Journal of Advanced Database Management & Systems
Journal of Advanced Database Management & Systems (JoADMS) ISSN: 2393-8730 Click here for complete Editorial Board Scientific Journal Impact Factor (SJIF): 5.844 Journal of Advanced Database Management & Systems (JoADMS) is a journal focused towards the rapid publication of fundamental research papers on all areas of Advance database Management & systems. It's a triannual journal, started in 2014. Journal DOI no.: 10.37591/ JoADMS Focus and Scope Cover Systems Analysis and Design Semantic Web and Ontology’s Knowledge Modeling and Processing Sensor data Management Temporal, Spatial and High dimensional databases Database System and applications Data warehousing and data mining, OLAP Distributed ,Parallel , P2P and Grid -based Database Emerging trends in XML Technology Digital Libraries Agile systems development Bio-informatics Cognitive modeling Conceptual modeling Data quality Database for advanced applications Database security and integrity E-business and m-commerce models and architectures Object oriented methods and methodologies Software engineering Web design methods and methodologies Web database and Web-based information systems Readership: Graduate, Postgraduate, Research Scholar, Faculties, Institutions, and in IT Companies. Indexing: The Journal is index in DRJI, Google Scholar Submission of Paper: All contributions to the journal are rigorously refereed and are selected on the basis of quality and originality of the work. The journal publishes the most significant new research papers or any other original contribution in the form of reviews and reports on new concepts in all areas pertaining to its scope and research being done in the world, thus ensuring its scientific priority and significance. Manuscripts are invited from academicians, students, research scholars and faculties for publication consideration. Papers are accepted for editorial consideration through email info@stmjournals.com or nikita@stmjournals.com Abbreviation: JoADMS Frequency: Three issues per year Peer Reviewed Policy Editorial Board Instructions to Authors Publisher: STM Journals A division of: Consortium eLearning Network Private Ltd Address: A-118, 1st Floor, Sector-63, Noida, Uttar Pradesh-201301, India Phone no.: 0120-4746-214/ Email: nikita@stmjournals.com
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Concept of Semantic Web and Its Indexing Mechanism
The present paper discusses various aspects of Semantic web. The semantic web is a tool of retrieving information for web on the basis of semantics conveyed by keywords. The semantic web has an improved system of information storage where the meaning and associated words of a keywords are also stored. The indexing mechanism of for retrieval of information is improved than normal keyword based search. In this paper the indexing mechanism of semantic web is also discussed. Keywords: Semantic web, synonymy, metadata, indexing mechanism, automatic knowledge sharing, search engine.
Design and Implementation of Security System for Medical Test Data Mining
The growth of data mining process began when company data was first stored on computers, unrelenting with improvements in data access. Data mining deals with bulky database which may hold insightful information. Medical test security system is a major factor in our society especially this system is used for special purpose like inspection of many healthy man powers for a specific organization. In this project software is implement which is useful for detection of healthy man power to avoid the wrong way entry in Army, Navy and Air force i.e., Government and Non Government sector. In this system first of all candidate registration should be done in this registration candidate firstly give their home address and age. The identification of candidate with figure print saved in server computer along with generated barcode where medical tests will be performed. Keywords: MTSS, thumb, data mining (DM), knowledge discovery in database (KDD), data link library (DLL)
An Implimentation on Extraction and Detection of Leak Data using Fake Tupples
Modern business activity contains the email exchange, sometimes email leakage is occurred in the company and it is widespread problem in the industry particularly in IT industry .so we study the following problem as data distributor (owner) has given sensitive data to trusted third parties called as (agents) and if that data leaked by trusted agents, particularly the leaked data is find on website also or can find out by using legal ways , we want to find out who has leaked that sensitive data. in early days for secure these sensitive data (information) Access Control Mechanism and Watermarking Technique were used, in Watermarking Technique unique code is embedded within data but it is not so useful for sensitive data. For keeping sensitive information of the company and finding out to this trusted third agent we implement this technique. This paper contains the results of Implementation of data leakage and detection of data using fake tupples.To find solution on this issue we proposed a system to find the Data leaker or identify the third party who leaks the data by using the fake Tupples. Keywords: Data leakage, agents, fake tupples, distributor, access control mechanism, watermarking technique
Identification of Thyroid Disease Severity using Fuzzy C-Means
Clustering is a primary data description method in data mining which group’s most similar data. Data clustering is one of the important problems in the fields of data mining, bio-informatics and pattern recognition. Various algorithms are used to solve this problem. This paper presents the performance analysis of k-means clustering algorithm and compares it with fuzzy C- Means (FCM) algorithm on Thyroid disease data set. We also find the accuracy of both the algorithms on Thyroid data set. Fuzzy clustering is useful to mine multi-dimensional and complex data sets, where the members have fuzzy or partial relations. Among the various techniques developed so far, fuzzy-C-Means (FCM) algorithm is the most popular one, where a partial membership is assigned to a piece of data with each of the pre-defined cluster centers. Moreover, in FCM, the cluster centers are virtual, that is, they are chosen at random and thus might be out of the data set. With in some iteration the cluster centers and membership values of the data points are updated. The FCM employs fuzzy portioning such that a point can belong to more than one group with different membership grades between 0 and 1. Using this membership we are going to determine the severity of thyroid disease in a person. Keywords: K-means, fuzzy C-means clustering, Euclidean distance, Manhattan distance, sigmoid membership, t-norms
Boosting Decision Tree Algorithm for Weather Prediction
Data mining is the computer assisted process which predicts behaviors and future trends by digging through and analyzing enormous sets of data and then extracting the meaningful data. It is used to answer questions that traditionally were very time consuming to resolve. This feature makes it useful to predict meteorological data that is weather prediction. Weather prediction is a necessary application in meteorology and has been one of the most scientifically and technologically challenging problems across the world in the last century. Predicting the weather is essential to help people preparing for the best and the worst of the climate. Accurately predicting the weather has been one of the most challenging problems around the world. Many weather predictions like thunderstorm prediction, rainfall prediction, predicting wind conditions, and cloud burst are major challenges for atmospheric research. The data for this work is authentic and has been collected from India Meteorological Department for city Shimla (India) from the period of January 2010 to December 2013. This paper compared three decision tree algorithms named C4.5, CART, LMT without boosting and with boosting their performance by using boosting algorithm AdaBoost. AdaBoost was run with three different iterations. It has been observed that with every increase in iteration, the accuracy of decision tree algorithm is improved. It has been also observed that LMT shows the best improvement among the decision tree algorithms after boosting. Therefore, boosted LMT is used to predict the weather of Shimla. The prediction accuracy of boosted LMT is nearly 100% when taken yearly and comparatively better than un-boosted LMT when the dataset is taken separately for each month. Keywords: data mining, decision trees, boosting, prediction, C4.5, CART, LMT, AdaBoost
Theoretic Study of Image Registration Techniques:A Survey
Image registration is the methodology of adjusting two or more images of the same scene taken from different viewpoints, at different times, and from different sensors. Image registration adjusts two picturesgeometrically, i.e., sense picture and target picture. There are diverse methodologies of image registration and these methodologies are arranged as indicated by their tendency that is area based and feature based. These methodologies are likewise ordered as per the four straightforward steps of image registration process—feature detection, feature matching, function mapping and transformation and resampling. The purpose of this paper is to portray the image registration procedures based on its comparison. Keywords: image registration, feature detection, feature matching, mapping function, MI
Bayesian Inference to Time Series Data Mining
The time series data mining (TSDM) framework is a fundamental contribution to the field of time series analysis and data mining in the recent past. Methods based on the TSDM framework are able to successfully characterize and predict complex, nonperiodic, irregular and chaotic time series. The TSDM methods overcome limitations including stationarity and linearity requirements of traditional time series analysis techniques by adopting data mining concepts for analyzing time series. This paper discusses the Bayesian Inference to data mining in time series through a case study on the IBM stock prices data. Keywords: Time series model, autoregressive model, TSDM, variance change, posterior distribution.
Integrity Protection of Text Document Data from Unauthorized Modifications
Digital watermarking provides authentication, validation and copyright protection for multimedia contents over the internet. Text is the most widely used means of communication in addition to image, audio and video. So it needs to be protected. Text watermarking techniques that have been developed in the past protects the text from illegal copying, imitation, and prevents copyright violations. In this paper, we have proposed an algorithm that ensures the integrity and confidentiality of the document. In this technique, watermark is created on the basis of the contents of the document and embeds it without changing the contents of the document and also encrypts the text to provide confidentiality. To authenticate and prove the integrity of the document the watermark can be easily extracted and verified for tampering. Keywords: confidentiality, integrity, encryption, shuffling, substitution
An Overview on Time Series Data Clustering
This paper gives an overview on time series clustering, which is an important topic now days. Time series data is has importance because of its applications, such as financial data processing like Stock Exchange, network monitoring, web click-stream analysis, sensor data mining, marketing research, and anomaly detection. Therefore in the data mining clustering such data is an important issue. Time series data are a special type of data set and large to process. This paper overview different methodology for some time series data and provides uniqueness and limitation of previous research on various application domains. Keywords: clustering, time series data, stock exchange, sensor data mining, anomaly detection
Applying the XML-based Technique to Support Keyword Search in Graph Data
Graphs have been important and proper structures to represent data with complex relationships in the Web, and keyword search provides a convenient way to querying graphs. Some researchers considered to return r-cliques as answers for keyword search in graph databases. Such concepts can retrieve meaningful information effectively, but the existing approach is computationally expensive. In this paper, we propose to utilize the efficient technique originally designed for keyword search in XML data to help identify answers. The main idea is first transforming the input graph into a tree, and then applying the efficient ELCA technique on the transformed tree to find the subtrees which consist of closely-related matches. The specially-designed SRE and SRE* algorithms are then applied on these subtrees to identify r-cliques. We have proposed several strategies for transforming graphs into trees and designed a series of experiments to examine their performance. Experimental results show that our approaches perform very well in terms of efficiency and recall. Keywords: Keyword search, Graph database, ELCA
Comparison of Education in Europe on Specialty Information Technologies and Computer Science
Europe. It is also important that the author writes about the change needed in teaching in Kazakhstan, as computer science is considered very important in today's world. This article describes the modern European education in the field of computer science in high schools considering the leading European universities (Oxford and Berlin Technical Universities) with different educations systems. Nevertheless, no matter how unique each system, there are several positive features, which can be emulated by Kazakhstan Universities and studied computer science and management bodies for the further improvement of higher education in the country and training graduates in Kazakhstan's Universities. Comparisons of education in Europe on specialty information technologies and computer science are consideredin this paper. Keywords: European education, educational systems, Oxford, Berlin Technical Universities, Computer science.
NoSQL Databases- Analysis, Techniques, and Classification
In recent years in big data and real-time web applications, NoSQL database solutions are gaining popularity in handling massive increase in size of data storage. The design of traditional relational database management systems makes it difficult for them to take advantage of virtually unlimited processing power and economical storage available via cloud computing. In contrast to this NoSQL databases support dynamic schemas, auto-sharding, automatic replication and integrated caching capabilities prominently among many other features making it suitable to be used as storage mechanism in cloud computing environments. Due to the big number of available storage solutions classification of NoSQL databases based on various criteria is analyzed. To cope with this look, I discuss techniques common to NoSQL datastores and analyze four prominent solutions (MongoDB, SimpleDB, Riak, and Bigtable). Keywords: NoSQL, RDBMS, MapReduce, BigTable, MongoDB
An Improved SentiWordNet for Opinion Mining and Sentiment Analysis
Opinion Mining and Sentiment Analysis is an emergent research area, spanning over multiple disciplines such as data mining, text mining, etc. Opinion mining is an art of extracting the opinions from the huge set of opinion set or reviews. Sentiment analysis is a type of natural language processing for tracking the mood of the public about a particular product or topic. The existing works of opinion mining used Sentiwordnet as a lexical resource. The major drawback of this existing Sentiwordnet is non-determination of score count, i.e., it doesn’t provide the details of number of positively, negatively and neutrally scored words. This information is necessary because without the knowledge of score count if the further data mining techniques are applied, it may give inaccurate results. To facilitate the opinion mining task, this work focus on design of Improved Sentiwordnet so that it can produce the count of scored words by distinguishing them into positive, negative and neutral words. Experiments are conducted on standard movie review and product review datasets. This work also make use of Stanford POS tagger for tagging the dataset. The counted words can be used to improve the results comparatively better. Keywords: Opinion mining, sentiment analysis, POS tagging, scoring using improved SentiWordNet
An Empirical Study on Evaluating Graph Based Clustering for HD Data Using Attribute Selection
An attribute subset selection can be showed as a process of identifying and eliminating or removing a number of irrelevant and surplus attributes (features) because irrelevant attributes do not give predictive accuracy and the surplus attributes provide the information that is already present in the other attributes. Attribute selection involves identifying a subset of the most useful attributes that produces the similar results as the final set of results. An attribute (feature) selection algorithm may be evaluated from two points of view. First one concerns the time required to get the subset of attributes and the second one concerns quality of the subset of attributes. Based on these criteria, graph-based clustering for attribute selection algorithm, GRACE is proposed. This algorithm works in two steps. In the first step, attributes are divided into clusters by using graph-theoretic clustering methods. In the second step, most similar attributes that are strongly related to the object classes are selected from each cluster from a subset of attributes. Attributes in different clusters are relatively independent. To ensure the efficiency of this algorithm, the authors implemented the minimum spanning tree clustering method. Keywords: Graph-based clustering, filter method, attribute subset selection
Abstract The purpose of this document is to examine in detail the technologies used to revolutionize and connect the world with various technologies, such as the Internet of Things (IoT) and the MIMO system. The basic idea of this document is to face the challenges of 5G technology. 5G is not an authorized term for any detail or particular file used by broadcasters or institutional bodies, such as media companies WiMAX Forum, 3GPP or ITU-R. The new 3GPP standard downloads beyond LTE and 4G advanced, which are in process, but they are not observed as a multifaceted era. The implementation of policies within a 5G cap would almost certainly occur around 2020. 5G technology means 5th generation mobile telecommunication. The user has never experienced the technology of such high value. The primary goal of the fifth era remote system (5G) is to build up the best system in the world beyond the restrictions and blunders of past ages. The 5G innovation has changed the use of mobile phones with an exceptionally high transmission capacity. The innovations of 5G incorporate all kinds of innovative aspects, making 5G innovation the most dominant and sought after in the near future. Keywords: Internet of things, MIMO system, 5G technology, challenges Cite this Article Gunjan Bansal, Amit Kumar Jain, Tarun Mishra. 5G Technology and Their Challenges. Journal of Advanced Database Management & Systems. 2019; 6(2): 1–7p.
AI Therapist using CBT Module and DNN Algorithm
Based on the WHO report, there are various kinds of mental disorders like depression, anxiety disorders, eating disorders, and trauma-related disorders [. Despite having access to competent health care and social services, those suffering from mental illnesses are unable to seek treatment due to the stigma associated with the disease. As a result, we are creating an AI therapist chatbot that aims to be as effective as a human therapist. The first line of support for mental health patients yet provide privacy and anonymity. The solution proposes the use of CBT to analyze the user’s frame of mind. The DNN is used to pre-set weighted words from the user’s response to questions. Our program detects how the user is feeling. He may be feeling depressed, feared, angry, etc. A recommendation system can then be used to demonstrate content based on his current emotions. For instance, the program shows motivating thoughts, blogs, etc. if the user feels depressed. The result will provide guidance to the user.
Society Management Application based on Flutter
This project is to develop an Android and iOS application to serve as a common platform for performing most of the day-to-day activities of society. Itincludes housing societies as well which have to do their day-to-day activities which include posting notices, reviewing proposals, conducting meetings, sending bills to residents, etc. The functions of the application include but are not limited to sending soft copies of proposals to be reviewed by other committee members.Other committee members can send their approval or disapproval of the considered proposal or document and add remarks, send general or personalized notices to the flat owners, review the complaints of the flat owners, register complaints regarding various matters, read posted notices, etc. The application will be developed using Flutter and Google Firebase databases
Fuzzy based Hybrid Genetic Algorithm for Efficient Cloud Job Scheduling
Abstract These days enterprises need to keep up a lot of uses getting to buy a large number of clients everywhere over the world. Keeping up their own foundation, overseeing programming prerequisites, and taking care of exorbitant excessive internet is difficult. This makes them move towards cloud computing. Cloud computing is a service provisioning strategy where clients can lease any assets like equipment, programming, or stage to build up an application. Clients need to pay just for how much the assets were devoured, and can powerfully increase or decrease the asset limit depending on the situation. Since clients are paying for the administrations, they anticipate the nature of administration from the supplier. Giving the nature of administration and pulling in the clients is a difficult issue for the suppliers. If not, clients will move towards other cloud suppliers. Hence, Service Level Agreement (SLA) is made among suppliers and clients that incorporate assistance quality, assets capacity, versatility, commitments, and outcomes in the event of an infringement. Fulfilling SLA is vital and a difficult issue. In this paper, diverse systems and strategies proposed by the various creators for givingthe nature of administration and keeping up SLA are discussed. We propose a cloud service planning model that is alluded to as the Task Scheduling System (TSS). In the client module, the interaction time of each task is as per an overall distribution. In the task scheduling module, we take a weighted amount of makes skillet and stream time as the target capacity and utilize a fluffy-based Genetic Algorithm (GA) to take care of the issue of cloud task planning. Simulation results show that the convergence speed and output performance of fuzzy-based Ant Colony Optimization (F-ACO) are optimal.
Pandemic as an Opportunity – A Study on Companies Survival by Innovation
It is said that every problem comes with an opportunity and it is said rightly as this Pandemic also arrives with Bucket full of opportunities. The opportunity to innovate, to create, to grow, to learn, to move forward with calculated risk. In this paper I have analyzed and tried to look for those companies who have done innovations by tapping in the opportunities during this pandemic times and because of it have seen the growth in the last quarters during and after lockdown time period. The Objectives of this Study is to 1). Review about the companies and startups who have found the new normal of doing the work. 2). Acknowledging about the innovations done by various companies during this and after this lockdown 3).Analysis about the growth of various companies in the last quarters. 4).To know about different sectors who has changed or had planned to be change in their process of doing work during this pandemic time.
Interview Analyser for Remote Interviews
The project is ideally based on a web-based format and can also be converted into an application format where it is designed for recruitment purposes. It is developed by using theartificial intelligence which consists of TensorFlow libraries and deep learning modules. This project can ease the work of the human resource department by recruiting the right and suitable candidate required by the company. Interview analyser can handle 3 modules namely resume, tone, and face analysis.
A Review of Techniques for Securing Big Data Integrated with Cloud
Abstract In the era of big data and cloud computing, which are the most sophisticated technologies individually with enormous potential; these technologies when integrated can extend themselves seamlessly to solve complicated problems of day-today life. We generate enormous amount of precious data every second related to personal, business to professional field through our digital lives. Big data perfectly fits in this scenario to help us to store, process and manage this massive data. Cloud computing on the other hand is a technology with its characteristic features like on-demand service, flexibility, rapid scalability, metered usage etc. are very useful in storing and managing big data by catering the needs of broad spectrum of users ranging from individuals, medium scale enterprises to large scale organizations. Cloud computing is bundled with benefits like low cost implementation, globally advanced in technological services with zero maintenance overhead for users. With these alluring benefits there are some issues which we need to focus and fix, like security and privacy of the big data stored and accessed in cloud in order to create foolproof secure solutions. In this study we focus on surveying recently proposed techniques for securing big data over the cloud by iterating through techniques in order to find their intricacies. Keywords: Big data, cloud computing, security, privacy, encryption
Abstract YARN, yet another resource negotiator is a large-scale, distributed operating system for big data applications and software rewrite that is capable of decoupling MapReduce's resource management and scheduling capabilities from the data processing component. In this study we have briefly described the evolution of YARN for big data analysis from the year 2017 to the recent times. Keywords: Apache HADOOP, HDFS, MapReduce, Apache Samza, Apache Flink
Business Intelligence and Its Challenges
Abstract Big data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. Huge amount of data produced in business can be processed to get useful information using Business intelligence (BI). Business intelligence comprises the strategies and technologies used by enterprises for the data analysis of business information. BI technologies provide historical, current and predictive views of business operations. Mobile business intelligence (MBI) is the ability to access BI-related data such as Key Performance Indicators (KPIs), business metrics, and dashboards on mobile devices. The MBI provides users with necessary information and content, regardless of their location and time, in order to gain business insights through information analysis using applications optimized for mobile devices. Although in recent years, the business intelligence and analytics has gained an increasing amount of interest among researchers and practitioners, there is a little academic literature available on MBI. This survey is intended to study the adoption of Business Intelligence (BI) with special emphasis on mobile business intelligence in various fields during past 10 years and highlights the challenges faced by users in its implementation. The study also emphasized on relationship between MBI and big data analytics, application of MBI, and difficulties in using the MBI tools. Keywords: Mobile business intelligence, business intelligence, big data, analytic
Abstract Recent advances in new generation information technologies, cutting-edge digital technologies are being harnessed to optimize and automate production, including upstream supply-chain processes. The ultimate goal of industry is that always-connected sensors embedded in machines, components, and works-in-progress will transmit real-time data to networked IT systems. These, in turn, apply machine learning and artificial intelligence algorithms to analyse and gain insights from this big data and adjust processes automatically as needed. In other words, the pain point is not generating and collecting data but being able to effectively extract value from it. Keywords: Big data, Industry 4.0, data mining, social network analysis, Machine Learning; Artificial Intelligence
The Review on Big Data Sharding and Replication
Abstract Sharding is a partitioning pattern that places each partition in potentially separate servers; potentially all over the world. A shard is managed by a single server. Data replication is the process of storing data in more than one site or node. It is useful in improving the availability of data. It is simply copying data from a database from one server to another server so that all the users can share the same data without any inconsistency. In this survey we briefly describe the evolution of sharding and replication techniques from the year 2018 to the recent times. Keywords: Sharding, replication, master-slave replication, peer-to-peer replication