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Über dieses Buch

This volume, the 35th issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains five fully-revised selected regular papers focusing on data quality, social-data artifacts, data privacy, predictive models, and e-health. Specifically, the five papers present and discuss a data-quality framework for the Estonian public sector; a data-driven approach to bridging the gap between the business and social worlds; privacy-preserving querying on privately encrypted data in the cloud; algorithms for the prediction of norovirus concentration in drinking water; and cloud computing in healthcare organizations in Saudi Arabia.



The Data Quality Framework for the Estonian Public Sector and Its Evaluation

Establishing a Systematic Process-Oriented Viewpoint on Cross-Organizational Data Quality
In this paper, we describe a data quality framework for public sector organizations is proposed. It includes a data quality methodology for the public sector organizations (DQMP) and a complex of state-level activities for improving data quality in the public sector. The DQMP comprises a data quality model, a data quality maturity model, and a data quality management process. Based on this framework, two guidelines have been developed: a data quality handbook for public sector organizations and guidelines for improving data quality in the public sector. The results of the evaluation of the data quality handbook on three basic Estonian state registers highlight lessons learned and confirm the usefulness of the approach. The paper aims at researchers investigating data quality in various environments and practitioners involved in information systems management, development, and maintenance.
Jaak Tepandi, Mihkel Lauk, Janar Linros, Priit Raspel, Gunnar Piho, Ingrid Pappel, Dirk Draheim

Bridging the Gap Between the Business and Social Worlds: A Data Artifact-Driven Approach

The widespread adoption of Web 2.0 applications has forced enterprises to rethink their ways of doing business. To support enterprises in their endeavors, this paper puts forward business-data artifact and social-data artifact to capture, respectively, the intrinsic characteristics of the business world (associated with business process management systems) and social world (associated with Web 2.0 applications), and, also, to make these two worlds work together. While the research community has extensively looked into business-data artifacts, there is a limited knowledge about/interest in social-data artifacts. This paper defines social-data artifact, analyzes the interactions between business- and social-data artifacts, and develops an architecture to support these interactions. For demonstration purposes, an implementation of a socially-flavored faculty-hiring scenario is discussed in the paper. The implementation calls for specialized components known as social machines that support artifact interaction.
Zakaria Maamar, Vanilson Burégio, Mohamed Sellami, Nelson Souto Rosa, Zhengshuai Peng, Zachariah Subin, Nayana Prakash, Djamal Benslimane, Roosevelt Silva

Privacy-Preserving Querying on Privately Encrypted Data in the Cloud

Cloud services provide clients with highly scalable network, storage, and computational resources. However, these service come with the challenge of guaranteeing the confidentiality of the data stored on the cloud. Rather than attempting to prevent adversaries from compromising the cloud server, we aim in this paper to propose a protocol for secure querying in the cloud, while preserving the privacy of the participants and assuming the existence of a passive adversary able to access all data stored in the cloud. In this paper, we address this problem by proposing a network protocol that would allow a third party, such as a health organization, to query privately encrypted data without relying on a trusted entity. The protocol we propose preserves the privacy of the data owners and the querying entity. The protocol relies on homomorphic cryptography, threshold cryptography, differential privacy, and randomization to allow for secure, distributed, and privacy-preserving queries. We evaluate the performance of our protocol and report on the results of the implementation.
Feras Aljumah, Makan Pourzandi, Mourad Debbabi

Comparison of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Gaussian Process for Machine Learning (GPML) Algorithms for the Prediction of Norovirus Concentration in Drinking Water Supply

Monitoring of Norovirus in drinking water supply is a complicated, rather expensive, process. Norovirus represent a leading cause of acute gastroenteritis in most developed countries. Modeling of general microbial occurrence in drinking water is a very active field of study and provides reliable information for predicting microbial risks in drinking water. In this work, adaptive neuro-fuzzy inference system (ANFIS) and Gaussian Process for Machine Learning (GPML) are proposed as predicting models for the total number of Norovirus in raw surface water in terms of water quality parameters such as water pH, turbidity, conductivity, temperature and rain. The predictive models were based on data from Nødre Romrike Vannverk water treatment plant in Oslo, Norway. Based on the model performance indices used in this study, the GPML model showed comparable accuracy to the ANFIS model. However, the ANFIS model generally demonstrated more superior prediction ability of the number of Norovirus in drinking water, with lower MSE and MAE values relative to the GPML model. In addition, the ability of the ANFIS model to explain potential effects of interactions among the water quality variables on the number of Norovirus in the raw water makes the technique more efficient for use in water quality modeling.
Hadi Mohammed, Ibrahim A. Hameed, Razak Seidu

Cloud Computing Adoption in Healthcare Organisations: A Qualitative Study in Saudi Arabia

This paper provides a comprehensive review of Cloud Computing by discussing the benefits and challenges of implementing such solution and discusses various Cloud Computing adoption models. The paper describes Cloud Computing in healthcare domains. It provides also information about Cloud Computing in Saudi Arabia and how it could be applied for healthcare domain. The paper presents a qualitative study which provides an in-depth understanding of the Cloud Computing adoption decision-making process in healthcare organisations in Saudi Arabia. The paper discusses the factors which will affect Cloud Computing decision making process in Saudi Arabia. The findings of the study showed that the factors affecting Cloud Computing adoption can be divided into five main categories, Technological, Business, Environmental, Organisational and Human. This paper also identifies some of the key drivers and challenges of Cloud Computing adoption in Saudi healthcare organisations. This study will help both Saudi healthcare organisations and Cloud Computing vendors in understanding healthcare organisations’ attitude towards the adoption of Cloud Computing.
Fawaz Alharbi, Anthony Atkins, Clare Stanier


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