An MDA approach for developing secure OLAP applications: Metamodels and transformations

Decision makers query enterprise information stored in DataWarehouses (DW) by using tools (such as On-Line Analytical Processing (OLAP) tools) which employ specific views or cubes from the corporate DW or Data Marts, based on multidimensional modelling. Since the information managed is critical, security constraints have to be correctly established in order to avoid unauthorized access. In previous work we defined a Model-Driven based approach for developing a secure DW repository by following a relational approach. Nevertheless, it is also important to define security constraints in the metadata layer that connects the DW repository with the OLAP tools; that is, over the same multidimensional structures that end users manage. This paper incorporates a proposal for developing secure OLAP applications within our previous approach: it improves a UML profile for conceptual modelling; it defines a logical metamodel for OLAP applications; and it defines and implements transformations from conceptual to logical models, as well as from logical models to secure implementation in a specific OLAP tool (SQL Server Analysis Services). © 2015 ComSIS Consortium. All rights reserved.This research is part of the following projects: SIGMA-CC (TIN2012-36904), GEODAS-BC (TIN2012-37493-C01) and GEODAS-BI (TIN2012-37493-C03) funded by the Ministerio de Economía y Competitividad and Fondo Europeo de Desarrollo Regional FEDER

Middleware non-repudiation service for the data warehouse

Nowadays, storing the information is fundamental for the correct functioning of any organization. The critical factor is to guarantee the security of the stored data. In the traditional database systems the security requirements are limited to confidentiality, integrity, availability of the data and user authorization. The criticality of the database system and data repositories for modern business with the new requirements of law and governments, makes the development of new system architecture necessary which ensures sophisticated set of security services. In this paper we propose the database architecture that ensures the non-repudiation of the user queries and data warehouse actions. These security services are accomplished by means of the middleware layer in the data warehouse architecture

A Distributed Intelligent Agent-Based Spatial Decision Support System

GIS usage has resulted in large volumes of spatial data, and organizations see the need for incorporating this data into their analysis and decision making. Managers are beginning to understand the potential of using DSS and EIS that are enhanced with spatial and temporal capabilities in addressing issues related to marketing, demographics, routing, etc. Traditional GIS have lagged behind in providing tools that support upper management in decision making and cooperative problem solving. As pointed out by Keenan (1997), and Mennecke (1997), there are ample opportunities for cross fertilization of ideas from IS and GIS research in this regard

The BioRef Infrastructure, a Framework for Real-Time, Federated, Privacy-Preserving, and Personalized Reference Intervals: Design, Development, and Application.

BACKGROUND Reference intervals (RIs) for patient test results are in standard use across many medical disciplines, allowing physicians to identify measurements indicating potentially pathological states with relative ease. The process of inferring cohort-specific RIs is, however, often ignored because of the high costs and cumbersome efforts associated with it. Sophisticated analysis tools are required to automatically infer relevant and locally specific RIs directly from routine laboratory data. These tools would effectively connect clinical laboratory databases to physicians and provide personalized target ranges for the respective cohort population. OBJECTIVE This study aims to describe the BioRef infrastructure, a multicentric governance and IT framework for the estimation and assessment of patient group-specific RIs from routine clinical laboratory data using an innovative decentralized data-sharing approach and a sophisticated, clinically oriented graphical user interface for data analysis. METHODS A common governance agreement and interoperability standards have been established, allowing the harmonization of multidimensional laboratory measurements from multiple clinical databases into a unified "big data" resource. International coding systems, such as the International Classification of Diseases, Tenth Revision (ICD-10); unique identifiers for medical devices from the Global Unique Device Identification Database; type identifiers from the Global Medical Device Nomenclature; and a universal transfer logic, such as the Resource Description Framework (RDF), are used to align the routine laboratory data of each data provider for use within the BioRef framework. With a decentralized data-sharing approach, the BioRef data can be evaluated by end users from each cohort site following a strict "no copy, no move" principle, that is, only data aggregates for the intercohort analysis of target ranges are exchanged. RESULTS The TI4Health distributed and secure analytics system was used to implement the proposed federated and privacy-preserving approach and comply with the limitations applied to sensitive patient data. Under the BioRef interoperability consensus, clinical partners enable the computation of RIs via the TI4Health graphical user interface for query without exposing the underlying raw data. The interface was developed for use by physicians and clinical laboratory specialists and allows intuitive and interactive data stratification by patient factors (age, sex, and personal medical history) as well as laboratory analysis determinants (device, analyzer, and test kit identifier). This consolidated effort enables the creation of extremely detailed and patient group-specific queries, allowing the generation of individualized, covariate-adjusted RIs on the fly. CONCLUSIONS With the BioRef-TI4Health infrastructure, a framework for clinical physicians and researchers to define precise RIs immediately in a convenient, privacy-preserving, and reproducible manner has been implemented, promoting a vital part of practicing precision medicine while streamlining compliance and avoiding transfers of raw patient data. This new approach can provide a crucial update on RIs and improve patient care for personalized medicine

Advanced Information Systems Engineering: 23rd International Conference, CAiSE 2011, London, UK, June 20-24, 2011. Proceedings

International audienc

Towards an Italian Energy Data Space

The efficient use and the sustainable production of energy are some of the main challenges to face the ever increasing request for energy and the need to limit the damages to the Earth. Smart energy grids, pervasive computing and communication technologies have enabled the stakeholders in the energy industry to collect large amounts of useful and highly granular energy data. They are generated in large volumes and in a variety of different formats, depending on their originating systems and prospected purposes. Moreover, the data type can be structured and unstructured, in open or proprietary formats. This work focuses on harnessing the power of Big Data Management to propose a first model of an Italian Energy Data Lake: the goal is to create a repository of national energy data that respects the FAIRness' key principles [1], aimed at providing a decision support system and the availability of FAIR data for open science. Starting from data of two thematic areas that are part of the nine common European Data Spaces identified in the European Data Strategy[2], namely the Green Deal data space and the Energy data space, an open and extensible platform to enable secure, resilient acquisition and sharing of information will be presented, for enabling the Green Deal priority actions on issues such as climate change, circular economy, pollution, biodiversity, and deforestation

Data warehouse applications in modern day business

Data warehousing provides organizations with strategic tools to achieve the competitive advantage that organazations are constantly seeking. The use of tools such as data mining, indexing and summaries enables management to retrieve information and perform thorough analysis, planning and forcasting to meet the changes in the market environment. in addition, The data warehouse is providing security measures that, if properly implemented and planned, are helping organizations ensure that their data quality and validity remain intact

Query Health: standards-based, cross-platform population health surveillance

Objective: Understanding population-level health trends is essential to effectively monitor and improve public health. The Office of the National Coordinator for Health Information Technology (ONC) Query Health initiative is a collaboration to develop a national architecture for distributed, population-level health queries across diverse clinical systems with disparate data models. Here we review Query Health activities, including a standards-based methodology, an open-source reference implementation, and three pilot projects. Materials and methods Query Health defined a standards-based approach for distributed population health queries, using an ontology based on the Quality Data Model and Consolidated Clinical Document Architecture, Health Quality Measures Format (HQMF) as the query language, the Query Envelope as the secure transport layer, and the Quality Reporting Document Architecture as the result language. Results: We implemented this approach using Informatics for Integrating Biology and the Bedside (i2b2) and hQuery for data analytics and PopMedNet for access control, secure query distribution, and response. We deployed the reference implementation at three pilot sites: two public health departments (New York City and Massachusetts) and one pilot designed to support Food and Drug Administration post-market safety surveillance activities. The pilots were successful, although improved cross-platform data normalization is needed. Discussions This initiative resulted in a standards-based methodology for population health queries, a reference implementation, and revision of the HQMF standard. It also informed future directions regarding interoperability and data access for ONC's Data Access Framework initiative. Conclusions: Query Health was a test of the learning health system that supplied a functional methodology and reference implementation for distributed population health queries that has been validated at three sites

The Cost- Saving Role of Blockchain Technology As a Data Integrity Tool: E-health Scenario

The digital economy of health and its global proliferation have led to the use of health information systems in the daily health services delivery to patients. Consequently, there is a development of web-based electronic healthcare aimed at providing electronic health services in real-time. In this way,  through the implementation of  the concept of electronic health, there is an exchange of health information among all stakeholders of the health organization, all with the aim of monitoring the health status of patients, timely intervention and adequate allocation of medical resources. Processing and sharing a large amount of health data in real time, with the constant need to maintain a high level of interoperability and scalability of network infrastructure, requires the highest possible level of security in accessing data, in order to reduce the misuse of health data. By using blockchain technology, the risk of misusing health information, asymmetry of information and the risk of increasing transaction costs are reduced in a very short time. Blockchain is a robust mathematical algorithm that can provide maximum security of the transaction using cryptographic methods. This type of technology is based on a distributed database that contains encrypted data that can not be changed or disturbed. For this reason, the application of this technology as a data integration tool is increasingly reflected in the electronic business of health organizations - electronic healthcare. Blockchain technology is especially used in information-intensive electronic healthcare records and medical applications, which ultimately results in reduced costs of providing health services, especially when it comes to system maintenance and security costs, interoperability and data redundancy. According to above-mentioned cost-saving role of blockchain technology in processing, sharing and analyzing healthcare data, in this paper, there will be more to say about the positive economic impact of blockchain technology on electronic healthcare, especially in the case of Estonia. This European country is a pioneer in creating, implementing and using the e-Health concept as an integral part of health information system through its healthcare system, in order to increase efficiency of healthcare services. Keywords: Blockchain technology, data integrity, e-Health, health economic

Data Behind the Walls An Advanced Architecture for Data Privacy Management

In today's highly connected society, we are constantly asked to provide personal information to retailers, voter surveys, medical professionals, and other data collection efforts. The collected data is stored in large data warehouses. Organisations and statistical agencies share and use this data to facilitate research in public health, economics, sociology, etc. However, this data contains sensitive information about individuals, which can result in identity theft, financial loss, stress and depression, embarrassment, abuse, etc. Therefore, one must ensure rigorous management of individuals' privacy. We propose, an advanced data privacy management architecture composed of three layers. The data management layer consists of de-identification and anonymisation, the access management layer for re-enforcing data access based on the concepts of Role-Based Access Control and the Chinese Wall Security Policy, and the roles layer for regulating different users. The proposed system architecture is validated on healthcare datasets.Comment: 7 page