A Review of Contemporary Data Quality Issues in Data Warehouse ETL Environment

In today’s scenario, Extraction–transformation– loading (ETL) tools have become important pieces of software responsible for integrating heterogeneous information from several sources. The task of carrying out the ETL process is potentially a complex, hard and time consuming. Organisations now –a-days are concerned about vast qualities of data. The data quality is concerned with technical issues in data warehouse environment. Research in last few decades has laid more stress on data quality issues in a data warehouse ETL process. The data quality can be ensured cleaning the data prior to loading the data into a warehouse. Since the data is collected from various sources, it comes in various formats. The standardization of formats and cleaning such data becomes the need of clean data warehouse environment. Data quality attributes like accuracy, correctness, consistency, timeliness are required for a Knowledge discovery process. The present state -of –the- art purpose of the research work is to deal on data quality issues at all the aforementioned stages of data warehousing 1) Data sources, 2) Data integration 3) Data staging, 4) Data warehouse modelling and schematic design and to formulate descriptive classification of these causes. The discovered knowledge is used to repair the data deficiencies. This work proposes a framework for quality of extraction transformation and loading of data into a warehouse

WBSN based safe lifestyle: A case study of heartrate monitoring system

A Heart is the vital organ of the body. According to the “world health statistics, 2017” by WHO, about 460,000 people die due to fatal heart attacks every year. To reduce the death rate due to fatal heart attacks and malfunctioning of the cardiovascular system, this paper proposed a Wireless Body Sensor Network (WBSN) based, portable, easily affordable, miniatured, accurate “Heartrate Monitoring System (HMS)”. HMS can be used to regularly examine the cardiac condition at home or hospital to avoid or early detection of any serious condition. Heartrate Monitoring Algorithm (HMA) was designed to observe the spread heartbeat spectrum and worked at the backend of HMS. A case study was performed for forty healthy young subjects. Each subject data was computed for (sub) ̅-3S_

Getting Bulk Data Through Google: An empirical study

To store the information in a database is one of the major tasks. The efficient storage of data is important for future use. Information retrieval is a method of gathering information related to input queries from the various sources or stored databases. To retrieve the information, a search engine plays an important role. A web search engine creates an index to match queries. The quality of information is improved with the help of search engine. For retrieving the information, a search engine comprises some modules such as query processor, a searching and matching function, document processor and page rank capability. This paper focuses on the retrieval of web documents against input queries and stores them in to database. A Google search API can be used to fetch the results. It analyses the data by processing through these modules and downloads the content available in different formats

Reengineering framework for open source software using decision tree approach

A Software engineering is an approach to software development. Once software gets developed and delivered, it needs maintenance. Changes in software incur due to new requirements of the end-user, identification of bug in software or failure to achieve system objective. It has been observed that successive maintenance in the developed software reduces software quality and degrades the performance of software system. Reengineering is an approach of retaining the software quality and improving maintainability of the software system. But the question arises “when to reengineer the software”. The paper proposed a framework for software reengineering process using decision tree approach which helps decision makers to decide whether to maintain or reengineer the software systems

SRC Model to Identify Beguiling Reviews

Today, e-trade sites are giving colossal number of a platform to clients in which they can express their perspectives,  their suppositions and post their audits about the items on the web. Such substance helped by clients is accessible for different clients and makers as a significant wellspring of data.  This data is useful in taking imperative business choices.  Despite the fact that this data impact the purchasing choice of a client, however quality control on this client created information is not guaranteed, as audit area is an open stage accessible to all. anybody  can  compose  anything  on  web  which may incorporate surveys which are not true. as the prevalence of e-commerce destinations are hugely expanding, nature of the surveys is deteriorating step by step subsequently influencing clients’ purchasing choices. This has turned into an enormous social issue.  From numerous years, email spam and web spam were the two primary highlighted social issues. at the same time these days, because of notoriety of clients’ enthusiasm toward internet shopping and their reliance on the online audits, it turned into a real focus for audit spammers to delude clients by composing sham surveys for target items. To the best of our insight, very little study is accounted for in regards to this issue reliability of online reviews. To begin with paper was distributed in 2007 by NITIN  JINDAL  &  BING  LIU in regards to  review Spam detection.  In the past few years, variety of techniques has been recommended by researchers to accord with this trouble. This paper intends to introduce Suspicious review Classifier model (SrC) for identifying suspicious review, review spammers and their group

Augmented Reality and GPS-Based Resource Efficient Navigation System for Outdoor Environments: Integrating Device Camera, Sensors, and Storage

Contemporary navigation systems rely upon localisation accuracy and humongous spatial data for navigational assistance. Such spatial-data sources may have access restrictions or quality issues and require massive storage space. Affordable high-performance mobile consumer hardware and smart software have resulted in the popularity of AR and VR technologies. These technologies can help to develop sustainable devices for navigation. This paper introduces a robust, memory-efficient, augmented-reality-based navigation system for outdoor environments using crowdsourced spatial data, a device camera, and mapping algorithms. The proposed system unifies the basic map information, points of interest, and individual GPS trajectories of moving entities to generate and render the mapping information. This system can perform map localisation, pathfinding, and visualisation using a low-power mobile device. A case study was undertaken to evaluate the proposed system. It was observed that the proposed system resulted in a 29 percent decrease in CPU load and a 35 percent drop in memory requirements. As spatial information was stored as comma-separated values, it required almost negligible storage space compared to traditional spatial databases. The proposed navigation system attained a maximum accuracy of 99 percent with a root mean square error value of 0.113 and a minimum accuracy of 96 percent with a corresponding root mean square value of 0.17

Emotion Quantification Using Variational Quantum State Fidelity Estimation

Sentiment analysis has been instrumental in developing artificial intelligence when applied to various domains. However, most sentiments and emotions are temporal and often exist in a complex manner. Several emotions can be experienced at the same time. Instead of recognizing only categorical information about emotions, there is a need to understand and quantify the intensity of emotions. The proposed research intends to investigate a quantum-inspired approach for quantifying emotional intensities in runtime. The inspiration comes from manifesting human cognition and decision-making capabilities, which may adopt a brief explanation through quantum theory. Quantum state fidelity was used to characterize states and estimate emotion intensities rendered by subjects from the Amsterdam Dynamic Facial Expression Set (ADFES) dataset. The Quantum variational classifier technique was used to perform this experiment on the IBM Quantum Experience platform. The proposed method successfully quantifies the intensities of joy, sadness, contempt, anger, surprise, and fear emotions of labelled subjects from the ADFES dataset

Using Modified Technology Acceptance Model to Evaluate the Adoption of a Proposed IoT-Based Indoor Disaster Management Software Tool by Rescue Workers

Advancements in IoT technology have been instrumental in the design and implementation of various ubiquitous services. One such design activity was carried out by the authors of this paper, who proposed a novel cloud-centric IoT-based disaster management framework and developed a multimedia-based prototype that employed real-time geographical maps. The multimediabased system can provide vital information on maps that can improve the planning and execution of evacuation tasks. This study was intended to explore the acceptance of the proposed technology by the specific set of users that could potentially lead to its adoption by rescue agencies for carrying out indoor rescue and evacuation operations. The novelty of this study lies in the concept that the acceptability of the proposed system was ascertained before the complete implementation of the system, which prevented potential losses of time and other resources. Based on the extended Technology Acceptance Model (TAM), we proposed a model included factors such as perceived usefulness, perceived ease of use, attitude, and behavioural intention. Other factors include trust in the proposed system, job relevance, and information requirement characteristics. Online survey data collected from the respondents were analyzed using structural equation modelling (SEM) revealed that although perceived ease of use and job relevance had significant impacts on perceived usefulness, trust had a somewhat milder impact on the same. The model also demonstrated a statistically moderate impact of trust and perceived ease of use on behavioural intention. All other relationships were statistically strong. Overall, all proposed relationships were supported, with the research model providing a better understanding of the perceptions of users towards the adoption of the proposed technology. This would be particularly useful while making decisions regarding the inclusion of various features during the industrial production of the proposed system

Estimating fuel-efficient air plane trajectories using machine learning

Airline industry has witnessed a tremendous growth in the recent past. Percentage of people choosing air travel as first choice to commute is continuously increasing. Highly demanding and congested air routes are resulting in inadvertent delays, additional fuel consumption and high emission of greenhouse gases. Trajectory planning involves creation identification of cost-effective flight plans for optimal utilization of fuel and time. This situation warrants the need of an intelligent system for dynamic planning of optimized flight trajectories with least human intervention required. In this paper, an algorithm for dynamic planning of optimized flight trajectories has been proposed. The proposed algorithm divides the airspace into four dimensional cubes and calculate a dynamic score for each cube to cumulatively represent estimated weather, aerodynamic drag and air traffic within that virtual cube. There are several constraints like simultaneous flight separation rules, weather conditions like air temperature, pressure, humidity, wind speed and direction that pose a real challenge for calculating optimal flight trajectories. To validate the proposed methodology, a case analysis was undertaken within Indian airspace. The flight routes were simulated for four different air routes within Indian airspace. The experiment results observed a seven percent reduction in drag values on the predicted path, hence indicates reduction in carbon footprint and better fuel economy