A unified view of data-intensive flows in business intelligence systems : a survey

Data-intensive flows are central processes in today’s business intelligence (BI) systems, deploying different technologies to deliver data, from a multitude of data sources, in user-preferred and analysis-ready formats. To meet complex requirements of next generation BI systems, we often need an effective combination of the traditionally batched extract-transform-load (ETL) processes that populate a data warehouse (DW) from integrated data sources, and more real-time and operational data flows that integrate source data at runtime. Both academia and industry thus must have a clear understanding of the foundations of data-intensive flows and the challenges of moving towards next generation BI environments. In this paper we present a survey of today’s research on data-intensive flows and the related fundamental fields of database theory. The study is based on a proposed set of dimensions describing the important challenges of data-intensive flows in the next generation BI setting. As a result of this survey, we envision an architecture of a system for managing the lifecycle of data-intensive flows. The results further provide a comprehensive understanding of data-intensive flows, recognizing challenges that still are to be addressed, and how the current solutions can be applied for addressing these challenges.Peer ReviewedPostprint (author's final draft

Data and Artificial Intelligence Strategy: A Conceptual Enterprise Big Data Cloud Architecture to Enable Market-Oriented Organisations

Market-Oriented companies are committed to understanding both the needs of their customers, and the capabilities and plans of their competitors through the processes of acquiring and evaluating market information in a systematic and anticipatory manner. On the other hand, most companies in the last years have defined that one of their main strategic objectives for the next years is to become a truly data-driven organisation in the current Big Data context. They are willing to invest heavily in Data and Artificial Intelligence Strategy and build enterprise data platforms that will enable this Market-Oriented vision. In this paper, it is presented an Artificial Intelligence Cloud Architecture capable to help global companies to move from the use of data from descriptive to prescriptive and leveraging existing cloud services to deliver true Market-Oriented in a much shorter time (compared with traditional approaches).This paper has been elaborated with the financing of FEDER funds in the Spanish National research project TIN2016-75850-R from Spanish Department for Economy and Competitiveness

Data and Artificial Intelligence Strategy: A Conceptual Enterprise Big Data Cloud Architecture to Enable Market-Oriented Organisations

Market-Oriented companies are committed to understanding both the needs of their customers, and the capabilities and plans of their competitors through the processes of acquiring and evaluating market information in a systematic and anticipatory manner. On the other hand, most companies in the last years have defined that one of their main strategic objectives for the next years is to become a truly data-driven organisation in the current Big Data context. They are willing to invest heavily in Data and Artificial Intelligence Strategy and build enterprise data platforms that will enable this Market-Oriented vision. In this paper, it is presented an Artificial Intelligence Cloud Architecture capable to help global companies to move from the use of data from descriptive to prescriptive and leveraging existing cloud services to deliver true Market-Oriented in a much shorter time (compared with traditional approaches)

TEXTUAL DATA MINING FOR NEXT GENERATION INTELLIGENT DECISION MAKING IN INDUSTRIAL ENVIRONMENT: A SURVEY

This paper proposes textual data mining as a next generation intelligent decision making technology for sustainable knowledge management solutions in any industrial environment. A detailed survey of applications of Data Mining techniques for exploiting information from different data formats and transforming this information into knowledge is presented in the literature survey. The focus of the survey is to show the power of different data mining techniques for exploiting information from data. The literature surveyed in this paper shows that intelligent decision making is of great importance in many contexts within manufacturing, construction and business generally. Business intelligence tools, which can be interpreted as decision support tools, are of increasing importance to companies for their success within competitive global markets. However, these tools are dependent on the relevancy, accuracy and overall quality of the knowledge on which they are based and which they use. Thus the research work presented in the paper uncover the importance and power of different data mining techniques supported by text mining methods used to exploit information from semi-structured or un-structured data formats. A great source of information is available in these formats and when exploited by combined efforts of data and text mining tools help the decision maker to take effective decision for the enhancement of business of industry and discovery of useful knowledge is made for next generation of intelligent decision making. Thus the survey shows the power of textual data mining as the next generation technology for intelligent decision making in the industrial environment

BIG DATA AND ANALYTICS AS A NEW FRONTIER OF ENTERPRISE DATA MANAGEMENT

Big Data and Analytics (BDA) promises significant value generation opportunities across industries. Even though companies increase their investments, their BDA initiatives fall short of expectations and they struggle to guarantee a return on investments. In order to create business value from BDA, companies must build and extend their data-related capabilities. While BDA literature has emphasized the capabilities needed to analyze the increasing volumes of data from heterogeneous sources, EDM researchers have suggested organizational capabilities to improve data quality. However, to date, little is known how companies actually orchestrate the allocated resources, especially regarding the quality and use of data to create value from BDA. Considering these gaps, this thesis – through five interrelated essays – investigates how companies adapt their EDM capabilities to create additional business value from BDA. The first essay lays the foundation of the thesis by investigating how companies extend their Business Intelligence and Analytics (BI&A) capabilities to build more comprehensive enterprise analytics platforms. The second and third essays contribute to fundamental reflections on how organizations are changing and designing data governance in the context of BDA. The fourth and fifth essays look at how companies provide high quality data to an increasing number of users with innovative EDM tools, that are, machine learning (ML) and enterprise data catalogs (EDC). The thesis outcomes show that BDA has profound implications on EDM practices. In the past, operational data processing and analytical data processing were two “worlds” that were managed separately from each other. With BDA, these "worlds" are becoming increasingly interdependent and organizations must manage the lifecycles of data and analytics products in close coordination. Also, with BDA, data have become the long-expected, strategically relevant resource. As such data must now be viewed as a distinct value driver separate from IT as it requires specific mechanisms to foster value creation from BDA. BDA thus extends data governance goals: in addition to data quality and regulatory compliance, governance should facilitate data use by broadening data availability and enabling data monetization. Accordingly, companies establish comprehensive data governance designs including structural, procedural, and relational mechanisms to enable a broad network of employees to work with data. Existing EDM practices therefore need to be rethought to meet the emerging BDA requirements. While ML is a promising solution to improve data quality in a scalable and adaptable way, EDCs help companies democratize data to a broader range of employees

IoT and Industry 4.0 technologies in Digital Manufacturing Transformation

The evolution of internet of things, cyber physical system, digital twin and artificial intelligence is stimulating the transformation of the product-centric processes toward smart control digital service-oriented ones. With the implementation of artificial intelligence and machine learning algorithms, IoT has accelerated the movement from connecting devices to the Internet to collecting and analyzing data by using sensors to extract data throughout the lifecycle of the product, in order to create value and knowledge from the huge amount of the collected data, such as the knowledge of the product performance and conditions. The importance of internet of things technology in manufacturing comes from its ability to collect real time data and extract valuable knowledge from these huge amount of data which can be supported through the implementation of smart IoT-based servitization framework which was presented in this research together with a 10-steps approach diagram. Moreover, literature review has been carried out to develop the research and deepen the knowledge in the field of IoT, CPS, DT and Artificial Intelligence, and then interviews with experts have been conducted to validate the contents, since DT is a quite new technology, so there are different points of view about certain concepts of this technology. The main scope and objective of this research is to allow organizational processes and companies to benefit form the value added information that can be achieved through the right implementation of advanced technologies such as IoT, DT, CPS, and artificial intelligence which can provide financial benefits to the manufacturing companies and competitive advantages to make them stand among the other competitors in the market. The effectiveness of such technologies can not only improve the financial benefits of the companies, but the workers\u2019 safety and health through the real time monitoring of the work environment. Here in this research the main aim is to present the right frameworks that can be used in the literature through companies and researchers to allow them to implement these technologies correctly in the boundaries of their businesses. In addition to that, the Smart factory concept, as introduced in the context of Industry 4.0, promotes the development of a new interconnected manufacturing environment where human operators cooperate with machines. While the role of the operator in the smart factory is substantially being rediscussed, the industrial approach towards safety and ergonomics still appears frequently outdated and inadequate. This research approaches such topic referring to the vibration risk, a well-known cause of work-related pathologies, and proposes an original methodology for mapping the risk exposure related to the performed activities. A miniaturized wearable device is employed to collect vibration data, and the obtained signals are segmented and processed in order to extract the significant features. An original machine learning classifier is then employed to recognize the worker\u2019s activity and evaluate the related exposure to vibration risks. Finally, the results obtained from the experimental analysis demonstrate feasibility and the effectiveness of the proposed methodology

TLAD 2011 Proceedings:9th international workshop on teaching, learning and assesment of databases (TLAD)

This is the ninth in the series of highly successful international workshops on the Teaching, Learning and Assessment of Databases (TLAD 2011), which once again is held as a workshop of BNCOD 2011 - the 28th British National Conference on Databases. TLAD 2011 is held on the 11th July at Manchester University, just before BNCOD, and hopes to be just as successful as its predecessors.The teaching of databases is central to all Computing Science, Software Engineering, Information Systems and Information Technology courses, and this year, the workshop aims to continue the tradition of bringing together both database teachers and researchers, in order to share good learning, teaching and assessment practice and experience, and further the growing community amongst database academics. As well as attracting academics from the UK community, the workshop has also been successful in attracting academics from the wider international community, through serving on the programme committee, and attending and presenting papers.Due to the healthy number of high quality submissions this year, the workshop will present eight peer reviewed papers. Of these, six will be presented as full papers and two as short papers. These papers cover a number of themes, including: the teaching of data mining and data warehousing, databases and the cloud, and novel uses of technology in teaching and assessment. It is expected that these papers will stimulate discussion at the workshop itself and beyond. This year, the focus on providing a forum for discussion is enhanced through a panel discussion on assessment in database modules, with David Nelson (of the University of Sunderland), Al Monger (of Southampton Solent University) and Charles Boisvert (of Sheffield Hallam University) as the expert panel

TLAD 2011 Proceedings:9th international workshop on teaching, learning and assesment of databases (TLAD)

This is the ninth in the series of highly successful international workshops on the Teaching, Learning and Assessment of Databases (TLAD 2011), which once again is held as a workshop of BNCOD 2011 - the 28th British National Conference on Databases. TLAD 2011 is held on the 11th July at Manchester University, just before BNCOD, and hopes to be just as successful as its predecessors.The teaching of databases is central to all Computing Science, Software Engineering, Information Systems and Information Technology courses, and this year, the workshop aims to continue the tradition of bringing together both database teachers and researchers, in order to share good learning, teaching and assessment practice and experience, and further the growing community amongst database academics. As well as attracting academics from the UK community, the workshop has also been successful in attracting academics from the wider international community, through serving on the programme committee, and attending and presenting papers.Due to the healthy number of high quality submissions this year, the workshop will present eight peer reviewed papers. Of these, six will be presented as full papers and two as short papers. These papers cover a number of themes, including: the teaching of data mining and data warehousing, databases and the cloud, and novel uses of technology in teaching and assessment. It is expected that these papers will stimulate discussion at the workshop itself and beyond. This year, the focus on providing a forum for discussion is enhanced through a panel discussion on assessment in database modules, with David Nelson (of the University of Sunderland), Al Monger (of Southampton Solent University) and Charles Boisvert (of Sheffield Hallam University) as the expert panel

Incorporation of ontologies in data warehouse/business intelligence systems - A systematic literature review

Semantic Web (SW) techniques, such as ontologies, are used in Information Systems (IS) to cope with the growing need for sharing and reusing data and knowledge in various research areas. Despite the increasing emphasis on unstructured data analysis in IS, structured data and its analysis remain critical for organizational performance management. This systematic literature review aims at analyzing the incorporation and impact of ontologies in Data Warehouse/Business Intelligence (DW/BI) systems, contributing to the current literature by providing a classification of works based on the field of each case study, SW techniques used, and the authors’ motivations for using them, with a focus on DW/BI design, development and exploration tasks. A search strategy was developed, including the definition of keywords, inclusion and exclusion criteria, and the selection of search engines. Ontologies are mainly defined using the Ontology Web Language standard to support multiple DW/BI tasks, such as Dimensional Modeling, Requirement Analysis, Extract-Transform-Load, and BI Application Design. Reviewed authors present a variety of motivations for ontology-driven solutions in DW/BI, such as eliminating or solving data heterogeneity/semantics problems, increasing interoperability, facilitating integration, or providing semantic content for requirements and data analysis. Further, implications for practice and research agenda are indicated.info:eu-repo/semantics/publishedVersio