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

Knowledge Management for Public Administrations: Technical Realizations of an Enterprise Attention Management System

The improvement of governments’ efficiency has gained great importance and validity especially in the current times of economic downturn. E-Government constitutes the most contemporary techno-managerial proposition in the track of possible interventions. The paper addresses, more specifically, empowerments necessitated by Public Administration (PA) organizations. Anchored on the needs of three real-life cases, the paper describes the conception and the realization of an IT artefact together with its methodological appeals aiming at improving information access and delivery and thus PAs’ decision making capacity. Our proposition constitutes a novel approach for managing users’ attention in knowledge intensive organizations which goes beyond informing a user about changes in relevant information towards proactively supporting the user to react on changes. The approach is based on an expressive attention model, which is realized by combining ECA (Event-Condition-Action) rules with ontologies. The technical realizations described in the paper constitute the underlying infrastructure of an Enterprise Attention Management System

A study of BIM collaboration requirements and available features in existing model collaboration systems

Established collaboration practices in the construction industry are document centric and are challenged by the introduction of Building Information Modelling (BIM). Document management collaboration systems (e.g. Extranets) have significantly improved the document collaboration in recent years; however their capabilities for model collaboration are limited and do not support the complex requirements of BIM collaboration. The construction industry is responding to this situation by adopting emerging model collaboration systems (MCS), such as model servers, with the ability to exploit and reuse information directly from the models to extend the current intra-disciplinary collaboration towards integrated multi-disciplinary collaboration on models. The functions of existing MCSs have evolved from the manufacturing industry and there is no concrete study on how these functions correspond to the requirements of the construction industry, especially with BIM requirements. This research has conducted focus group sessions with major industry disciplines to explore the user requirements for BIM collaboration. The research results have been used to categorise and express the features of existing MCS which are then analysed in selected MCS from a user’s perspective. The potential of MCS and the match or gap in user requirements and available model collaboration features is discussed. This study concludes that model collaborative solutions for construction industry users are available in different capacities; however a comprehensive custom built solution is yet to be realized. The research results are useful for construction industry professionals, software developers and researchers involved in exploring collaborative solutions for the construction industry

When Things Matter: A Data-Centric View of the Internet of Things

With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. While IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy, and continuous. This article surveys the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed

Issues in digital preservation: towards a new research agenda

Digital Preservation has evolved into a specialized, interdisciplinary research discipline of its own, seeing significant increases in terms of research capacity, results, but also challenges. However, with this specialization and subsequent formation of a dedicated subgroup of researchers active in this field, limitations of the challenges addressed can be observed. Digital preservation research may seem to react to problems arising, fixing problems that exist now, rather than proactively researching new solutions that may be applicable only after a few years of maturing. Recognising the benefits of bringing together researchers and practitioners with various professional backgrounds related to digital preservation, a seminar was organized in Schloss Dagstuhl, at the Leibniz Center for Informatics (18-23 July 2010), with the aim of addressing the current digital preservation challenges, with a specific focus on the automation aspects in this field. The main goal of the seminar was to outline some research challenges in digital preservation, providing a number of "research questions" that could be immediately tackled, e.g. in Doctoral Thesis. The seminar intended also to highlight the need for the digital preservation community to reach out to IT research and other research communities outside the immediate digital preservation domain, in order to jointly develop solutions

Using Fuzzy Linguistic Representations to Provide Explanatory Semantics for Data Warehouses

A data warehouse integrates large amounts of extracted and summarized data from multiple sources for direct querying and analysis. While it provides decision makers with easy access to such historical and aggregate data, the real meaning of the data has been ignored. For example, "whether a total sales amount 1,000 items indicates a good or bad sales performance" is still unclear. From the decision makers' point of view, the semantics rather than raw numbers which convey the meaning of the data is very important. In this paper, we explore the use of fuzzy technology to provide this semantics for the summarizations and aggregates developed in data warehousing systems. A three layered data warehouse semantic model, consisting of quantitative (numerical) summarization, qualitative (categorical) summarization, and quantifier summarization, is proposed for capturing and explicating the semantics of warehoused data. Based on the model, several algebraic operators are defined. We also extend the SQL language to allow for flexible queries against such enhanced data warehouses

Recommendation domains for pond aquaculture

This publication introduces the methods and results of a research project that has developed a set of decision-support tools to identify places and sets of conditions for which a particular target aquaculture technology is considered feasible and therefore good to promote. The tools also identify the nature of constraints to aquaculture development and thereby shed light on appropriate interventions to realize the potential of the target areas. The project results will be useful for policy planners and decision makers in national, regional and local governments and development funding agencies, aquaculture extension workers in regional and local governments, and researchers in aquaculture systems and rural livelihoods. (Document contains 40 pages