Deed, Guideline, Gratification, Judgment, Model, Need, Ownership, Promotion, Rate, Review, Selling, Support, View, and Violation, is explored and the core knowledge of the concept of consumer complaints and protection is developed visually as well as in detail. Useful SAP and SDP templates are included for each concept. The main contribution of this thesis is the creation of stable, reusable templates that build an unlimited number of applications for the consumer complaints and protection concept

Unified Software Engineering Reuse: A Methodology for Effective Software Reuse

Software is a necessity in the modern world, and that need is continuously growing. As expensive as the creation of all this new software is, the maintenance costs are even greater. One solution to this problem is software reuse, whereby already written software can be applied to new problems after some modification, thus reducing the overall input of new code. The goal in traditional software reuse is to produce a piece of software with enough flexibility to be used at least twice. Unfortunately, there are many difficulties in achieving software reuse using modern programming techniques. Even software built specifically for reuse is severely restricted in its utility for new applications. It is easy for new programs to require entirely new logic or new objects. Because of this, they become quickly outdated, and any labor spent creating reusable software is nullified. The solution is a method to vastly increase the reusability of software by concentrating on the base knowledge and overall goals of software rather than the details on a case-by-case basis. Finding patterns in the problem and solution spaces allows unification into a smaller solution set. Instead of each problem receiving its own solution from marginally reusable components, multiple problems are resolved with the same architecture and object set. As an added benefit, this solution will not only vastly improve software reuse, but it will make feasible systems that can construct software architecture on demand and provide the first steps to fully automated software development

Ontology Based Data Access in Statoil

Ontology Based Data Access (OBDA) is a prominent approach to query databases which uses an ontology to expose data in a conceptually clear manner by abstracting away from the technical schema-level details of the underlying data. The ontology is ‘connected’ to the data via mappings that allow to automatically translate queries posed over the ontology into data-level queries that can be executed by the underlying database management system. Despite a lot of attention from the research community, there are still few instances of real world industrial use of OBDA systems. In this work we present data access challenges in the data-intensive petroleum company Statoil and our experience in addressing these challenges with OBDA technology. In particular, we have developed a deployment module to create ontologies and mappings from relational databases in a semi-automatic fashion; a query processing module to perform and optimise the process of translating ontological queries into data queries and their execution over either a single DB of federated DBs; and a query formulation module to support query construction for engineers with a limited IT background. Our modules have been integrated in one OBDA system, deployed at Statoil, integrated with Statoil’s infrastructure, and evaluated with Statoil’s engineers and data

Data-Driven Anomaly Detection in Industrial Networks

Since the conception of the first Programmable Logic Controllers (PLCs) in the 1960s, Industrial Control Systems (ICSs) have evolved vastly. From the primitive isolated setups, ICSs have become increasingly interconnected, slowly forming the complex networked environments, collectively known as Industrial Networks (INs), that we know today. Since ICSs are responsible for a wide range of physical processes, including those belonging to Critical Infrastructures (CIs), securing INs is vital for the well-being of modern societies. Out of the many research advances on the field, Anomaly Detection Systems (ADSs) play a prominent role. These systems monitor IN and/or ICS behavior to detect abnormal events, known or unknown. However, as the complexity of INs has increased, monitoring them in the search of anomalous trends has effectively become a Big Data problem. In other words, IN data has become too complex to process it by traditional means, due to its large scale, diversity and generation speeds. Nevertheless, ADSs designed for INs have not evolved at the same pace, and recent proposals are not designed to handle this data complexity, as they do not scale well or do not leverage the majority of the data types created in INs. This thesis aims to fill that gap, by presenting two main contributions: (i) a visual flow monitoring system and (ii) a multivariate ADS that is able to tackle data heterogeneity and to scale efficiently. For the flow monitor, we propose a system that, based on current flow data, builds security visualizations depicting network behavior while highlighting anomalies. For the multivariate ADS, we analyze the performance of Multivariate Statistical Process Control (MSPC) for detecting and diagnosing anomalies, and later we present a Big Data, MSPCinspired ADS that monitors field and network data to detect anomalies. The approaches are experimentally validated by building INs in test environments and analyzing the data created by them. Based on this necessity for conducting IN security research in a rigorous and reproducible environment, we also propose the design of a testbed that serves this purpose

ICE-MILK: Intelligent Crowd Engineering using Machine-based Internet of Things Learning and Knowledge Building

Title from PDF of title page viewed June 1, 2022Dissertation advisor: Sejun SongVitaIncludes bibliographical references (pages 136-159)Thesis (Ph.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2022The lack of proper crowd safety control and management often leads to spreading human casualties and infectious diseases (e.g., COVID-19). Many Machine Learning (ML) technologies inspired by computer vision and video surveillance systems have been developed for crowd counting and density estimation to prevent potential personal injuries and deaths at densely crowded political, entertaining, and religious events. However, existing crowd safety management systems have significant challenges and limitations on their accuracy, scalability, and capacity to identify crowd characterization among people in crowds in real-time, such as a group characterization, impact of occlusions, mobility and contact tracing, and distancing. In this dissertation, we propose an Intelligent Crowd Engineering platform using Machine-based Internet of Things Learning, and Knowledge Building approaches (ICE-MILK) to enhance the accuracy, scalability, and crowd safety management capacity in real-time. Specifically, we design an ICE-MILK structure with three critical layers: IoT-based mobility characterization, ML-based video surveillance, and semantic information-based application layers. We built an IoT-based mobility characterization system by predicting and preventing potential disasters through real-time Radio Frequency (RF) data characterization and analytics. We tackle object group identification, speed, direction detection, and density for the mobile group among the many crowd mobility characteristics. Also, we tackled an ML-based video surveillance approach for effective dense crowd counting by characterizing scattered occlusions, named CSONet. CSONet recognizes the implications of event-induced, scene-embedded, and multitudinous obstacles such as umbrellas and picket signs to achieve an accurate crowd analysis result. Finally, we developed a couple of group semantics to track and prevent crowd-caused infectious diseases. We introduce a novel COVID-19 tracing application named Crowd-based Alert and Tracing Services (CATS) and a novel face masking and social distancing monitoring system for Modeling Safety Index in Crowd (MOSAIC). CATS and MOSAIC apply privacy-aware contact tracing, social distancing, and calculate spatiotemporal Safety Index (SI) values for the individual community to provide higher privacy protection, efficient penetration of technology, greater accuracy, and effective practical policy assistance.Introduction -- Literature review -- IoT-based mobility characterization -- ML-based video/image surveillance -- Semantic knowledge information-based tracing application -- Conclusions and future directions -- Appendi

Architectural stability of self-adaptive software systems

This thesis studies the notion of stability in software engineering with the aim of understanding its dimensions, facets and aspects, as well as characterising it. The thesis further investigates the aspect of behavioural stability at the architectural level, as a property concerned with the architecture's capability in maintaining the achievement of expected quality of service and accommodating runtime changes, in order to delay the architecture drifting and phasing-out as a consequence of the continuous unsuccessful provision of quality requirements. The research aims to provide a systematic and methodological support for analysing, modelling, designing and evaluating architectural stability. The novelty of this research is the consideration of stability during runtime operation, by focusing on the stable provision of quality of service without violations. As the runtime dimension is associated with adaptations, the research investigates stability in the context of self-adaptive software architectures, where runtime stability is challenged by the quality of adaptation, which in turn affects the quality of service. The research evaluation focuses on the effectiveness, scale and accuracy in handling runtime dynamics, using the self-adaptive cloud architectures