Papers by Shastri Nimmagadda
Advances in Science, Technology & Innovation/Advances in science, technology & innovation, 2024
Advances in Science, Technology & Innovation/Advances in science, technology & innovation, 2024
Zenodo (CERN European Organization for Nuclear Research), Mar 15, 2023
In the entire life cycle of the exploratory drilling campaigns, the technical and financial docum... more In the entire life cycle of the exploratory drilling campaigns, the technical and financial documents, Geotechnical Order (GTO), and Authorization for Expenditure (AFE) have a role in stakeholder and portfolio management, including investment opportunities. Executing GTO and AFE documents has challenges in committing huge capital outlays contributing to operational costs. Lack of operational awareness, manual documents, and disparate databases, including discrete accounting and reporting systems, make the upstream businesses challenging with shattered budgets. Other issues are intricate supply chains, disengaged standards, rigid government regulations, political uncertainties, and inconsistent fiscal systems. The authors investigate the existing inconsistent guidelines of exploratory drilling campaigns, information barriers, uncertainties, and bottlenecks of implementing Joint Ventures (JV) under the Production Sharing Contract (PSC) with stakeholders. We design Information System (IS) articulated techno-economic decision tree template and its associated multidimensional attribute journey modelling to integrate the technical and financial documents with exploration business knowledge. High Performance Computing (HPC), Business Information Systems (BIS) and Cloud-and fogcomputing services can be added tools for investments in the oil and natural gas industry. Customer Relations Management and Enterprise Resources Planning extranets can collaborate with the techno-economic framework and template. In addition, a multidimensional repository system with fine-grained data structures can attract the Exploration and Production (E & P) portfolios. The techno-economic facts-guided decision-tree template can digitally customize the exploratory drilling campaigns and make realistic technical and financial decisions. The methodology can deliver timely, valuable, and accurate information to explorers, investors and managers involved in the E & P projects, including two-way communication and business-to-business interactions. The decision-tree template can be reusable and interoperable in multiple domains of the E & P industry. The techno-economic decision tree template is usable in various oil and gas finds, easing stakeholder and portfolio management, including flexible GTO and AFE document structures.
International Conference on Geoinformatics, 2013
Worldwide conventional resources are on declining trend. Alternate resources are unconventional s... more Worldwide conventional resources are on declining trend. Alternate resources are unconventional sources. Volume of datasets exists in unconventional resources, but they are neither evaluated nor unknown. They are even unaccounted for during drilling campaigns and evaluations. Because of exploration and development setbacks, productibility, recovery costs and environmental concerns, exploitation of unconventional resources is held up on global market. In order to address these issues, authors propose, multidimensional and heterogeneous, data warehousing and mining approach, supported by ontology. Data integration and exploring multiple connections among attributes of multiple dimensions of unconventional petroleum ecosystems (of different geographic, geological and production regimes) are needed. Authors attempt to make use of ontologies, written for multiple dimensions including periodically (longitudinal dimension) and geographically (distantly, lateral dimension) varying dimensions, within an unconventional resource basin. The proposed methodology is robust and can resolve issues associated with organization of unconventional resources and extend help to technology adaptation. The proposed methodology can be applied in any basin for all unconventional reservoir ecosystems worldwide
This paper introduces the problem of mining frequent integrated-geophysical data patterns and spa... more This paper introduces the problem of mining frequent integrated-geophysical data patterns and spatial association rules that are prevalent in spatio-temporal data. Due to the heterogeneous nature, large volume in size and file storage requirements, data patterns and trends hidden in these voluminous integrated data, non-trivial data mining and interpretation solutions are required. Data in different formats and domains have different dimensions, properties and attribute strengths. Conventional data mining and interpretation methods alone cannot discern data patterns and unlock the latent geological knowledge. Use of a single geophysical method of exploration and field development in any project may yield ambiguous results. Even after integration, geophysical anomalies and their trends are not well understood. Gravity, magnetic, seismic, electrical and electromagnetic datasets are typical geophysical domains, including their sub-surface geophysical data domain, when integrated for processing and interpretation of different geophysical anomalies in a single prospect domain can be effective for data mining and interpretation. Authors propose a novel approach to extract trends, correlations and patterns from different geophysical data domains, especially when they are interpreted with petroleum databases that are narrated by period and geographic dimensions using geo-ontologies representing prior knowledge. The aim of this study is to show how large amount of knowledge represented in geo-ontologies, can be used to avoid the extraction of data patterns that are previously known to be “ambiguous”.
Lecture notes in information systems and organisation, 2023
In recent years, Big Data sources and their analytics have been the focus of many researchers on ... more In recent years, Big Data sources and their analytics have been the focus of many researchers on multiple ecosystems in both commercial and research organizations. The authors, currently focus on embedded ecosystems with Big Data motivation. The embedded systems hold large volumes and variety of heterogeneous, multidimensional data and their sources complicate the organization, accessibility, presentation and interpretation in producing and service companies. For example, the authors model various events associated with human_environment_economic ecosystems (HEEE) and exploit the impacts of human and environment ecosystems with respect to economic ecosystems. The objectives of the current research are to provide an understanding of the ecosystems and their inherent connectivity through integration of multiple ecosystems' Big Data sources using data warehousing and mining approaches. Domain ontologies are described for exploring the connectivity through an effective data integration process. To this extent, data patterns and trends hidden among Big Data sources of embedded ecosystems are analyzed for new domain knowledge and its interpretation. Data structures and implementation models deduced in the current work can guide ecosystems' researchers for forecasting of resources with a scope for developing information systems and their applications. Analyzing multiple domains and systems with robust methodologies facilitates the researchers to explore future alternatives and new opportunities of Big Data in the embedded ecosystems' research arena.
Americas Conference on Information Systems, 2019
The Human Anatomy, Physiology and Psychology Integrated Egghead Relationships (HAPPIER) are embed... more The Human Anatomy, Physiology and Psychology Integrated Egghead Relationships (HAPPIER) are embedded domains of a human ecosystem. In the digital ecosystem perspective, the idiom “Integrated-Egghead-Relationships” describes composite domains with encapsulation of multiple data attribute dimensions. The increase in physiological and psychological disorders among the mass population in periodic and geographic dimensions has reported an outburst of volumes and varieties of data in healthcare ecosystems. The data heterogeneity and multidimensionality are other challenges constraining the data integration process, at times disconnecting patients and medical practitioners. Knowledge-based data models are needed to manage unstructured cognitive healthcare Big Data. The purpose of the research is to build a framework and integrate multilayered models in multidimensional warehouse repository. The integrated framework can engage multiple scopes of HAPPIER and collaborate with human cognitive anatomy and physiological data events in large-scale depositories. The repositories can bring values in new knowledge domains, making healthcare projects operationally successful.
Advances in science, technology & innovation, Dec 30, 2018
Volumes of geological and geophysical (G & G) data sources exist in the Nile Delta basin, cov... more Volumes of geological and geophysical (G & G) data sources exist in the Nile Delta basin, covering approximately 60,000 km2 in both onshore and offshore areas. Although several varieties of data continue to support the exploration and field development activities in the basin, the Big Data sources are largely unstructured, heterogeneous and multidimensional. Connecting various G & G events of onshore-transition-offshore zones and integrating them into a single repository is a complicated process. The authors proposed a holistic data warehousing and mining methodology that can support logical and physical data organization, easing the data integration process in the warehouse repository. In addition, an implementable framework, the Nile Delta Digital Petroleum Ecosystem (NDDPE) was articulated, assessing its usability and interoperability with associated data artefacts. The NDDPE can deliver Big Data guided digital ecosystem solutions that can minimize the risk of exploration.
Environmental Modeling & Assessment, Feb 13, 2016
Effective use of historical volumes of heterogeneous and multidimensional data is a major challen... more 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.
arXiv (Cornell University), Aug 8, 2022
Relevance judgment of human assessors is inherently subjective and dynamic when evaluation datase... more Relevance judgment of human assessors is inherently subjective and dynamic when evaluation datasets are created for Information Retrieval (IR) systems. However, a small group of experts' relevance judgment results are usually taken as ground truth to "objectively" evaluate the performance of the IR systems. Recent trends intend to employ a group of judges, such as outsourcing, to alleviate the potentially biased judgment results stemmed from using only a single expert's judgment. Nevertheless, different judges may have different opinions and may not agree with each other, and the inconsistency in human relevance judgment may affect the IR system evaluation results. In this research, we introduce a Relevance Judgment Convergence Degree (RJCD) to measure the quality of queries in the evaluation datasets. Experimental results reveal a strong correlation coefficient between the proposed RJCD score and the performance differences between the two IR systems.
Advances in Remote Sensing and Geo Informatics Applications, 2018
Data heterogeneity and multidimensionality are major challenges when dealing with the integration... more Data heterogeneity and multidimensionality are major challenges when dealing with the integration of exploration data sources in the frontier basins. We took advantage of the fact that the geology and geophysics (G & G) do not have boundaries either with the continents or their associated countries. Frontier basins may have generated enormous digital-data on geological structures and reservoirs in areas where the continents and their tectonic plates drifted. The digital data are indeed in Big Data scale, characterized by volumes and varieties in spatial dimensions that may have emerged in the form of conceptualization and contextualization attributes. The frontier basin research thus needs knowledge-based ecosystems, with entities, dimensions and their logical data models interpreted in geo-informatics focus. An integrated framework is proposed, generating a multidimensional metadata structure for meta-knowledge. The meta-knowledge derivable from metadata models can immensely be use...
Journal of Business Research, 2018
The emerging Big Data integration imposes diverse challenges, compromising the sustainable busine... more The emerging Big Data integration imposes diverse challenges, compromising the sustainable business research practice. Heterogeneity, multi-dimensionality, velocity, and massive volumes that challenge Big Data paradigm may preclude the effective data and system integration processes. Business alignments get affected within and across joint ventures as enterprises attempt to adapt to changes in industrial environments rapidly. In the context of the Oil and Gas industry, we design integrated artefacts for a resilient multidimensional warehouse repository. With access to several decades of resource data in upstream companies, we incorporate knowledge-based data models with spatial-temporal dimensions in data schemas to minimize ambiguity in warehouse repository implementation. The design considerations ensure uniqueness and monotonic properties of dimensions, maintaining the connectivity between artefacts and achieving the business alignments. The multidimensional attributes envisage Big Data analysts a scope of business research with valuable new knowledge for decision support systems and adding further business values in geographic scales.
Second EAGE Eastern Africa Petroleum Geoscience Forum, 2016
In spite of high pace of exploration activity in the Lake Albert basin, appraisal and field devel... more In spite of high pace of exploration activity in the Lake Albert basin, appraisal and field development become challenging in the Albertine Graben of Western Uganda. The volumes and variety of exploration data sources in these basins exist in different scales, sizes and formats in multiple dimensions (including periodic and geographic dimensions) and domains. Modelling and integrating such unstructured data need a new direction, in particular, the data structuring, storage and retrieval. We propose Big Data tools since the data in terabyte scale in multiple domains are needed to bring them together in an upstream business. We aim at a holistic information system development, simulating Petroleum Digital Ecosystem (PDE) and Petroleum Management Information System (PMIS) articulations with data modelling, data warehousing and mining, visualization and interpretation artefacts. This approach facilitates the data management not only in the Albertine Graben but from basins of Sudan, Uganda, Kenya, Tanzania, Rwanda and Burundi in the western arm of the East African Rift System (EARS). We evaluate Big Data, exploring the connectivity among multiple oil and gas fields and their associated petroleum systems, providing new insights on data integration and management, adding values to data analytics and exploration projects in the Albertine Graben context.
Geoinformatics 2013, 2013
Worldwide conventional resources are on declining trend. Alternate resources are unconventional s... more Worldwide conventional resources are on declining trend. Alternate resources are unconventional sources. Volume of datasets exists in unconventional resources, but they are neither evaluated nor unknown. They are even unaccounted for during drilling campaigns and evaluations. Because of exploration and development setbacks, productibility, recovery costs and environmental concerns, exploitation of unconventional resources is held up on global market. In order to address these issues, authors propose, multidimensional and heterogeneous, data warehousing and mining approach, supported by ontology. Data integration and exploring multiple connections among attributes of multiple dimensions of unconventional petroleum ecosystems (of different geographic, geological and production regimes) are needed. Authors attempt to make use of ontologies, written for multiple dimensions including periodically (longitudinal dimension) and geographically (distantly, lateral dimension) varying dimensions, within an unconventional resource basin. The proposed methodology is robust and can resolve issues associated with organization of unconventional resources and extend help to technology adaptation. The proposed methodology can be applied in any basin for all unconventional reservoir ecosystems worldwide.
Zenodo (CERN European Organization for Nuclear Research), Mar 15, 2023
In oil and gas industries, operational digital solutions are challenging because of spatially ext... more In oil and gas industries, operational digital solutions are challenging because of spatially extended large-size installations and the criticality of implementation solutions in business contexts, where rising global demand for energy needs is stirring. Other challenges include using obsolete tools and technology, time-consuming digital makeovers, inflexible data systems for decision support, low levels of digital maturity, and interoperable supply chain articulations between petroleum companies. To meet the demands of digital petroleum, we construct five-layer digital data models for better integration and automation in energy industries. The research aims to develop five primary platforms, a uniform transmission network, even repository systems, sensor technology, ontology interchange language (OIL) and application layer in the digital petroleum field model. An Industrial Ethernet is proposed based on a network transmission platform for the exploration industry. The network platform is an information superhighway to gather and integrate the existing automation sub-systems, di information and provide standard interfaces for future sub-systems. Sensor technology is dependable for data acquisition qualities. The OIL layer supports the digital petroleum to interchange the petroleum Big Data and their ontology descriptions into executable computing languages. Each layer gathers information, integrates ontology-driven data in a warehouse environment, and transmits data to different petroleum industry units. Uniform hardware and software platforms of distributed networks and their data structures improve petroleum resource management. Cloud-based onshore and offshore digital petroleum entities can connect petroleum-bearing basins, including sub-basins and share information in industrial automation projects. Data geoscience emerges as a digital analytics tool, irrespective of conventional or unconventional energy sources. Future energy relies on renewable sources in pollution-free environments, and the digital model is extendable in industries where industry integration and automation are required. We conceptualise the five-layer digital energy model as a digital solution development in unconventional energy industries, extending its articulations in green energy automation.
The challenges faced by domain experts, commitments made to domain-specific rule (DSR) languages ... more The challenges faced by domain experts, commitments made to domain-specific rule (DSR) languages and process design are described. We investigate the business application developments and existing challenges of evaluation strategies of DSR articulations. Often, multiple domain scenarios pose end-user predicaments complicating the computational ability of DSR. In addition, implementation of DSR and its configuration are belated due to poorly evaluated usability criteria. A new framework is needed, facilitating the DSR language and enhancing the computational intelligence. We intend to evaluate the performance of DSR generation and framework integration with variety of usability conditions including efficiency and effectiveness of configuration through system usability score (SUS). Empirical research involving experimental data, questionnaire surveys, and interview outcomes provide conclusive evaluation attributes and their fact instances from SUS. Both manual and semi-automatic configurations are tested. Semiautomatic configuration appears to be more efficient and satisfactory with regard to artefact performance, quality, learnability, user-friendly and reliability.
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Papers by Shastri Nimmagadda