Examining the quality and management of non-geometric building information modelling data at project hand-over

Through the exponential global increase of Building Information Modelling (BIM) adoption across the Construction industry, and the emergence of inter-connected, strategic and data-rich solutions; such as Big Data, the Internet of Things and Smart Cities, the importance associated with activities and decisions reliant on exact data input, transaction, analysis, and resulting actions becomes exponentially magnified. The supply of inaccurate BIM data may negatively impact on systems and processes that require fully assured data of appropriate quality/veracity, to support informed decision making, deliver functionality, facilitate services, or direct strategic actions within the built environment. This preliminary research intends to provide a catalyst for discussion, analysis and information retrieval relating to Building Information Modelling (BIM) processes where non-geometric data errors may; or are predicted to occur within a project environment. This may result in the delivery of data that cannot be described as representing truth or of good quality, and therefore of little value or use to the data user. The wider aspects of this research investigates specifically non-geometric data veracity & associated dimensions of data quality; in order to discover and explore future solutions to resolve current industry data quality assessment challenges. This paper provides feedback from the research focusing on the current state, presenting existing industry challenges and proposes further research areas based on initial findings

Data science

Even though it has only entered public perception relatively recently, the term "data science" already means many things to many people. This chapter explores both top-down and bottom-up views on the field, on the basis of which we define data science as "a unique blend of principles and methods from analytics, engineering, entrepreneurship and communication that aim at generating value from the data itself". The chapter then discusses the disciplines that contribute to this "blend", briefly outlining their contributions and giving pointers for readers interested in exploring their backgrounds further

Development of a heart failure filter for Medline: an objective approach using evidence-based clinical practice guidelines as an alternative to hand searching

<p>Abstract</p> <p>Background</p> <p>Heart failure is a highly debilitating syndrome with a poor prognosis primarily affecting the elderly. Clinicians wanting timely access to heart failure evidence to provide optimal patient care can face many challenges in locating this evidence.</p> <p>This study developed and validated a search filter of high clinical utility for the retrieval of heart failure articles in OvidSP Medline.</p> <p>Methods</p> <p>A Clinical Advisory Group was established to advise study investigators. The study set of 876 relevant articles from four heart failure clinical practice guidelines was divided into three datasets: a Term Identification Set, a Filter Development Set, and a Filter Validation Set. A further validation set (the Cochrane Validation Set) was formed using studies included in Cochrane heart failure systematic reviews. Candidate search terms were identified via word frequency analysis. The filter was developed by creating combinations of terms and recording their performance in retrieving items from the Filter Development Set. The filter's recall was then validated in both the Filter Validation Set and the Cochrane Validation Set. A precision estimate was obtained post-hoc by running the filter in Medline and screening the first 200 retrievals for relevance to heart failure.</p> <p>Results</p> <p>The four-term filter achieved a recall of 96.9% in the Filter Development Set; 98.2% in the Filter Validation Set; and 97.8% in the Cochrane Validation Set. Of the first 200 references retrieved by the filter when run in Medline, 150 were deemed relevant and 50 irrelevant. The post-hoc precision estimate was therefore 75%.</p> <p>Conclusions</p> <p>This study describes an objective method for developing a validated heart failure filter of high recall performance and then testing its precision post-hoc. Clinical practice guidelines were found to be a feasible alternative to hand searching in creating a gold standard for filter development. Guidelines may be especially appropriate given their clinical utility. A validated heart failure filter is now available to support health professionals seeking reliable and efficient access to the heart failure literature.</p

Mind the Gap: Transitions Between Concepts of Information in Varied Domains

The concept of 'information' in five different realms – technological, physical, biological, social and philosophical – is briefly examined. The 'gaps' between these conceptions are dis‐ cussed, and unifying frameworks of diverse nature, including those of Shannon/Wiener, Landauer, Stonier, Bates and Floridi, are examined. The value of attempting to bridge the gaps, while avoiding shallow analogies, is explained. With information physics gaining general acceptance, and biology gaining the status of an information science, it seems rational to look for links, relationships, analogies and even helpful metaphors between them and the library/information sciences. Prospects for doing so, involving concepts of complexity and emergence, are suggested

Decentralized Industrial IoT Data Management Based on Blockchain and IPFS

The wide application of Internet of Things (IoT) has fostered the development of Industry 4.0. In manufacturing domain, Industrial IoT (IIoT) are key components of the Factories of the Future (FoF). The big IIoT data are the foundation of implementing data-driven strategies. In current industrial practice, most of these IIoT data are wasted or fragmented in data silos due to security and privacy concerns. Novel data management approaches are required to replace traditional centralized data management systems. The rapid development of blockchain technologies provides a novel solution for this challenge lever-aging its unique characteristics such as decentralization, immutability and traceability. However, blockchain is inefficient for exchanging big data due to transaction throughput limits. The peer-to-peer InterPlanetary File System (IPFS) provides a suitable complement for blockchain. Therefore, this paper aims to propose a decentralized IIoT data management approach based on blockchain and IPFS technology. The architecture and enabling technologies of the proposed system are introduced. A proof-of-concept implementation is realized and relevant experiments are conducted. The results demonstrated the feasibility of the proposed approach