Software engineering techniques for the development of systems of systems

This paper investigates how existing software engineering techniques can be employed, adapted and integrated for the development of systems of systems. Starting from existing system-of-systems (SoS) studies, we identify computing paradigms and techniques that have the potential to help address the challenges associated with SoS development, and propose an SoS development framework that combines these techniques in a novel way. This framework addresses the development of a class of IT systems of systems characterised by high variability in the types of interactions between their component systems, and by relatively small numbers of such interactions. We describe how the framework supports the dynamic, automated generation of the system interfaces required to achieve these interactions, and present a case study illustrating the development of a data-centre SoS using the new framework.

Run-time connector synthesis for autonomic systems of systems

A key objective of autonomic computing is to reduce the cost and expertise required for the management of complex IT systems. As a growing number of these systems are implemented as hierarchies or federations of lower-level systems, techniques that support the development of autonomic systems of systems are required. This article introduces one such technique, which involves the run-time synthesis of autonomic system connectors. These connectors are specified by means of a new type of autonomic computing policy termed a resource definition policy, and enable the dynamic realisation of collections of collaborating autonomic systems, as envisaged by the original vision of autonomic computing. We propose a framework for the formal specification of autonomic computing policies, and use it to define the new policy type and to describe its application to the development of autonomic system of systems. To validate the approach, we present a sample data-centre application that was built using connectors synthesised from resource-definition policies.

Various techniques and procedures for refining ERS-1 orbits

The satellite ERS-1 was launched in July 1991 in a period of high solar activity. Sparse laser tracking and the failure of the experimental microwave system (PRARE) compounded the orbital errors which resulted from mismodelling of atmospheric density and hence surface forces. Three attempts are presented here to try and refine the coarse laser orbits of ERS-1, made prior to the availability of the full altimetric dataset. The results of the first attempt indicate that by geometrically modelling the satellite shape some improvement in orbital precision may be made for any satellite; especially one where no area tables already exist. The second and third refinement attempts are based on the introduction of data from some second satellite; in these examples SPOT-2 and TOPEX/Poseidon are employed. With SPOT-2 the method makes use of the orbital similarities to produce along-track corrections for the more fully tracked SPOT-2. Transferring these corrections to ERS-1 produces improvements in the precise orbits thus determined. With TOPEX/Poseidon the greater altitude results in a more precise orbit (gravity field and atmospheric errors are of less importance). Thus, by computing height differences at crossover points of the TOPEX/Poseidon and ERS-1 ground tracks the poorer orbit of ERS-1 may be improved by the addition of derived radial corrections. In the positive light of all three results several potential modification are suggested and some further avenues of investigation indicated.

An iterative-style approach to constructing intelligent tutoring systems in mathematics

Mathematics is highly structured and also underpins most of science and engineering. For this reason, it has proved a very suitable domain for Intelligent Tutoring System (ITS) research, with the result that probably more tutoring systems have been constructed for the domain than any other. However, the literature reveals that there still exists no consensus on a credible approach or approaches for the design of such systems, despite numerous documented efforts. Current approaches to the construction of ITSs leave much to be desired. Consequently, existing ITSs in the domain suffer from a considerable number of shortcomings which render them 'unintelligent'. The thesis examines some of the reasons why this is the case. Following a critical review of existing ITSs in the domain, and some pilot studies, an alternative approach to their construction is proposed (the 'iterative-style' approach); this supports an iterative style, and also improves on at least some of the shortcomings of existing approaches. The thesis also presents an ITS for fractions which has been developed using this approach, and which has been evaluated in various ways. It has, demonstrably, improved on many of the limitations of existing ITSs; furthermore, it has been shown to be largely 'intelligent', at least more so than current tutors for the domain. Perhaps more significantly, the tutor has also been evaluated against real students with, so far, very encouraging results. The thesis thus concludes that the novel iterative-style approach is a more credible approach to the construction of ITSs in mathematics than existing techniques.

Automatic summary assessment for intelligent tutoring systems

Summary writing is an important part of many English Language Examinations. As grading students' summary writings is a very time-consuming task, computer-assisted assessment will help teachers carry out the grading more effectively. Several techniques such as latent semantic analysis (LSA), n-gram co-occurrence and BLEU have been proposed to support automatic evaluation of summaries. However, their performance is not satisfactory for assessing summary writings. To improve the performance, this paper proposes an ensemble approach that integrates LSA and n-gram co-occurrence. As a result, the proposed ensemble approach is able to achieve high accuracy and improve the performance quite substantially compared with current techniques. A summary assessment system based on the proposed approach has also been developed.

Quantitative authorship attribution:an evaluation of techniques

The basic assumption of quantitative authorship attribution is that the author of a text can be selected from a set of possible authors by comparing the values of textual measurements in that text to their corresponding values in each possible author's writing sample. Over the past three centuries, many types of textual measurements have been proposed, but never before have the majority of these measurements been tested on the same dataset. A large-scale comparison of textual measurements is crucial if current techniques are to be used effectively and if new and more powerful techniques are to be developed. This article presents the results of a comparison of thirty-nine different types of textual measurements commonly used in attribution studies, in order to determine which are the best indicators of authorship. Based on the results of these tests, a more accurate approach to quantitative authorship attribution is proposed, which involves the analysis of many different textual measurements.

Evaluating the performance of a EuroDivisia index using artificial intelligence techniques

This paper compares two methods to predict in°ation rates in Europe. One method uses a standard back propagation neural network and the other uses an evolutionary approach, where the network weights and the network architecture are evolved. Results indicate that back propagation produces superior results. However, the evolving network still produces reasonable results with the advantage that the experimental set-up is minimal. Also of interest is the fact that the Divisia measure of money is superior as a predictive tool over simple sum.

Multimodal 'Eyes-Free' interaction techniques for mobile devices

Mobile and wearable computers present input/output prob-lems due to limited screen space and interaction techniques. When mobile, users typically focus their visual attention on navigating their environment - making visually demanding interface designs hard to operate. This paper presents two multimodal interaction techniques designed to overcome these problems and allow truly mobile, 'eyes-free' device use. The first is a 3D audio radial pie menu that uses head gestures for selecting items. An evaluation of a range of different audio designs showed that egocentric sounds re-duced task completion time, perceived annoyance, and al-lowed users to walk closer to their preferred walking speed. The second is a sonically enhanced 2D gesture recognition system for use on a belt-mounted PDA. An evaluation of the system with and without audio feedback showed users' ges-tures were more accurate when dynamically guided by au-dio-feedback. These novel interaction techniques demon-strate effective alternatives to visual-centric interface de-signs on mobile devices.

Improving scheduling techniques in heterogeneous systems with dynamic, on-line optimisations

Computational performance increasingly depends on parallelism, and many systems rely on heterogeneous resources such as GPUs and FPGAs to accelerate computationally intensive applications. However, implementations for such heterogeneous systems are often hand-crafted and optimised to one computation scenario, and it can be challenging to maintain high performance when application parameters change. In this paper, we demonstrate that machine learning can help to dynamically choose parameters for task scheduling and load-balancing based on changing characteristics of the incoming workload. We use a financial option pricing application as a case study. We propose a simulation of processing financial tasks on a heterogeneous system with GPUs and FPGAs, and show how dynamic, on-line optimisations could improve such a system. We compare on-line and batch processing algorithms, and we also consider cases with no dynamic optimisations.

Pushing the Quality Level in Networked News Business: Semantic-Based Content Retrieval and Composition in International News Publishing

Electronic publishing exploits numerous possibilities to present or exchange information and to communicate via most current media like the Internet. By utilizing modern Web technologies like Web Services, loosely coupled services, and peer-to-peer networks we describe the integration of an intelligent business news presentation and distribution network. Employing semantics technologies enables the coupling of multinational and multilingual business news data on a scalable international level and thus introduce a service quality that is not achieved by alternative technologies in the news distribution area so far. Architecturally, we identified the loose coupling of existing services as the most feasible way to address multinational and multilingual news presentation and distribution networks. Furthermore we semantically enrich multinational news contents by relating them using AI techniques like the Vector Space Model. Summarizing our experiences we describe the technical integration of semantics and communication technologies in order to create a modern international news network.

Comparing interaction techniques for serious games through brain-computer interfaces:a user perception evaluation study

This paper examines the application of commercial and non-invasive electroencephalography (EEG)-based brain-computer (BCIs) interfaces with serious games. Two different EEG-based BCI devices were used to fully control the same serious game. The first device (NeuroSky MindSet) uses only a single dry electrode and requires no calibration. The second device (Emotiv EPOC) uses 14 wet sensors requiring additional training of a classifier. User testing was performed on both devices with sixty-two participants measuring the player experience as well as key aspects of serious games, primarily learnability, satisfaction, performance and effort. Recorded feedback indicates that the current state of BCIs can be used in the future as alternative game interfaces after familiarisation and in some cases calibration. Comparative analysis showed significant differences between the two devices. The first device provides more satisfaction to the players whereas the second device is more effective in terms of adaptation and interaction with the serious game.

Enhancing the performance of intelligent control systems in the face of higher levels of complexity and uncertainty

Modern advances in technology have led to more complex manufacturing processes whose success centres on the ability to control these processes with a very high level of accuracy. Plant complexity inevitably leads to poor models that exhibit a high degree of parametric or functional uncertainty. The situation becomes even more complex if the plant to be controlled is characterised by a multivalued function or even if it exhibits a number of modes of behaviour during its operation. Since an intelligent controller is expected to operate and guarantee the best performance where complexity and uncertainty coexist and interact, control engineers and theorists have recently developed new control techniques under the framework of intelligent control to enhance the performance of the controller for more complex and uncertain plants. These techniques are based on incorporating model uncertainty. The newly developed control algorithms for incorporating model uncertainty are proven to give more accurate control results under uncertain conditions. In this paper, we survey some approaches that appear to be promising for enhancing the performance of intelligent control systems in the face of higher levels of complexity and uncertainty.

Development of prediction models for freeway incident durations using data mining techniques

The nation's freeway systems are becoming increasingly congested. A major contribution to traffic congestion on freeways is due to traffic incidents. Traffic incidents are non-recurring events such as accidents or stranded vehicles that cause a temporary roadway capacity reduction, and they can account for as much as 60 percent of all traffic congestion on freeways. One major freeway incident management strategy involves diverting traffic to avoid incident locations by relaying timely information through Intelligent Transportation Systems (ITS) devices such as dynamic message signs or real-time traveler information systems. The decision to divert traffic depends foremost on the expected duration of an incident, which is difficult to predict. In addition, the duration of an incident is affected by many contributing factors. Determining and understanding these factors can help the process of identifying and developing better strategies to reduce incident durations and alleviate traffic congestion. A number of research studies have attempted to develop models to predict incident durations, yet with limited success. ^ This dissertation research attempts to improve on this previous effort by applying data mining techniques to a comprehensive incident database maintained by the District 4 ITS Office of the Florida Department of Transportation (FDOT). Two categories of incident duration prediction models were developed: "offline" models designed for use in the performance evaluation of incident management programs, and "online" models for real-time prediction of incident duration to aid in the decision making of traffic diversion in the event of an ongoing incident. Multiple data mining analysis techniques were applied and evaluated in the research. The multiple linear regression analysis and decision tree based method were applied to develop the offline models, and the rule-based method and a tree algorithm called M5P were used to develop the online models. ^ The results show that the models in general can achieve high prediction accuracy within acceptable time intervals of the actual durations. The research also identifies some new contributing factors that have not been examined in past studies. As part of the research effort, software code was developed to implement the models in the existing software system of District 4 FDOT for actual applications. ^

Enabling adaptability in service aggregates using transparent shaping techniques

Distributed applications are exposed as reusable components that are dynamically discovered and integrated to create new applications. These new applications, in the form of aggregate services, are vulnerable to failure due to the autonomous and distributed nature of their integrated components. This vulnerability creates the need for adaptability in aggregate services. The need for adaptation is accentuated for complex long-running applications as is found in scientific Grid computing, where distributed computing nodes may participate to solve computation and data-intensive problems. Such applications integrate services for coordinated problem solving in areas such as Bioinformatics. For such applications, when a constituent service fails, the application fails, even though there are other nodes that can substitute for the failed service. This concern is not addressed in the specification of high-level composition languages such as that of the Business Process Execution Language (BPEL). We propose an approach to transparently autonomizing existing BPEL processes in order to make them modifiable at runtime and more resilient to the failures in their execution environment. By transparent introduction of adaptive behavior, adaptation preserves the original business logic of the aggregate service and does not tangle the code for adaptive behavior with that of the aggregate service. The major contributions of this dissertation are: first, we assessed the effectiveness of BPEL language support in developing adaptive mechanisms. As a result, we identified the strengths and limitations of BPEL and came up with strategies to address those limitations. Second, we developed a technique to enhance existing BPEL processes transparently in order to support dynamic adaptation. We proposed a framework which uses transparent shaping and generative programming to make BPEL processes adaptive. Third, we developed a technique to dynamically discover and bind to substitute services. Our technique was evaluated and the result showed that dynamic utilization of components improves the flexibility of adaptive BPEL processes. Fourth, we developed an extensible policy-based technique to specify how to handle exceptional behavior. We developed a generic component that introduces adaptive behavior for multiple BPEL processes. Fifth, we identify ways to apply our work to facilitate adaptability in composite Grid services.

Actionable pattern analysis in computing and business intelligence systems

With advances in science and technology, computing and business intelligence (BI) systems are steadily becoming more complex with an increasing variety of heterogeneous software and hardware components. They are thus becoming progressively more difficult to monitor, manage and maintain. Traditional approaches to system management have largely relied on domain experts through a knowledge acquisition process that translates domain knowledge into operating rules and policies. It is widely acknowledged as a cumbersome, labor intensive, and error prone process, besides being difficult to keep up with the rapidly changing environments. In addition, many traditional business systems deliver primarily pre-defined historic metrics for a long-term strategic or mid-term tactical analysis, and lack the necessary flexibility to support evolving metrics or data collection for real-time operational analysis. There is thus a pressing need for automatic and efficient approaches to monitor and manage complex computing and BI systems. To realize the goal of autonomic management and enable self-management capabilities, we propose to mine system historical log data generated by computing and BI systems, and automatically extract actionable patterns from this data. This dissertation focuses on the development of different data mining techniques to extract actionable patterns from various types of log data in computing and BI systems. Four key problems—Log data categorization and event summarization, Leading indicator identification , Pattern prioritization by exploring the link structures , and Tensor model for three-way log data are studied. Case studies and comprehensive experiments on real application scenarios and datasets are conducted to show the effectiveness of our proposed approaches.