A web-based system architecture for ontology-based data integration in the domain of IT benchmarking

In the domain of IT benchmarking (ITBM), a variety of data and information are collected. Although these data serve as the basis for business analyses, no unified semantic representation of such data yet exists. Consequently, data analysis across different distributed data sets and different benchmarks is almost impossible. This paper presents a system architecture and prototypical implementation for an integrated data management of distributed databases based on a domain-specific ontology. To preserve the semantic meaning of the data, the ITBM ontology is linked to data sources and functions as the central concept for database access. Thus, additional databases can be integrated by linking them to this domain-specific ontology and are directly available for further business analyses. Moreover, the web-based system supports the process of mapping ontology concepts to external databases by introducing a semi-automatic mapping recommender and by visualizing possible mapping candidates. The system also provides a natural language interface to easily query linked databases. The expected result of this ontology-based approach of knowledge representation and data access is an increase in knowledge and data sharing in this domain, which will enhance existing business analysis methods.     

多数据源数据信息交换中间件的设计与实现

网络环境下的异构数据交换技术是数据集成、电子商务应用和Web服务合成系统等实际应用的核心支撑技术。文章基于"协作者"模式,以关系数据库和XML文档为异构数据源的典型代表,给出了Web服务环境下的数据交换模型,介绍了数据交换模型的4种基本操作的算法思想和利用Web服务合成技术实现数据交换的方法,通过一个示例,验证了文章提出的数据交换方法的有效性。     

QaaS (quality as a service) model for web services using big data technologies

Quality of service (QoS) determines the service usability and utility and both of which influence the service selection process. The QoS varies from one service provider to other. Each web service has its own methodology for evaluating QoS. The lack of transparent QoS evaluation model makes the service selection challenging. Moreover, most QoS evaluation processes do not consider their historical data which not only helps in getting more accurate QoS but also helps for future prediction, recommendation and knowledge discovery. QoS driven service selection demands a model where QoS can be provided as a service to end users. This paper proposes a layered QaaS (quality as a service) model in the same line as PaaS and software as a service, where users can provide QoS attributes as inputs and the model returns services satisfying the user’s QoS expectation. This paper covers all the key aspects in this context, like selection of data sources, its transformation, evaluation, classification and storage of QoS. The paper uses server log as the source for evaluating QoS values, common methodology for its evaluation and big data technologies for its transformation and analysis. This paper also establishes the fact that Spark outperforms the Pig with respect to evaluation of QoS from logs.     

Metafrontier efficiency analysis for hospital pharmacy services using dynamic network DEA framework

Medication management is a complex but critical process within the healthcare delivery system. The pharmacy department in healthcare facilities is responsible for acquisition, distribution and dispensing of medication to patients that involves a series of network process. This process can be categorized into two main subprocesses: i) pharmacy store and ii) patient services (i.e., ambulatory and inpatient pharmacy). The objective of this study was to investigate the relative efficiency level of the two subprocesses of pharmacy services in Malaysia's public hospitals. We used a nonconvex metafrontier technology to estimate technical efficiency of hospital pharmacies between specialist and nonspecialist hospital categories under the dynamic network data envelopment analysis (DNDEA) framework. For overall efficiency, both specialist and nonspecialist categories had similar mean efficiency scores. However, when measuring the efficiency level according to pharmacy store and patient services separately, the nonspecialist category had a higher mean efficiency score in pharmacy store, whereas for patient service efficiency, both categories had similar results. The patient service efficiency level was lower consistently over time, suggesting that the overall inefficiency of hospital pharmacy is greatly influenced by the inefficiency of the patient service. This suggests that systematic improvements in patient service efficiency should be a higher priority than pharmacy store efficiency to further increase the overall efficiency level. We believe that our findings will provide valuable information to policymakers towards achieving an efficient public healthcare service.     

A conceptual framework for product lifecycle modelling

Product lifecycle modelling is to define and represent product lifecycle data and to maintain data interdependencies. To build a complete, reusable and highly consistent product lifecycle information model, the product lifecycle is divided into five stages: requirement analysis, conceptual design, engineering design, manufacturing, and service and support. Accordingly, five stage product models (requirement analysis model, conceptual design model, engineering design model, manufacturing model, and service and support model) are discussed. To integrate all information of a product lifecycle and support networked manufacturing mode, the key elements of product lifecycle modelling are discussed and a framework of product lifecycle modelling is proposed. Further, the relationship and evolvement of product models at different stages are described. Finally, a Web-based integration framework is proposed to support interoperability of distributed product data sources.     

A framework for the identification of reusable processes

A significant challenge that faces IT management is that of aligning the IT infrastructure of an enterprise with its business goals and practices, also called business-IT alignment. A particular business-IT alignment approach, the foundation for execution approach, was well-accepted by practitioners due to a novel construct, called the operating model (OM). The OM supports business-IT alignment by directing the coherent and consistent design of business and IT components. Even though the OM is a popular construct, our previous research detected the need to enhance the OM, since the OM does not specify methods to identify opportunities for data sharing and process reuse in an enterprise. In this article, we address one of the identified deficiencies in the OM. We present a process reuse identification framework (PRIF) that could be used to enhance the OM in identifying process reuse opportunities in an enterprise. We applied design research to develop PRIF as an artefact, where the development process of PRIF was facilitated by means of the business-IT alignment model (BIAM). We demonstrate the use of the PRIF as well as report on the results of evaluating PRIF in terms of its usefulness and ease-of-use, using experimentation and a questionnaire.     

A general framework for frontier estimation with panel data

The main objective of the paper is to present a general framework for estimating production frontier models with panel data. A sample of firms i = 1, ..., N is observed on several time periods t = 1, ... T. In this framework, nonparametric stochastic models for the frontier will be analyzed. The usual parametric formulations of the literature are viewed as particular cases and the convergence of the obtained estimators in this general framework are investigated. Special attention is devoted to the role of N and of T on the speeds of convergence of the obtained estimators. First, a very general model is investigated. In this model almost no restriction is imposed on the structure of the model or of the inefficiencies. This model is estimable from a nonparametric point of view but needs large values of T and of N to obtain reliable estimates of the individual production functions and estimates of the frontier function. Then more specific nonparametric firm effect models are presented. In these cases, only NT must be large to estimate the common production function; but again both large N and T are needed for estimating individual efficiencies and for estimating the frontier. The methods are illustrated through a numerical example with real data.     

A framework for service enterprise workflow simulation with multi-agents cooperation

Process dynamic modelling for service business is the key technique for Service-Oriented information systems and service business management, and the workflow model of business processes is the core part of service systems. Service business workflow simulation is the prevalent approach to be used for analysis of service business process dynamically. Generic method for service business workflow simulation is based on the discrete event queuing theory, which is lack of flexibility and scalability. In this paper, we propose a service workflow-oriented framework for the process simulation of service businesses using multi-agent cooperation to address the above issues. Social rationality of agent is introduced into the proposed framework. Adopting rationality as one social factor for decision-making strategies, a flexible scheduling for activity instances has been implemented. A system prototype has been developed to validate the proposed simulation framework through a business case study.     

A visualization tool for exploratory analysis of cyclic multivariate data

This paper presents an interactive visualization tool for the qualitative exploration of multivariate data that may exhibit cyclic or periodic behavior. Glyphs are used to encode each multivariate data point, and linear, stacked, and spiral glyph layouts are employed to help convey both intra-cycle and inter-cycle relationships within the data. Users may interactively select glyph and layout types, modify cycle lengths and the number of cycles to display, and select the specific data dimensions to be included. We validate the usefulness of the system with case studies and describe our future plans for expanding the system's capabilities.     

Topics of statistical theory for register‐based statistics and data integration

Official statistics production based on a combination of data sources, including sample survey, census and administrative registers, is becoming more and more common. Reduction of response burden, gains of production cost efficiency as well as potentials for detailed spatial‐demographic and longitudinal statistics are some of the major advantages associated with the use of integrated statistical data. Data integration has always been an essential feature associated with the use of administrative register data. But survey and census data should also be integrated, so as to widen their scope and improve the quality. There are many new and difficult challenges here that are beyond the traditional topics of survey sampling and data integration. In this article we consider statistical theory for data integration on a conceptual level. In particular, we present a two‐phase life‐cycle model for integrated statistical microdata, which provides a framework for the various potential error sources, and outline some concepts and topics for quality assessment beyond the ideal of error‐free data. A shared understanding of these issues will hopefully help us to collocate and coordinate efforts in future research and development.     

A method towards Web service combination for cross-organisational business process using QoS and cluster

How to quickly combine various Web services to support cross-organisational business processes is the key technical problem in service computing. Because of the changeability of QoS of Web services, the traditional methods are unadaptable to the new environments. In this paper, a new service composition method, called Improved Self-organising neural network Method for Web Service Composition, is proposed to achieve QoS-aware Web service combination, according to using the clustering technology. First, fuzzy mathematics is used to express each QoS attribute and the improved self-organising neural network is used to cluster services to reduce the number of candidate services. Secondly, all the centre of each cluster is selected and formed a composite service by using an exhaustive algorithm. Thirdly, the service cluster that is represented by the concrete services. Finally, the optimal service combination can be selected using integer programming or genetic algorithm. The experimental result shows the efficiency of Web service composition and demonstrates the applicability.     

A top-down approach for approximate data anonymisation

Data sharing in today's information society poses a threat to individual privacy and organisational confidentiality. k-anonymity is a widely adopted model to prevent the owner of a record being re-identified. By generalising and/or suppressing certain portions of the released dataset, it guarantees that no records can be uniquely distinguished from at least other k?1 records. A key requirement for the k-anonymity problem is to minimise the information loss resulting from data modifications. This article proposes a top-down approach to solve this problem. It first considers each record as a vertex and the similarity between two records as the edge weight to construct a complete weighted graph. Then, an edge cutting algorithm is designed to divide the complete graph into multiple trees/components. The Large Components with size bigger than 2k?1 are subsequently split to guarantee that each resulting component has the vertex number between k and 2k?1. Finally, the generalisation operation is applied on the vertices in each component (i.e. equivalence class) to make sure all the records inside have identical quasi-identifier values. We prove that the proposed approach has polynomial running time and theoretical performance guarantee O(k). The empirical experiments show that our approach results in substantial improvements over the baseline heuristic algorithms, as well as the bottom-up approach with the same approximate bound O(k). Comparing to the baseline bottom-up O(logk)-approximation algorithm, when the required k is smaller than 50, the adopted top-down strategy makes our approach achieve similar performance in terms of information loss while spending much less computing time. It demonstrates that our approach would be a best choice for the k-anonymity problem when both the data utility and runtime need to be considered, especially when k is set to certain value smaller than 50 and the record set is big enough to make the runtime have to be taken into account.     

Web服务Qos评价的模糊新算法

质量评价在Web服务生命周期的服务选择、实时监控、管理和计费等阶段占有越来越重要的地位,且模糊理论已应用于该领域。文章从区分冗余隶属度的角度对模糊综合评判算法进行改进,以消除指标隶属度中对目标分类不起作用的冗余数值,在没有冗余数据的条件下实现隶属度转换,并应用于Web服务QoS的评价。     

Web服务QoS评价的模糊新算法

质量评价在Web服务生命周期的服务选择、实时监控、管理和计费等阶段占有越来越重要的地位,且模糊理论已应用于该领域。文章从区分冗余隶属度的角度对模糊综合评判算法进行改进,以消除指标隶属度中对目标分类不起作用的冗余数值,在没有冗余数据的条件下实现隶属度转换,并应用于Web服务QoS的评价。     

A proposed configurable approach for recommendation systems via data mining techniques

This study presents a configurable approach for recommendations which determines the suitable recommendation method for each field based on the characteristics of its data, the method includes determining the suitable technique for selecting a representative sample of the provided data. Then selecting the suitable feature weighting measure to provide a correct weight for each feature based on its effect on the recommendations. Finally, selecting the suitable algorithm to provide the required recommendations. The proposed configurable approach could be applied on different domains. The experiments have revealed that the approach is able to provide recommendations with only 0.89 error rate percentage.     

SLA-based optimisation of virtualised resource for multi-tier web applications in cloud data centres

Dynamic virtualised resource allocation is the key to quality of service assurance for multi-tier web application services in cloud data centre. In this paper, we develop a self-management architecture of cloud data centres with virtualisation mechanism for multi-tier web application services. Based on this architecture, we establish a flexible hybrid queueing model to determine the amount of virtual machines for each tier of virtualised application service environments. Besides, we propose a non-linear constrained optimisation problem with restrictions defined in service level agreement. Furthermore, we develop a heuristic mixed optimisation algorithm to maximise the profit of cloud infrastructure providers, and to meet performance requirements from different clients as well. Finally, we compare the effectiveness of our dynamic allocation strategy with two other allocation strategies. The simulation results show that the proposed resource allocation method is efficient in improving the overall performance and reducing the resource energy cost.     

An effective framework for finding similar cases of dengue from audio and text data using domain thesaurus and case base reasoning

Dengue, also known as break-bone fever, is a tropical disease transmitted by mosquitoes. If the similarity between dengue infected users can be identified, it can help government’s health agencies to manage the outbreak more effectively. To find similarity between cases affected by Dengue, user’s personal and health information are the two fundamental requirements. Identification of similar symptoms, causes, effects, predictions and treatment procedures, is important. In this paper, an effective framework is proposed which finds similar patients suffering from dengue using keyword aware domain thesaurus and case base reasoning method. This paper focuses on the use of ontology dependent domain thesaurus technique to extract relevant keywords and then build cases with the help of case base reasoning method. Similar cases can be shared with users, nearby hospitals and health organizations to manage the problem more adequately. Two million case bases were generated to test the proposed similarity method. Experimental evaluations of proposed framework resulted in high accuracy and low error rate for finding similar cases of dengue as compared to UPCC and IPCC algorithms. The framework developed in this paper is for dengue but can easily be extended to other domains also.     

The role of HRM and social capital configuration for knowledge sharing in post-M&A integration: a framework for future empirical investigation

The literature on human resource management (HRM) indicates that HRM plays an important role in merger and acquisition (M&A) integration success, but pays little attention to the mechanisms for knowledge sharing in post-M&A integration. Limited work has been carried out to provide understanding on how social capital and HRM practices influence intra-organizational knowledge sharing in M&A integration. This paper primarily focuses on the phenomenon of social capital and HRM practices – one of the primary means by which knowledge sharing can occur within firms. The main aim of this paper is to provide an alternative framework that introduces the literature on HRM and social capital to discuss how HRM practices and the various dimensions of social capital may enhance knowledge sharing in post-M&A integration. Drawing on the literature on social capital and HRM, we offer an alternative view on the issue of knowledge sharing in M&A integration by explaining how specific HRM practices that have an impact on employees’ knowledge, skills and abilities for participating in knowledge sharing activities may depend on relational, cognitive and structural social capital. We isolate a number of HRM practices and social capital variables that may enhance knowledge sharing in post-M&A integration, and develop a research model and propositions for future empirical investigation.     

Adjusting for the environment in DEA: A comparison of alternative models based on empirical data

Due to the existence of free software and pedagogical guides, the use of Data Envelopment Analysis (DEA) has been further democratized in recent years. Nowadays, it is quite usual for practitioners and decision makers with no or little knowledge in operational research to run their own efficiency analysis. Within DEA, several alternative models allow for an environmental adjustment. Four alternative models, each user-friendly and easily accessible to practitioners and decision makers, are performed using empirical data of 90 primary schools in the State of Geneva, Switzerland. Results show that the majority of alternative models deliver divergent results. From a political and a managerial standpoint, these diverging results could lead to potentially ineffective decisions. As no consensus emerges on the best model to use, practitioners and decision makers may be tempted to select the model that is right for them, in other words, the model that best reflects their own preferences. Further studies should investigate how an appropriate multi-criteria decision analysis method could help decision makers to select the right model.     

A partial order toolbox for building synthetic indicators of sustainability with ordinal data

This paper designs and implements a statistical toolbox for the construction of synthetic indicators of sustainability and rankings of multidimensional systems of ordinal variables. The toolbox employs results from partial order theory to provide a purely ordinal way to perform the classical steps of indicator construction (i.e., with neither quantification nor aggregation of the input variables) and thus fills a gap in the statistical literature. “Ordinal” non-aggregative procedures are developed to score statistical units and assess the reliability of the final rankings as well as account for exogenous information relating to variable importance. The toolbox is introduced and shown in action through the real example of Sustainable Development Goal 16 (Peace, Justice, and Strong Institutions), specifically as it relates to Political Pluralism and Participation in North and South American countries.