4 research outputs found
Autonomic care platform for optimizing query performance
Background: As the amount of information in electronic health care systems increases, data operations get more complicated and time-consuming. Intensive Care platforms require a timely processing of data retrievals to guarantee the continuous display of recent data of patients. Physicians and nurses rely on this data for their decision making. Manual optimization of query executions has become difficult to handle due to the increased amount of queries across multiple sources. Hence, a more automated management is necessary to increase the performance of database queries. The autonomic computing paradigm promises an approach in which the system adapts itself and acts as self-managing entity, thereby limiting human interventions and taking actions. Despite the usage of autonomic control loops in network and software systems, this approach has not been applied so far for health information systems.
Methods: We extend the COSARA architecture, an infection surveillance and antibiotic management service platform for the Intensive Care Unit (ICU), with self-managed components to increase the performance of data retrievals. We used real-life ICU COSARA queries to analyse slow performance and measure the impact of optimizations. Each day more than 2 million COSARA queries are executed. Three control loops, which monitor the executions and take action, have been proposed: reactive, deliberative and reflective control loops. We focus on improvements of the execution time of microbiology queries directly related to the visual displays of patients' data on the bedside screens.
Results: The results show that autonomic control loops are beneficial for the optimizations in the data executions in the ICU. The application of reactive control loop results in a reduction of 8.61% of the average execution time of microbiology results. The combined application of the reactive and deliberative control loop results in an average query time reduction of 10.92% and the combination of reactive, deliberative and reflective control loops provides a reduction of 13.04%.
Conclusions: We found that by controlled reduction of queries' executions the performance for the end-user can be improved. The implementation of autonomic control loops in an existing health platform, COSARA, has a positive effect on the timely data visualization for the physician and nurse
SeaNet -- Towards A Knowledge Graph Based Autonomic Management of Software Defined Networks
Automatic network management driven by Artificial Intelligent technologies
has been heatedly discussed over decades. However, current reports mainly focus
on theoretic proposals and architecture designs, works on practical
implementations on real-life networks are yet to appear. This paper proposes
our effort toward the implementation of knowledge graph driven approach for
autonomic network management in software defined networks (SDNs), termed as
SeaNet. Driven by the ToCo ontology, SeaNet is reprogrammed based on Mininet (a
SDN emulator). It consists three core components, a knowledge graph generator,
a SPARQL engine, and a network management API. The knowledge graph generator
represents the knowledge in the telecommunication network management tasks into
formally represented ontology driven model. Expert experience and network
management rules can be formalized into knowledge graph and by automatically
inferenced by SPARQL engine, Network management API is able to packet
technology-specific details and expose technology-independent interfaces to
users. The Experiments are carried out to evaluate proposed work by comparing
with a commercial SDN controller Ryu implemented by the same language Python.
The evaluation results show that SeaNet is considerably faster in most
circumstances than Ryu and the SeaNet code is significantly more compact.
Benefit from RDF reasoning, SeaNet is able to achieve O(1) time complexity on
different scales of the knowledge graph while the traditional database can
achieve O(nlogn) at its best. With the developed network management API, SeaNet
enables researchers to develop semantic-intelligent applications on their own
SDNs