Greedy Selection of Materialized Views

Greedy based approach for view selection at each step selects a beneficial view that fits within the space available for view materialization. Most of these approaches are focused around the HRU algorithm, which uses a multidimensional lattice framework to determine a good set of views to materialize. The HRU algorithm exhibits high run time complexity as the number of possible views is exponential with respect to the number of dimensions. The PGA algorithm provides a scalable solution to this problem by selecting views for materialization in polynomial time relative to the number of dimensions. This paper compares the HRU and the PGA algorithm. It was experimentally deduced that the PGA algorithm, in comparison with the HRU algorithm, achieves an improved execution time with lowered memory and CPU usages. The HRU algorithm has an edge over the PGA algorithm on the quality of the views selected for materialization

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

A Declarative Approach to View Selection Modeling

Abstract. View selection is important in many data-intensive systems e.g., commercial database and data warehousing systems. Given a database (or a data warehouse) schema and a query workload, view selection is to choose an appropriate set of views to be materialized that optimizes the total query cost, given a limited amount of resource, e.g., storage space and total view maintenance cost. The view selection problem is known to be a NP-complete problem. In this paper, we propose a declarative approach that involves a constraint programming technique which is known to be e cient for the resolution of NP-complete problems. The originality of our approach is that it provides a clear separation between formulation and resolution of the problem. For this purpose, the view selection problem is modeled as a constraint satisfaction problem in an easy and declarative way. Then, its resolution is performed automatically by the constraint solver. Furthermore, our approach is exible and extensible, in that it can easily model and handle new constraints and new heuristic search strategies to reduce the solution space. The performance results show that our approach outperforms the genetic algorithm which is known to provide the best trade-o between quality of solutions in terms of cost saving and execution time

Materialized View Construction Based on Clustering Technique

Part 4: Data Analysis and Information RetrievalInternational audienceMaterialized view is important to any data intensive system where answering queries at runtime is subject of interest. Users are not aware about the presence of materialized views in the system but the presence of these results in fast access to data and therefore optimized execution of queries. Many techniques have evolved over the period to construct materialized views. However the survey work reveals a few attempts to construct materialized views based on attribute similarity measure by statistical similarity function and thereafter applying the clustering techniques. In this paper we have proposed materialized view construction methodology at first by analyzing the attribute similarity based on Jaccard Index then clustering methodology is applied using similarity based weighted connected graph. Further the clusters are validated to check the correctness of the materialized views

Abstracts of 1st International Conference on Machine Intelligence and System Sciences

This book contains the abstracts of the papers presented at the International Conference on Machine Intelligence and System Sciences (MISS-2021) Organized by the Techno College of Engineering, Agartala, Tripura, India & Tongmyong University, Busan, South Korea, held on 1–2 November 2021. This conference was intended to enable researchers to build connections between different digital technologies based on Machine Intelligence, Image Processing, and the Internet of Things (IoT). Conference Title: 1st International Conference on Machine Intelligence and System SciencesConference Acronym: MISS-2021Conference Date: 1–2 November 2021Conference Location: Techno College of Engineering Agartala, Tripura(w), IndiaConference Organizer: Techno College of Engineering, Agartala, Tripura, India & Tongmyong University, Busan, South Korea