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Qualitative analysis is the process of identifying elements, ions, or compounds in an unknown sample. It is the first and most fundamental type of analysis based on the hierarchy of analytical goals. This hierarchy is significant as it provides a structured approach to scientific research, with qualitative analysis serving as the initial step, providing essential information before moving on to quantitative or other forms of analysis.
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For solutions containing mixtures of different cations, the identity of each cation can be determined by qualitative analysis. This technique involves a series of selective precipitations with different chemical reagents, each reaction producing a characteristic precipitate for a specific group of cations. Metal ions within a group are further separated by varying the pH, heating the mixture to redissolve a precipitate, or adding other reagents to form complex ions.
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Run charts serve as an essential instrument for visualizing the performance of various processes over time, enabling the identification of trends and patterns crucial for quality improvement. These charts map out a series of data points chronologically, offering insights into the stability and efficiency of a process. A run chart's creation involves plotting data points on a graph, with the time intervals on the horizontal axis and the specific measurements on the vertical axis. For...
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When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
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Analyzing the Quality of Twitter Data Streams.

Franco Arolfo1, Kevin Cortés Rodriguez1, Alejandro Vaisman1

  • 1Department of Information Engineering, Instituto Tecnológico de Buenos Aires Lavardén 315, C1437FBG Ciudad Autónoma de Buenos Aires, Argentina.

Information Systems Frontiers : a Journal of Research and Innovation
|November 10, 2020
PubMed
Summary

This study redefines data quality (DQ) metrics for Twitter data streams, developing a tool to assess and visualize DQ. Results show Twitter data quality is surprisingly high, challenging common assumptions.

Keywords:
Big dataData qualitySocial networksTwitter

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Area of Science:

  • Data Science
  • Information Science
  • Social Media Analytics

Background:

  • Commonly perceived low and unpredictable quality of Twitter data streams hinders reliable decision-making.
  • Traditional data quality (DQ) methods from relational databases are not directly applicable to dynamic Big Data like Twitter streams.

Purpose of the Study:

  • To adapt and redefine classic DQ dimensions and metrics for Twitter data streams.
  • To introduce a software tool for real-time DQ assessment of Twitter data.
  • To analyze the DQ of Twitter streams across various filtering and topic classification scenarios.

Main Methods:

  • Redefinition of DQ dimensions (Readability, Completeness, Usefulness, Trustworthiness) and metrics for Twitter data.
  • Development of a software tool for real-time data capture, DQ computation, and visualization.
  • Empirical analysis of DQ on unfiltered, keyword-filtered, and topic-classified Twitter streams, including geolocalization.

Main Results:

  • The study reveals that the data quality of Twitter streams is higher than commonly believed.
  • DQ varies across different filtering strategies and topics.
  • The analysis also investigated DQ related to tweet geolocalization, despite the low number of such tweets.

Conclusions:

  • The proposed DQ framework and tool effectively capture the characteristics of Twitter data streams.
  • The findings challenge the prevailing notion of low Twitter data quality, suggesting its potential for informed decision-making.
  • The flexibility in weighting DQ dimensions allows for context-specific data quality assessments.