However, existing mapping systems built on GIS databases fail to fully leverage relational database technology, mainly because most systems store geographic information-geometry and attributes-in the relational database, but store map definition and symbolization information in separate files. Such systems are also used to produce cartographic products including maps and mapping datasets. Geographic information systems centered on relational databases are a powerful and proven way to collect, store, and analyze geographic data. Existing obstacles in the automatisation process of map production, more in-depth understanding of the visualized map content adding Virtual Reality / Augmented Reality (VR / AR), usage of cloud technologies and supercomputers with software platforms for map creation are also recognized. Some examples of good and imperfect web map productions are shown in the context of BD visualization. The challenges that faced the modern cartography for creating better and cheaper cartographical multi-dimensional and multi-layered map products for less time on different map scales are shortly outlined. The potential to use commercial web GIS platforms and free and open-source software solutions for Web mapping (WM) are discussed. A conceptual framework encompassed several stages of multi-source data handling, separate stages of data management and data analytics, as well as data transformations from the real world into the map is described following the web-map creation process. Based on this approach and taking into account the technological advances, the authors describe which part of BD can be utilized for new mapping products and services. The primary purpose of the cartographic approach is to maximize the value of a user-oriented map rendering of the web map content. The recent development of GIS and web technologies have expanded and empowered cartography by providing actual, steadily growing and complex data and information, increasing the spatial and thematic accuracy of the contemporary maps. In cartography, the term "Big Data" (BD) has been started to use in the last decade. This paper investigates the potential of the representation framework by building and describing a set of representation rules and geoprocessing models that experiment with traditional cartographic depictions that have proven difficult to automate in the past. The intelligent combination of the components of the representation framework can result in intricate and insightful database cartography. The new framework offers geoprocessing tools that can automate aspects of symbolization and detect areas where symbols overlap even when the spatial geometry does not. Geometric effects within rules can dynamically alter spatial geometry before symbology is applied, and values within the feature class table can define symbol properties to customize the appearance of features. Symbology is applied intelligently to spatial features through powerful representation rules to achieve complex depictions. To address this, ArcGIS 9.2 introduces a rule-based way to store cartographic symbology alongside spatial data within a geodatabase. The convenience of database-driven cartography has traditionally been offset by the design limitations of automated symbology.
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