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Related Concept Videos

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The systematic method of obtaining and analyzing accurate information of a population is called data collection. A survey is a standard method of data collection that involves collecting information from a target human population about their experience, opinion, or knowledge of a product, service, or process. The responses are recorded and interpreted. The most common survey examples are written questionnaires, face-to-face or telephonic conversations, focus groups, and electronic (e-mail or...
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Data Collection by Observations01:08

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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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Convenience Sampling Method00:55

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population.
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Types of Global Positioning System Surveys01:30

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GPS surveying methods vary in application, accuracy, and data collection techniques, catering to diverse surveying and mapping needs. Static GPS, kinematic GPS, and real-time kinematic (RTK) surveying are widely used. Each technique offers distinct advantages.Static GPS involves placing one receiver at a known reference point and another at the target point. It collects exact positional data by observing multiple satellite ranges over an extended period, achieving centimeter-level accuracy for...
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GIS Software, Hardware, and Sources of GIS Data01:23

GIS Software, Hardware, and Sources of GIS Data

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A Geographic Information System (GIS) combines specialized software and hardware to effectively manage, analyze, and present spatial and related data. GIS software includes critical functionalities such as a user interface for easy navigation, database management tools for handling spatial and attribute data, and data retrieval features for efficient access. Analytical tools transform raw data into insights, while display functions produce maps and reports in various formats for effective...
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Selected Data About Geographic Locations01:25

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Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
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Semantic Data Mining in Ubiquitous Sensing: A Survey.

Grzegorz J Nalepa1,2, Szymon Bobek1,2, Krzysztof Kutt1

  • 1Institute of Applied Computer Science and Jagiellonian Human-Centered Artificial Intelligence Laboratory (JAHCAI), ul. Prof. Stanislawa Lojasiewicza 11, Jagiellonian University, 30-348 Krakow, Poland.

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|July 2, 2021
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Summary
This summary is machine-generated.

Semantic data mining enhances ubiquitous sensing by integrating domain knowledge for better explainability. This survey explores methods and applications in environmental, industrial, and social sensing.

Keywords:
data miningdeclarative methodsexplainabilityindustrial sensorssemantics

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Area of Science:

  • Computer Science
  • Data Science
  • Artificial Intelligence

Background:

  • Ubiquitous sensing generates large, heterogeneous data, posing challenges for data mining.
  • Explainability and interpretability of models are crucial for reliable data mining outcomes.
  • Integrating domain knowledge into semantic data mining is an emerging research direction.

Purpose of the Study:

  • To survey semantic data mining approaches and methods for ubiquitous sensing.
  • To examine applications of semantic data mining in environmental, industrial, and social sensing.
  • To provide an outlook on future research directions in this field.

Main Methods:

  • Literature review of semantic data mining techniques.
  • Analysis of prominent application areas for ubiquitous sensing.
  • Discussion of challenges and opportunities in semantic data mining.

Main Results:

  • Identified key semantic data mining approaches and their relevance to ubiquitous sensing.
  • Detailed selected applications in environmental, industrial, and social sensing.
  • Highlighted the importance of domain knowledge for enhanced data interpretation.

Conclusions:

  • Semantic data mining offers promising solutions for challenges in ubiquitous sensing.
  • The field is rapidly evolving with significant potential for future advancements.
  • Interdisciplinary approaches are essential for unlocking the full potential of ubiquitous sensing data.