Fuzzy Web Data Tables Integration Guided by an Ontological and Terminological Resource

Ontology based Data Integration (ONDINE) is an approach to integrate web data from heterogeneous resources with the help of ontologies. The ONDINE system is enhanced for querying the data warehouse opened in the web and updating the data. Using the ontological and terminological resources (OTR) we can load and query the data from the data warehouse. Especially, the system extracts the tables from multiple web pages and annotates the symbols and numeric constants in separated columns. The output in XML/RDF formats represents the fuzzy annotation of the integrated data tables. Using SPARQL, the fuzzy sets from the fuzzy RDF annotations are retrieved. In the proposed SEFOS system, fuzzy DL is used for querying. Thus the time to finish the task would be reduced and a decent precision rate is obtained on using fuzzy DL.

Documents are rich resources containing knowledge describing a specific domain. That's why their processing is a common task, which is based on the use of terminological and ontological resources. Various types of ontologies, thesauri, and a large list of resources are commonly used in the process of knowledge extraction. The modeling and reuse of these resources is intended to support knowledge management. In this paper, we propose a methodology and a model for ontological and terminological resource management. Our aim is to build a resources repository that offers operations for loading, storing, indexing, translating, generating and matching different resources. In this contribution we propose an ontology as a model of these resources and we explain how can we represent, annotate and load new resources into our repository.

This paper presents a flexible querying system of fuzzy RDF annotations which consists in translating fuzzy RDF annotations into fuzzy conceptual graphs and using an “approximate”-projection operation in order to compare fuzzy query graphs with fuzzy annotation graphs. The fuzzy sets in the query graphs having a semantic of preferences are compared with the fuzzy sets in the annotation graphs having a semantic of similarity or imprecision. These comparisons deliver several scores which are used by our flexible querying system to sort the answers according to a total order even if these scores are not commensurable.

Data that needs to be documented for clinical studies has often been acquired and documented in clinical routine. Usually this data is manually transferred to Case Report Forms (CRF) and/or directly into an electronic data capture (EDC) system. To enhance the documentation process of a large clinical follow-up study targeting patients admitted for acutely decompensated heart failure by accessing the data created during routine and study visits from a hospital information system (HIS) and by transferring it via a data warehouse (DWH) into the study's EDC system. This project is based on the clinical DWH developed at the University of Würzburg. The DWH was extended by several new data domains including data created by the study team itself. An R user interface was developed for the DWH that allows to access its source data in all its detail, to transform data as comprehensively as possible by R into study-specific variables and to support the creation of data and catalog tables. A data flow was established that starts with labeling patients as study patients within the HIS and proceeds with updating the DWH with this label and further data domains at a daily rate. Several study-specific variables were defined using the implemented R user interface of the DWH. This system was then used to export these variables as data tables ready for import into our EDC system. The data tables were then used to initialize the first 296 patients within the EDC system by pseudonym, visit and data values. Afterwards, these records were filled with clinical data on heart failure, vital parameters and time spent on selected wards. This solution focuses on the comprehensive access and transformation of data for a DWH-EDC system linkage. Using this system in a large clinical study has demonstrated the feasibility of this approach for a study with a complex visit schedule.

There are ambiguities and vagueness in solar radiation records during a day. As a consequence, the development and adaptation of automatic knowledge acquisition techniques under uncertainty is entirely advisable. Among them fuzzy sets theory formulizes and analyses the situations in which the uncertainty is due to non-precise environment. Any good decision is based on information. Decision makers should reduce their uncertainty by obtaining as much reliable and consistent information as possible. In this paper, we introduce a novel decision support system for handling uncertain climate data consolidated with SAS application system. This novel approach is based on the membership function and multidimensional analysis. From our findings, we conclude that data warehousing and data mining are essentials for an effective decision support system. Because organizations frequently process uncertain information, decision makers should reduce their uncertainty by obtaining as much reliable and consistent information as possible. Due to the potential benefits of a data warehouse as well as internal and external pressure for creating a competitive advantage, many organizations have launched data warehouse projects with the expectations of acquiring a consistent and reliable source of data for their DSS. Therefore, a DSS with data warehouse should improve the performance of users by improving information accessibility which positively affects the quality of decision making [1]. At present there is a great need to provide decision makers from middle management upward with information at the correct level of detail to support decision making. Data warehousing, on-line analytical processing (OLAP), and data mining provide this functionality [2]. Although there are researchers in fields of certainty, such as the natural science fields of physics, mathematics, biology, and in the social science fields of philosophy, economy, society, psychology, cognition, almost nobody places suspicion on the uncertain essence of the world. An increasing number of scientists believe that uncertainty is the charm of the world and only uncertainty itself is certain! For a long time, humans thought uncertainty was equal to randomness. Probability theory is the main mathematical tool to solve the problem of randomness. With more in-depth studies, people found a kind of uncertainties that could not be described with randomness. That was “fuzziness”. Fuzziness is a characteristic feature of modern science to describe quantitative relationships and space formation by using precise definitions and rigidly proven theorems, and to explore the laws of the objective world by using precisely controlled experimental methods, accurate measurements, and calculation so as to establish a rigorous theoretic system [3]. There are ambiguities and vagueness in solar radiation records during a day. There is a desired need for providing a simple technique whereby uncertainties in the process of solar radiation measurements being handled. In this paper, we introduce a novel decision support system for handling uncertain climate data consolidated with SAS application system. This novel approach is based on the membership function and multidimensional analysis. The rest of the paper is organized as follows. The next section reviews related work. In Section 3 we will describe the characteristics of decision support systems. In Section 4 we demonstrate the basic data warehousing concepts. Section 5 provides the definition of a general framework for fuzzy sets in the presence of uncertainty. Section 6 introduces climate case study. In Section 7 we present our approach for solving this problem. Section 8 presents the results analysis. Section 9 shows International Journal for Information Security Research (IJISR), Volume 1, Issue 2, June 2011 Copyright © 2011, Infonomics Society 35

Background: A key strength of population sciences and epidemiology is the breadth and depth of the data these studies, especially prospective cohorts, collect over time. However, publications from and websites of these studies tend to present collapsed or aggregate data in traditional tables, often underestimating the true complexity of information in those data. One goal of the NIH Strategic Plan for Data Science is to “support the development and dissemination of advanced data management, analytics, and visualization tools.” We recently developed a robust, innovative, and scalable data visualization environment for the California Teachers Study (CTS), a prospective cancer epidemiology cohort. Methods: We identified the need to eliminate the practice of storing CTS data in a collection of disparate data silos and constructed a data warehouse to compile two decades of study data, eliminating additional harmonization steps. We then developed a data commons, a remotely accessible environment where cohort data could be utilized by our researchers and study collaborators. This allowed questionnaire-based data, cancer registry information, and hospitalization and mortality data from our study participants to be easily accessed in a secure environment. Serving as a single unified source of information, the data warehouse, coupled with the industry-leading data visualization tool Tableau, was configured using the following key epidemiologic cohort activities: reports, cohort selection, calculated values, and exploring and transforming data variables for analysis. Results: With the ability to sort and filter on specific covariates we can define subsets of populations for cohort selection and endpoint-specific analysis. Using interactive reports with drags and drops, and creating calculated variables within Tableau, gives us the ability to visualize time between event data, such as time from study entry to a cancer diagnosis, multiple cancer diagnoses, hospitalizations, or mortality without requiring programmatic back-end coding. Conclusion: The creation of a data warehouse and data commons has given the CTS the ability to visualize information about the demographics and health-related patterns of our study participants, leading to faster answers to complex questions about our data. The interactive data visualizations that we have created allow users the ability to identify new patterns easily, and drill down into the data for a more detailed look. Defining analytic populations through cohort selection and adding geospatial data to our data warehouse has already resulted in the discovery of new patterns that had been unidentified before—simply by providing the same information but utilizing a more accessible and innovative approach to presenting it. As epidemiology uses and shares more of its data, it’s essential to help the widest possible community understand those data in detail. Data visualization can provide specific benefits to key, essential population sciences research activities. Citation Format: Nadia T. Chung, James V. Lacey Jr., Emma Spielfogel, Paul Hughes, Sandeep Chandra, Elena Martinez, Jennifer Benbow. Opportunities for and benefits of incorporating data visualization in population sciences [abstract]. In: Proceedings of the AACR Special Conference on Modernizing Population Sciences in the Digital Age; 2019 Feb 19-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(9 Suppl):Abstract nr A01.

With the increasing personalization of clinical therapies, translational research is evermore dependent on multisite research cooperations to obtain sufficient data and biomaterial. Distributed research networks rely on the availability of high-quality data stored in local databases operated by their member institutions. However, reusing data documented by independent health providers for the purpose of care, rather than research ("secondary use"), reveal a high variability in terms of data formats, as well as poor data quality, across network sites. The aim of this work is the provision of a process for the assessment of data quality with regard to completeness and syntactic accuracy across independently operated data warehouses using common definitions stored in a central (network-wide) metadata repository (MDR). For assessment of data quality across multiple sites, we employ a framework of so-called bridgeheads. These are federated data warehouses, which allow the sites to participate in a research network. A central MDR is used to store the definitions of the commonly agreed data elements and their permissible values. We present the design for a generator of quality reports within a bridgehead, allowing the validation of data in the local data warehouse against a research network's central MDR. A standardized quality report can be produced at each network site, providing a means to compare data quality across sites, as well as to channel feedback to the local data source systems, and local documentation personnel. A reference implementation for this concept has been successfully utilized at 10 sites across the German Cancer Consortium. We have shown that comparable data quality assessment across different partners of a distributed research network is feasible when a central metadata repository is combined with locally installed assessment processes. To achieve this, we designed a quality report and the process for generating such a report. The final step was the implementation in a German research network.

A new method to extract n-Ary relation instances from scientific documents

Objective: Biomedical ontologies exist to serve integration of clinical and experimental data, and it is critical to their success that they be put to widespread use in the annotation of data. How, then, can ontologies achieve the sort of user- friendliness, reliability, cost-effectiveness, and breadth of coverage that is necessary to ensure extensive usage? Methods: Our focus here is on two different sets of answers to these questions that have been proposed, on the one hand in medicine, by the SNOMED CT community, and on the other hand in biology, by the OBO Foundry. We address more specifically the issue as to how adherence to certain development principles can advance the usability and effectiveness of an ontology or terminology resource, for example by allowing more accurate maintenance, more reliable application, and more efficient interoperation with other ontologies and information resources. Results: SNOMED CT and the OBO Foundry differ considerably in their general approach. Nevertheless, a general trend towards more formal rigor and cross-domain interoperability can be seen in both and we argue that this trend should be accepted by all similar initiatives in the future. Conclusions: Future efforts in ontology development have t o address t he need f or harmonization and integration of ontologies across disciplinary borders, and for this, coherent formalization of ontologies is a pre-requisite.

Column scan, or predicate evaluation and filtering over a column of data in a database table, is an important primitive for data mining and data warehousing. In this paper, we present our study on accelerating column scan using a massively parallel accelerator. With a design that takes full advantage of the characteristics of the memory hierarchy and parallel execution in such processors, we have achieved very attractive speedup performance that significantly exceeds previously reported results, making the use of such an accelerator for this type of primitives much more viable. Running on a general purpose graphic processor unit (GPGPU), NVidia GTX 280 GPU, the GPU version is about 5-6 times faster than an implementation on an eight-core CPU, or over 40 times faster than that on a single-core CPU.

This research contributes to the body of literature on areal interpolation and dasymetric mapping by introducing algorithms that make use of local ancillary data sources. The address weighting (AW) and parcel distribution (PD) methods are based on county address points and parcels. The algorithms can be effectively applied in rural and transitional areas where geographies are large and population counts are low. These new methods were compared to existing algorithms that use nationally available land cover and street network datasets. Compared with existing methods, the new methods yielded significant improvement in reducing estimate error for the study areas. Both new methods succeeded in maintaining high accuracy in both urban and rural areas. The research presents opportunities for increasing the accuracy of both areal interpolation and dasymetric mapping in areas where accurate local data are available.

EnvDB is a database that classifies the environmental samples and their associated 16S rDNA sequences currently stored in GenBank. The samples were categorized in a three-level, hierarchical classification of media: the five upper levels (terrestrial, aquatic, thermal, host-associated and other) are further decomposed in 20 intermediate (such as marine, marine sediments, freshwater, soil, gut, etc.) and 47 lower levels (for instance, soil is further decomposed in forest, agricultural, wetlands, grasslands, tropical, arid, etc.). Each sample was also characterized with nine environmental features: polluted, diseased (for clinical samples), acidic, alkaline, hot environment, cold environment, saline, anoxic and restricted (when the study is focused only in particular taxonomic groups). The classification of samples was aided by text-mining techniques, complemented with careful curation and completion by human experts. EnvDB currently includes 359 928 sequences from 3502 samples. The sequences were clustered at several identity levels to obtain operative taxonomic units (OTUs). Sequences and OTUs have been taxonomically assigned to the maximum possible resolution by different procedures. The user can obtain information about sequences, OTUs, samples and environments, combining these tables using a flexible querying system that allows generating very diverse queries. Thus, the user can easily inspect the presence and abundance of particular taxa in particular samples and environments. The database also allows the users to run analyses with their own data: users can input their sequences and find the closest sequences or samples in the database. EnvDB can be accessed in the web address http://metagenomics.uv.es/envDB.

The Small&Medium steel enterprises is relatively weak on information technology, and the information management personnel lack the urgent need for information integration. According to the actual needs of the Small&Medium steel enterprises information management, three-tier architecture of information integration systems is proposed in this paper. By using SQL Server to implement database and data warehouse, data mining models is built and the design of information integration system is completed. The system covers the steel purchase, inventory, sale, distribution, contracts, finance, statistical analysis, decision support and other management functions. The proposed system can meet the Small&Medium steel enterprises scientific and orderly management for information resources, and the needs of managers forecasting, decision-making. Data warehouse and data mining applications can give an excellent support to the implementation of business intelligence in the future.

Aims: To estimate the size of the drug-using population in Lambeth, an inner city London borough, and the prevalence of blood-borne virus (BBV) infections in the drug-using population. To identify possible shortfalls in service provision for drug users.Methods: Direct and indirect estimates of the drug-using population size using national and local data sources. Prevalence estimates for BBV infections were derived from local survey data. Routine data and a survey of borough services for drug users were compiled.Findings: Based on indirect methods we estimated there were 3117 drug users in Lambeth in 2001–2002 (20 per 1000, 15- to 44-year-olds), of whom 1641 were injecting users (11 per 1000); 1353 were reported as being in drug treatment programmes. BBV prevalence rates: hepatitis B, 37%; hepatitis C, 71%; HIV, 6%. Estimated demand for needle exchange services, in terms of one syringe per injection, and methadone maintenance may be up to double local provision.Conclusions: More accurate and feasible methods are needed for the routine estimation of the population of drug users. Accurate information is needed for service planning.

Shifts in the physical environment in the Pacific Arctic and implications for ecological timing and conditions