Efficient Algorithms for the Closest Pair Problem and Applications

The closest pair problem (CPP) is one of the well studied and fundamental problems in computing. Given a set of points in a metric space, the problem is to identify the pair of closest points. Another closely related problem is the fixed radius nearest neighbors problem (FRNNP). Given a set of points and a radius $R$, the problem is, for every input point $p$, to identify all the other input points that are within a distance of $R$ from $p$. A naive deterministic algorithm can solve these problems in quadratic time. CPP as well as FRNNP play a vital role in computational biology, computational finance, share market analysis, weather prediction, entomology, electro cardiograph, N-body simulations, molecular simulations, etc. As a result, any improvements made in solving CPP and FRNNP will have immediate implications for the solution of numerous problems in these domains. We live in an era of big data and processing these data take large amounts of time. Speeding up data processing algorithms is thus much more essential now than ever before. In this paper we present algorithms for CPP and FRNNP that improve (in theory and/or practice) the best-known algorithms reported in the literature for CPP and FRNNP. These algorithms also improve the best-known algorithms for related applications including time series motif mining and the two locus problem in Genome Wide Association Studies (GWAS)

Information Systems and Healthcare XXXIV: Clinical Knowledge Management Systems—Literature Review and Research Issues for Information Systems

Knowledge Management (KM) has emerged as a possible solution to many of the challenges facing U.S. and international healthcare systems. These challenges include concerns regarding the safety and quality of patient care, critical inefficiency, disparate technologies and information standards, rapidly rising costs and clinical information overload. In this paper, we focus on clinical knowledge management systems (CKMS) research. The objectives of the paper are to evaluate the current state of knowledge management systems diffusion in the clinical setting, assess the present status and focus of CKMS research efforts, and identify research gaps and opportunities for future work across the medical informatics and information systems disciplines. The study analyzes the literature along two dimensions: (1) the knowledge management processes of creation, capture, transfer, and application, and (2) the clinical processes of diagnosis, treatment, monitoring and prognosis. The study reveals that the vast majority of CKMS research has been conducted by the medical and health informatics communities. Information systems (IS) researchers have played a limited role in past CKMS research. Overall, the results indicate that there is considerable potential for IS researchers to contribute their expertise to the improvement of clinical process through technology-based KM approaches

Information sharing platform to support collaborative teamwork in construction

Construction industry is one of the most challenging industries. It has many problems associated to its performance and the ability to satisfy the users’ requirements. One of the most critical issues that need to be addressed by construction is related to the lack of information sharing among the project stakeholders. Therefore this research has been undertaken to address this issue within the context of Collaborative Teamwork Environment (CTW) in construction. The aim and the objective of the research are to identify the important elements of project reporting towards establishment of communication framework to support collaborative teamwork information sharing in Malaysian construction industry. This research also developed the Matrix Measurement Guidelines (MMG-CTW) as a useful tool to gauge the level of collaborative teamwork achievement that was created by the communication platform. The different methodologies were employed to generate qualitative and quantitative data at different stages of the research. These methodologies were: literature review; single stage postal questionnaire survey; interview; and project case study analysis. The data collected using the triangulation approach with the stated methodologies from the expert industry player’s nationwide. The important elements and the current approach of project communication and the elements of the collaborative teamwork are the main variables that have been identified from the data collection and been used as the basis to develop the project communication platform. The research was divided into three main phases. The initial stage involved the evaluation of the current approach of the project communication and the collaborative teamwork environment in Malaysian construction industry. The second stage was the development of the MMG-CTW and the third stage was the establishment of the framework for the communication platform. This communication and information need have been integrated within the MS-SharePoint software and was tested its application on two selected case study projects and feedbacks generated from the users were used for improvement. As a result, the application of this groupware system is suitable for a medium size project because of its simplicity and user friendly. This research concludes that the implementation of CTW concept is still in the early stage in Malaysian construction industry. The practice of project reporting is still using simple tools such as email, telephone and traditional way of exchanging documents. The progress report, design concept, drawing and specification were identified as the top priority project reporting elements in project communication and information. There is an urgent need to improve the current communication system among project stakeholders. The groupware system developed in this research was validated by the expert panel can be a suitable tool for communication framework to support collaborative teamwork information sharing in Malaysian construction industry

Knowledge construction from time series data using a collaborative exploration system.

International audienceThis paper deals with the exploration of biomedical multivariate time series to construct typical parameter evolution or scenarios. This task is known to be difficult: the temporal and multivariate nature of the data at hand and the context-sensitive aspect of data interpretation hamper the formulation of a priori knowledge about the kind of patterns that can be detected as well as their interrelations. This paper proposes a new way to tackle this problem based on a human-computer collaborative approach involving specific annotations. Three grounding principles, namely autonomy, adaptability and emergence, support the co-construction of successive abstraction levels for data interpretation. An agent-based design is proposed to support these principles. Preliminary results in a clinical context are presented to support our proposal. A comparison with two well-known time series exploration tools is furthermore performed