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Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
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Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
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Subscribing to big data at scale.

Xikui Wang1, Michael J Carey1, Vassilis J Tsotras2

  • 1Donald Bren School of Information and Computer Sciences, University of California Irvine, Irvine, CA 92697 USA.

Distributed and Parallel Databases
|April 12, 2022
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Summary
This summary is machine-generated.

The Big Active Data (BAD) system actively collects and serves data, unlike traditional Big Data systems. BAD provides an end-to-end solution for both active and passive data needs at scale.

Keywords:
Cloud computingData warehousesParallel and distributed DBMSsPublish-subscribe/event-based architectures

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Area of Science:

  • Computer Science
  • Data Engineering

Background:

  • Modern applications generate vast amounts of data, emphasizing not just content but also relationships and user interactions.
  • Existing Big Data systems primarily focus on passively responding to user queries.
  • Satisfying both active data collection/processing and passive querying demands significant developer effort and system customization.

Purpose of the Study:

  • Introduce the Big Active Data (BAD) system as a comprehensive, out-of-the-box solution.
  • Address the limitations of existing Big Data systems in actively serving data at scale.
  • Provide a unified platform for both passive and active data services.

Main Methods:

  • Design and implementation of the BAD system architecture.
  • Integration of active data collection and processing with passive query answering capabilities.
  • Performance evaluation of the BAD system at scale.

Main Results:

  • The BAD system effectively supports both passive and active data services.
  • BAD demonstrates scalability in handling large-scale data operations.
  • The system design simplifies the provision of active data services compared to "glued" solutions.

Conclusions:

  • The BAD system offers an efficient, end-to-end solution for active and passive Big Data management.
  • BAD reduces the complexity and overhead associated with building active data services.
  • The proposed system facilitates scalable and proactive data serving to users.