EFFICIENT APPROACH FOR VIEW SELECTION FOR DATA WAREHOUSE USING TREE MINING AND EVOLUTIONARY COMPUTATION

Selection of a proper set of views to materialize plays an important role indatabase performance. There are many methods of view selection which uses different techniques and frameworks to select an efficient set of views for materialization. In this paper, we present a new efficient, scalable method for view selection under the given storage constraints using a tree mining approach and evolutionary optimization. Tree mining algorithm is designed to determine the exact frequency of (sub)queries in the historical SQL dataset. Query Cost model achieves the objective of maximizing the performance benefits from the final view set which is derived from the frequent view set given by tree mining algorithm. Performance benefit of a query is defined as a function of queryfrequency, query creation cost, and query maintenance cost. The experimental results shows that the proposed method is successful in recommending a solution which is fairly close to optimal solution

Mining Query Plans for Finding Candidate Queries and Sub-Queries for Materialized Views in BI Systems Without Cube Generation

Materialized views are important for optimizing Business Intelligence (BI) systems when they are designed without data cubes. Selecting candidate queries from large number of queries for materialized views is a challenging task. Most of the work done in the past involves finding out frequent queries from the past workload and creating materialized views from such queries by either manually analyzing workload or using approximate string matching algorithms using query text. Most of the existing methods suggest complete queries but ignore query components such as sub queries for creation of materialized views. This paper presents a novel method to determine on which queries and query components materialized views can be created to optimize aggregate and join queries by mining database of query execution plans which are in the form of binary trees. The proposed algorithm showed significant improvement in terms of more number of optimized queries because it is using the execution plan tree of the query as a basis of selection of query to be optimized using materialized views rather than choosing query text which is used by traditional methods. For selecting a correct set of queries to be optimized using materialized views, the paper proposes efficient specialized frequent tree component mining algorithm with novel heuristics to prune search space. These frequent components are used to determine the possible set of candidate queries for creation of materialized views. Experimentation on standard, real and synthetic data sets, and also the theoretical basis, proved that the proposed method is able to optimize a large number of queries with less number of materialized views and showed a significant improvement in performance compared to traditional methods

Color, Scale, and Rotation Independent Multiple License Plates Detection in Videos and Still Images

Most of the existing license plate (LP) detection systems have shown significant development in the processing of the images, with restrictions related to environmental conditions and plate variations. With increased mobility and internationalization, there is a need to develop a universal LP detection system, which can handle multiple LPs of many countries and any vehicle, in an open environment and all weather conditions, having different plate variations. This paper presents a novel LP detection method using different clustering techniques based on geometrical properties of the LP characters and proposed a new character extraction method, for noisy/missed character components of the LP due to the presence of noise between LP characters and LP border. The proposed method detects multiple LPs from an input image or video, having different plate variations, under different environmental and weather conditions because of the geometrical properties of the set of characters in the LP. The proposed method is tested using standard media-lab and Application Oriented License Plate (AOLP) benchmark LP recognition databases and achieved the success rates of 97.3% and 93.7%, respectively. Results clearly indicate that the proposed approach is comparable to the previously published papers, which evaluated their performance on publicly available benchmark LP databases

Probabilistic Page Replacement Policy in Buffer Cache Management for Flash-Based Cloud Databases

In the fast evolution of storage systems, the newly emerged flash memory-based Solid State Drives (SSDs) are becoming an important part of the computer storage hierarchy. Amongst the several advantages of flash-based SSDs, high read performance, and low power consumption are of primary importance. Amongst its few disadvantages, its asymmetric I/O latencies for read, write and erase operations are the most crucial for overall performance. In this paper, we proposed two novel probabilistic adaptive algorithms that compute the future probability of reference based on recency, frequency, and periodicity of past page references. The page replacement is performed by considering the probability of reference of cached pages as well as asymmetric read-write-erase properties of flash devices. The experimental results show that our proposed method is successful in minimizing the performance overheads of flash-based systems as well as in maintaining the good hit ratio. The results also justify the utility of a genetic algorithm in maximizing the overall performance gains

Intelligent Multidimensional Modelling

On-Line Analytical Processing (OLAP) systems considerably ease the process of analyzing business data and have become widely used in industry. Such systems primarily employ multidimensional data models to structure their data. However, current multidimensional data models fall short in time and skills to model the complex data found in some real-world application domains. Multidimensional data Analysis is based on Measure, Dimensions and Hierarchies. Process to find them manually is very crucial and time consuming because large and complex data is involved across multiple regions, products, and employees. This paper presents an Intelligent Multidimensional modelling system which helps the modeller in building multidimensional model and provides working at logical level by hiding heterogeneousity of physical database. The paper proposes the process to identify Measures, Dimensions, and Hierarchies to generate multidimensional model

Approaches to Avoid Traditional Multidimensional Data Cube: A Survey

Data analysis is the growing need of the current era. Data analysis is not only restricted to business domain only. Advancement in the technology opens the door of technology to the every common person and hence data generation is increasing exponentially day by day. Incorporating such huge amount of data in the data analysis system is the big challenge. Handling variety of data is also the difficult issue. The numerous options are coming out to solve these problems. To support the decision making system, Online Analytical Processing (OLAP) is the more suitable option. OLAP uses the multidimensional data analysis approach of data analysis. OLAP includes the analysis of current data as well history data and also the aggregated or summary data. To handle the aggregated data traditionally data cube is used. This paper focuses on the various research techniques to enhance the performance of the data cube

Recurrence Based Similarity Identification of Climate Data

Climate change has become a challenging and emerging research problem in many research related areas. One of the key parameters in analyzing climate change is to analyze temperature variations in different regions. The temperature variation in a region is periodic within the interval. Temperature variations, though periodic in nature, may vary from one region to another and such variations are mainly dependent on the location and altitude of the region and also on other factors like the nearness of sea and vegetation. In this paper, we analyze such periodic variations using recurrence plot (RP), cross recurrence plot (CRP), recurrence rate (RR), and correlation of probability of recurrence (CPR) methods to find similarities of periodic variations between and within climatic regions and to identify their connectivity trend. First, we test the correctness of our method by applying it on voice and heart rate data and then experimentation is performed on synthetic climate data of nine regions in the United States and eight regions in China. Finally, the accuracy of our approach is validated on both real and synthetic datasets and demonstrated using ANOVA, Kruskal–Wallis, and z-statistics significance tests

Albiglutide and cardiovascular outcomes in patients with type 2 diabetes and cardiovascular disease (Harmony Outcomes): a double-blind, randomised placebo-controlled trial

Background: Glucagon-like peptide 1 receptor agonists differ in chemical structure, duration of action, and in their effects on clinical outcomes. The cardiovascular effects of once-weekly albiglutide in type 2 diabetes are unknown. We aimed to determine the safety and efficacy of albiglutide in preventing cardiovascular death, myocardial infarction, or stroke. Methods: We did a double-blind, randomised, placebo-controlled trial in 610 sites across 28 countries. We randomly assigned patients aged 40 years and older with type 2 diabetes and cardiovascular disease (at a 1:1 ratio) to groups that either received a subcutaneous injection of albiglutide (30–50 mg, based on glycaemic response and tolerability) or of a matched volume of placebo once a week, in addition to their standard care. Investigators used an interactive voice or web response system to obtain treatment assignment, and patients and all study investigators were masked to their treatment allocation. We hypothesised that albiglutide would be non-inferior to placebo for the primary outcome of the first occurrence of cardiovascular death, myocardial infarction, or stroke, which was assessed in the intention-to-treat population. If non-inferiority was confirmed by an upper limit of the 95% CI for a hazard ratio of less than 1·30, closed testing for superiority was prespecified. This study is registered with ClinicalTrials.gov, number NCT02465515. Findings: Patients were screened between July 1, 2015, and Nov 24, 2016. 10 793 patients were screened and 9463 participants were enrolled and randomly assigned to groups: 4731 patients were assigned to receive albiglutide and 4732 patients to receive placebo. On Nov 8, 2017, it was determined that 611 primary endpoints and a median follow-up of at least 1·5 years had accrued, and participants returned for a final visit and discontinuation from study treatment; the last patient visit was on March 12, 2018. These 9463 patients, the intention-to-treat population, were evaluated for a median duration of 1·6 years and were assessed for the primary outcome. The primary composite outcome occurred in 338 (7%) of 4731 patients at an incidence rate of 4·6 events per 100 person-years in the albiglutide group and in 428 (9%) of 4732 patients at an incidence rate of 5·9 events per 100 person-years in the placebo group (hazard ratio 0·78, 95% CI 0·68–0·90), which indicated that albiglutide was superior to placebo (p<0·0001 for non-inferiority; p=0·0006 for superiority). The incidence of acute pancreatitis (ten patients in the albiglutide group and seven patients in the placebo group), pancreatic cancer (six patients in the albiglutide group and five patients in the placebo group), medullary thyroid carcinoma (zero patients in both groups), and other serious adverse events did not differ between the two groups. There were three (<1%) deaths in the placebo group that were assessed by investigators, who were masked to study drug assignment, to be treatment-related and two (<1%) deaths in the albiglutide group. Interpretation: In patients with type 2 diabetes and cardiovascular disease, albiglutide was superior to placebo with respect to major adverse cardiovascular events. Evidence-based glucagon-like peptide 1 receptor agonists should therefore be considered as part of a comprehensive strategy to reduce the risk of cardiovascular events in patients with type 2 diabetes. Funding: GlaxoSmithKline

DESIGN OPTIMIZATION PROCESS OF DIFFERENTIAL CASE

Overall weight of vehicle contributes significantly towards fuel efficiency. Components used in vehicle today have thicker section sizes than the actual requirement due to the manufacturing and material property restrictions in thinner sections. A better optimized component with combined use of improved design, material and manufacturing practices can reduce the weight of components by 40-50% which can in turn reduce the overall weight of vehicle and hence increase fuel efficiency. Differential case is selected as the component to be optimized from design point of view which is currently being used commercial vehicles. The design optimization process for reducing weight of differential case is discussed in this report. The main aim of this project is to figure out the best suitable process for design optimization of differential case. After finalizing on the process, finite element analysis is performed on the optimized part to check whether the stress is within limits. Differential case has ring gear attached to it which bears load due to pinion motion. 36 different load cases have been studied with load at every 10 degrees in order to take into account the location of maximum stress