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 incorrectmedications 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 patient?s 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 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

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 unknownrelationships 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 data warehouse modelling - a methodology for managing petroleum field ecosystems

Petroleum field ecosystems offer an interesting and productive domain for ontology based data warehousing model and methodology development. This paper explains the opportunities and challenges confronting modellers, methodologists, and managers operating in the petroleum business and provides some detailed techniques and suggested methods for constructing and using the ontology based warehouse.Ecologically sensitive operations such as well drilling, well production, exploration, and reservoir development can be guided and carefully planned based on data mined from a suitable constructed data warehouse. Derivation of business intelligence, simulations and vizualisation can also be driven by online analytical processing based on warehoused data and metadata

Design of petroleum company's metadata and an effective knowledge mapping methodology

Success of information flow depends on intelligent datastorage and its management in a multi-disciplinaryenvironment. Multi-dimensional data entities, data typesand ambiguous semantics, often pose uncertainty andinconsistency in data retrieval from volumes of petroleumdata sources. In our approach, conceptual schemas andsub-schemas have been described based on variousoperational functions of the petroleum industry. Theseschemas are integrated, to ensure their consistency andvalidity, so that the information retrieved from anintegrated metadata (in the form of a data warehouse)structure derives its authenticity from its implementation.The data integration process validating the petroleummetadata has been demonstrated for one of the Gulfoffshore basins for an effective knowledge mapping andinterpreting it successfully for the derivation of usefulgeological knowledge. Warehoused data are used formining data patterns, trends and correlations amongknowledge-base data attributes that led to interpretation ofinteresting geological features. These technologies appearto be more amenable for exploration of more petroleumresources in the mature gulf basins

Roles of multidimensionality and granularity in warehousing Australian resources data

Granularity of data modeled in multidimensional data structures is an important factor for a data warehouse. Grain sizes and number of dimensions participating in the model are critical in ascertaining the quality of analytical queries that are run on such data warehouses. In this paper, exploration and production data of Australian resources industry, pertinent to oil and gas, over the past five decades have been examined for multidimensionality and grain size. This research shows how using an ER approach combined with multidimensional data modeling helps in considerable reduction in the size of the data warehouse, making it more effective and efficient

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 differentgeographic 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

Mapping and modeling of oil and gas relational data objects for warehouse development and efficient data mining

Oil and gas industries archive volumes of heterogeneous data. These companies, by virtue of their diverse operations, comprise of complicated organizational structure. The nature of organizational set-up with several operational units, often results with communication barriers among operational units. In order to effectively and efficiently perform oil and gas company's business activities, the flow of data and information must be consistent and sharing among its units. In order to improve information sharing among oil and gas company's personnel, heterogeneous data from various sources are integrated. Data warehouse is a solution, in which, oil and gas data entities, identified as class objects, are used for multidimensional modeling. Relational data structures constructed using these class objects, are stored in a warehousing environment to minimize the complexity of heterogeneous data and enhances power of data integration and information sharing among different operational units

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 businessprocesses, 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