scholarly journals Evaluating Database Self-Tuning Strategies in a Comon Extensible Framework

2020 ◽  
Author(s):  
Rafael De Oliveira ◽  
Sergio Lifschitz ◽  
Marcos Kalinowski ◽  
Marx Viana ◽  
Carlos Lucena ◽  
...  
Keyword(s):  
Hot Spots ◽  
Data Extraction ◽  
Automatic Tuning ◽  
Materialized Views ◽  
Database Research ◽  
Framework Approach ◽  
Self Tuning ◽  
Tuning Strategy ◽  
Automated Database ◽  
Database Tuning

Database automatic tuning tools are an essential class of database applications for database administrators (DBAs) and researchers. These selfmanagement systems involve recurring and ubiquitous tasks, such as data extraction for workload acquisition and more specific features that depend on the tuning strategy, such as the specification of tuning action types and heuristics. Given the variety of approaches and implementations, it would be desirable to evaluate existing database self-tuning strategies, particularly recent and new heuristics, in a standard testbed. In this paper, we propose a reuseoriented framework approach towards assessing and comparing automatic relational database tuning strategies. We employ our framework to instantiate three customized automated database tuning tools extended from our framework kernel, employing strategies using combinations of different tuning actions (indexes, partial indexes, and materialized views) for various RDBMSs. Finally, we evaluate the effectiveness of these tools using a known database benchmark. Our results show that the framework enabled instantiating useful self-tuning tools for these multiple RDBMSs with low effort by just extending well-defined framework hot-spots. Additionally, the instantiated tools provided significant improvements in execution cost of a query workload generated from benchmark query templates. Our framework is made available as an open-source and extensible testbed for the database research community, thus facilitating the further evaluation of database self-tuning strategies.

scholarly journals Systematic Analysis of Metallo-β-Lactamases Using an Automated Database

2012 ◽  
Vol 56 (7) ◽  
pp. 3481-3491 ◽  
Author(s):  
Michael Widmann ◽  
Jürgen Pleiss ◽  
Peter Oelschlaeger
Keyword(s):  
Active Site ◽  
Hot Spots ◽  
Bacterial Resistance ◽  
Protein Sequences ◽  
Database System ◽  
Systematic Analysis ◽  
Link Type ◽  
Peptide Database ◽  
Class B ◽  
Automated Database

ABSTRACTMetallo-β-lactamases (MBLs) are enzymes that hydrolyze β-lactam antibiotics, resulting in bacterial resistance to these drugs. These proteins have caused concerns due to their facile transference, broad substrate spectra, and the absence of clinically useful inhibitors. To facilitate the classification, nomenclature, and analysis of MBLs, an automated database system was developed, the Metallo-β-Lactamase Engineering Database (MBLED) (http://www.mbled.uni-stuttgart.de). It contains information on MBLs retrieved from the NCBI peptide database while strictly following the nomenclature by Jacoby and Bush (http://www.lahey.org/Studies/) and the generally accepted class B β-lactamase (BBL) standard numbering scheme for MBLs. The database comprises 597 MBL protein sequences and enables systematic analyses of these sequences. A systematic analysis employing the database resulted in the generation of mutation profiles of assigned IMP- and VIM-type MBLs, the identification of five MBL protein entries from the NCBI peptide database that were inconsistent with the Jacoby and Bush nomenclature, and the identification of 15 new IMP candidates and 9 new VIM candidates. Furthermore, the database was used to identify residues with high mutation frequencies and variability (mutation hot spots) that were unexpectedly distant from the active site located in the ββ sandwich: positions 208 and 266 in the IMP family and positions 215 and 258 in the VIM family. We expect that the MBLED will be a valuable tool for systematically cataloguing and analyzing the increasing number of MBLs being reported.

A Systematic Review of Coding Systems Used in Pharmacoepidemiology and Database Research

2018 ◽  
Vol 57 (01/02) ◽  
pp. 01-42 ◽  
Author(s):  
Yong Chen ◽  
Marko Zivkovic ◽  
Su Su ◽  
Jianyi Lee ◽  
Edward Bortnichak ◽  
...  
Keyword(s):  
Real World ◽  
Clinical Setting ◽  
English Language ◽  
Data Extraction ◽  
Therapeutic Area ◽  
Coding System ◽  
Clinical Coding ◽  
Real World Data ◽  
Coding Systems ◽  
Database Research

Summary Background: Clinical coding systems have been developed to translate real-world healthcare information such as prescriptions, diagnoses and procedures into standardized codes appropriate for use in large healthcare datasets. Due to the lack of information on coding system characteristics and insufficient uniformity in coding practices, there is a growing need for better understanding of coding systems and their use in pharmacoepidemiology and observational real world data research. Objectives: To determine: 1) the number of available coding systems and their characteristics, 2) which pharmacoepidemiology databases are they adopted in, 3) what outcomes and exposures can be identified from each coding system, and 4) how robust they are with respect to consistency and validity in pharmacoepidemiology and observational database studies. Methods: Electronic literature database and unpublished literature searches, as well as hand searching of relevant journals were conducted to identify eligible articles discussing characteristics and applications of coding systems in use and published in the English language between 1986 and 2016. Characteristics considered included type of information captured by codes, clinical setting(s) of use, adoption by a pharmacoepidemiology database, region, and available mappings. Applications articles describing the use and validity of specific codes, code lists, or algorithms were also included. Data extraction was performed independently by two reviewers and a narrative synthesis was performed. Results: A total of 897 unique articles and 57 coding systems were identified, 17% of which included country-specific modifications or multiple versions. Procedures (55%), diagnoses (36%), drugs (38%), and site of disease (39%) were most commonly and directly captured by these coding systems. The systems were used to capture information from the following clinical settings: inpatient (63%), ambulatory (55%), emergency department (ED, 34%), and pharmacy (13%). More than half of all coding systems were used in Europe (59%) and North America (57%). 34% of the reviewed coding systems were utilized in at least 1 of the 16 pharmacoepidemiology databases of interest evaluated. 21% of coding systems had studies evaluating the validity and consistency of their use in research within pharmacoepidemiology databases of interest. The most prevalent validation method was comparison with a review of patient charts, case notes or medical records (64% of reviewed validation studies). The reported performance measures in the reviewed studies varied across a large range of values (PPV 0-100%, NPV 6-100%, sensitivity 0-100%, specificity 23-100% and accuracy 16-100%) and were dependent on many factors including coding system(s), therapeutic area, pharmacoepidemiology database, and outcome. Conclusions: Coding systems vary by type of information captured, clinical setting, and pharmacoepidemiology database and region of use. Of the 57 reviewed coding systems, few are routinely and widely applied in pharmacoepidemiology database research. Indication and outcome dependent heterogeneity in coding system performance suggest that accurate definitions and algorithms for capturing specific exposures and outcomes within large healthcare datasets should be developed on a case-by-case basis and in consultation with clinical experts.

scholarly journals Assessing The Completeness of Periodontal Disease Documentation in The EHR: A First Step in Measuring The Quality of Care

2021 ◽  
Author(s):  
Joanna Mullins ◽  
Alfa Yansane ◽  
Shwetha Kumar ◽  
Suhasini Bangar ◽  
Ana Neumann ◽  
...  
Keyword(s):  
Periodontal Disease ◽  
Disease Risk ◽  
Data Extraction ◽  
P Value ◽  
Related Information ◽  
Diabetes Status ◽  
Sensitivity Specificity ◽  
Manual Review ◽  
Automated Database

Abstract Background: Our objective was to measure the proportion of patients for which comprehensive periodontal charting, periodontal disease risk factors (diabetes status, tobacco use, and oral home care compliance), and periodontal diagnoses were documented in the electronic health record (EHR). We developed an EHR-based quality measure to assess how well four dental institutions documented periodontal disease-related information. An automated database script was developed and implemented in the EHR at each institution. The measure was validated by comparing the findings from the measure with a manual review of charts. Results: The overall measure scores varied significantly across the four institutions (site 1=20.47%, site 2=0.97%, site 3=22.27% site 4= 99.49%, p-value <0.0001). The largest gaps in documentation were related to periodontal diagnoses and capturing oral homecare compliance. A random sample of 1,224 charts were manually reviewed and showed excellent validity when compared with the data generated from the EHR-based measure (Sensitivity, Specificity, PPV, and NPV >80%). Conclusion: Our results demonstrate the feasibility of developing automated data extraction scripts using structured data from EHRs, and successfully implementing these to identify and measure the periodontal documentation completeness within and across different dental institutions.

Self-tuning strategy for a minimum variance control system of a highly disturbed process

2019 ◽  
Vol 46 ◽  
pp. 49-62 ◽  
Author(s):  
Ioan Filip ◽  
Cristian Vasar ◽  
Iosif Szeidert ◽  
Octavian Prostean
Keyword(s):  
Control System ◽  
Minimum Variance ◽  
Minimum Variance Control ◽  
Self Tuning ◽  
Tuning Strategy
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