Future Trends In Geospatial Data Management Process
Some of other solutions include storing files on an enhanced file server with spatial data access capabilities across the network. Even though these improvements have brought file-based solutions more useable, but problems such as data integrity, multiple user access, Data productivity ( cost utilization benefits ) and scalability needs to be solved. The most important advantages for a spatial database system include efficient data storage and retrieval, access to multiple users in an enterprise environment, serving data across multiple platforms, scalability, quantifiability, ratability, data security and integrity
Geo-Database industry
Geospatial data, information that references the geographic location of natural and manmade phenomena on the surface of the Earth, have become an indispensable information asset in today’s society. Geospatial data are the fuel that will drive an estimated $45 to $67 billion (US) world market for geomatics based products and services by the year ( Source estimates : open ).
From the design of the roads on which we travel, to the location of our place of work, nearly every facet of everyday life is touched, in some way, by geospatial data. Geospatial data are being produced by all levels of government and in the private sector at an unexampled rate.
Arenas
Spatiotemporal data, dynamic data, kinematics data and location-aware computing present important opportunities for research in the geospatial database and data mining arenas. Current database techniques use very simple representations of geographic objects and relationships (e.g., point objects, polygons, and Euclidean distances). Data structures, queries, indexes, and algorithms need to be expanded to handle other geographic objects (e.g., objects that move and evolve over time) and relationships (e.g., non-Euclidean distances, direction, and connectivity) (Miller and Han, 2001). One of the most serious challenges is integrating time into database representations. Another is integrating geospatial data sets from multiple sources (often with varied formats, semantics, precision, coordinate systems, and so forth). Geospatial databases management technology is exploited by a wide variety of businesses and agencies which themselves make a interesting cocktails of specific solvent orbits such as,
- Environmental studies
- Planning,
- Surveying,
- Internal security
- Mapping applications (Map and database publishers)
- Election administration
- Redistricting Infrastructure management
- Oil, gas, and mineral exploration
- Public health and safety
- Real estate information
- Research and education
- Transportation and logistics
- Consulting firms
- Government agencies (city, county, state, and federal)
- Marketing and sales companies
- Telecommunications
- Timber companies
- Utilities (electric, gas, water, wastewater, and telephone)
Future Trends
The ubiquitousness and longevity of the geo spatial relational database architecture lies mainly on its theoretical foundation, the significative nature of the query processing language, and its ability to truly separate the structure of the data from the software applications. Although there has been some research on both spatial and temporal databases, relatively little research has addressed the more complex issues associated with spatiotemporal characteristics. In addition, research investments are needed in geometric algorithms to manipulate efficiently the massive amounts of geospatial data being generated and stored. Despite advances in data mining methods over the past decade, considerable work remains to be done to improve the discovery of structure (in the form of rules, patterns, regularities, or models) in geo-spatial databases. A couple of authoritative co-occurrent trends in geo spatial databases are :
Geo- spatial database Benchmarking & Geospatial Data Integration
Data integration to combine data from heterogeneous, multidisciplinary, multidimensional sources into one coherent data set requires integration and benchmarking. The radical of the data typically employ different resolutions, measurement techniques, coordinate systems, spatial or temporal scales, and semantics. Perhaps the most obvious problem stems from positional accuracy & all must be adjusted to integrate the data into an singular effective result set. The positions recorded for geospatial data vary in accuracy, depending on how the location coordinates were derived, their spatial resolution, and so forth. The degree of difference may be unimportant or critical, depending on the requirements of the application. Conflation mainly comes from the integration of geospatial data sets that come from multiple sources, are of different scales, and have different positional accuracies. Conflation can be useful in several ways (Nusser):
- As a means of correcting or reducing errors in one data set through comparison with a second.
- As a process of averaging, in which the product is more accurate than either input.
- As a process of concatenation, in which the output data set preserves all of the information in the inputs.
- As a means of resolving unacceptable differences when data sets are overlaid.