A New Methodology for Storing Consistent Fuzzy Geospatial Data in Big Data Environment

In this era of big data, as relational databases are inefficient, NoSQL databases are a workable solution for data storage. In this context, one of the key issues is the veracity and therefore the data quality. Indeed, as with classic data, geospatial big data are generally fuzzy even though they are stored as crisp data (perfect data). Hence, if data are geospatial and fuzzy, additional complexities appear because of the complex syntax and semantic features of such data. The NoSQL databases do not offer strict data consistency. Therefore, new challenges are needed to be overcome to develop efficient methods that simultaneously ensure the performance and the consistency in storing fuzzy geospatial big data. This paper presents a new methodology that tackles the storage issues and validates the fuzzy spatial entities' consistency in a document-based NoSQL system. Consequently, first, to better express the structure of fuzzy geospatial data in such a system, we present a logical model called Fuzzy GeoJSON schema. Second, for consistent storage, we implement a schema-driven pipeline based on the Fuzzy GeoJSON schema and semantic constraints.

1 Introduction

Since the emergence of the era of big data, a classic consideration of the associated issues is the four Vs: volume, velocity, variety, and veracity. The last one is clearly less studied and is intrinsically linked with data quality issues, such as consistency, accuracy, imprecision, correctness, etc. In a geographic context, the significant growth of geospatial data is mainly due to the increasing volume of data produced from many sources, such as geo-located sensors, location-based social media data, and volunteer geographic information (VGI) [1]. This expansive volume, with varying formats, raises big data storage and big data analysis issues and poses additional challenges in verifying data quality [2]. To ensure a better quality of geographic data, it is important to consider its imperfect nature, study it, and integrate it into the analyzing process [3].