Users Integrity Constraints in SOLAP Systems. Application in Agroforestry

SpatialData Warehouse and Spatial On-Line Analytical Processing are decision support technologies which offer the spatial and multidimensional analysis of data stored in multidimensional structure. They are aimed also at supporting geographic knowledge discovery to help decision-maker in his job related to make the appropriate decision . However, if we don’t consider data quality in the spatial hypercubes and how it is explored, it may provide unreliable results. In this paper, we propose a system for the implementation of user integrity constraints in SOLAP namely “UIC-SOLAP”. It corresponds to a methodology for guaranteeing results quality in an analytical process effectuated by different users exploiting several facts tables within the same hypercube. We integrate users Integrity Constraints (IC) by specifying visualization ICs according to their preferences and we define inter-facts ICs in this case. In order to validate our proposition, we propose the multidimensional modeling by UML profile to support constellation schema of a hypercube with several fact tables related to subjects of analysis in forestry management. Then, we propose implementation of some ICs related to users of such a system

A Semantic Approach to Secure Collaborative Inter-Organizational eBusiness Processes (SSCIOBP)

The information supply chain (ISC) involves the exchange, organization, selection, and synthesis of relevant knowledge and information about production, purchase planning, demand forecasting, and inventory among collaborating business partners in a value chain. Information and knowledge sharing in an ISC occurs in a business process context. Seamless knowledge exchange within and across organizations involved in secure business processes is critically needed to secure and cultivate the information supply chain. Extant literature does not explicitly consider or systematically represent component knowledge, process knowledge and security knowledge for business processes within and across organizations. As a result, organizations engaged in collaborative inter-organizational processes continue to be plagued with issues such as semantic conflict issues, lack of integration of heterogeneous systems, and lack of security knowledge regarding authorized access to resources. Without appropriate security controls, manual interventions lead to unauthorized access to resources. These problems motivate our Semantic Approach to Secure Collaborative Inter-Organizational eBusiness Processes (SSCIOBP). We follow a design science paradigm to identify meta-requirements of SSCIOBP and develop the design artifact. SSCIOBP is evaluated using observational and descriptive evaluation methods following Hevner et al. (2004). We apply our approach to show how the Collaborative Planning Forecasting and Replenishment (CPFR) industry standard models can be enhanced using the proposed design artifact. We apply SSCIOBP to a case study to illustrate its applicability in mapping core business processes of organizations to solve semantic inter-operability issues and systematically incorporate component, process and security knowledge in the design of secure business processes across the information supply chain

Outcome-Driven Supply Chain Perspective on Dry Ports

The hinterland leg of maritime containerized transport as a part of supply chain has been increasingly pressured by larger volumes, as well as by a need to fulfill sustainability requirements that are expressed by social opinion and formal regulations. There is a potential to relieve this pressure through integration of a dry port, as a seaport’s inland interface, in the supply chain. Therefore, this paper aims to explain how a supply chain can benefit or enhance its outcomes of cost, responsiveness, security, environmental performance, resilience, and innovation, by the integration of a dry port. The data for this case study is collected through interviews and site visits from the privately owned Skaraborg dry port, Sweden; and the study is limited to the actors of the transport system involved in the development and operations of the dry port integrated setup. The results show that the six supply chain outcomes (cost, responsiveness, security, environmental performance, resilience, and innovation) are perceived by the actors as being desirable, and can be enhanced by the integration of a dry port in the supply chains. In particular, the enhancement of the supply chain outcomes can be achieved due to intermodality and reliability of rail transportation and customization of services associated with the dry port integrated setup, and by increasing the capacity of transportation system

Big Data in Laboratory Medicine—FAIR Quality for AI?

Laboratory medicine is a digital science. Every large hospital produces a wealth of data each day—from simple numerical results from, e.g., sodium measurements to highly complex output of “-omics” analyses, as well as quality control results and metadata. Processing, connecting, storing, and ordering extensive parts of these individual data requires Big Data techniques. Whereas novel technologies such as artificial intelligence and machine learning have exciting application for the augmentation of laboratory medicine, the Big Data concept remains fundamental for any sophisticated data analysis in large databases. To make laboratory medicine data optimally usable for clinical and research purposes, they need to be FAIR: findable, accessible, interoperable, and reusable. This can be achieved, for example, by automated recording, connection of devices, efficient ETL (Extract, Transform, Load) processes, careful data governance, and modern data security solutions. Enriched with clinical data, laboratory medicine data allow a gain in pathophysiological insights, can improve patient care, or can be used to develop reference intervals for diagnostic purposes. Nevertheless, Big Data in laboratory medicine do not come without challenges: the growing number of analyses and data derived from them is a demanding task to be taken care of. Laboratory medicine experts are and will be needed to drive this development, take an active role in the ongoing digitalization, and provide guidance for their clinical colleagues engaging with the laboratory data in research

Practices that organizations employ to enhance business intelligence agility

In today's rapidly changing business environment, organizations strive to be agile in order to accommodate changes and seize opportunities. Since organizations use information system as a tool to serve their needs, it is important for these systems also to be agile. One prominent type of such systems is business intelligence, which provides organizations with information to gain and retain competitive advantage. This thesis focuses on business intelligence agility, which is widely discussed in practice however not extensively covered in information systems literature. Therefore, this thesis seeks to identify the practices employed by organizations to enhance business intelligence agility. To find the answer to the research question this thesis first compiles a theoretical framework on business intelligence, information systems agility in general and business intelligence agility in specific using academic literature and market white papers. This compiled framework is comprised of four enabling factors 1) sensing business changes, 2) development approach, 3) IT governance, and 4) technical factors. This thesis conducts a qualitative research based on semi-structured interviews with business intelligence experts. Based on analysis of the empirical data this thesis identified a set of practices organized in terms of the enabling factors. The practices in sensing business changes are enabling business staff to sense changes and incorporating business staff feedback into data requirements. Regarding development approach, this thesis identifies the practices as applying an iterative development approach, building collaborative team of skilled members, enabling a centric role of business staff, reducing use of approval documents and learning from each project. In IT governance, applying a centralized or decentralized development were the two practices. Regarding practices in technical factors, this thesis identifies integrating data through either building an enterprise-wide data warehouse or applying an appropriate modeling approach while managing multiple data warehouses, using multiple front-end applications, and adopting cloud business intelligence. The findings of this thesis provide organizations with a pool of practices that can be used to enhance business intelligence agility

Implications and challenges to using data mining in educational research in the Canadian context

Canadian institutions of higher education are major players on the international arena for educating future generations and producing leaders around the world in various fields. In the last decade, Canadian universities have seen an influx in their incoming international students, who contribute over 3.5 billion to the Canadian economy (Madgett & Bélanger 2008, p. 195). Research in Canadian post-secondary institutions is booming, especially in education (SSHRC, 2011)—for the academic year 2010-2011, of the 12 subject areas, the total SSHRC funding for projects in education, ranked fourth, exceeding 27 million. All of these variables place Canadian higher education in a leading and strategic position in several educational research fields. One can imagine the wealth of knowledge about trends in higher education that could be revealed if the large amount of data generated by Canadian universities were systematically analyzed and handled using techniques such as data mining. However, not much can be achieved from the unharnessed knowledge accumulated on a daily basis, as the advancement of data mining research that would provide the ultimate tool to learn about trends and changes in Canadian institutions is often held back by inadequate data warehousing, as well as by privacy, confidentiality, and copyright regulations. In this paper, we engage in a critical discussion/analysis of the interface between data mining research in higher education and the legal implications of such a tool.Les établissements canadiens d'enseignement supérieur jouent un rôle majeur sur la scène internationale dans l'éducation des générations futures et dans la formation de leaders dans divers domaines à travers le monde. Au cours de la dernière décennie, les universités canadiennes ont connu un afflux d'étudiants internationaux, qui contribuent plus de 3,5 milliards de dollars à l'économie canadienne (Bélanger & Madgett 2008, p. 195). La recherche dans les institutions canadiennes d'enseignement postsecondaire est en plein essor, en particulier en matière d'éducation (CRSH, 2011) - pour l'année académique 2010-2011, parmi les 12 domaines de recherche, le financement total du CRSH pour les projets portant sur l'éducation, au quatrième rang, s'élevait à plus de 27 millions de dollars. Toutes ces variables placent l'enseignement supérieur canadien dans une position stratégique et de premier plan dans plusieurs domaines de recherche en éducation. On peut imaginer la richesse des informations sur les tendances dans l'enseignement supérieur qui pourrait être révélée si la masse de données générée par les universités canadiennes était systématiquement analysée et traitée en utilisant des techniques telle que l'exploration de données. Cependant, on ne peut guère obtenir grand chose à partir des informations accumulées sur une base quotidienne, étant donné que l'avancement de la recherche à exploration de données, qui serait l'outil ultime pour en apprendre davantage sur les tendances et les changements dans les institutions canadiennes, est souvent freinée par un entreposage de données insuffisant, ainsi que par les règlementations sur la protection des renseignements personnels, la confidentialité et le droit d'auteur. Dans cet article, nous engageons une discussion et une analyse critiques de l'interface entre la recherche à exploration de données dans l'enseignement supérieur et les implications juridiques d'un tel outil

Integration of Distributed Services and Hybrid Models Based on Process Choreography to Predict and Detect Type 2 Diabetes

[EN] Life expectancy is increasing and, so, the years that patients have to live with chronic diseases and co-morbidities. Type 2 diabetes is one of the most prevalent chronic diseases, specifically linked to being overweight and ages over sixty. Recent studies have demonstrated the effectiveness of new strategies to delay and even prevent the onset of type 2 diabetes by a combination of active and healthy lifestyle on cohorts of mid to high risk subjects. Prospective research has been driven on large groups of the population to build risk scores that aim to obtain a rule for the classification of patients according to the odds for developing the disease. Currently, there are more than two hundred models and risk scores for doing this, but a few have been properly evaluated in external groups and integrated into a clinical application for decision support. In this paper, we present a novel system architecture based on service choreography and hybrid modeling, which enables a distributed integration of clinical databases, statistical and mathematical engines and web interfaces to be deployed in a clinical setting. The system was assessed during an eight-week continuous period with eight endocrinologists of a hospital who evaluated up to 8080 patients with seven different type 2 diabetes risk models implemented in two mathematical engines. Throughput was assessed as a matter of technical key performance indicators, confirming the reliability and efficiency of the proposed architecture to integrate hybrid artificial intelligence tools into daily clinical routine to identify high risk subjects.The authors wish to acknowledge the consortium of the MOSAIC project (funded by the European Commission, Grant No. FP7-ICT 600914) for their commitment during concept development, which led to the development of the research reported in this manuscriptMartinez-Millana, A.; Bayo-Monton, JL.; Argente-Pla, M.; Fernández Llatas, C.; Merino-Torres, JF.; Traver Salcedo, V. (2018). Integration of Distributed Services and Hybrid Models Based on Process Choreography to Predict and Detect Type 2 Diabetes. Sensors. 18 (1)(79):1-26. https://doi.org/10.3390/s18010079S12618 (1)79Thomas, C. C., & Philipson, L. H. (2015). Update on Diabetes Classification. Medical Clinics of North America, 99(1), 1-16. doi:10.1016/j.mcna.2014.08.015Kahn, S. 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Biomedical data integration in computational drug design and bioinformatics

[Abstract In recent years, in the post genomic era, more and more data is being generated by biological high throughput technologies, such as proteomics and transcriptomics. This omics data can be very useful, but the real challenge is to analyze all this data, as a whole, after integrating it. Biomedical data integration enables making queries to different, heterogeneous and distributed biomedical data sources. Data integration solutions can be very useful not only in the context of drug design, but also in biomedical information retrieval, clinical diagnosis, system biology, etc. In this review, we analyze the most common approaches to biomedical data integration, such as federated databases, data warehousing, multi-agent systems and semantic technology, as well as the solutions developed using these approaches in the past few years.Red Gallega de Investigación sobre Cáncer Colorrectal; Ref. 2009/58Programa Iberoamericano de Ciencia y Tecnología para el Desarrollo; 209RT- 0366Instituto de Salud Carlos III; PIO52048Instituto de Salud Carlos III; RD07/0067/0005Ministerio de Industria, Turismo y Comercio; TSI-020110-2009-