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... The documents' transcripts may comprise multiple conceptualized and contextualized semantic, schematic, and syntactic expressions (Alemi and Ginsparg, 2015). Ontological relationships between various words and alphanumeric characteristics of Big textual Data can facilitate improvements in the veracity and quality of documents with readability judgements (Rudra andNimmagadda, 2005 andLacasta et al. 2010). The research aims to establish the relationship between the various attributes of veracity and the reading accuracy of the text. ...
... The documents' transcripts may comprise multiple conceptualized and contextualized semantic, schematic, and syntactic expressions (Alemi and Ginsparg, 2015). Ontological relationships between various words and alphanumeric characteristics of Big textual Data can facilitate improvements in the veracity and quality of documents with readability judgements (Rudra andNimmagadda, 2005 andLacasta et al. 2010). The research aims to establish the relationship between the various attributes of veracity and the reading accuracy of the text. ...
Often the textual data are either disorganized or misinterpreted because of unstructured Big Data in multiple dimensions. Managing readable textual alphanumeric data and its analytics is challenging. In spatial dimensions, the facts can be ambiguous and inconsistent, posing interpretation and new knowledge discovery challenges. The information can be wordy, erratic, and noisy. The research aims to assimilate the data characteristics through Information System (IS) artefacts that are appropriate to data analytics, especially in application domains that involve big data sources. Data heterogeneity and multidimensionality can make and preclude IS-guided veracity models in the data integration process, including customer analytics services. The veracity of big data thus can impact visualization and value, including knowledge enhancement in the vast amount of textual data qualitatively. The manner the veracity features construed in each schematic, semantic and syntactic attribute dimension in several IS artefacts and relevant documents can enhance the readability of textual data robustly.
... Data visualization and interpretation are the other two major challenges inherently linked to the implementation of warehoused metadata models. In our approach, alphanumeric characters are ontologically modelled to their atomic levels [15], intelligently integrated and stored in a warehouse environment as unified metadata. Textual data mining and interpreting the words, sentences, text, contexts and phrases relevant to various documents in the industry scenarios are pertinent to artefacts discussed in [19]. ...
... They vary in size and depend on other related documents, their structures, concepts and contexts. If any part of the text changes, the other parts do change including their semantic, schematic and syntactic content [14,15]. When a document ecosystem generates an interpretable new knowledge, we say it is sustainable document [6,8]. ...
... 3. Semantic heterogeneitydifferences in the interpretation of the meaning of alphanumeric textual data from multiple sources and contexts. 4. System heterogeneityreconciling and accommodating the differences in the operating and hardware systems, keeping in view the differences in formats and dimensions of the document ecosystems [15,16], [9,10] and [5]. ...
The multidisciplinary textual-data are often disorganized and misinterpreted in many documents, which can obscure the information retrieval and its interpretation in company networks and even the World Wide Web. Managing textual information in particular with large-size alphanumeric data sources is challenging and at times can preclude the prompt delivery of good quality document services to diverse customers. Optimizing the words, sentences and alphanumeric characters of a script is the purpose of research, without losing intelligibility, semantics, perception, content flow and the contextual scenarios, represented as dimensions. We interpret the manuscript as a document ecosystem, within which different dimensions are construed. We choose different lexes, sentences, paragraphs and pages that possess frequent alphanumeric characters, interpreted in multiple domains and contexts. The ontologies of alphanumeric textual-data dimensions and their metaphors are presented in several data schemas, connecting various contexts of document ecosystems. The domain ontologies that can deliver text-mining, the semantic and schematic information of textual data, can expedite the textual-data integration process in the multidimensional warehouse modelling procedure. Diverse views and contexts that are generic within the document ecosystems are analysed for contextual knowledge. The ontologically structured document ecosystems that can facilitate more legibility and reproducibility to a variety of document designers are research outcomes. Data analysts, text mining experts and document managers can benefit the current research.
... The data modelling, warehousing, and mining, visualization and interpretation artefacts are articulated for adding values for the integrated interpretation project. The framework is a formulation of an ontology-based data warehousing and mining (Nimmagadda, 2015aand 2015band Rudra and Nimmagadda, 2005 approach. It predictably resolves the issues of scaling and formatting the heterogeneous and multidimensional datasets that may have inherited Dreher, 2008 andNimmagadda et al. 2012) from a large number of domains, basins and their types in the NWS. ...
... At each navigational point dimension, the structure, reservoir, source, seal data attribute dimensions and their instances are interpreted, as demonstrated in the implementation framework in Fig. 3. Extracting useful knowledge on favourable geological structures that trap the productive reservoirs of petroleum ecosystems has significance in the application of the Big Data technology, in particular its implementation for data and business analytics. We use the OLAP tools (Chen et al. 2012 andRudra andNimmagadda, 2005) for viewing the data from metadata cubes for visualization. Several data mining operations performed on metadata cubes are shown in Fig. 4. The data views extracted for exploring the connections are from porosity metadata cubes in an onshore-offshore ecosystem (of NWS) for visualization and interpretation of porosity connections among multiple reservoir systems, as interpreted in a bubble plot view in Fig. 5. ...
... At each navigational point dimension, the structure, reservoir, source, seal data attribute dimensions and their instances are interpreted, as demonstrated in the implementation framework in Fig. 3. Extracting useful knowledge on favourable geological structures that trap the productive reservoirs of petroleum ecosystems has significance in the application of the Big Data technology, in particular its implementation for data and business analytics. We use the OLAP tools (Chen et al. 2012 andRudra andNimmagadda, 2005) for viewing the data from metadata cubes for visualization. Several data mining operations performed on metadata cubes are shown in Fig. 4. The data views extracted for exploring the connections are from porosity metadata cubes in an onshore-offshore ecosystem (of NWS) for visualization and interpretation of porosity connections among multiple reservoir systems, as interpreted in a bubble plot view in Fig. 5. ...
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.
... The data modelling, warehousing, and mining, visualization and interpretation artefacts are articulated for adding values for the integrated interpretation project. The framework is a formulation of an ontology-based data warehousing and mining (Nimmagadda, 2015aand 2015band Rudra and Nimmagadda, 2005 approach. It predictably resolves the issues of scaling and formatting the heterogeneous and multidimensional datasets that may have inherited Dreher, 2008 andNimmagadda et al. 2012) from a large number of domains, basins and their types in the NWS. ...
... At each navigational point dimension, the structure, reservoir, source, seal data attribute dimensions and their instances are interpreted, as demonstrated in the implementation framework in Fig. 3. Extracting useful knowledge on favourable geological structures that trap the productive reservoirs of petroleum ecosystems has significance in the application of the Big Data technology, in particular its implementation for data and business analytics. We use the OLAP tools (Chen et al. 2012 andRudra andNimmagadda, 2005) for viewing the data from metadata cubes for visualization. Several data mining operations performed on metadata cubes are shown in Fig. 4. The data views extracted for exploring the connections are from porosity metadata cubes in an onshore-offshore ecosystem (of NWS) for visualization and interpretation of porosity connections among multiple reservoir systems, as interpreted in a bubble plot view in Fig. 5. ...
... At each navigational point dimension, the structure, reservoir, source, seal data attribute dimensions and their instances are interpreted, as demonstrated in the implementation framework in Fig. 3. Extracting useful knowledge on favourable geological structures that trap the productive reservoirs of petroleum ecosystems has significance in the application of the Big Data technology, in particular its implementation for data and business analytics. We use the OLAP tools (Chen et al. 2012 andRudra andNimmagadda, 2005) for viewing the data from metadata cubes for visualization. Several data mining operations performed on metadata cubes are shown in Fig. 4. The data views extracted for exploring the connections are from porosity metadata cubes in an onshore-offshore ecosystem (of NWS) for visualization and interpretation of porosity connections among multiple reservoir systems, as interpreted in a bubble plot view in Fig. 5. ...
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.
... Data visualization and interpretation are the other two major challenges inherently linked to the implementation of warehoused metadata models. In our approach, alphanumeric characters are ontologically modelled to their atomic levels [15], intelligently integrated and stored in a warehouse environment as unified metadata. Textual data mining and interpreting the words, sentences, text, contexts and phrases relevant to various documents in the industry scenarios are pertinent to artefacts discussed in [19]. ...
... They vary in size and depend on other related documents, their structures, concepts and contexts. If any part of the text changes, the other parts do change including their semantic, schematic and syntactic content [14,15]. When a document ecosystem generates an interpretable new knowledge, we say it is sustainable document [6,8]. ...
... 3. Semantic heterogeneitydifferences in the interpretation of the meaning of alphanumeric textual data from multiple sources and contexts. 4. System heterogeneityreconciling and accommodating the differences in the operating and hardware systems, keeping in view the differences in formats and dimensions of the document ecosystems [15,16], [9,10] and [5]. ...
Many enterprises and companies strive with information overload in particular with large size alphanumeric data including research manuscripts in the educational institutes. The purpose of the research is to optimize the sentences, words and alphanumeric characters of various kinds of manuscripts without compromising the clarity, semantics and quality of textual data. In this context, we present the concept of digital document ecosystem framework within which various dimensions related to the alphanumeric data are interpreted. Domain ontologies are described that can make connections between alphanumeric dimensions and their attribute instances. Semantic, schematic and syntactic based ontologies can facilitate the textual data integration and connectivity between various attribute dimensions. The quality of the document depends on how logically dimensions are structured in various schemas to accommodate in the form of Meta models in a multidimensional warehouse repository. These repositories, representing the digital document ecosystems provide digital solutions for complex contextual documents.
... In other words, generalization and specialization, feasible and applicable in IS research and practice, are conceptualized and contextualized in multiple data structures and problem domains. Data structuring describes fi negrained data schemas (Rudra & Nimmagadda, 2005 ) in multiple domains and integration of domain ontologies. The multidimensional big data structuring process supports heterogeneity, multidimensionality, and granularity, among multiple data sources and domains. ...
... Multidimensional models designed and developed for various ecosystems are accommodated in the warehouse modeling for storage, integration, and processing for metadata, mining, and visualization purposes as demonstrated in Fig. 5.4 . Domain ontologies and fi ne-grained denormalized multidimensional data structures (Rudra & Nimmagadda, 2005 ) are accommodated in the integrated framework (Fig. 5.4 ). Metadata is generated for mining, visualization, and interpretation purposes. ...
Big data sources and their mining from multitude of ecosystems have been the focus of many researchers in both commercial and research organizations. The authors in the current research have focused on embedded ecosystems with big data motivation. Embedded systems hold volumes and a variety of heterogeneous, multidimensional data, and their sources complicate their organization, accessibility, presentation, and interpretation. Objectives of the current research are to provide improved understanding of ecosystems and their inherent connectivity by integrating multiple ecosystems’ big data sources in a data warehouse environment and their analysis with multivariate attribute instances and magnitudes. Domain ontologies are described for connectivity, effective data integration, and mining of embedded ecosystems. The authors attempt to exploit the impacts of disease and environment ecosystems on human ecosystems. To this extent, data patterns, trends, and correlations hidden among big data sources of embedded ecosystems are analyzed for domain knowledge. Data structures and implementation models deduced in the current work can guide the researchers of health care, welfare, and environment for forecasting of resources and managing information systems that involve with big data. Analyzing embedded ecosystems with robust methodologies facilitates the researchers to explore scope and new opportunities in the domain research.
... This makes data integration process more effective. In addition, denormalization and granularity [34] of attribute relationships do make schematic and semantic ontologies flexible and efficient for data mining purposes. ...
... Ontology-based fine-grained multidimensional data structuring approach [34] appears feasible and applicable, when data are syntactically, schematically, and semantically ...
... Data models and views deduced from warehouses are interpreted for successful implementation and evaluation of interpreted data views for knowledge mapping and discovery purposes. Rudra and Nimmagadda [34] discuss major challenges and implementation issues, to overcome these challenges, and multidimensional data are made fine-grained [17]. Knowledge building analysis, including interpretation of data views from massive data structures, has been an intricate issue. ...
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 (CO2) emissions and its modeling process. The purpose of the research is to address major challenges and how best a robust “ontology-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 CO2 emissions worldwide.
... Commonly, we store structured data in the multidimensional warehouse repositories. Data views are extracted using structured query languages (Rudra and Nimmagadda, 2005). Data structures evolve through a variety of data types and their relationships. ...
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.
... The authors address the issues associated with embedded systems including the applicability and feasibility of integrated framework. HEEE produces large volumes of geographically and time-varying data [18,19]. The authors document and organize historical data sources of human, economic and environmental ecosystems' for warehousing, mining, and visualization and for interpretation and analysis of data patterns at different geographic locations and periodic intervals. ...
... Warehouse modelling Multidimensional data models designed and developed from various ecosystems, are stored in the warehouse for integration and metadata computations. The fine-grained and denormailzed multidimensional data structures [19] are accommodated in the integrated framework for effective data mining, visualization and new knowledge interpretation. ...
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
a variety of heterogeneous, multidimensional data and the
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 an 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 hide 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 facilitate the
researchers to explore future alternatives and new opportunities
of Big Data in the embedded ecosystems’ research arena.
... In the context of multidimensional ontology [4] (analogous to classified interacted dimension model), several classes of dimensions, their attributes and the relationships are used in dimensional structuring. Data models are represented in different schemas and the data relationships among the dimensions are denormalized so that the structures evolved, are fine-grained [12] and suitable for effective data mining purposes. ER and EER models [5] represent same domain knowledge, but multidimensional models represent narration of complex spatio-temporal data sources, unlike ER models have limitations in their knowledge representations. ...
Ontologies are described for elements, processes and chains of petroleum systems. Sedimentary basins, comprising of multiple petroleum systems are simulated in an integrated framework in which domain ontologies are integrated to generate a metadata. Several data views are generated from various data mining schemes. Domain knowledge is interpreted from these data views for decision support systems.
