58 research outputs found
BEAR: Benchmarking the Efficiency of RDF Archiving
There is an emerging demand on techniques addressing the
problem of efficiently archiving and (temporal) querying different versions of evolving semantic Web data. While systems archiving and/or temporal querying are still in their early days, we consider this a good time to discuss benchmarks for evaluating storage space efficiency for
archives, retrieval functionality they serve, and the performance of various retrieval operations. To this end, we provide a blueprint on benchmarking archives of semantic data by defining a concise set of operators that cover the major aspects of querying of and interacting with such
archives. Next, we introduce BEAR, which instantiates this blueprint to serve a concrete set of queries on the basis of real-world evolving data. Finally, we perform an empirical evaluation of current archiving techniques that is meant to serve as a first baseline of future developments
on querying archives of evolving RDF data. (authors' abstract)Series: Working Papers on Information Systems, Information Business and Operation
Iterative Learning of Relation Patterns for Market Analysis with UIMA
Blohm S, Umbrich J, Cimiano P, Sure Y. Iterative Learning of Relation Patterns for Market Analysis with UIMA. In: UIMA Workshop at GLDV Frühjahrstagung. 2007
Evaluating Query and Storage Strategies for RDF Archives
There is an emerging demand on efficiently archiving and (temporal) querying different versions of evolving semantic Web data. As novel archiving systems are starting to address this challenge, foundations/standards for benchmarking RDF archives are needed to evaluate its storage space efficiency and the performance of different retrieval operations. To this end, we provide theoretical foundations on the design of data and queries to evaluate emerging RDF archiving systems. Then, we instantiate these foundations along a concrete set of queries on the basis of a real-world evolving dataset. Finally, we perform an empirical evaluation of various current archiving techniques and querying strategies on this data that is meant to serve as a baseline of future developments on querying archives of evolving RDF data
PLoS One
ObjectivesTo identify the reasons patients miss taking their antiretroviral therapy (ART) and the proportion who miss their ART because of symptoms; and to explore the association between symptoms and incomplete adherence.MethodsSecondary analysis of data collected during a cross-sectional study that examined ART adherence among adults from 18 purposefully selected sites in Tanzania, Uganda, and Zambia. We interviewed 250 systematically selected patients per facility ( 6518 years) on reasons for missing ART and symptoms they had experienced (using the HIV Symptom Index). We abstracted clinical data from the patients\u2019 medical, pharmacy, and laboratory records. Incomplete adherence was defined as having missed ART for at least 48 consecutive hours during the past 3 months.ResultsTwenty-nine percent of participants reported at least one reason for having ever missed ART (1278/4425). The most frequent reason was simply forgetting (681/1278 or 53%), followed by ART-related hunger or not having enough food (30%), and symptoms (12%). The median number of symptoms reported by participants was 4 (IQR: 2\u20137). Every additional symptom increased the odds of incomplete adherence by 12% (OR: 1.1, 95% CI: 1.1\u20131.2). Female participants and participants initiated on a regimen containing stavudine were more likely to report greater numbers of symptoms.ConclusionsSymptoms were a common reason for missing ART, together with simply forgetting and food insecurity. A combination of ART regimens with fewer side effects, use of mobile phone text message reminders, and integration of food supplementation and livelihood programmes into HIV programmes, have the potential to decrease missed ART and hence to improve adherence and the outcomes of ART programmes.2016PEPFAR/United States26788919PMC4720476703
A Hybrid Framework for Querying Linked Data Dynamically
As of today, the Web has evolved to become the largest collection of information
made available by mankind. Researchers and developers are continuously working
on transforming this loosely connected data collection into a giant knowledge
base. As part of this trend, the Semantic Web community has started a movement
to transform the Web of unstructured text into the so called \u27Web of Data\u27-a
framework to create, share and reuse data by humans and machines alike across
application, enterprise, and community boundaries. From this movement, Linked
Data has emerged as a set of best practices to publish, connect and discover structured
data on the Web using standard formats. As of today, there are over thirty
billion public facts which can be accessed, reused and combined by individuals as
well as organisations and companies.
As the Web of Data continues to expand and diversify, it becomes more and
more dynamic with data being constantly generated, removed and updated, e.g.,
from sensor/stream sources. New querying techniques are required to eXciently
keep up with this trend. While traditional approaches facilitate fast query times
by replicating Web data in optimised oYine index structures , they cannot deal
eXciently with dynamic data and cannot guarantee up-to-date results. A new generation
of distributed Linked Data query engines address this problem and deliver
up-to-date results by retrieving query relevant data immediately before or during
query execution. However fetching data at runtime from potentially hundreds or
thousands of relevant Web sources is slow compared to optimised index lookups.
This thesis studies and improves distributed query approaches for Linked Data
and develops a hybrid query framework that oUers fresh and fast query results by
combining centralised and distributed query techniques with a novel query planning
approach based on knowledge about the dynamicity of data.
We start by identifying the diUerent levels of dynamicity within Linked Data
and highlight the challenges for centralised query approaches to deliver up-to-date
results if operating over such dynamic data.We then present a study of link traversal
based query execution approaches for Linked Data and show how the query
performance can be improved by providing reasoning extensions.We have also developed
an approximate index structure that summarises the graph-structured content
of Web sources, and provide an algorithm that exploits this source summary
index. Finally, we propose and evaluate a novel hybrid query engine framework
that combines the execution strength of materialised query approaches with the
live results from distributed query approaches. The query planning phase uses a
cost-model that combines standard selectivity and novel dynamicity estimates to
enable fast and fresh results
Web of Data Plumbing - Lowering the Barriers to Entry
Publishing and consuming content on the Web of Data often requires considerable expertise in the underlying technologies, as the expected services to achieve this are either not packaged in a simple and accessible manner, or are simply lacking. In this poster, we address selected issues by briefly introducing the following essential Web of Data services designed to lower the entry-barrier for Web developers: (i) a multi-ping service, (ii) a meta search service, and (iii) a universal discovery service
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