Organic Databases

Jagadish, H. V.; Nandi, Arnab; Li, Qian

doi:10.1007/978-3-642-25731-5_5

Cited by 9 publications

(6 citation statements)

References 61 publications

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“…Collecting all these information for large number of bacteria types and strains in various experimental conditions and storing them in large database systems would be of prime importance to the development of biotechnology (Li et al, 2015;Jagadish et al, 2015;Madaan and Chu, 2015).…”

Section: Discussionmentioning

confidence: 99%

Radio-frequency and microwave spectroscopy investigation of bacteria solutions: determination of the aggregation threshold

Merabet

2019

IJES

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This paper presents novel approaches to assess and characterise the biomass concentration of bacteria in a broth culture medium. It is based on the use of the colloid inductive permittivity probe, which is not affected by the electrode polarisation, and on the analysis of the distribution of relaxation processes. Specifically, the frequency-dependent dielectric responses of Lactobacilli in DMEM are investigated in the [75 kHZ-30 MHz] and in the [130 MHz to 20 GHz] ranges. Moreover, due to the direct measurement over wide concentration ranges, the concentration threshold of the bacteria aggregation can be precisely identified. As a matter of fact, the behaviour of the amplitude of the medium-frequency relaxation process shows two distinct linear evolutions with an intersection corresponding to the aggregation threshold. The dielectric method can be easily adapted to the online monitoring of the growth of biological cells and to the control of the fermentation processes.

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Section: Discussionmentioning

confidence: 99%

Radio-frequency and microwave spectroscopy investigation of bacteria solutions: determination of the aggregation threshold

Merabet

2019

IJES

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“…Furthermore, there has been a significant push towards flexible database architectures where we can tune the architecture to the task at hand, for example by having a declarative interface for the data layouts [17] or for the whole engine [42,43]. In another vision, organic databases are proposed to continuously match incoming data and queries [34].…”

Section: Database Layermentioning

confidence: 99%

Overview of Data Exploration Techniques

Idreos

Papaemmanouil

Chaudhuri

2015

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data

201

103

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Data exploration is about efficiently extracting knowledge from data even if we do not know exactly what we are looking for. In this tutorial, we survey recent developments in the emerging area of database systems tailored for data exploration. We discuss new ideas on how to store and access data as well as new ideas on how to interact with a data system to enable users and applications to quickly figure out which data parts are of interest. In addition, we discuss how to exploit lessons-learned from past research, the new challenges data exploration crafts, emerging applications and future research directions.

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“…Most experts use structured interfaces such as forms [ 213 ] or free-text notes [ 214 ] for data entry or querying and spreadsheet interfaces for data exploration [ 104 ]. Following user-centered interface design and adhering to latency constraints is even more essential for these systems.…”

Section: Taxonomy Of Expertise Amplificationmentioning

confidence: 99%

Amplifying Domain Expertise in Clinical Data Pipelines

2020

Self Cite

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Digitization of health records has allowed the health care domain to adopt data-driven algorithms for decision support. There are multiple people involved in this process: a data engineer who processes and restructures the data, a data scientist who develops statistical models, and a domain expert who informs the design of the data pipeline and consumes its results for decision support. Although there are multiple data interaction tools for data scientists, few exist to allow domain experts to interact with data meaningfully. Designing systems for domain experts requires careful thought because they have different needs and characteristics from other end users. There should be an increased emphasis on the system to optimize the experts’ interaction by directing them to high-impact data tasks and reducing the total task completion time. We refer to this optimization as amplifying domain expertise. Although there is active research in making machine learning models more explainable and usable, it focuses on the final outputs of the model. However, in the clinical domain, expert involvement is needed at every pipeline step: curation, cleaning, and analysis. To this end, we review literature from the database, human-computer information, and visualization communities to demonstrate the challenges and solutions at each of the data pipeline stages. Next, we present a taxonomy of expertise amplification, which can be applied when building systems for domain experts. This includes summarization, guidance, interaction, and acceleration. Finally, we demonstrate the use of our taxonomy with a case study.

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Organic Databases

Cited by 9 publications

References 61 publications

Radio-frequency and microwave spectroscopy investigation of bacteria solutions: determination of the aggregation threshold

Radio-frequency and microwave spectroscopy investigation of bacteria solutions: determination of the aggregation threshold

Overview of Data Exploration Techniques

Amplifying Domain Expertise in Clinical Data Pipelines

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