6,503 research outputs found
Building Data Warehouses with Semantic Web Data
The Semantic Web (SW) deployment is now a realization and the amount of
semantic annotations is ever increasing thanks to several initiatives that promote
a change in the current Web towards the Web of Data, where the semantics of
data become explicit through data representation formats and standards such as
RDF/(S) and OWL. However, such initiatives have not yet been accompanied
by e cient intelligent applications that can exploit the implicit semantics and
thus, provide more insightful analysis. In this paper, we provide the means for
e ciently analyzing and exploring large amounts of semantic data by combining
the inference power from the annotation semantics with the analysis capabilities
provided by OLAP-style aggregations, navigation, and reporting. We formally
present how semantic data should be organized in a well-de ned conceptual
MD schema, so that sophisticated queries can be expressed and evaluated. Our
proposal has been evaluated over a real biomedical scenario, which demonstrates
the scalability and applicability of the proposed approach
Proximal business intelligence on the semantic web
This is the post-print version of this article. The official version can be accessed from the link below - Copyright @ 2010 Springer.Ubiquitous information systems (UBIS) extend current Information System thinking to explicitly differentiate technology between devices and software components with relation to people and process. Adapting business data and management information to support specific user actions in context is an ongoing topic of research. Approaches typically focus on providing mechanisms to
improve specific information access and transcoding but not on how the information
can be accessed in a mobile, dynamic and ad-hoc manner. Although web ontology has been used to facilitate the loading of data warehouses, less research has been carried out on ontology based mobile reporting. This paper explores how business data can be modeled and accessed using the web ontology
language and then re-used to provide the invisibility of pervasive access; uncovering
more effective architectural models for adaptive information system strategies of this type. This exploratory work is guided in part by a vision of business intelligence that is highly distributed, mobile and fluid, adapting to sensory understanding of the underlying environment in which it operates. A proof-of concept mobile and ambient data access architecture is developed in order to further test the viability of such an approach. The paper concludes with an ontology engineering framework for systems of this type – named UBIS-ONTO
Using Ontologies for the Design of Data Warehouses
Obtaining an implementation of a data warehouse is a complex task that forces
designers to acquire wide knowledge of the domain, thus requiring a high level
of expertise and becoming it a prone-to-fail task. Based on our experience, we
have detected a set of situations we have faced up with in real-world projects
in which we believe that the use of ontologies will improve several aspects of
the design of data warehouses. The aim of this article is to describe several
shortcomings of current data warehouse design approaches and discuss the
benefit of using ontologies to overcome them. This work is a starting point for
discussing the convenience of using ontologies in data warehouse design.Comment: 15 pages, 2 figure
Knowledge and Metadata Integration for Warehousing Complex Data
With the ever-growing availability of so-called complex data, especially on
the Web, decision-support systems such as data warehouses must store and
process data that are not only numerical or symbolic. Warehousing and analyzing
such data requires the joint exploitation of metadata and domain-related
knowledge, which must thereby be integrated. In this paper, we survey the types
of knowledge and metadata that are needed for managing complex data, discuss
the issue of knowledge and metadata integration, and propose a CWM-compliant
integration solution that we incorporate into an XML complex data warehousing
framework we previously designed.Comment: 6th International Conference on Information Systems Technology and
its Applications (ISTA 07), Kharkiv : Ukraine (2007
Semantic processing of EHR data for clinical research
There is a growing need to semantically process and integrate clinical data
from different sources for clinical research. This paper presents an approach
to integrate EHRs from heterogeneous resources and generate integrated data in
different data formats or semantics to support various clinical research
applications. The proposed approach builds semantic data virtualization layers
on top of data sources, which generate data in the requested semantics or
formats on demand. This approach avoids upfront dumping to and synchronizing of
the data with various representations. Data from different EHR systems are
first mapped to RDF data with source semantics, and then converted to
representations with harmonized domain semantics where domain ontologies and
terminologies are used to improve reusability. It is also possible to further
convert data to application semantics and store the converted results in
clinical research databases, e.g. i2b2, OMOP, to support different clinical
research settings. Semantic conversions between different representations are
explicitly expressed using N3 rules and executed by an N3 Reasoner (EYE), which
can also generate proofs of the conversion processes. The solution presented in
this paper has been applied to real-world applications that process large scale
EHR data.Comment: Accepted for publication in Journal of Biomedical Informatics, 2015,
preprint versio
Profiling risk factors for chronic uveitis in juvenile idiopathic arthritis: a new model for EHR-based research.
BackgroundJuvenile idiopathic arthritis is the most common rheumatic disease in children. Chronic uveitis is a common and serious comorbid condition of juvenile idiopathic arthritis, with insidious presentation and potential to cause blindness. Knowledge of clinical associations will improve risk stratification. Based on clinical observation, we hypothesized that allergic conditions are associated with chronic uveitis in juvenile idiopathic arthritis patients.MethodsThis study is a retrospective cohort study using Stanford's clinical data warehouse containing data from Lucile Packard Children's Hospital from 2000-2011 to analyze patient characteristics associated with chronic uveitis in a large juvenile idiopathic arthritis cohort. Clinical notes in patients under 16Â years of age were processed via a validated text analytics pipeline. Bivariate-associated variables were used in a multivariate logistic regression adjusted for age, gender, and race. Previously reported associations were evaluated to validate our methods. The main outcome measure was presence of terms indicating allergy or allergy medications use overrepresented in juvenile idiopathic arthritis patients with chronic uveitis. Residual text features were then used in unsupervised hierarchical clustering to compare clinical text similarity between patients with and without uveitis.ResultsPreviously reported associations with uveitis in juvenile idiopathic arthritis patients (earlier age at arthritis diagnosis, oligoarticular-onset disease, antinuclear antibody status, history of psoriasis) were reproduced in our study. Use of allergy medications and terms describing allergic conditions were independently associated with chronic uveitis. The association with allergy drugs when adjusted for known associations remained significant (OR 2.54, 95% CI 1.22-5.4).ConclusionsThis study shows the potential of using a validated text analytics pipeline on clinical data warehouses to examine practice-based evidence for evaluating hypotheses formed during patient care. Our study reproduces four known associations with uveitis development in juvenile idiopathic arthritis patients, and reports a new association between allergic conditions and chronic uveitis in juvenile idiopathic arthritis patients
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