Real-world Information Systems Research Articles

A hybridized red deer and rough set clinical information retrieval system for hepatitis B diagnosis

Healthcare is a big concern in the current booming population. Many approaches for improving health are imposed, such as early disease identification, treatment, and prevention. Therefore, knowledge acquisition is highly essential at different stages of decision-making. Inferring knowledge from the information system, which necessitates multiple steps for extracting useful information, is one technique to address this problem. Handling uncertainty throughout data analysis is also another challenging task. Computer intelligence is a step forward to this end while selecting characteristics, classification, clustering, and developing clinical information retrieval systems. According to recent studies, swarm optimization is a useful technique for discovering key features while resolving real-world issues. However, it is ineffective in managing uncertainty. Conversely, a rough set helps a decision system generate decision rules. This produces decision rules without any additional information. In order to assess real-world information systems while managing uncertainties, a hybrid strategy that combines a rough set and red deer algorithm is presented in this research. In the red deer optimization algorithm, the suggested method selects the optimal characteristics in terms of the degree of dependence on the rough set. In order to determine the decision rules, further a rough set is used. The efficiency of the suggested model is also contrasted with that of the decision tree algorithm and the conventional rough set. An empirical study on hepatitis disease illustrates the viability of the proposed research as compared to the decision tree and crisp rough set. The proposed hybridization of rough set and red deer algorithm achieves an accuracy of 91.7% accuracy. The acquired accuracy for the decision tree, and rough set methods is 82.9%, and 88.9%, respectively. It suggests that the proposed research is viable.

Learning with Euler Collaborative Representation for Robust Pattern Analysis

The Collaborative Representation (CR) framework has provided various effective and efficient solutions to pattern analysis. By leveraging between discriminative coefficient coding (l 2 regularization) and the best reconstruction quality (collaboration), the CR framework can exploit discriminative patterns efficiently in high-dimensional space. Due to the limitations of its linear representation mechanism, the CR must sacrifice its superior efficiency for capturing the non-linear information with the kernel trick. Besides this, even if the coding is indispensable, there is no mechanism designed to keep the CR free from inevitable noise brought by real-world information systems. In addition, the CR only emphasizes exploiting discriminative patterns on coefficients rather than on the reconstruction. To tackle the problems of primitive CR with a unified framework, in this article we propose the Euler Collaborative Representation (E-CR) framework. Inferred from the Euler formula, in the proposed method, we map the samples to a complex space to capture discriminative and non-linear information without the high-dimensional hidden kernel space. Based on the proposed E-CR framework, we form two specific classifiers: the Euler Collaborative Representation based Classifier (E-CRC) and the Euler Probabilistic Collaborative Representation based Classifier (E-PROCRC). Furthermore, we specifically designed a robust algorithm for E-CR (termed as R-E-CR ) to deal with the inevitable noises in real-world systems. Robust iterative algorithms have been specially designed for solving E-CRC and E-PROCRC. We correspondingly present a series of theoretical proofs to ensure the completeness of the theory for the proposed robust algorithms. We evaluated E-CR and R-E-CR with various experiments to show its competitive performance and efficiency.

Anomalous node detection in attributed social networks using dual variational autoencoder with generative adversarial networks

Many types of real-world information systems, including social media and e-commerce platforms, can be modelled by means of attribute-rich, connected networks. The goal of anomaly detection in artificial intelligence is to identify illustrations that deviate significantly from the main distribution of data or that differ from known cases. Anomalous nodes in node-attributed networks can be identified with greater precision if both graph and node attributes are taken into account. Almost all of the studies in this area focus on supervised techniques for spotting outliers. While supervised algorithms for anomaly detection work well in theory, they cannot be applied to real-world applications owing to a lack of labelled data. Considering the possible data distribution, our model employs a dual variational autoencoder (VAE), while a generative adversarial network (GAN) assures the model is robust to adversarial training. The dual VAEs are used in another capacity: as a fake-node generator. Adversarial training is used to ensure that our latent codes have a Gaussian or uniform distribution. To provide a fair presentation of the graph, the discriminator instructs the generator to generate latent variables with distributions that are more consistent with the actual distribution of the data. Once the model has been learned, the discriminator is used for anomaly detection via reconstruction loss it has been trained to distinguish between the normal and artificial distributions of data. First, using a dual VAE, our model simultaneously captures cross-modality interactions between topological structure and node characteristics and overcomes the problem of unlabeled anomalies, allowing us to better understand the network sparsity and nonlinearity. Second, the proposed model considers the regularization of the latent codes while solving the issue of unregularized embedding techniques that can quickly lead to unsatisfactory representation. Finally, we use the discriminator reconstruction loss for anomaly detection as the discriminator is well-trained to separate the normal and generated data distributions because reconstruction-based loss does not include the adversarial component. Experiments conducted on attributed networks demonstrate the effectiveness of the proposed model and show that it greatly surpasses the previous methods. The area under the curve scores of our proposed model for the BlogCatalog, Flickr, and Enron datasets are 0.83680, 0.82020, and 0.71180, respectively, proving the effectiveness of the proposed model. The result of the proposed model on the Enron dataset is slightly worse than the other models; we attribute this to the dataset's low dimensionality as the most probable explanation.

Qualitative Comparative Analysis (QCA) In Information Systems Research: Status Quo, Guidelines, and Future Directions

Qualitative comparative analysis (QCA) allows researchers to study how configurations of conditions lead to outcomes and, thereby, richly explain the dynamics of complex digital phenomena. To advance discussion on QCA in the information systems (IS) discipline, we introduce its fundamental concepts and offer guidelines for authors on how to apply QCA to advance IS research. We also provide checklists for reviewers of QCA papers. We illustrate how to apply our guidelines through two exemplar studies. In the first exemplar study, we focus on IT-business strategic alignment to study the influence that different forms of alignment have on firm performance. In the second exemplar study, we use the perspective of the integrated technology acceptance model to explain an individual’s intention to use a digital assistant. The contrasting results from both studies highlight how to use QCA to derive robust and reproducible results. By doing so, we contribute to encouraging IS scholars to use QCA to develop sophisticated models that accurately depict real-world IS phenomena.

Beyond Relevance Ranking: A General Graph Matching Framework for Utility-Oriented Learning to Rank

Learning to rank from logged user feedback, such as clicks or purchases, is a central component of many real-world information systems. Different from human-annotated relevance labels, the user feedback is always noisy and biased. Many existing learning to rank methods infer the underlying relevance of query–item pairs based on different assumptions of examination, and still optimize a relevance based objective. Such methods rely heavily on the correct estimation of examination, which is often difficult to achieve in practice. In this work, we propose a general framework U-rank+ for learning to rank with logged user feedback from the perspective of graph matching. We systematically analyze the biases in user feedback, including examination bias and selection bias. Then, we take both biases into consideration for unbiased utility estimation that directly based on user feedback, instead of relevance. In order to maximize the estimated utility in an efficient manner, we design two different solvers based on Sinkhorn and LambdaLoss for U-rank+ . The former is based on a standard graph matching algorithm, and the latter is inspired by the traditional method of learning to rank. Both of the algorithms have good theoretical properties to optimize the unbiased utility objective while the latter is proved to be empirically more effective and efficient in practice. Our framework U-rank+ can deal with a general utility function and can be used in a widespread of applications including web search, recommendation, and online advertising. Semi-synthetic experiments on three benchmark learning to rank datasets demonstrate the effectiveness of U-rank+ . Furthermore, our proposed framework has been deployed on two different scenarios of a mainstream App store, where the online A/B testing shows that U-rank+ achieves an average improvement of 19.2% on click-through rate and 20.8% improvement on conversion rate in recommendation scenario, and 5.12% on platform revenue in online advertising scenario over the production baselines.

An anomaly detection framework for time-evolving attributed networks

Real-world information systems are naturally dynamic, and they are usually represented as a time-evolving attributed network (i.e., a sequence of static attributed networks). Recently, there is a surge of research interests in finding anomalous nodes upon attributed networks due to its significant implications in many high-impact applications, such as financial fraud detection, network intrusion detection, and opinion spam detection, to name a few. Despite the importance of anomaly detection in time-evolving attributed network, a vast majority of existing methods fail to capture the evolution of the underlying networks properly, as they regard the whole system as static and neglect the evolution process. Meanwhile, they treat all the attributes and the instances equally, ignoring the existence of noisy which may lead to the adverse effects to the detection results. To tackle these problems, in this paper, we propose a novel dynamic anomaly detection framework on time-evolving attributed networks on the basis of residual analysis, namely AMAD. Under the assumption of temporal smoothness property, AMAD leverages the small smooth disturbance between two consecutive time stamps to characterize the evolution of networks for incrementally update. Experiments conducted on both synthetic and real-world time-evolving attributed networks show the superiority of our proposed method in detecting anomalies. Moreover, our method is competitive in terms of efficiency compared to the existing work.

A blockchain based framework for information system integrity

A blockchain based system integrity (BCSI) framework for assuring the integrity of information system processes is presented. BCSI is well suited for a broad class of large scale real-world information systems. Under the BCSI framework, the integrity of any information system I is assured by executing the finite state machine model for system I processes in a blockchain network. The BCSI framework is compared and contrasted with the Clark-Wilson (CW) system integrity model, and existing blockchain based frameworks like Ethereum.

A framework for on-line timing error detection in software systems

On-line timing error detection entails gathering and analyzing monitoring data to pinpoint deviations from the expected timing behavior of a given software system. Current solutions for system monitoring and runtime analysis present several practical drawbacks that limit their usability in real industrial systems, such as the need of kernel-level probes or the coarse per-node/per-process monitoring granularity. This paper proposes a novel framework for timing error detection that capitalizes on the systematic interleaving of logging instructions across the functional code in order to overcome above limitations. The paper faces the practical challenges related to the specification and implementation of a log weaving technique, detection algorithms, and a data centralization platform to collect and analyze fine-grained execution traces in distributed systems. We experiment the proposed framework in two real-world critical information systems from the Crisis Management and the Air Traffic Control domains. Results show that our framework achieves 95% timing error coverage and allows reconstructing error trends with high statistical confidence at negligible performance overhead.

Towards a Standard for a Reliable Execution Environment for Security Protocols

Approaches for securing digital assets of information systems can be classified as active approaches based on attack models, and passive approaches based on system-models. Passive approaches are inherently superior to active ones. However, taking full advantage of passive approaches calls for a rigorous standard for a low-complexity-high-integrity execution environment for security protocols. We sketch broad outlines of mirror network (MN) modules, as a candidate for such a standard. Their utility in assuring real-world information systems is illustrated with examples.

Evaluating the added-value of online bus arrival prediction schemes

Online predictions of bus arrival times have the potential to reduce the uncertainty associated with bus operations. By better anticipating future conditions, online predictions can reduce perceived and actual passenger travel times as well as facilitate more proactive decision making by service providers. Even though considerable research efforts were devoted to the development of computationally expensive bus arrival prediction schemes, real-world real-time information (RTI) systems are typically based on very simple prediction rules. This paper narrows down the gap between the state-of-the-art and the state-of-the-practice in generating RTI for public transport systems by evaluating the added-value of schemes that integrate instantaneous data and dwell time predictions. The evaluation considers static information and a commonly deployed scheme as a benchmark. The RTI generation algorithms were applied and analyzed for a trunk bus network in Stockholm, Sweden. The schemes are assessed and compared based on their accuracy, reliability, robustness and potential waiting time savings. The impact of RTI on passengers waiting times are compared with those attained by service frequency and regularity improvements. A method which incorporates information on downstream travel conditions outperforms the commonly deployed scheme, leading to a 25% reduction in the mean absolute error. Furthermore, the incorporation of instantaneous travel times improves the prediction accuracy and reliability, and contributes to more robust predictions. The potential waiting time gains associated with the prediction scheme are equivalent to the gains expected when introducing a 60% increase in service frequency, and are not attainable by service regularity improvements.

Order Degree Evaluation of Information System Based on Improved Structural Entropy

To solve the problem that available methods of order degree evaluation ignore the heterogeneous information interactions between subsystems, an improved method of order degree evaluation, based on structural entropy, is proposed from the perspective of heterogeneous interactions between subsystems. A case study is proposed to test our model, and order degree under the two scenarios is contrasted. Result shows that directed information flow increases timeliness of information and decreases accuracy of information, which is repeatedly verified in practice. The proposed model, which is more in line with the actual structure of information system, not only further improves order degree evaluation in theory, but also provides operational method to order degree evaluation in real-world information systems.

An Assessment of Risk Management Strategies for Financial Information Systems by Financial Institutions in Kenya

Decision making is an important aspect of software processes management. Most organizations allocate resources based on predictions. Improving the accuracy of such predictions reduces costs and helps in efficient resources management. risk management is of vital importance for any financial institution (and enterprise for that matter) to keep its information systems secure at an acceptable level, the key issues focus on both how to reduce the probability of risk occurrence and decrease the loss of risk consequence. The main tasks for the implementation of such requirements involve the determination of the causes of risks, the estimation of risk occurrence probability, and the evaluation of risk consequence severity, which are all included in the risk analysis. In the process of risk analysis for information systems, models are built in order to analyze and better understand the risk factors and their causal relationships in real-world information systems. Establishing an appropriate model suitable for the target risk problem is a crucial task that will ultimately influence the effectiveness of risk analysis results. In the existing literature, most the approaches either assumed that the structure of the model was provided by domain expert experience and knowledge, or assumed that the structure was chosen from some general well-known class of model structures, thus, the results of risk analysis were relatively subjective. To overcome these drawbacks, not only expert have the experience and knowledge that needs to be taken into account, but also, the database of observed cases from information systems should be utilized in the process of modeling. With the growth of the dependency on IT, the impact of risk concerns on the development and exploitation of information systems has also increased exponentially. The risk management system focuses on specific phases of the software life cycle, without recognizing that risks in one stage can have an impact on other stages. This paper explores the risk situation as it is in the financial institutions in Kenya and suggests ways through which risk management can be brought a notch higher in order to minimise the losses incurred when faced by these risk situations.

Bichromatic buckets: An effective technique to improve the accuracy of histograms for geographic data points

Histograms have been widely used for estimating selectivity in query optimization. In this paper, we propose a new technique to improve the accuracy of histograms for two-dimensional geographic data points that are used in many real-world Geographic Information Systems. Typically, a histogram consists of a collection of rectangular regions, called buckets. The main idea of our technique is to use a straight line to convert each rectangular bucket to a new one with two separating regions. The converted buckets, called bichromatic buckets, can approximate the distribution of data objects better while preserving the simplicity of originally rectangular ones. To construct bichromatic buckets, we propose an adaptive algorithm to find good separating lines. Two strategies to find the separating lines, one based on the potential skewness gains of the candidate lines and the other based on the difference of density levels of the data regions, are proposed and used flexibly within our algorithm. Then, we describe how to apply the proposed technique to existing histogram construction methods to improve the accuracy of the constructed histograms further. Results from extensive experiments using real-life data sets demonstrate that our technique improves the accuracy of the histograms by 2 times on average.

CONCEPT LOCATION MODELING THROUGH BUSINESS PROCESS VIEWS

Concept location is a key activity during software modernization since it allows maintainers to exactly determine what pieces of source code support a specific concept. Real-world business processes and information systems providing operational IT support for respective processes can be misaligned as a consequence of uncontrolled maintenance over time. When concepts supported by an information system are getting outdated or misaligned, concept location becomes a time-consuming and error-prone task. Moreover, enterprise information systems (which implement business processes) embed significant business knowledge over time that is neither present nor documented anywhere else. To support the evolution of existing information systems, the embedded knowledge must first be retrieved and depicted in up-to-date business process models and then be mapped to the source code. This paper addresses this issue through a concept location approach that considers business activities as the key concept to be located and discovers different partial business process views for each piece of source code. Thus, the concept location problem becomes the problem of extracting such views. This approach follows model-driven development principles and an automatic model transformation is implemented to facilitate its adoption. Moreover, a case study involving two real-life information system demonstrates its feasibility.

A semantic metrics suite for evaluating modular ontologies

Ontologies, which are formal representations of knowledge within a domain, can be used for designing and sharing conceptual models of enterprises information for the purpose of enhancing understanding, communication and interoperability. For representing a body of knowledge, different ontologies may be designed. Recently, designing ontologies in a modular manner has emerged for achieving better reasoning performance, more efficient ontology management and change handling. One of the important challenges in the employment of ontologies and modular ontologies in modeling information within enterprises is the evaluation of the suitability of an ontology for a domain and the performance of inference operations over it. In this paper, we present a set of semantic metrics for evaluating ontologies and modular ontologies. These metrics measure cohesion and coupling of ontologies, which are two important notions in the process of assessing ontologies for enterprise modeling. The proposed metrics are based on semantic-based definitions of relativeness, and dependencies between local symbols, and also between local and external symbols of ontologies. Based on these semantic definitions, not only the explicitly asserted knowledge in ontologies but also the implied knowledge, which is derived through inference, is considered for the sake of ontology assessment. We present several empirical case studies for investigating the correlation between the proposed metrics and reasoning performance, which is an important issue in applicability of employing ontologies in real-world information systems.

An ontology-based knowledge management framework for a distributed water information system

With the increasing complexity of hydrologic problems, data collection and data analysis are often carried out in distributed heterogeneous systems. Therefore it is critical for users to determine the origin of data and its trustworthiness. Provenance describes the information life cycle of data products. It has been recognised as one of the most promising methods to improve data transparency. However, due to the complexity of the information life cycle involved, it is a challenge to query the provenance information which may be generated by distributed systems, with different vocabularies and conventions, and may involve knowledge of multiple domains. In this paper, we present a semantic knowledge management framework that tracks and integrates provenance information across distributed heterogeneous systems. It is underpinned by the Integrated Knowledge model that describes the domain knowledge and the provenance information involved in the information life cycle of a particular data product. We evaluate the proposed framework in the context of two real-world water information systems.

A Tool for Visualizing the Behavior of Fuzzy Constraint Satisfaction Solvers

This paper presents a software tool to intuitively comprehend and analyze fuzzy constraint satisfaction problems (FCSPs) through effective visualization with animation. FCSPs are an extension of constraint satisfaction problems (CSPs), which are used for formulating problems in real-world information systems. Once we formulate a problem as an FCSP, it can be solved (in principle) by any existing general-purpose FCSP solvers developed so far. However, the formulation is sometimes difficult because it requires high abstraction of real-world problems and affects the performance of the solvers. The authors believe that the tool will improve this situation by increasing the visibility of the behavior of the solvers.

Team projects in a Systems Analysis and Design class: elements of success

An essential part of many Systems Analysis and Design capstone courses is the development of operational, meaningful and real-world information systems. The development of such systems typically takes place in a small group setting to realistically simulate the future work environment of Management Information System (MIS) students. We investigated how previous work experience, time spent on the project, project complexity, and the relationships among project team members and between team members and project stakeholders affected the outcome of the system development projects. Our findings indicate that three factors: prior work experience in information technology; experience with project management; the ability to select relatively simple projects positively affected the project outcome. While difficulties among team members showed a negative impact on the outcome of the projects, difficulties in the relationships with the stakeholders and total time spent on the project did not have significant effects.

Semantic Web applications: a framework for industry and business exploitation – What is needed for the adoption of the Semantic Web from the market and industry

In recent years, Semantic Web (SW) research has resulted in significant outcomes. Various industries have adopted SW technologies, while the 'deep web' is still pursuing the critical transformation point, in which the majority of data found on the deep web will be exploited through SW value layers. In this article we analyse the SW applications from a 'market' perspective. We are setting the key requirements for real-world information systems that are SW-enabled and we discuss the major difficulties for the SW uptake that has been delayed. This article contributes to the literature of SW and knowledge management providing a context for discourse towards best practices on SW-based information systems.

1A1-A06 ヒューマノイドにおけるモダリティ分散を特徴としたタスクシナリオ記述と実世界デモンストレーションへの応用(ヒューマノイド)

In "Real World Information Systems Project" in "21st Century COE Program", we demonstrated research progress, called "A Living Room Of The Future". This shows an example of situations of humanoids daily life supporting, where HRP-2W, with HRP-2JSK, follows an order from a guest, serve tea, and take away the cup. We use "Reactive Server System", activating behaviors by events thrown by each module, for realization of daily task using functional abilities of grasping a tea cup, moving around the stage, visual recognition of cup location, speech recognition, sound source localization, voice synthesis and so on. In this paper, we will describe summary of implementaion, artifice and problems in deed, and will describe solutions and future prospects.