Data Quality Management and Query Optimization
Summary
Data quality management encompasses the methods and tools used to ensure accuracy, consistency and completeness of data throughout its lifecycle. Core activities include data profiling to assess the current state of datasets, anomaly detection and automated cleaning procedures to correct or remove erroneous records, and ongoing monitoring to prevent degradation over time. Query optimization, by contrast, focuses on executing retrieval requests against large data collections with minimal resource consumption and latency. It relies on metadata such as cardinality estimates, histograms and data dependencies to generate efficient execution plans. The two domains intersect where high-quality data enables more reliable metadata and statistics, which in turn support more precise cost models and execution strategies. Advances in distributed processing, sampling techniques and machine learning have further strengthened this synergy, allowing real-time cleaning of streaming data and adaptive optimisation of complex query workloads in cloud and edge environments. Collectively, these developments underpin reliable decision-making in fields as diverse as finance, healthcare and scientific research.
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Data Quality Management and Query Optimization publication trend
The graph below shows the total number of articles in data quality management and query optimization across all publications each year (not limited to Nature Index journals).
Technical terms
Data profiling: The assessment of data characteristics and quality metrics to identify anomalies and guide cleaning activities.
Data cleaning: The process of detecting and correcting or removing erroneous, duplicate or incomplete records in a dataset.
Functional dependency: A relationship between attributes in which the value of one attribute uniquely determines the value of another.
Query optimization: The selection of an efficient execution plan for a database query, based on cost estimates and metadata.
Selection rule: A tuple-level constraint derived from the selection operator in relational algebra, used to identify and correct inconsistent records.
Sampling: The technique of examining a representative subset of data to estimate global properties or accelerate profiling and optimisation tasks.
References
- Data dependencies for query optimization: a survey. The VLDB Journal (2021).
- Cleaning Data With Selection Rules. IEEE Access (2022).
- An Efficient and Scalable Algorithm to Mine Functional Dependencies from Distributed Big Data. Sensors (2022).
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