Shastri L. Nimmagadda

Ontology based data warehouse modeling and managing ecology of human body for disease and drug prescription management

Health care sector is currently experiencing a major crisis with information overload. With the increasing prevalence of chronic diseases and the ageing population the amount of paper-work is more than ever before. In the US, a hospital admission of one patient generates an estimate of 60 pieces of paper. The federal governments of various countries have passed policies and initiatives that focus on introducing information systems into the health care sector. Technology will immensely reduce the cost of managing patients and even reduce the risks of mis-diagnosing and prescribing incorrect medications to patients. This paper primarily focuses on introducing the concept of ontology based warehouse modelling and managing ecology of human body for disease and drug prescription management. Disorders of the human body and factors such as the patientpsilas age, living and working conditions, familial and genetic influences can be simulated into Metadata in a warehousing environment. In this environment, various relationships are identified and described between these factors and the diseases. Secondly, we also introduce ontological representation of the various human body systems such as the digestive, musculoskeletal and nervous system in disease processes. Although this is an extensive and complex knowledge domain, the work in this paper is one of the first to attempt to introduce the use of ontology based data warehousing and data mining conceptually. We also aim at implementing and applying this research in practice.

Ontology based data warehouse modeling and mining of earthquake data: prediction analysis along Eurasian-Australian continental plates

Seismological observatories archive volumes of heterogeneous types of earthquake data. These organizations, by virtue of their geographic operations, handle complicated hierarchical data structures. In order to effectively and efficiently perform seismological observatories business activities, the flow of data and information must be consistent and information is shared among its units, situated at different geographic locations. In order to improve information sharing among observatories, heterogeneous nature of earthquake data from various sources are intelligently integrated. Data warehouse is a solution, in which, earthquake data entities are modeled using ontology-base multidimensional representation. These data are structured and stored in multi-dimensions in a warehousing environment to minimize the complexity of heterogeneous data. Authors are of the view that data integration process adds value to knowledge building and information sharing among different observatories. Authors suggest that warehoused data modeling facilitates earthquake prediction analysis more effectively.

Data warehouse structuring methodologies for efficient mining of Western Australian petroleum data sources

Representing the knowledge domain of a petroleum system is a complex problem. In the present study, logical modelling of shared attributes of resources industry entities (dimensions or objects) has been used for construction of a dynamic and time-variant metadata model. This work demonstrates effectiveness of multidimensional data modelling for petroleum industry, which will be further investigated for fine-grain data presentation and interpretation for quality knowledge discovery.

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

Resources businesses often undergo turbulent and volatile periods, due to rapid increase of resource demand and poorly organised resources data volumes. This volatile industry operates multifaceted business units that manage heterogeneous data sources. Data integration and interactive business processes, distributed across complex business environments, need attention. Historical resources data, geographically (spatial dimension) archived for decades (periodic dimension), are source of analysing past business data dimensions and predicting their future turbulences. Periodic data, modelled in an integrated and robust warehouse environment, are explored using data mining methodologies. The data models presented, will optimise future inputs in the turbulent resources business environments.

On Robust Methodologies for Managing Public Health Care Systems

Authors focus on ontology-based multidimensional data warehousing and mining methodologies, addressing various issues on organizing, reporting and documenting diabetic cases and their associated ailments, including causalities. Map and other diagnostic data views, depicting similarity and comparison of attributes, extracted from warehouses, are used for understanding the ailments, based on gender, age, geography, food-habits and other hereditary event attributes. In addition to rigor on data mining and visualization, an added focus is on values of interpretation of data views, from processed full-bodied diagnosis, subsequent prescription and appropriate medications. The proposed methodology, is a robust back-end application, for web-based patient-doctor consultations and e-Health care management systems through which, billions of dollars spent on medical services, can be saved, in addition to improving quality of life and average life span of a person. Government health departments and agencies, private and government medical practitioners including social welfare organizations are typical users of these systems.

On new emerging concepts of modeling petroleum digital ecosystems by multidimensional data warehousing and mining approaches

Petroleum system and its ingredients 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. Significance of this concept is to make connectivity among petroleum systems through attributes of ingredients and their contextualization and specification. Most popularly known ingredients are geological structure, reservoir, source and seal rocks. Other ingredients involved are in the form of process of these ingredients such as maturation (of source rocks) and migration and timing (of formation of structure, reservoir and seal rocks). One can notice the connectivity among primary petroleum system ingredients, through different processes, such as maturation of source rocks and charging capability and migration of hydrocarbons into suitable structural (structure) entrapment areas of reservoir. Unless the phenomenon of interconnectivity is understood; integration between ingredients and processes in the context of digital representation and visualization, petroleum system existence and its survival cannot be well explained. Its value cannot be added in terms of petroleum accumulations and volumes, unless these phenomena are explicit. Authors propose ontology based data warehousing and data mining technologies, in which, conceptualization and contextualization of multiple data dimensions (petroleum systems ingredients and processes), integration (within data warehouse environment) and data mining of interpretable emerging petroleum digital ecosystems are accomplished. Multidimensional data warehousing and mining facilitate an effective interpretation of petroleum systems, minimizing the ambiguities involved during structure and reservoir qualifications and quantifications.

Ontology of Western Australian petroleum data for effective data warehouse design and data mining

Volumes of petroleum resources data are archived in different companies. These are of heterogeneous form - either in relational, hierarchical or network structures. With the widespread use of these databases and explosive growth in their sizes, petroleum businesses face a problem of information overload. Effectively utilizing these data volumes is a major challenge for this type of industry. Data search becomes tedious, at times when specific queries are made, due to data accumulated in several geographic locations. In this research work, we revisit these data and propose to simplify these heterogeneous data structures through an ontological data modelling approach particularly to address the issues of data integration and information sharing. Various ontological models have been described in the context of Western Australian petroleum systems. An ontological framework, with a mechanism to integrate petroleum resources data in a warehouse environment has been investigated. Ontology design benefits and its impacts on designing data mining algorithms are discussed. Ontology approach ensures petroleum data validity that supports petroleum knowledge mining process.

Knowledge Base Smarter Articulations for the Open Directory Project in a Sustainable Digital Ecosystem

We examine the volumes and varieties of data sources of the Open Directory Project (ODP), which can endure, regenerate and flourish with new knowledge. The ODP motivates us in building a knowledge base smarter multidimensional data constructs and models. We articulate the models with new artefacts, addressing the heterogeneity and multidimensionality of the data. The conceptualization and contextualization of various entities and dimensions have emerged with innovation that led us to develop a digital ecosystem-based inventory. The ODP based domain ontologies support the warehouse repository, which accommodates multidimensional data relationships. The concept of a digital ecosystem in the ODP context is to bring the dimensions together and unite with multidimensional schemas. We explore the Big Data, incorporating their characteristics in the ODP constructs and models. The volumes and varieties of the ODP data are logically organized and integrated in the warehouse repositories. The multidimensional data modelling makes the ODP more smart and flexible in an environment, where varieties of business rules and constraints change rapidly. The visualization and interpretation are the other artefacts of the Big Data facilitating us use, reuse, test the interoperability and effectiveness of the data models for sustainable ODP digital ecosystem. We compute the polynomial regressions, based on the data fluctuations of the ODP as observed in the scatter plots, providing new data mining models for knowledge interpretation.

Multidimensional ontology modeling of human digital ecosystems affected by social behavioural data patterns

Relational and hierarchical data modeling studies are carried out, using simple and explicit comparison based ontology. The comparison is basically performed on relationally and hierarchically structured data entities/dimensions. This methodology is adopted to understand the human ecosystem that is affected by human behavioural and social disorder data patterns. For example, the comparison may be made among human systems, which could be between male and female, fat and slim, disabled and normal (physical impairment), again normal and abnormal (psychological), smokers and non-smokers and among different age group domains. There could be different hierarchies among which, different super-type dimensions are conceptualized into several subtype dimensions and integrated them by connecting the interrelated several common data attributes. Domain ontologies are built based on the known-knowledge mining and thus unknown relationships are modeled that are affected by social behaviour data patterns. This study is useful in understanding human situations, behavioral patterns and social ecology that can facilitate health and medical practitioners, social workers and psychologists, while treating their patients and clients.

Ontology based warehouse modeling of fractured reservoir ecosystems — For an effective borehole and petroleum production management

Exploration business deals with structure and reservoir data. The carbonate reservoirs, (especially of Jurassic age), establish its hydrocarbon potential and production on commercial scale in Middle Eastern onshore petroleum systems. There is an immense scope of exploration and production from the fractured horizons and their associated reservoirs. Most of the fractures are networked or interconnected through fluid media. The wells drilled in carbonate reservoir areas, have been under an unbalanced-stress system that exhibits commonly two types of borehole failures, shear and tensile failure, where the rocks drilled, are replaced with drilling mud. Rocks undergo hoop and radial stresses that occur by drilling and also natural fracturing. A robust methodology is needed to address issues of integrating multiple fracture systems. Issues relevant to borehole management are addressed through ontology modeling of networked fractures. Authors propose data warehousing approach supported by ontology that can integrate data attributes associated with fractures of multiple horizons from several wells, geographically (distantly) located within a producing basin. Authors attempt to make connectivity between structure and reservoir data attributes. Integration is done by mapping and modeling conceptually (more logically) interpreted relationships among multidimensional inter-dependent data structures and attributes through their data property instances that are described from different fracture systems. Data mining can separate out these stresses, so that driller or well planner knows in advance the fracture systems that are being drilled. The proposed methodology is robust and can resolve issues relevant to deviation and smart drilling in the fractured reservoir systems. This approach integrates and makes connectivity among varying common and conceptualized attributes associated with structure and reservoir. If the proposed methodology is successful, it can be applied any fractured shales and tight-gas reservoir systems worldwide.