The K Group Nearest-Neighbor Query on Non-indexed RAM-Resident Data

Data sets that are used for answering a single query only once (or just a few times) before they are replaced by new data sets appear frequently in practical applications. The cost of buiding indexes to accelerate query processing would not be repaid for such data sets. We consider an extension of the popular (K) Nearest-Neighbor Query, called the (K) Group Nearest Neighbor Query (GNNQ). This query discovers the (K) nearest neighbor(s) to a group of query points (considering the sum of distances to all the members of the query group) and has been studied during recent years, considering data sets indexed by efficient spatial data structures. We study (K) GNNQs, considering non-indexed RAM-resident data sets and present an existing algorithm adapted to such data sets and two Plane-Sweep algorithms, that apply optimizations emerging from the geometric properties of the problem. By extensive experimentation, using real and synthetic data sets, we highlight the most efficient algorithm

Ensuring competency in end-of-life care: controlling symptoms

BACKGROUND: Palliative medicine is assuming an increasingly important role in patient care. The Education for Physicians in End-of-life Care (EPEC) Project is an ambitious program to increase core palliative care skills for all physicians. It is not intended to transmit specialty level competencies in palliative care. METHOD: The EPEC Curriculum was developed to be a comprehensive syllabus including trainer notes, multiple approaches to teaching the material, slides, and videos of clinical encounters to trigger discussion are provided. The content was developed through a combination of expert opinion, participant feedback and selected literature review. Content development was guided by the goal of teaching core competencies not included in the training of generalist and non-palliative medicine specialist physicians. RESULTS: Whole patient assessment forms the basis for good symptom control. Approaches to the medical management of pain, depression, anxiety, breathlessness (dyspnea), nausea/vomiting, constipation, fatigue/weakness and the symptoms common during the last hours of life are described. CONCLUSION: While some physicians will have specialist palliative care services upon which to call, most in the world will need to provide the initial approaches to symptom control at the end-of-life

Similarity queries: their conceptual evaluation, transformation and processing

Many application scenarios can significantly benefit from the identification and processing of similarities in the data. Even though some work has been done to extend the semantics of some operators, for example join and selection, to be aware of data similarities, there has not been much study on the role and implementation of similarity-aware operations as first-class database operators. Furthermore, very little work has addressed the problem of evaluating and optimizing queries that combine several similarity operations. The focus of this paper is the study of similarity queries that contain one or multiple first-class similarity database operators such as Similarity Selection, Similarity Join, and Similarity Group-by. Particularly, we analyze the implementation techniques of several similarity operators, introduce a consistent and comprehensive conceptual evaluation model for similarity queries, and present a rich set of transformation rules to extend cost-based query optimization to the case of similarity queries