Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 1409.2819

A Sample-Based Approach to Data Quality Assessment in Spatial Databases with Application to Mobile Trajectory Nearest-Neighbor Search

Bagher Saberi·Nasser Ghadiri
View original source ↗Read source document ↗Export metadata ↓

Abstract

Spatial data is playing an emerging role in new technologies such as web and mobile mapping and Geographic Information Systems (GIS). Important decisions in political, social and many other aspects of modern human life are being made using location data. Decision makers in many countries are exploiting spatial databases for collecting information, analyzing them and planning for the future. In fact, not every spatial database is suitable for this type of application. Inaccuracy, imprecision and other deficiencies are present in location data just as any other type of data and may have a negative impact on credibility of any action taken based on unrefined information. So we need a method for evaluating the quality of spatial data and separating usable data from misleading data which leads to weak decisions. On the other hand, spatial databases are usually huge in size and therefore working with this type of data has a negative impact on efficiency. To improve the efficiency of working with spatial big data, we need a method for shrinking the volume of data. Sampling is one of these methods, but its negative effects on the quality of data are inevitable. In this paper we are trying to show and assess this change in quality of spatial data that is a consequence of sampling. We used this approach for evaluating the quality of sampled spatial data related to mobile user trajectories in China which are available in a well-known spatial database. The results show that sample-based control of data quality will increase the query performance significantly, without losing too much accuracy. Based on this results some future improvements are pointed out which will help to process location-based queries faster than before and to make more accurate location-based decisions in limited times.

Explore related subjects

cs.DB

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Bagher Saberi, Nasser Ghadiri. 2016-05-14. A Sample-Based Approach to Data Quality Assessment in Spatial Databases with Application to Mobile Trajectory Nearest-Neighbor Search. https://arxiv.org/abs/1409.2819

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Compass: General Filtered Search across Vector and Structured Data

The increasing prevalence of hybrid vector and relational data necessitates efficient, general support for queries that combine high-dimensional vector search with complex relational filtering. However, existing filtered search solutions are fundamentally limited by specialized indices, which restrict arbitrary filtering and hinder integration with general-purpose DBMSs. This work introduces \textsc{Compass}, a unified framework that enables general filtered search across vector and structured data without relying on new index designs. Compass leverages established index structures -- such as HNSW and IVF for vector attributes, and B+-trees for relational attributes -- implementing a principled cooperative query execution strategy that coordinates candidate generation and predicate evaluation across modalities. Uniquely, Compass maintains generality by allowing arbitrary conjunctions, disjunctions, and range predicates, while ensuring robustness even with highly-selective or multi-attribute filters. Comprehensive empirical evaluations demonstrate that Compass consistently outperforms NaviX, the only existing performant general framework, across diverse hybrid query workloads. It also matches the query throughput of specialized single-attribute indices in their favorite settings with only a single attribute involved, all while maintaining full generality and DBMS compatibility. Overall, Compass offers a practical and robust solution for achieving truly general filtered search in vector database systems.

cs.DB↗
arXiv2026

NeurIDA: Dynamic Modeling for Effective In-Database Analytics

Relational Database Management Systems (RDBMS) manage complex, interrelated data and support a broad spectrum of analytical tasks. With the growing demand for predictive analytics, the deep integration of machine learning (ML) into RDBMS has become critical. However, a fundamental challenge hinders this evolution: conventional ML models are static and task-specific, whereas RDBMS environments are dynamic and must support diverse analytical queries. Each analytical task entails constructing a bespoke pipeline from scratch, which incurs significant development overhead and hence limits wide adoption of ML in analytics. We present NeurIDA, an autonomous end-to-end system for in-database analytics that dynamically "tweaks" the best available base model to better serve a given analytical task. In particular, we propose a novel paradigm of dynamic in-database modeling to pre-train a composable base model architecture over the relational data. Upon receiving a task, NeurIDA formulates the task and data profile to dynamically select and configure relevant components from the pool of base models and shared model components for prediction. For friendly user experience, NeurIDA supports natural language queries; it interprets user intent to construct structured task profiles, and generates analytical reports with dedicated LLM agents. By design, NeurIDA enables ease-of-use and yet effective and efficient in-database AI analytics. Extensive experiment study shows that NeurIDA consistently delivers up to 12% improvement in AUC-ROC and 25% relative reduction in MAE across ten tasks on five real-world datasets. The source code is available at https://github.com/Zrealshadow/NeurIDA

cs.DB↗
arXiv2026

kgsteward: a tool for building, reproducing and maintaining distributed knowledge graphs

Collaborative research projects in life sciences increasingly need to integrate private, embargoed consortium data with public reference databases in order to reach statistically meaningful interpretations. The Resource Description Framework (RDF) is well suited to this task: it facilitates the integration of heterogeneous data sources, and allows researchers to keep data and their documentation as metadata in the same place, provided the knowledge graph itself remains private during the time course of the project. Nevertheless, the development and long-term maintenance of a scientific knowledge graph remains a challenging, labour-intensive endeavour owing to the state of constant flux of most public resources. To tackle this challenge, we present kgsteward, a Python command-line tool that builds and maintains knowledge graphs inside RDF stores from a single, version-controlled configuration file. kgsteward supports multiple triplestores, keeps the local graph up-to-date with its external sources possibly already in RDF, or transformed into it on the fly, and uses SPARQL 1.1 UPDATE commands to amend further imported RDF on the fly. It can also validate the resulting graph with SPARQL queries that double as usage examples for both human users and AI agents. kgsteward has already been used in several collaborative projects at the SIB Swiss Institute of Bioinformatics, and we demonstrate its applicability in two real-world international research projects: one that builds a library of plant extracts with chemical analyses and associated bio-activities, and a second that reconciles public reference resources for human metabolic-network reconstruction.

cs.DB↗