Evolving an Information Systems Research Strategy

Many new IS research groups have come into existence over the past several years. This is especially so in the Asia Pacific region, as reflected in the Asia Pacific Directory of Information Systems Researchers (APDISR), which in its first edition (1994) includes approximately 1,300 staff from 150 institutions in fourteen countries. New IS research groups also continue to be established throughout North America and Europe (the North American, Europzm and Asia Pacific directories in sum reflect approximately 4,000 researchers from 800 institutions in 40 countries). The circumstances of these “new” IS groups are often quite different (e.g., younger staff, lack of research focus) from more established groups. Also, the circumstances of Asia Pacific groups may be quite different from those in North America a i d Europe (e.g., geographic proximity, external validity of findings, reward systems). Rather than attempt to track research directions or present a framework for research, this panel will focus on more practical issues of evolving a research strategy that maximizes the effectiveness of these new IS groups, taking account of their unique circumstances and comparative advantages

Role of Health IT in LTPAC Facilities

Although some of the impacts of evolving HIT and EHRs have been studied in acute care hospitals and physician office settings, there is a dearth of information about the deployment and effectiveness of Health IT in long-term and post-acute care facilities. How does health IT implementation and use affect the fast-growing long term and post-acute care (LTPAC) sector of the U.S. healthcare economy? The goal of this paper is to fill the gap in the current research by assessing the role of health information systems within the LTPAC industry by adopting configurational perspective towards the organized complexity of LTPAC transitions of care business strategy. We examine data obtained from an organization that operates more than 200 long term care and post-acute care facilities (LTPAC) across multiple states. Our research investigates parsimonious configurations for the high qualitative performance of LTPAC facilities with Health IT characterized by organizational complexity

Reengineering DoD through enterprise-wide migration to open systems

The Department of Defense cannot afford to develop and deploy information systems that have no growth potential. Legacy systems must be replaced with flexible, highly interoperable systems that produce high residual values. With shrinking budgets, depreciation of existing hardware, and rising maintenance of legacy systems, organizations must deploy systems that are capable of evolving with changing business requirements. The Department of Defense enterprise vision for information management (IM) emphasizes integration, interoperability, flexibility, and efficiency through the development of a common, multipurpose, standards-based technical infrastructure. This vision requires a new paradigm for building information systems. The new paradigm relies on open systems, which make it easier, less expensive, and faster to develop and change applications and to employ new technology features. This research examines open systems and provides a strategy for organizations to migrate to them. A case study of the Naval Postgraduate School illustrates the strategy. Provisionally, a prototype application models the desired characteristics of an open system.http://archive.org/details/reengineeringdod1094532221NAU.S. Navy (USN) authorU.S. Army (USA) authorApproved for public release; distribution is unlimited

Evolving Ensemble Fuzzy Classifier

The concept of ensemble learning offers a promising avenue in learning from data streams under complex environments because it addresses the bias and variance dilemma better than its single model counterpart and features a reconfigurable structure, which is well suited to the given context. While various extensions of ensemble learning for mining non-stationary data streams can be found in the literature, most of them are crafted under a static base classifier and revisits preceding samples in the sliding window for a retraining step. This feature causes computationally prohibitive complexity and is not flexible enough to cope with rapidly changing environments. Their complexities are often demanding because it involves a large collection of offline classifiers due to the absence of structural complexities reduction mechanisms and lack of an online feature selection mechanism. A novel evolving ensemble classifier, namely Parsimonious Ensemble pENsemble, is proposed in this paper. pENsemble differs from existing architectures in the fact that it is built upon an evolving classifier from data streams, termed Parsimonious Classifier pClass. pENsemble is equipped by an ensemble pruning mechanism, which estimates a localized generalization error of a base classifier. A dynamic online feature selection scenario is integrated into the pENsemble. This method allows for dynamic selection and deselection of input features on the fly. pENsemble adopts a dynamic ensemble structure to output a final classification decision where it features a novel drift detection scenario to grow the ensemble structure. The efficacy of the pENsemble has been numerically demonstrated through rigorous numerical studies with dynamic and evolving data streams where it delivers the most encouraging performance in attaining a tradeoff between accuracy and complexity.Comment: this paper has been published by IEEE Transactions on Fuzzy System

Exploration of applying a theory-based user classification model to inform personalised content-based image retrieval system design

© ACM, 2016. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published at http://dl.acm.org/citation.cfm?id=2903636To better understand users and create more personalised search experiences, a number of user models have been developed, usually based on different theories or empirical data study. After developing the user models, it is important to effectively utilise them in the design, development and evaluation of search systems to improve users’ overall search experiences. However there is a lack of research has been done on the utilisation of the user models especially theory-based models, because of the challenges on the utilization methodologies when applying the model to different search systems. This paper explores and states how to apply an Information Foraging Theory (IFT) based user classification model called ISE to effectively identify user’s search characteristics and create user groups, based on an empirically-driven methodology for content-based image retrieval (CBIR) systems and how the preferences of different user types inform the personalized design of the CBIR systems

Online Tool Condition Monitoring Based on Parsimonious Ensemble+

Accurate diagnosis of tool wear in metal turning process remains an open challenge for both scientists and industrial practitioners because of inhomogeneities in workpiece material, nonstationary machining settings to suit production requirements, and nonlinear relations between measured variables and tool wear. Common methodologies for tool condition monitoring still rely on batch approaches which cannot cope with a fast sampling rate of metal cutting process. Furthermore they require a retraining process to be completed from scratch when dealing with a new set of machining parameters. This paper presents an online tool condition monitoring approach based on Parsimonious Ensemble+, pENsemble+. The unique feature of pENsemble+ lies in its highly flexible principle where both ensemble structure and base-classifier structure can automatically grow and shrink on the fly based on the characteristics of data streams. Moreover, the online feature selection scenario is integrated to actively sample relevant input attributes. The paper presents advancement of a newly developed ensemble learning algorithm, pENsemble+, where online active learning scenario is incorporated to reduce operator labelling effort. The ensemble merging scenario is proposed which allows reduction of ensemble complexity while retaining its diversity. Experimental studies utilising real-world manufacturing data streams and comparisons with well known algorithms were carried out. Furthermore, the efficacy of pENsemble was examined using benchmark concept drift data streams. It has been found that pENsemble+ incurs low structural complexity and results in a significant reduction of operator labelling effort.Comment: this paper has been published by IEEE Transactions on Cybernetic

Minority games, evolving capitals and replicator dynamics

We discuss a simple version of the Minority Game (MG) in which agents hold only one strategy each, but in which their capitals evolve dynamically according to their success and in which the total trading volume varies in time accordingly. This feature is known to be crucial for MGs to reproduce stylised facts of real market data. The stationary states and phase diagram of the model can be computed, and we show that the ergodicity breaking phase transition common for MGs, and marked by a divergence of the integrated response is present also in this simplified model. An analogous majority game turns out to be relatively void of interesting features, and the total capital is found to diverge in time. Introducing a restraining force leads to a model akin to replicator dynamics of evolutionary game theory, and we demonstrate that here a different type of phase transition is observed. Finally we briefly discuss the relation of this model with one strategy per player to more sophisticated Minority Games with dynamical capitals and several trading strategies per agent.Comment: 19 pages, 7 figure

Theory-based user modeling for personalized interactive information retrieval

In an effort to improve users’ search experiences during their information seeking process, providing a personalized information retrieval system is proposed to be one of the effective approaches. To personalize the search systems requires a good understanding of the users. User modeling has been approved to be a good method for learning and representing users. Therefore many user modeling studies have been carried out and some user models have been developed. The majority of the user modeling studies applies inductive approach, and only small number of studies employs deductive approach. In this paper, an EISE (Extended Information goal, Search strategy and Evaluation threshold) user model is proposed, which uses the deductive approach based on psychology theories and an existing user model. Ten users’ interactive search log obtained from the real search engine is applied to validate the proposed user model. The preliminary validation results show that the EISE model can be applied to identify different types of users. The search preferences of the different user types can be applied to inform interactive search system design and development

A programme management approach for ensuring coherence in IS (higher) education

This paper examines IS higher education, concentrating on issues of ‘coherence’ in IS curricula. While curriculum coherence can be jeopardized by poor curriculum design, misalignment between module content and/or misalignment between module or course aims can cause serious coherence issues over time. Misalignment of this type is exacerbated by the traditional processes of curriculum (re)design, which rely heavily on the (singular) interpretation of highly abstract documents, such as module syllabi and course specifications – often produced by curriculum designers in isolation. To improve curriculum coherence, this paper examines the use of a programme management framework as a means of (a) ‘humanizing’ the abstract aims and goals of curricula schemes and (b) managing the delivery and evolution of curricula in relation to the stakeholders in the process of delivery. The practical use of the framework is examined in the context of a Masters-level course in ‘Information Systems Management’. An action research approach is used to demonstrate the practical utility of the framework in terms of (a) improving communication of curricula, (b) improving the coherence between modules and between modules and course and (c) removing content redundancy. Guidelines are presented that generalize the findings in order that key practices may be adopted by others