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Business Intelligence and Geographic Information System for Hydrogeology
Abstract
We have developed the Hydrogeological Information System (HgIS). Its purpose is to load data from available data sources of any kind, to visualize and analyze data and to implement simple models. HgIS is mostly built upon the Pentaho business intelligence (BI) platform. HgIS uses only some components of BI in comparison to enterprise BI solutions. Adequacy and limitation of data warehousing and BI application for groundwater data is discussed. Data extraction, transformation and loading is focused on integration of wide variety of structured and semi-structured data. Data warehouse uses a hybrid snowflake/star schema. Inmon’s paradigm is used because data semantics is known and the volume of data is limited. HgIS is data agnostic, database agnostic, scalable and interoperable. The architecture of the system corresponds to a spatial business intelligence solution (GeoBI) – a combination of BI and geographic information systems (GIS). Groundwater practitioners have worked with GIS software for decades but BI technologies and tools have not previously been applied to groundwater data.
Organizations must quickly adapt their processes to understand the dynamic nature of modern business environments. As highlighted in the literature, centralized governance supports decision-making and performance measurement processes in technology companies. For this reason, a reliable decision-making system with an integrated data model that enables the rapid collection and transformation of data stored in heterogeneous and different sources is needed. Therefore, this paper proposes the design of a data model to implement data-driven governance through a literature review of adopted approaches. The lack of a standardized procedure and a disconnection between theoretical frameworks and practical application has emerged. This paper documented the suggested approach following these steps: (i) mapping of monitoring requirements to the data structure, (ii) documentation of ER diagram design, and (iii) reporting dashboards used for monitoring and reporting. The paper helped fill the gaps highlighted in the literature by supporting the design and development of a DWH data model coupled with a BI system. The application prototype shows benefits for top management, particularly those responsible for governance and operations, especially for risk monitoring, audit compliance, communication, knowledge sharing on strategic areas of the company, and identification and implementation of performance improvements and optimizations.
Research on practices to share and reuse data will inform the design of infrastructure to support data collection, management, and discovery in the long tail of science and technology. These are research domains in which data tend to be local in character, minimally structured, and minimally documented. We report on a ten-year study of the Center for Embedded Network Sensing (CENS), a National Science Foundation Science and Technology Center. We found that CENS researchers are willing to share their data, but few are asked to do so, and in only a few domain areas do their funders or journals require them to deposit data. Few repositories exist to accept data in CENS research areas.. Data sharing tends to occur only through interpersonal exchanges. CENS researchers obtain data from repositories, and occasionally from registries and individuals, to provide context, calibration, or other forms of background for their studies. Neither CENS researchers nor those who request access to CENS data appear to use external data for primary research questions or for replication of studies. CENS researchers are willing to share data if they receive credit and retain first rights to publish their results. Practices of releasing, sharing, and reusing of data in CENS reaffirm the gift culture of scholarship, in which goods are bartered between trusted colleagues rather than treated as commodities.
Ground water systems dominated by iron- or sulfate-reducing conditions may be distinguished by observing concentrations of dissolved iron (Fe(2+)) and sulfide (sum of H(2)S, HS(-), and S(=) species and denoted here as "H(2)S"). This approach is based on the observation that concentrations of Fe(2+) and H(2)S in ground water systems tend to be inversely related according to a hyperbolic function. That is, when Fe(2+) concentrations are high, H(2)S concentrations tend to be low and vice versa. This relation partly reflects the rapid reaction kinetics of Fe(2+) with H(2)S to produce relatively insoluble ferrous sulfides (FeS). This relation also reflects competition for organic substrates between the iron- and the sulfate-reducing microorganisms that catalyze the production of Fe(2+) and H(2)S. These solubility and microbial constraints operate in tandem, resulting in the observed hyperbolic relation between Fe(2+) and H(2)S concentrations. Concentrations of redox indicators, including dissolved hydrogen (H(2)) measured in a shallow aquifer in Hanahan, South Carolina, suggest that if the Fe(2+)/H(2)S mass ratio (units of mg/L) exceeded 10, the screened interval being tapped was consistently iron reducing (H(2) approximately 0.2 to 0.8 nM). Conversely, if the Fe(2+)/H(2)S ratio was less than 0.30, consistent sulfate-reducing (H(2) approximately 1 to 5 nM) conditions were observed over time. Concomitantly high Fe(2+) and H(2)S concentrations were associated with H(2) concentrations that varied between 0.2 and 5.0 nM over time, suggesting mixing of water from adjacent iron- and sulfate-reducing zones or concomitant iron and sulfate reduction under nonelectron donor-limited conditions. These observations suggest that Fe(2+)/H(2)S mass ratios may provide useful information concerning the occurrence and distribution of iron and sulfate reduction in ground water systems.
New requirements to understand geological properties in three dimensions have led to the development of PropBase, a data structure and delivery tools to deliver this. At the BGS, relational database management systems (RDBMS) has facilitated effective data management using normalised subject-based database designs with business rules in a centralised, vocabulary controlled, architecture. These have delivered effective data storage in a secure environment. However, isolated subject-oriented designs prevented efficient cross-domain querying of datasets. Additionally, the tools provided often did not enable effective data discovery as they struggled to resolve the complex underlying normalised structures providing poor data access speeds. Users developed bespoke access tools to structures they didn't fully understand sometimes delivering them incorrect results.
This paper presents the application of Data Warehouse (DW) and On-Line Analytical Processing (OLAP) technologies to the field of water quality assessment. The European Water Framework Directive (DCE, 2000) underlined the necessity of having operational tools to help in the interpretation of the complex and abundant information regarding running waters and their functioning. Several studies have exemplified the interest in DWs for integrating large volumes of data and in OLAP tools for data exploration and analysis.
Based on free software tools, we propose an extensible relational OLAP system for the analysis of physicochemical and hydrobiological watercourse data. This system includes: (i) two data cubes; (ii) an Extract, Transform and Load (ETL) tool for data integration; and (iii) tools for OLAP exploration. Many examples of OLAP analysis (thematic, temporal, spatiotemporal, and multiscale) are provided. We have extended an existing framework with complex aggregate functions that are used to define complex analysis indicators. Additional analysis dimensions are also introduced to allow their calculation and also for purposes of rendering information. Finally, we propose two strategies to address the problem of summarizing heterogeneous measurement units by: (i) transforming source data at the ETL tier, and (ii) introducing an additional analysis dimension at the OLAP server tier.
Groundwater data management system
- K Nešetřil
- J Šembera
- J M Gómez
- M Sonnenschein
- U Vogel
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Ossietzky University Oldenburg, Oldenburg (2014)
An information system for groundwater data and modelling
- K Nešetřil
- J Šembera
Nešetřil, K., Šembera, J.: An information system for groundwater data and modelling. In:
Sauvage, S., Sánchez-Pérez, J.M., Rizzoli, A.E. (eds.) Proceedings of the 8th International
Congress on Environmental Modelling and Software, 10-14 July, Toulouse, France, pp. 747-752 (2016)