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

Abstract

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.

NoSQL Database Modeling and Management: A Systematic Literature Review

“…Likewise, in five studies, the modeling was carried out according to criteria. In [57], the authors propose a methodology to store fuzzy geospatial data by adding a Revista Facultad de Ingeniería (Rev. Fac.…”

“…These are in a format called a shape file, which consists of data vectors. In [57], the authors, working with fuzzy geospatial data, describe that these are naturally imprecise due to the distribution of city centers, whose location is difficult to identify due to the constant growth of these cities; also, due to the complexity of determining where a forest starts. They also indicate that the semantic characteristics must be validated so that the JSON format, the fuzzy structure, and the semantic restrictions must be respected to store the data.…”

Review on Integrating Geospatial Big Datasets and Open Research Issues

“…NoSQL databases generally have many veracity issues because they fail to apply data integrity or durability techniques unlike relational databases, which apply strict data consistency policies to guarantee the simultaneous delivery of similar data to all users. Khalfi et al [61] presented another technique for the big data environment to address capacity issues and ensure geospatial data consistency in a document-based NoSQL framework through a particular consistency approval workflow. GeoJSON used schema as a logical model to optimize both relational and NoSQL databases along with semantic constraints to support consistent storage.…”