Data warehousing and mining technologies for adaptability in turbulent resources business environments

Nimmagadda, Shastri L.; Dreher, Heinz

doi:10.1504/ijbidm.2011.039409

Cited by 12 publications

(5 citation statements)

References 16 publications

(23 reference statements)

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“…The dimension schema represents the details of dimension modeling. Nimmagadda and Dreher [20,21,[24][25][26][27] propose ontology-based data warehousing of multidimensional and heterogeneous data structures for mining from multiple sources. They discuss several domains and application scenarios.…”

Section: Data Structuring Methodologiesmentioning

confidence: 99%

“…The multitude of data tables causes problems in many analysis techniques that are designed to work in a data warehouse environment. To avoid this problem, tables are merged, implying that the data are denormalized in flat files [27]. For historical data analysis, denormalized relationships are more flexible.…”

Section: Allocation Of Estimate Typementioning

confidence: 99%

“…Taniar et al [36] and Tjioe and Taniar [37] propose association mining rules to data warehouse scenarios. Nimmagadda and Dreher [27] propose classical mining algorithms for analyzing the metadata views.…”

Section: Data Characterization and Miningmentioning

confidence: 99%

“…16) the information contained in the data warehouse. Essentially, the process of influencing the cubes of information is OLAP [27,33] and how it is presented for interpretation. BH^is a structural-high attribute and most ideal for CCS drilling campaign and BL^is structural-low attribute.…”

Section: Extraction Of Olap Views From Warehoused Metadatamentioning

confidence: 99%

See 3 more Smart Citations

On a Holistic Modeling Approach for Managing Carbon Emission Ecosystems

Nimmagadda

Dreher

Rudra

2016

Environ Model Assess

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Effective use of historical volumes of heterogeneous and multidimensional data is a major challenge, especially projects associated with potential applications of carbon emission ecosystems. Data science in these applications becomes tedious when such varied data are accumulated and or distributed in multiple domains. Design, development, and implementation of sustainable geological storages are crucial for managing carbon dioxide (CO 2 ) emissions and its modeling process. The purpose of the research is to address major challenges and how best a robust Bontology-based multidimensional data warehousing and mining^approach can resolve issues associated with carbon ecosystems. The conceptualized relationships deduced among multiple domains, integration of domain ontologies, data mining, visualization, and interpretation artefacts are highlights of the study. Several data, plot, and map views are extracted from metadata storage for interpreting new knowledge on carbon emissions. Statistical mining models describe data attributes' correlations, patterns, and trends that can help in predicting future forecast of CO 2 emissions worldwide.

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Section: Data Structuring Methodologiesmentioning

confidence: 99%

Section: Allocation Of Estimate Typementioning

confidence: 99%

Section: Data Characterization and Miningmentioning

confidence: 99%

Section: Extraction Of Olap Views From Warehoused Metadatamentioning

confidence: 99%

See 2 more Smart Citations

On a Holistic Modeling Approach for Managing Carbon Emission Ecosystems

Nimmagadda

Dreher

Rudra

2016

Environ Model Assess

Self Cite

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“…It includes machine-interpretable definitions of basic classes (concepts) in different domains and their respective data relationships to arrive at a meaningful metadata. To analyze the domain knowledge from warehoused NWS metadata, data mining, visualization and interpretation artefacts Dreher, 2009a andNimmagadda, 2015b) are articulated. The Big Data characteristics described in Fig.…”

Section: B Big Data Role In the Nws Geo-informaticsmentioning

confidence: 99%

The North West Shelf (NWS), a Digital Petroleum Ecosystem (PDE) in a Big Data Scale

Nimmagadda¹,

Rudra²,

Reiners³

2018

ASEG Extended Abstracts

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The North West Shelf (NWS) and its associated petroleum systems have varied geographies, geomorphologies and complex geological environments. In spite of the ongoing exploration activities in many sedimentary basins, the appraisal and field development campaigns are challenging. Besides, interpreting the connectivity between petroleum systems is challenging. The heterogeneity and multidimensionality of multi-stacked reservoirs associated with multiple oil and gas fields complicate the data integration process. Volumes and varieties of data existing in these basins are in different scales, sizes and formats, demanding new storage and retrieval methods, emphasizing both data integration and data structuring. Since the data are in terabyte size; the multiple dimensions and domains need to be brought in a single repository, we take advantage of Big Data tools and technologies. In this context, we aim at articulating the digital petroleum ecosystems and petroleum database management systems, with new data modelling, data warehousing and mining, visualization and interpretation artefacts. This approach facilitates the data management not only for individual basins but groups of basins of the NWS. Warehoused cuboid metadata can explore the connections providing new insights in the data interpretation and knowledge of new prospective areas. The multidimensional warehousing repository that supported by cloud computing, data analytics and virtualization features, provide new opportunities for delivering quality and just-in-time online ecosystem services. Other goals are deducing an integrated unified metadata model and characterizing the connectivity among the basins of the NWS and associated oil & gas fields. The study supports the features of PDE and its knowledge management.

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On new emerging concepts of petroleum digital ecosystem

Nimmagadda¹,

Dreher²

2012

WIREs Data Min & Knowl

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Petroleum‐bearing sedimentary basin is an emerging digital ecosystem. Petroleum system and its elements are narrated for each and every oil and gas field in each and every petroleum‐bearing sedimentary basin. A new concept of ecosystem and its digitization are emerging within the generic petroleum system. The Significance of this concept comes with an ability to establish connectivity among petroleum subsystems, for example, Albertine Graben (Western Uganda), through attributes of their components and their contextualization and specifications. Unless the phenomenon of integration of elements and processes among subbasins in the context of digital representation, visualization, and interpretation are understood, the existence of petroleum system (both at local and global scales) and its survival, the phenomenon of ‘interconnectivity’ among ecosystems cannot be well explained. Its value cannot be added in terms of petroleum accumulations and volumes, unless these phenomena are made explicit. Authors propose ontology‐based data warehousing and data mining technologies in which conceptualization and contextualization of multiple data dimensions' are modeled (elements and processes of petroleum system), besides their integration (within data warehouse environment) and data mining of interpretable emerging petroleum digital ecosystem. Multidimensional data warehousing and mining facilitate an effective interpretation of petroleum ecosystems, minimizing the ambiguities involved during structure and reservoir qualifications and quantifications, especially during reserve computations. © 2012 Wiley Periodicals, Inc.This article is categorized under: Fundamental Concepts of Data and Knowledge > Data Concepts Fundamental Concepts of Data and Knowledge > Knowledge Representation Technologies > Visualization

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Data warehousing and mining technologies for adaptability in turbulent resources business environments

Cited by 12 publications

References 16 publications

On a Holistic Modeling Approach for Managing Carbon Emission Ecosystems

On a Holistic Modeling Approach for Managing Carbon Emission Ecosystems

The North West Shelf (NWS), a Digital Petroleum Ecosystem (PDE) in a Big Data Scale

On new emerging concepts of petroleum digital ecosystem

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