4,261 research outputs found
On Big Data guided Unconventional Digital Ecosystems and their Knowledge Management
Establishing the reservoir connections is paramount in exploration and exploitation of unconventional petroleum systems and their reservoirs. In Big Data scale, multiple petroleum systems hold volumes and varieties of data sources. The connectivity between petroleum reservoirs and their existence in a single petroleum ecosystem is often ambiguously interpreted. They are heterogeneous and unstructured in multiple domains. They need better data integration methods to interpret the interplay between elements and processes of petroleum systems. Largescale infrastructure is needed to build data relationships between different petroleum systems. The purpose of the research is to establish the connectivity between petroleum systems through resource data management and visual analytics. We articulate a Design Science Information System (DSIS) approach, bringing various artefacts together from multiple domains of petroleum provinces. The DSIS emerges as a knowledge-based digital ecosystem innovation, justifying its need, connecting geographically controlled petroleum systems and building knowledge of oil and gas prospects
Data geo-Science Approach for Modelling Unconventional Petroleum Ecosystems and their Visual Analytics
Storage, integration and interoperability are critical
challenges in the unconventional exploration data
management. With a quest to explore unconventional
hydrocarbons, in particular, shale gas from fractured shales,
we aim at investigating new petroleum data geoscience
approaches. The data geo-science describes the
integration of geoscience-domain expertise, collaborating
mathematical concepts, computing algorithms, machine learning
tools, including data and business analytics.
Further, to strengthen data-science services among
producing companies, we propose an integrated
multidimensional repository system, for which factual
instances are acquired on gas shales, to store, process and
deliver fractured-data views in new knowledge domains.
Data dimensions are categorized to examine their
suitability in the integrated prototype articulations that use
fracture-networks and attribute dimension model
descriptions. The factual instances are typically from
seismic attributes, seismically interpreted geological
structures and reservoirs, well log, including production
data entities. For designing and developing
multidimensional repository systems, we create various
artefacts, describing conceptual, logical and physical
models. For exploring the connectivity between seismic
and geology entities, multidimensional ontology models
are construed using fracture network attribute dimensions
and their instances. Different data warehousing and mining
are added support to the management of ontologies that can
bring the data instances of fractured shales, to unify and
explore the associativity between high-dense fractured
shales and their orientations.
The models depicting collaboration of geology,
geophysics, reservoir engineering and geo-mechanics
entities and their dimensions can substantially reduce the
risk and uncertainty involved in modelling and interpreting
shale- and tight-gas reservoirs, including traps associated
with Coal Bed Methane (CBM). Anisotropy, Poisson's
ratio and Young's modulus properties corroborate the
interpretation of stress images from the 3D acoustic
characterization of shale reservoirs. The statistical analysis
of data-views, their correlations and patterns further
facilitate us to visualize and interpret geoscientific
metadata meticulously. Data geo-science guided integrated
methodology can be applied in any basin, including frontier
basins
On Logistics Management for Prosumer Business Information System Development and Implementation
Managing prosumer businesses is challenging with different types of renewable and non-renewable energy resources. The development and implementation of energy systems pose additional challenges when prosumers pursue sustainable production while simultaneously trying to mitigate gas emissions and energy losses. Issues associated with energy emissions and supply shortfalls must be addressed before developing prosumer business information systems and reaping their benefits. Innovative IS (Information System) solutions are needed to align different energy systems and prosumer coalitions that require cautious implementations. The purpose of the research is to develop IS artefacts, strategizing energy systems, establishing essential logistics requirements for smart-grids to ensure sustainable energy supplies. A conceptual Prosumer Business-based Design Science Information System (PBDSIS) framework is developed, collaborating IS articulations of prosumer business data artefacts in ecologies, where energy production and distribution need crucial logistics support and implementation of IS artefacts. The framework is implemented in prosumer business domain using open-source data
Big Data guided Digital Petroleum Ecosystems for Visual Analytics and Knowledge Management
The North West Shelf (NWS) interpreted as a Total
Petroleum System (TPS), is Super Westralian Basin with
active onshore and offshore basins through which shelf, -
slope and deep-oceanic geological events are construed. In
addition to their data associativity, TPS emerges with
geographic connectivity through phenomena of digital
petroleum ecosystem. The super basin has a multitude of
sub-basins, each basin is associated with several petroleum
systems and each system comprised of multiple oil and gas
fields with either known or unknown areal extents. Such
hierarchical ontologies make connections between
attribute relationships of diverse petroleum systems.
Besides, NWS has a scope of storing volumes of instances
in a data-warehousing environment to analyse and
motivate to create new business opportunities.
Furthermore, the big exploration data, characterized as
heterogeneous and multidimensional, can complicate the
data integration process, precluding interpretation of data
views, drawn from TPS metadata in new knowledge
domains. The research objective is to develop an
integrated framework that can unify the exploration and
other interrelated multidisciplinary data into a holistic TPS
metadata for visualization and valued interpretation.
Petroleum digital ecosystem is prototyped as a digital oil
field solution, with multitude of big data tools. Big data
associated with elements and processes of petroleum
systems are examined using prototype solutions. With
conceptual framework of Digital Petroleum Ecosystems
and Technologies (DPEST), we manage the
interconnectivity between diverse petroleum systems and
their linked basins. The ontology-based data warehousing
and mining articulations ascertain the collaboration
through data artefacts, the coexistence between different
petroleum systems and their linked oil and gas fields that
benefit the explorers. The connectivity between systems
further facilitates us with presentable exploration data
views, improvising visualization and interpretation. The
metadata with meta-knowledge in diverse knowledge
domains of digital petroleum ecosystems ensures the
quality of untapped reservoirs and their associativity
between Westralian basins
Digital Web Ecosystem Development for Managing Social Network Data Science
The World Wide Web (WWW) unfolds with diverse domains and associated data sources, complicating the network data science. In addition, heterogeneity and multidimensionality can make data management, documentation, and even integration more challenging. The WWW emerges as a complex digital ecosystem on Big Data scale, and we conceptualize the web network as a Digital Web Ecosystem (DWE) in an analytical space. The purpose of the research is to develop a framework, explore the association between attributes of social networks and assess their strengths. We have experimented network users and usability attributes of social networks and tools, including misgivings. We construe new insights from data views of DWE metadata. For leveraging the usability and popularity-sentiment attribute relationships, we compute map views and several regressions between instances of technology and society dimensions, interpreting their strengths and weaknesses. Visual analytics adds values to the DWE meta-knowledge, establishing cognitive data usability in the WWW
Exploring Unconventional Sources in Big Data: A Data Lifecycle Approach for Social and Economic Analysis with Machine Learning
This study delves into the realm of leveraging unconventional sources within the domain of Big Data for conducting insightful social and economic analyses. Employing a Data Lifecycle Approach, the research focuses on harnessing the potential of linear regression, random forest, and XGBoost techniques to extract meaningful insights from unconventional data sources. The study encompasses a structured methodology involving data collection, preprocessing, feature engineering, model selection, and iterative refinement. By applying these techniques to diverse datasets, encompassing sources like social media content, sensor data, and satellite imagery, the study aims to provide a comprehensive understanding of social and economic trends. The results obtained through these methods contribute to an enhanced comprehension of the intricate relationships within societal and economic systems, further highlighting the importance of unconventional data sources in driving valuable insights for decision-makers and researchers alike
Ontology based data warehousing for mining of heterogeneous and multidimensional data sources
Heterogeneous and multidimensional big-data sources are virtually prevalent in all business environments. System and data analysts are unable to fast-track and access big-data sources. A robust and versatile data warehousing system is developed, integrating domain ontologies from multidimensional data sources. For example, petroleum digital ecosystems and digital oil field solutions, derived from big-data petroleum (information) systems, are in increasing demand in multibillion dollar resource businesses worldwide. This work is recognized by Industrial Electronic Society of IEEE and appeared in more than 50 international conference proceedings and journals
Creating a Discipline-specific Commons for Infectious Disease Epidemiology
Objective: To create a commons for infectious disease (ID) epidemiology in
which epidemiologists, public health officers, data producers, and software
developers can not only share data and software, but receive assistance in
improving their interoperability. Materials and Methods: We represented 586
datasets, 54 software, and 24 data formats in OWL 2 and then used logical
queries to infer potentially interoperable combinations of software and
datasets, as well as statistics about the FAIRness of the collection. We
represented the objects in DATS 2.2 and a software metadata schema of our own
design. We used these representations as the basis for the Content, Search,
FAIR-o-meter, and Workflow pages that constitute the MIDAS Digital Commons.
Results: Interoperability was limited by lack of standardization of input and
output formats of software. When formats existed, they were human-readable
specifications (22/24; 92%); only 3 formats (13%) had machine-readable
specifications. Nevertheless, logical search of a triple store based on named
data formats was able to identify scores of potentially interoperable
combinations of software and datasets. Discussion: We improved the findability
and availability of a sample of software and datasets and developed metrics for
assessing interoperability. The barriers to interoperability included poor
documentation of software input/output formats and little attention to
standardization of most types of data in this field. Conclusion: Centralizing
and formalizing the representation of digital objects within a commons promotes
FAIRness, enables its measurement over time and the identification of
potentially interoperable combinations of data and software.Comment: 12 pages, 6 figure
Fostering Sustainable Creativity and Innovation in Islamic Creative Organizations
One of the prominent trends in modern creative organizations is involving organizational stakeholders in idea development, promoting cooperation, sharing knowledge, and fostering creative expression. Changes in the value writing process have become essential factors in creating innovation. This research aims to analyze how innovative organizations, such as publishers, utilize their ability to manage communication channels to foster stakeholder involvement in the value writing process collectively during innovation. Through a case study approach, this research involves 25 Islamic publishers of the Indonesian Publishers Association, utilizing websites and the social network Facebook as communication tools. The research findings indicate that idea and project development, networking, collaboration, knowledge sharing, opportunities for non-formal learning, discussion platform creation, and feedback are crucial factors driving stakeholder involvement in enhancing added value in the innovation proces
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