756 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
Large Scale Visual Recommendations From Street Fashion Images
We describe a completely automated large scale visual recommendation system
for fashion. Our focus is to efficiently harness the availability of large
quantities of online fashion images and their rich meta-data. Specifically, we
propose four data driven models in the form of Complementary Nearest Neighbor
Consensus, Gaussian Mixture Models, Texture Agnostic Retrieval and Markov Chain
LDA for solving this problem. We analyze relative merits and pitfalls of these
algorithms through extensive experimentation on a large-scale data set and
baseline them against existing ideas from color science. We also illustrate key
fashion insights learned through these experiments and show how they can be
employed to design better recommendation systems. Finally, we also outline a
large-scale annotated data set of fashion images (Fashion-136K) that can be
exploited for future vision research
Contextual Media Retrieval Using Natural Language Queries
The widespread integration of cameras in hand-held and head-worn devices as
well as the ability to share content online enables a large and diverse visual
capture of the world that millions of users build up collectively every day. We
envision these images as well as associated meta information, such as GPS
coordinates and timestamps, to form a collective visual memory that can be
queried while automatically taking the ever-changing context of mobile users
into account. As a first step towards this vision, in this work we present
Xplore-M-Ego: a novel media retrieval system that allows users to query a
dynamic database of images and videos using spatio-temporal natural language
queries. We evaluate our system using a new dataset of real user queries as
well as through a usability study. One key finding is that there is a
considerable amount of inter-user variability, for example in the resolution of
spatial relations in natural language utterances. We show that our retrieval
system can cope with this variability using personalisation through an online
learning-based retrieval formulation.Comment: 8 pages, 9 figures, 1 tabl
Techniques for improving efficiency and scalability for the integration of information retrieval and databases
PhDThis thesis is on the topic of integration of Information Retrieval (IR) and Databases (DB), with
particular focuses on improving efficiency and scalability of integrated IR and DB technology
(IR+DB). The main purpose of this study is to develop efficient and scalable techniques for
supporting integrated IR and DB technology, which is a popular approach today for handling
complex queries over text and structured data.
Our specific interest in this thesis is how to efficiently handle queries over large-scale text
and structured data. The work is based on a technology that integrates probability theory and
relational algebra, where retrievals for text and data are to be expressed in probabilistic logical
programs such as probabilistic relational algebra or probabilistic Datalog. To support efficient
processing of probabilistic logical programs, we proposed three optimization techniques
that focus on aspects covered logical and physical layers, which include: scoring-driven query
optimization using scoring expression, query processing with top-k incorporated pipeline, and
indexing with relational inverted index.
Specifically, scoring expressions are proposed for expressing the scoring or probabilistic semantics
of implied scoring functions of PRA expressions, so that efficient query execution plan
can be generated by rule-based scoring-driven optimizer. Secondly, to balance efficiency and
effectiveness so that to improve query response time, we studied methods for incorporating topk
algorithms into pipelined query execution engine for IR+DB systems. Thirdly, the proposed
relational inverted index integrates IR-style inverted index and DB-style tuple-based index, which
can be used to support efficient probability estimation and aggregation as well as conventional
relational operations.
Experiments were carried out to investigate the performances of proposed techniques. Experimental
results showed that the efficiency and scalability of an IR+DB prototype have been
improved, while the system can handle queries efficiently on considerable large data sets for a
number of IR tasks
An Extensible "SCHEMA-LESS" Database Framework for Managing High-Throughput Semi-Structured Documents
Object-Relational database management system is an integrated hybrid cooperative approach to combine the best practices of both the relational model utilizing SQL queries and the object-oriented, semantic paradigm for supporting complex data creation. In this paper, a highly scalable, information on demand database framework, called NETMARK, is introduced. NETMARK takes advantages of the Oracle 8i object-relational database using physical addresses data types for very efficient keyword search of records spanning across both context and content. NETMARK was originally developed in early 2000 as a research and development prototype to solve the vast amounts of unstructured and semistructured documents existing within NASA enterprises. Today, NETMARK is a flexible, high-throughput open database framework for managing, storing, and searching unstructured or semi-structured arbitrary hierarchal models, such as XML and HTML
Query optimization and processing in Federated Database Systems
this paper, we have selected a minimal set of core operations that includes the set of relational operations, i.e. foe,ß,1,\Gamma,[g, as well as three other operators which are useful in specifying the federated views. These are 2-way Outerjoin Operator
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