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

Impact of CRM adoption on organizational performance: Moderating role of technological turbulence

Purpose Customer relationship management (CRM) is instrumental to attain and sustain organizational competitive advantage. Innovation in terms of CRM adoption is the key to gain competitive advantage, and being innovative is dependent on how well organizations know about changing demands of customers and their changing ways to gain access to the market. There is hence a need to develop ongoing empirical insights from diverse management perspectives into the effect of CRM adoption on organizational performance. In this context, the purpose of this study is to develop empirical insights in relation to the moderation of technological turbulence in the banking sector. Design/methodology/approach Primary data were collected and analyzed from 277 CRM staff-members of the banking sector in Pakistan to test a conceptual model. Frequencies of demographics are calculated with correlation and regression analyses using SPSS. The correlation analysis was performed to identify the direction that exists between the dependent and independent variables, and the regression analysis was performed to study the strength/intensity of the independent variable over the dependent variable. Moderating regression analysis was performed to find the moderation effect of technological turbulence on CRM adoption and organizational performance. Findings The CRM adoption has a critical positive impact on organizational performance in the settings of business-to-customer (B2C) perspective in the banking sector. Moreover, the results uncover that improved client satisfaction through CRM adoption prompts better organizational performance in the B2C organization. The authors also have found that technological turbulence has a negative guiding impact on the association linking with CRM adoption, as well as organizational performance. Research limitations/implications The conceptual model that is proposed in this study and supported by empirical insights offers researchers to develop future research studies on the moderating role of technological turbulence to analyze the influence of CRM adoption on organizational performance. Practical implications The empirical insights of this study are valuable for the professionals in the banking sector and other B2C organizations to enrich their organizational performance through CRM adoption while considering the moderating role of technological turbulence. Originality/value Based on an empirical study, in support of an original conceptual model, the insights of this paper contribute to the extant literature in the CRM, bank marketing and management, service management, B2C marketing and the emerging economy knowledge streams

Addressing information flow in lean production management and control in construction

Traditionally, production control on construction sites has been a challenging area, where the ad-hoc production control methods foster uncertainty - one of the biggest enemies of efficiency and smooth production flow. Lean construction methods such as the Last Planner System have partially tackled this problem by addressing the flow aspect through means such as constraints analysis and commitment planning. However, such systems have relatively long planning cycles to respond to the dynamic production requirements of construction, where almost daily if not hourly control is needed. New solutions have been designed by researchers to improve this aspect such as VisiLean, but again these types of software systems require the proximity and availability of computer devices to workers. Given this observation, there is a need for a communication system between the field and site office that is highly interoperable and provides real-time task status information. A High-level communication framework (using VisiLean) is presented in this paper, which aims to overcome the problems of system integration and improve the flow of information within the production system. The framework provides, among other things, generic and standardized interfaces to simplify the “push” and “pull” of the right (production) information, whenever needed, wherever needed, by whoever needs it. Overall, it is anticipated that the reliability of the production control will be improve

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

Data Mining with Big data applications, its challenges and Future Research

Big data is the term for a collection of data sets which are enormous and complex, it contain organized and unstructured both kind of data. Data originates from all over the place, sensors used to assemble atmosphere information, presents via web-based networking media destinations, computerized pictures and recordings and so forth, This data is known as big data. Valuable data can be separated from this big data with the assistance of data mining. Data mining is a strategy for finding intriguing examples just as enlightening, reasonable models from enormous scale data. Right now reviewed sorts of big data and difficulties in big data for future. Separating valuable information from huge data-set like in all science and designing space, There will be most energizing open door in up and coming a very long time for big data. This paper incorporates big data, Data mining, Data mining with big data, Challenging issue and study papers of different organizations identified with big-data. Each organization concentrated on the most proficient method to oversee huge arrangement of data and how much organizations put resources into big-data just as what kind of return they get. Numerous specialized difficulties like implementations and visualizations are to be thought about in future. To oversee and dissect edge data investigate business openings getting from the research of edge data. Team up with the business to comprehend existing edge framework and the potential use for data. It concluded from the discoveries that Enterprise are as yet searching for the correct foundation instruments that will empower them to successfully deal with their big-data with their business needs

Developing front-end Web 2.0 technologies to access services, content and things in the future Internet

The future Internet is expected to be composed of a mesh of interoperable web services accessible from all over the web. This approach has not yet caught on since global user?service interaction is still an open issue. This paper states one vision with regard to next-generation front-end Web 2.0 technology that will enable integrated access to services, contents and things in the future Internet. In this paper, we illustrate how front-ends that wrap traditional services and resources can be tailored to the needs of end users, converting end users into prosumers (creators and consumers of service-based applications). To do this, we propose an architecture that end users without programming skills can use to create front-ends, consult catalogues of resources tailored to their needs, easily integrate and coordinate front-ends and create composite applications to orchestrate services in their back-end. The paper includes a case study illustrating that current user-centred web development tools are at a very early stage of evolution. We provide statistical data on how the proposed architecture improves these tools. This paper is based on research conducted by the Service Front End (SFE) Open Alliance initiative

Towards robust and reliable multimedia analysis through semantic integration of services

Thanks to ubiquitous Web connectivity and portable multimedia devices, it has never been so easy to produce and distribute new multimedia resources such as videos, photos, and audio. This ever-increasing production leads to an information overload for consumers, which calls for efficient multimedia retrieval techniques. Multimedia resources can be efficiently retrieved using their metadata, but the multimedia analysis methods that can automatically generate this metadata are currently not reliable enough for highly diverse multimedia content. A reliable and automatic method for analyzing general multimedia content is needed. We introduce a domain-agnostic framework that annotates multimedia resources using currently available multimedia analysis methods. By using a three-step reasoning cycle, this framework can assess and improve the quality of multimedia analysis results, by consecutively (1) combining analysis results effectively, (2) predicting which results might need improvement, and (3) invoking compatible analysis methods to retrieve new results. By using semantic descriptions for the Web services that wrap the multimedia analysis methods, compatible services can be automatically selected. By using additional semantic reasoning on these semantic descriptions, the different services can be repurposed across different use cases. We evaluated this problem-agnostic framework in the context of video face detection, and showed that it is capable of providing the best analysis results regardless of the input video. The proposed methodology can serve as a basis to build a generic multimedia annotation platform, which returns reliable results for diverse multimedia analysis problems. This allows for better metadata generation, and improves the efficient retrieval of multimedia resources

Applying federated learning to combat food fraud in food supply chains

Ensuring safe and healthy food is a big challenge due to the complexity of food supply chains and their vulnerability to many internal and external factors, including food fraud. Recent research has shown that Artificial Intelligence (AI) based algorithms, in particularly data driven Bayesian Network (BN) models, are very suitable as a tool to predict future food fraud and hence allowing food producers to take proper actions to avoid that such problems occur. Such models become even more powerful when data can be used from all actors in the supply chain, but data sharing is hampered by different interests, data security and data privacy. Federated learning (FL) may circumvent these issues as demonstrated in various areas of the life sciences. In this research, we demonstrate the potential of the FL technology for food fraud using a data driven BN, integrating data from different data owners without the data leaving the database of the data owners. To this end, a framework was constructed consisting of three geographically different data stations hosting different datasets on food fraud. Using this framework, a BN algorithm was implemented that was trained on the data of different data stations while the data remained at its physical location abiding by privacy principles. We demonstrated the applicability of the federated BN in food fraud and anticipate that such framework may support stakeholders in the food supply chain for better decision-making regarding food fraud control while still preserving the privacy and confidentiality nature of these data

Design of Back-End of Recommendation Systems Using Collective Intelligence Social Tagging

Recommendation systems are the tools whose purpose is to suggest relevant products or services to the customers. In a movie business website, the recommendation system provides users with more options, classify movies under different types to assist in arriving at a decision. Although, with current e-commerce giants focusing on hybrid filtering approach, we have decided to explore the functionality of Content-based recommendation system. This research paper aims to delve deeper into the content-based recommendation system and adding tags to enhance its functionality. The content-based approach is more fit to the movie recommendation as it overcomes the ‘cold start’ issue faced by the collaborative filtering approach, meaning, even with no ratings for a movie, it can still be recommended. The proposed method is to solve the less ‘data categorization’ issue in content-based filtering. Collective Intelligence Social Tagging System (CIST) aims at making a significant difference in content-based recommendation system to enrich the item profile and provide more accurate suggestions. The main gist of CIST is to involve the users to contribute in tagging to build a more robust system in online movie businesses. Tags in the millennial world are the ‘go to’ words that everyone looks up to in an online world of E-commerce. It’s the easiest way of telling a story without actual long sentences. We recommended three main solutions for the concerns of CIST, (a) clustering of tags to avoid synonymous tag confusion and create a metadata for movies under same tags, (b) 5 criteria model to motivate and give the most amount of genuine information for end users to trust and eventually contribute in tagging, and (c) clear way of distinguishing and displaying tags to separate primary tags and secondary tags and give a chance to the users to assess whether the given tags reflect the relevant theme of the film