Journal of Advanced Database Management & Systems(JoADMS)

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STM JOURNALSScientific Technical Medical

September–December 2016

ISSN 2393-8730 (Online)

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Journal of Advanced Database Management & Systems

ISSN: 2393-8730 (online)

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It is my privilege to present the print version of the [Volume 3 Issue 3] of our Journal of Advanced

Database Management & Systems, 2016. The intension of JoADMS is to create an atmosphere that

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STM JOURNALS

1. A Review on Big Data Divya Chauhan, K.L. Bansal 1

2. Effective Implementation of Apriori Algorithm to Develop a Suggestion System Based on Sales History Using Hadoop Environment Aishwarya Rani M.R., Shivanand R.D. 8

3. IoT Based Biometric SystemK.P. Swain, M.V.S.V. Prasad, J. Sahoo, S. Sharma, G. Palai 17

4. A Review on Inventory Management System for Improving Efficiency of Project Development CycleSagar S. Mehta, Prasad S. Puranik, Satish B. Sharma 24

5. Information Privacy and Security in Data MiningLikhitha A.R., Vandana B.S., Savitha C.K., Ujwal U.J. 30

ContentsJournal of Advanced Database Management & Systems

JoADMS (2016) 1-7 © STM Journals 2016. All Rights Reserved Page 1

Journal of Advanced Database Management & Systems ISSN: 2393-8730(online)

Volume 3, Issue 3

www.stmjournals.com

A Review on Big Data

Divya Chauhan*, K.L. Bansal Department of Computer Science, Himachal Pradesh University, Shimla, Himachal Pradesh, India

Abstract The goal of Big Data is to help organizations make better business decisions based on

information by enabling researchers, data scientists and other analytics professionals to

analyze large volumes of operational data, as well as other forms of data that may not be

discovered by conventional Business Intelligence (BI) programs. This gives rise to the need

for an analytical review of recent developments in the big data technology. Cloud services

were used to process huge amount of data and it has turned into new Big Data model to meet

the on demand services. This paper aims to provide a comprehensive review of big data

applications and challenges, which it is facing. In addition to that, several research areas

have been highlighted for future directions. The survey will be beneficial for the further

enhancement and enrichment of Big Data Analytics in various research perspectives.

Keywords: Big Data, Hadoop, Big Data Analytics

INTRODUCTION Businesses have always struggled long to find

an optimized approach for capturing

information about their customers, services

and products. Managing and analyzing data for

organizations have always offered the greatest

benefits and the greatest challenges across all

industries. But with the advent in time, the

market with which they worked has grown

with the time. Previously there used to be few

customers, the information about them was

straightforward and easy to handle. It is not

only the plight of business, the research and

development (R&D) organizations have

struggled to get enough computing power to

run sophisticated models or to process images

or other sources of scientific data. When there

is so much information, that too in so many

different forms, it is difficult to deal with it

with the traditional data management ways.

Working in the era of enormous data, Big Data

is important because it enables organizations

to store, manage, gather, and manipulate vast

amounts data at the right time, at the right

speed, to gain the right insights. Big Data is

the result of last 50 years of technology

evolution. It is not a stand-alone technology.

The Big Data is nothing but a data, available at

heterogeneous, autonomous sources, in

extremely large amount, which are updated in

fractions of seconds [1]. The next section

describes the waves or journey of the data.

Subsequent sections give the overview of five

V’s of Big Data and discuss the technical

changes being faced by Big Data. Later in the

paper, applications of Big Data are also

discussed. A separate section highlights the

research area and recent evolvement in Big

Data and last section gives the conclusion.

THE WAVES OF MANAGING DATA The revolution in the data from generations

has been classified into three waves, where

each wave describes the journey of the data

and techniques evolved to handle it [2].

Wave1: Creating Manageable Data

Structures

Initially data used to be stored in files. To give

it a level of abstraction, RDBMS was

incarnated to give programmers ease in

extracting values from data to satisfy the

growing needs of the business. But the data

volume kept growing. So the data were

fragmented and distributed over different

locations. But this added duplication and

redundancy to the data. At this stage, an urgent

need was felt to find a new set of technologies

to support the relational model. Entity

relationship models served the purpose by

adding additional abstraction to increase the

usability of the data. Data warehouse also

played there part when data grew out of

control. But with time, data warehouse grew

too complex and large and did not provide the

speed and agility that the businesses were

expecting. So there was further refinement of

JoADMS (2016) 8-16 © STM Journals 2016. All Rights Reserved Page 8

Journal of Advanced Database Management & Systems ISSN: 2393-8730(online)

Volume 3, Issue 3

www.stmjournals.com

Effective Implementation of Apriori Algorithm to Develop

a Suggestion System Based on Sales History Using Hadoop

Environment

Aishwarya Rani M.R.*, Shivanand R.D.2

Department of Computer Science and Engineering, Bapuji Institute of Engineering and Technology,

Davanagere, Karnataka, India

Abstract The need for comprehensive support system to analyze and predict the nature of the dynamic

market based on the previous records is very vital in competent industries today. The data

mining is a process of extracting implicit, previously unknown and potentially useful

information from data. Mining is search for relationships and global patterns that exist in the

large databases but that are hidden among vast amount of data. Since the data collected tends

to be huge and complex, it is necessary to structure it and process it using evolving

technologies like Hadoop. Apriori algorithm is an algorithm for frequent itemsets mining and

association rule learning over transactional database. This algorithm helps in finding out the

frequent itemsets and thus deriving the association rule between the itemsets. Apriori

algorithm when used in Hadoop framework is proven to be highly effective, with regard to

time complexity. A MapReduce framework is a major part of Hadoop and performs filtering

and sorting and a summary operation. This paper aims at providing solution to the task of

analyzing the sales and distribution function of an enterprise using the latest Hadoop

technology.

Keywords: A priori algorithm, data mining, frequent itemsets, association rules, Hadoop

MapReduce

INTRODUCTION Big data means data sets whose volume is very

large and of wide variety, some commonly

used software tools are not able enough to

manage data. Data mining is a process of

identifying valid, novel, potentially useful and

ultimately understandable patterns in data.

Apriori algorithm is one of the data mining

algorithms for frequent itemset mining and

association rule learning over transactional

database [1]. It helps in finding out the

frequent itemsets from a given data repository

and thus deriving the association rules

between the itemsets. The key idea of Apriori

algorithm is to make multiple passes over the

database. The working of Apriori algorithm

fairly depends upon the Apriori property

which states that “All nonempty subsets of a

frequent itemsets must be frequent”. The main

objective of the proposed work is to reduce the

response time of Apriori algorithm and to

speed-up the algorithm. Thus, in order to find

the frequent itemsets, there is a need to scan

the database again and again. The main

limitation of Apriori algorithm is costly

wasting of time to hold a vast number of

candidate sets.

In addition, single processor’s memory and

CPU resources are very limited, which make

the algorithm performance inefficient.

Furthermore, because of growth of

information, enterprises have to deal with

growing amount of data. So, the solution to

this problem is parallel and distributed

computing. This can be achieved by Hadoop

Map-Reduce model. Hadoop is an open source

framework for processing and storing large

datasets over a cluster and it is used in

handling large and complex data which may

be structured, unstructured or semi-structured

[2]. Hadoop distributed file system (HDFS) is

a distributed file system, which rests on top of

the native file system and is written in java. It

is highly fault tolerant and is designed for

commodity hardware. HDFS has a high

JoADMS (2016) 17-23 © STM Journals 2016. All Rights Reserved Page 17

Journal of Advanced Database Management & Systems ISSN: 2393-8730(online)

Volume 3, Issue 3

www.stmjournals.com

IoT Based Biometric System

K.P. Swain1,*, M.V.S.V. Prasad

2, J. Sahoo

2, S. Sharma

3, G. Palai

1

1Department of Electronics and Communication Engineering, Gandhi Institute for Technological

Advancement, Bhubaneswar, Odisha, India 2Department of Electrical and Electronics Engineering, Gandhi Institute for Technological

Advancement, Bhubaneswar, Odisha, India 3Deptartment of Computer Science and Engineering, International Institute of Information

Technology, Bhubaneswar, Odisha, India

Abstract Research on “Internet of Things” (IoT) is burgeoning time to time owing to its real

application in the field of science and technology. The combination of cloud computing, big

data, future internet, robotics, semantic technologies, and IoT deal with several applications

in every automation pitch. IoT integrates the real world data and services into current

information networking for realizing different practical devices. In view of importance of

Internet of Things, this paper design and implement, the cloud-based biometric attendance

system using a low-cost IoT device Raspberry Pi. Present work has many advantages like

desktop notification about the individual attendance info to the concerned person and

checking own database by using both web and Android App from anywhere in the globe.

Keywords: IoT, Raspberry Pi, biometric, real-time

INTRODUCTION Biometric authentication, most often termed as

simply biometrics is used to verify the identity

of a living person depending upon some

physiological parameters, despite the entire cross

individual similarities [1]. It is always used with

some computational devices, which give more

accurate result in many real-time applications

like ATM, Aadhar Cards, voting machine,

security systems, attendance systems, etc.

Internet of Things (IoT) was first described by

Kevin Ashton, a British scientist in 1999

where the physical object is connected to the

Internet by using sensors [2]. He illustrated

how RFID can be used in a supply chain

system to count and track goods by the help of

the internet which drastically reduce the

human intervention. Afterwards, Internet of

Things turns out to be gigantic widespread in

automatic system by significantly reducing the

manpower.

At present, IoT acquired a very lucrative trend

by the combination of cloud computing and

the low cost device like Raspberry Pi,

Arduino, Edison.

In this work, a Raspberry Pi 2 board is used

along with a fingerprint sensor and a touch

screen LCD is used to log daily attendance for

an educational organization. Here, Raspberry

Pi 2 board us acts a Linux based minicomputer

used to store individual information in its

database and synchronize with remote

database.

RELATED WORK In the investigations [3, 4], Raspberry Pi is

efficiently used as an authentication node in a

cloud based biometric system for remote

enrolment. A Raspberry Pi along with

Arduino, Xbee and relay modules used in

smart drip irrigation system where user

command is processed at Raspberry Pi using

python language [5]. By using Zigbee

protocol, on/off command is received by

Arduino microcontroller from Raspberry Pi.

Between Raspberry Pi and end user

communication star topology is used. A

wireless sensor network system is developed

using Raspberry Pi and Zigbee, which can be

used for variety of environment monitoring

application [6]. Also, in this Raspberry Pi is

used a base station to collect different sensors

JoADMS (2016) 24-29 © STM Journals 2016. All Rights Reserved Page 24

Journal of Advanced Database Management & Systems ISSN: 2393-8730(online)

Volume 3, Issue 3

www.stmjournals.com

A Review on Inventory Management System for

Improving Efficiency of Project Development Cycle

Sagar S. Mehta1,*, Prasad S. Puranik

1, Satish B. Sharma

2

1Department of Mechanical Engineering, Atmiya Institute of Technology and Science Rajkot, Gujarat,

India 2Space Application Centre, Indian Space Research Organization, Ahmedabad, Gujarat, India

Abstract Inventory is a major element of many organizations. Consequently, its proper control is

crucial for the profitability of the organization and development of circumventing

communities. Inventory Management System (IMS) enables the visualization, specification,

and documentation of a software-intensive system. The software was tested for enhancing the

workflow and providing a timely and efficient handling. The manual system requires everyday

counting of items in the inventory, human errors are very prevalent during counting and

recording and all the manual inventory records will be damaged and irretrievable. In light of

the discoveries this paper highlights the possible solutions to the above quandaries; a

computerized IMS to issue and update the stocks.

Keywords: Inventory Management System, Project Development Life Cycle

INTRODUCTION Inventory Management System (IMS)

provides a flexible and easily understood way

of analyzing complicated problems.

The method has been used in several areas

including performance evaluation, project

management, inventory management, resource

allocation, budgeting decisions, etc.

Low inventory may lead to stock outs, which

result in production halts, inability to meet

deadlines, customer dissatisfaction and loss of

goodwill. On the other hand, high inventory

levels block huge capital, which is a scarce

resource for any organization.

For organizations that maintain thousands of

inventory items, it is unrealistic to provide

equal consideration to each item. Inventory is

one of the largest and most important assets of

a manufacturing business.

The main purpose of the inventory

management practices in all production

companies is to have the required items ready

to be processed right on the required time with

incurring minimum cost. The need of

inventory IMS emerges from the way that

manual taking care of may bring about human

blunders, which may influence the inventory

utilization. With a specific end goal to robotize

the procedure, a thorough study on the system

should be conducted.

The essential objective of Inventory

Administration System is to give a

documentation that is effortlessly

comprehended by all clients inside the

association.

IMS plays an important role for a successful

enterprise. With a correct framework, it is

easier to provide coordination between units,

eliminate waste, and make faster and better

decisions.

It is intended to those organizations that need

to receive and ship goods, while keeping up an

ideal use of space and knowing particularly

where all products are put away at any given

time.

IMS enhances real-time data capture, and the

automation of warehouse. The common

warehouse tasks can all be optimized to save

time to make for greater profits. Inventory

management is the process of productively

JoADMS (2016) 30-44 © STM Journals 2016. All Rights Reserved Page 30

Journal of Advanced Database Management & Systems ISSN: 2393-8730(online)

Volume 3, Issue 3

www.stmjournals.com

Information Privacy and Security in Data Mining

Likhitha A.R.*, Vandana B.S., Savitha C.K., Ujwal U.J. Department of Computer Science, KVG College of Engineering, Sullia, Karnataka, India

Abstract The improving popularity and growing of data mining technologies bring very serious effect

to the security of individual's sensitive information. In the recent years, the privacy preserving

data mining (PPDM), has been extensively studied and it is an emerging research subject in

data mining. Without accommodating the security of sensitive information contained in the

data, the basic idea of PPDM is to implement the data in such a manner so as to execute data

mining algorithms effectively. While in fact, data collecting, data publishing, and information

delivering happen only in the process of unwanted disclosure of sensitive information. Here,

the privacy issues equal to the data mining from a wider perspective and investigate many

different approaches which can help to save or protect the sensitive information. In data

mining, there are four different types of users involved, namely data provider, data collector,

data miner and decision maker. The four types of users, which discuss user privacy and

concerns the methods that can adopted to protect sensitive information. The basics of parallel

research topics, evaluate state-of-the-art approaches existing, some preliminary thoughts on

upcoming research directions are introduced briefly here. Each type of user exploring the

privacy-preserving approaches; and also find the game theoretical approaches. In data

mining scenario, the approaches are proposed for analyzing the interaction among different

users, each of information is based on the valuation on the sensitive information. Sensitive

information are, differentiating the responsibilities of different users with respect to security,

this would provide some of useful insights into the study of PPDM.

Keywords: Data mining, privacy preserving data mining (PPDM), sensitive information,

state-of-the-art approaches

INTRODUCTION Nowadays, the many industrial areas and

governments around the world are

experiencing unprecedented increase by three

different things, they are volume, variety and

velocity of information, it is reason to the

deployment of the mobile networks of new

generations and number of increased use of

smart phones.

There is explosion in numbers of subscribers,

the services offered by the multitude, online

transactions and the rise of social media. The

consideration of massive data is a gold mine

that must be tapped to enjoy, to do this, the

appropriate technology proves by the big data

[1]. This big data allows data analysis in

immeasurable depths, highlights the hidden

meanings in data tsunami, by showing

correlations brings up the information,

unsuspected association by the underlying

mechanisms and shows things in a new and

unexpected angle [1]. The big data will

differentiate themselves from their

competitors, gain market share, achieve key

objectives, increase revenue and benefit from

new innovative services in the field of

business organizations. Here, the big data

technology is introduced along with its

importance and its uses in the modern world

and its key fields and substantial issues have

also been highlighted.

Data mining has attracted more and more

attention in the recent days, it is because of the

popularity of the “big data'' concept. Data

mining is the process of inventing or searching

interesting patterns and knowledge from many

numbers of large amounts of data [2]. Data

mining has been successfully applied to many

domains such as business intelligence, web

search, scientific discovery, digital libraries

etc., it is because of its usage as a highly

application-driven discipline.

Journal of Advanced Database Management & Systems(JoADMS)

www.stmjournals.com

STM JOURNALSScientific Technical Medical

September–December 2016

ISSN 2393-8730 (Online)