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

Information System Guided Supply Chains and their Visual Analytics in Integrated Project Management

From a digital ecosystem perspective, sustainability is a manifestation of a composite entity with multiple data attribute dimensions. The data relationships may emerge between geographically distributed supply chain management ecosystems and their linked human, economic and environment ecologies. The ecosystems may exhibit inherent connections and interactions. For making connections more resilient, we characterize models that serve multiple industries through numerous data associations, even in Big Data scales. In the context of Integrated Project Management (IPM), the knowledge of boundaries between systems is mysterious, analysing diverse ecosystems through a sustainable framework can uncover new insights of inherent connections. The purpose of this research is to develop a holistic information system approach, in which multidimensional data and their connectivity are analysed, recognizing the ontological cogency, uniqueness of ecosystems and their data sources. The research outcome has facilitated the tactical development of strategies for ameliorating the sustainability challenges in the IPM contexts

Relevance Judgment Convergence Degree – A Measure of Inconsistency among Assessors for Information Retrieval

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

On knowledge-based design science information system (DSIS) for managing the unconventional digital petroleum ecosystems

© Springer International Publishing AG, part of Springer Nature 2019. The unconventional digital petroleum ecosystems are associated with fractured reservoirs that are usually unpredictable, but can produce for longer periods depending on size of petroleum systems and basins. Currently, conventional reservoirs do produce oil & gas even without integrated workflows and solutions. The heterogeneity and multidimensionality of data sources at times can make the data documentation and integration complicated affecting the exploration and field development. We examine the conventional database technologies and their failures in organizing the data of unconventional digital ecosystems. Big Data driven intelligent information system solutions are needed for addressing the issues of complex data systems of unconventional digital ecosystems. Geographically distributed petroleum systems and their associated reservoirs too demand such integrated and innovative digital ecosystem solutions. We propose an innovative design science information system (DSIS), an integrated digital framework solution to explorers, dealing with unconventional fractured reservoirs. The integrated Big Data analytics solutions are effective in interpreting unconventional digital petroleum ecosystems that are impacted by shale prospect businesses worldwide

Design and Development of a Real Time Vision Enhancement System using Image Fusion - an Algorithmic Approach

The images enhanced in different viewpoints can be registered and fused within surveillance systems. Most of the existing systems are not capable of providing a real-time image fusion. This paper embodies the design and application development of a real-time vision enhancement system using real-time image fusion. In feature-based image registration, corners are extracted, and a suitable transformation matrix is derived with which the unregistered frame is transformed. The transformed register- and reference frames are fused with discrete wavelet transform (DWT) based on maximum selection image fusion algorithm. These algorithms are implemented and validated using MATLAB/Simulink. The developed vision enhancement system provides 30 fps, and it is jitter free. The response time of the developed system is 155ms. The execution time of an un-optimized and implemented real-time image fusion algorithm on the DM642 processor stretch to 770ms. A unique experimental setup designed has enabled us to achieve an optimum vision enhancement for security and surveillance applications. The algorithm is further optimized to provide us an average execution time of 740ms. The development of the system is extended to two dissimilar cameras moving in different directions. In the current research, the feasibility and applicability of the real-time vision and fusion systems are explored in various security and surveillance application scenarios

On Managing Contextual Knowledge of Digital Document Ecosystems, characterized by Alphanumeric Textual Data

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

Design of a SWOT Analysis Model and its Evaluation in Diverse Digital Business Ecosystem Contexts

Investigating the Strengths, Weaknesses, Opportunities and Threats (SWOT) of enterprise systems is popular among business researchers in major organizations. Many establishments carry out SWOT analysis at strategic planning, quality control while formulating government policies and legislations. In the digital ecosystems scenarios, the SWOT activities need a great deal of attention, in particular, while designing and promoting new strategies of multiple industry scenarios in the Integrated Project Management context, keeping in view complex business operations. Information solutions may not have choices, failing to address priorities and provide alternate solutions. We focus on digital ecosystem methodologies, in which the business and organizational issues, challenges and priorities are addressed. The purpose of the research is designing a new SWOT model in which the elements are modelled to interrogate managersâ views to oversee new insights of a variety of business contexts that can guide SWOT analyzers and provide digital ecosystem services in multiple industry operations in an optimum manner. Issues and challenges of elements of SWOT of several public and private sector companies are analyzed, documented and modelled to evaluate unified metadata representing multiple industry views, their visualization and interpretation in new knowledge domains

On Modelling Big Data Guided Supply Chains in Knowledge-Base Geographic Information Systems

We examine the existing goals of business- and geographic - information systems and their influence on logistics and supply chain management systems. Modelling supply chain management systems is held back because of lack of consistent and poorly aligned data with supply chain elements and processes. The issues constraining the decision-making process limit the connectivity between supply chains and geographically controlled database systems. The heterogeneous and unstructured data are added challenges to connectivity and integration processes. The research focus is on analysing the data heterogeneity and multidimensionality relevant to supply chain systems and geographically controlled databases. In pursuance of the challenges, a unified methodological framework is designed with data structuring, data warehousing and mining, visualization and interpretation artefacts to support connectivity and integration process. Multidimensional ontologies, ecosystem conceptualization and Big Data novelty are added motivations, facilitating the relationships between events of supply chain operations. The models construed for optimizing the resources are analysed in terms of effectiveness of the integrated framework articulations in global supply chains that obey laws of geography. The integrated articulations analysed with laws of geography can affect the operational costs, sure for better with reduced lead times and enhanced stock management

On a robust data modelling approach for managing the fractured reservoirs in an onshore colombian oil & gas field

Neither the limits of elements and processes of the petroleum systems of Colombian sedimentary basins are known nor interpreted without ambiguities undermining the reservoir complexities and hampering the data integration in the upstream business. Partly it is due to poor understanding of the datasets and poorly articulated data modelling, visualization and interpretation artefacts in complex geological regimes. We propose an ontology based multidimensional warehouse repository approach with ontology constructs and models for various data sources, acquired from multiple domains of upstream business. We choose several data volumes, variety of multidimensional data attributes and their fact instances for interpreting seismically integrated geological horizons. Structure and fracture attribute map views are computed to ascertain the density of fractures and their orientations, calibrating the fracture signatures with production data existing within the interpreted faulted compartments. Field development plans are assessed based on new knowledge, obtained from domain ontology descriptions, exploring connections among multi-stacked fractured reservoirs. Though we find no structural bearing on the accumulations of oil and gas in the study area, the fracture density and orientation appear to have definite bearing on production. Integrated framework minimizes the ambiguity involved in the interpretation of fractures, their density and orientations in the study area