The document proposes a technique to recommend indexes for high-dimensional databases based on query workloads. It detects when query patterns change and dynamically adjusts indexes to maintain good performance. Lower-dimensional indexes that represent user access patterns are used to accurately prune large portions of data irrelevant to queries. As query patterns evolve over time, the technique monitors workloads and detects changes to evolve indexes and preserve query response speeds.
IEEE PROJECTS 2015
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Dot Net
DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
Eligibility
Final Year students of
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2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
1. DOT NET
2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
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12. MAT LAB
13. LAB VIEW
14. Multi Sim
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Tamin Nadu, INDIA - 600 026
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website:1croreprojects.com
Phone : +91 97518 00789 / +91 72999 51536
We are the company providing Complete Solution for all Academic Final Year/Semester Student Projects. Our projects are
suitable for B.E (CSE,IT,ECE,EEE), B.Tech (CSE,IT,ECE,EEE),M.Tech (CSE,IT,ECE,EEE) B.sc (IT & CSE), M.sc (IT & CSE),
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Nexgen Technology Address:
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No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
Overview of basic concepts related to Data Mining: database, data model, fuzzy sets, information retrieval, data warehouse, dimensional modeling, data cubes, OLAP, machine learning.
An efficient feature selection algorithm for health care data analysisjournalBEEI
Diabete is a silent killer, which will slowly kill the person if it goes undetected. The existing system which uses F-score method and K-means clustering of checking whether a person has diabetes or not are 100% accurate, and anything which isn't a 100% is not acceptable in the medical field, as it could cost the lives of many people. Our proposed system aims at using some of the best features of the existing algorithms to predict diabetes, and combine these and based on these features; This research work turns them into a novel algorithm, which will be 100% accurate in its prediction. With the surge in technological advancements, we can use data mining to predict when a person would be diagnosed with diabetes. Specifically, we analyze the best features of chi-square algorithm and advanced clustering algorithm (ACA). This research work is done using the Pima Indian Diabetes dataset provided by National Institutes of Diabetes and Digestive and Kidney Diseases. Using classification theorems and methods we can consider different factors like age, BMI, blood pressure and the importance given to these attributes overall, and singles these attributes out, and use them for the prediction of diabetes.
IEEE PROJECTS 2015
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Dot Net
DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
1. DOT NET
2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
11. EMBEDDED
12. MAT LAB
13. LAB VIEW
14. Multi Sim
CONTACT US
1 CRORE PROJECTS
Door No: 214/215,2nd Floor,
No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai,
Tamin Nadu, INDIA - 600 026
Email id: 1croreprojects@gmail.com
website:1croreprojects.com
Phone : +91 97518 00789 / +91 72999 51536
We are the company providing Complete Solution for all Academic Final Year/Semester Student Projects. Our projects are
suitable for B.E (CSE,IT,ECE,EEE), B.Tech (CSE,IT,ECE,EEE),M.Tech (CSE,IT,ECE,EEE) B.sc (IT & CSE), M.sc (IT & CSE),
MCA, and many more..... We are specialized on Java,Dot Net ,PHP & Andirod technologies. Each Project listed comes with
the following deliverable: 1. Project Abstract 2. Complete functional code 3. Complete Project report with diagrams 4.
Database 5. Screen-shots 6. Video File
SERVICE AT CLOUDTECHNOLOGIES
IEEE, WEB, WINDOWS PROJECTS ON DOT NET, JAVA& ANDROID TECHNOLOGIES,EMBEDDED SYSTEMS,MAT LAB,VLSI DESIGN.
ME, M-TECH PAPER PUBLISHING
COLLEGE TRAINING
Thanks&Regards
cloudtechnologies
# 304, Siri Towers,Behind Prime Hospitals
Maitrivanam, Ameerpet.
Contact:-8121953811,8522991105.040-65511811
cloudtechnologiesprojects@gmail.com
http://cloudstechnologies.in/
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Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
Overview of basic concepts related to Data Mining: database, data model, fuzzy sets, information retrieval, data warehouse, dimensional modeling, data cubes, OLAP, machine learning.
An efficient feature selection algorithm for health care data analysisjournalBEEI
Diabete is a silent killer, which will slowly kill the person if it goes undetected. The existing system which uses F-score method and K-means clustering of checking whether a person has diabetes or not are 100% accurate, and anything which isn't a 100% is not acceptable in the medical field, as it could cost the lives of many people. Our proposed system aims at using some of the best features of the existing algorithms to predict diabetes, and combine these and based on these features; This research work turns them into a novel algorithm, which will be 100% accurate in its prediction. With the surge in technological advancements, we can use data mining to predict when a person would be diagnosed with diabetes. Specifically, we analyze the best features of chi-square algorithm and advanced clustering algorithm (ACA). This research work is done using the Pima Indian Diabetes dataset provided by National Institutes of Diabetes and Digestive and Kidney Diseases. Using classification theorems and methods we can consider different factors like age, BMI, blood pressure and the importance given to these attributes overall, and singles these attributes out, and use them for the prediction of diabetes.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Providing healthcare as-a-service using fuzzy rule-based big data analytics i...nexgentechnology
GET IEEE BIG DATA, JAVA ,DOTNET,ANDROID ,NS2,MATLAB,EMBEDED AT LOW COST WITH BEST QUALITY PLEASE CONTACT BELOW NUMBER
FOR MORE INFORMATION PLEASE FIND THE BELOW DETAILS:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com
Mobile: 9791938249
Telephone: 0413-2211159
www.nexgenproject.com
Certain Investigation on Dynamic Clustering in Dynamic Dataminingijdmtaiir
Clustering is the process of grouping a set of objects
into classes of similar objects. Dynamic clustering comes in a
new research area that is concerned about dataset with dynamic
aspects. It requires updates of the clusters whenever new data
records are added to the dataset and may result in a change of
clustering over time. When there is a continuous update and
huge amount of dynamic data, rescan the database is not
possible in static data mining. But this is possible in Dynamic
data mining process. This dynamic data mining occurs when
the derived information is present for the purpose of analysis
and the environment is dynamic, i.e. many updates occur.
Since this has now been established by most researchers and
they will move into solving some of the problems and the
research is to concentrate on solving the problem of using data
mining dynamic databases. This paper gives some
investigation of existing work done in some papers related with
dynamic clustering and incremental data clustering
With the development of database, the data volume stored in database increases rapidly and in the large
amounts of data much important information is hidden. If the information can be extracted from the
database they will create a lot of profit for the organization. The question they are asking is how to extract
this value. The answer is data mining. There are many technologies available to data mining practitioners,
including Artificial Neural Networks, Genetics, Fuzzy logic and Decision Trees. Many practitioners are
wary of Neural Networks due to their black box nature, even though they have proven themselves in many
situations. This paper is an overview of artificial neural networks and questions their position as a
preferred tool by data mining practitioners.
Test Data Generation is seen to be a complex problem and though a lot of solutions have come forth most of them are limited to toy programs. DTM Data Generator is a simple, powerful and fully customizable tool that generates data for database testing purposes. The project is a set of generation rules.
It's a free, open source tool written in JavaScript, PHP and MySQL that lets you quickly generate large volumes of custom data in a variety of formats for use in testing software, populating databases, and... so on and so forth.
Classification of data is a data mining technique based on machine learning is used to classification of each item set in as a set of dataset into a set of predefined labelled as classes or groups. Classification is tasks for different application such as text classification, image classification, class’s predictions, data Classification etc. In this paper, we presenting the major classification techniques used for prediction of classes using supervised learning dataset. Several major types of classification method including Random Forest, Naive Bayes, Support Vector Machine (SVM) techniques. The goal of this review paper is to provide a review, accuracy and comparative between different classification techniques in data mining.
Document Classification Using Expectation Maximization with Semi Supervised L...ijsc
As the amount of online document increases, the demand for document classification to aid the analysis and management of document is increasing. Text is cheap, but information, in the form of knowing what classes a document belongs to, is expensive. The main purpose of this paper is to explain the expectation maximization technique of data mining to classify the document and to learn how to improve the accuracy while using semi-supervised approach. Expectation maximization algorithm is applied with both supervised and semi-supervised approach. It is found that semi-supervised approach is more accurate and effective. The main advantage of semi supervised approach is “DYNAMICALLY GENERATION OF NEW CLASS”. The algorithm first trains a classifier using the labeled document and probabilistically classifies the
unlabeled documents. The car dataset for the evaluation purpose is collected from UCI repository dataset in which some changes have been done from our side.
An exploratory analysis on half hourly electricity load patterns leading to h...acijjournal
Accurate prediction of electricity demand can bring
extensive benefits to any country as the forecaste
d
values help the relevant authorities to take decisi
ons regarding electricity generation, transmission
and
distribution appropriately. The literature reveals
that, when compared to conventional time series
techniques, the improved artificial intelligent app
roaches provide better prediction accuracies. Howev
er,
the accuracy of predictions using intelligent appro
aches like neural networks are strongly influenced
by the
correct selection of inputs and the number of neuro
-forecasters used for prediction. Deshani, Hansen,
Attygalle, & Karunarathne (2014) suggested that a c
luster analysis could be performed to group similar
day types, which contribute towards selecting a bet
ter set of neuro-forecasters in neural networks. Th
e
cluster analysis was based on the daily total elect
ricity demands as their target was to predict the d
aily
total demands using neural networks. However, predi
cting half-hourly demand seems more appropriate
due to the considerable changes of electricity dema
nd observed during a particular day. As such cluste
rs
are identified considering half-hourly data within
the daily load distribution curves. Thus, this pape
r is an
improvement to Deshani et. al. (2014), which illust
rates how the half hourly demand distribution withi
n a
day, is incorporated when selecting the inputs for
the neuro-forecasters.
https://www.youtube.com/watch?v=Y_-o-4rKxUk
Machine learning powered metabolomic network analysis
Dmitry Grapov PhD,
Director of Data Science and Bioinformatics,
CDS- Creative Data Solutions
www.createdatasol.com
Metabolomic network analysis can be used to interpret experimental results within a variety of contexts including: biochemical relationships, structural and spectral similarity and empirical correlation. Machine learning is useful for modeling relationships in the context of pattern recognition, clustering, classification and regression based predictive modeling. The combination of developed metabolomic networks and machine learning based predictive models offer a unique method to visualize empirical relationships while testing key experimental hypotheses. The following presentation focuses on data analysis, visualization, machine learning and network mapping approaches used to create richly mapped metabolomic networks. Learn more at www.createdatasol.com
Internet becomes the most popular surfing environment which increases the
service oriented data size. As the data size grows, finding and retrieving the most
similar data from the large volume of data would become more difficult task. This
problem is focused in the various research methods, which attempts to cluster the
large volume of data. In the existing research method Clustering-based Collaborative
Filtering approach (ClubCF) is introduced whose main goal is to cluster the similar
kind of data together, so that retrieval time cost can be reduced considerably.
However, existing research methods cannot find the similar reviews accurately which
needs to be focused more for efficient and accurate recommendation system. This is
ensured in the proposed research method by introducing the novel research technique
namely Modified Collaborative Filtering and Clustering with Regression (MoCFCR).
In this research method, initially k means algorithm is used to cluster the similar
movie reviewer together, so that recommendation process can be done in the easier
way. In order to handle the large volume of data this research work adapts the map
reduce framework which will divide the entire data into subsets which will assigned
on separate nodes with individual key values. After clustering, the clustered outcome
is merged together using inverted index procedure in which similarity between movies
would be calculated. Here collaborative filtering is applied to remove the movies that
are not relevant to input. Finally recommendations of movies are made in the accurate
way by using the logistic regression method. The overall evaluation of the proposed
research method is done in Hadoop from which it can be proved that the proposed
research technique can lead to provide better outcome than the existing research
techniques
Using Classification and Clustering with Azure Machine Learning Models shows how to use classification and clustering algorithms with Azure Machine Learning.
Final year M.E IEEE PROJECTS TITLES 2014-2015 Final year IEEE PROJECTS TITLES 2014-2015 Final year M.TECH IEEE PROJECTS TITLES 2014-2015 Final year B.E IEEE
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Providing healthcare as-a-service using fuzzy rule-based big data analytics i...nexgentechnology
GET IEEE BIG DATA, JAVA ,DOTNET,ANDROID ,NS2,MATLAB,EMBEDED AT LOW COST WITH BEST QUALITY PLEASE CONTACT BELOW NUMBER
FOR MORE INFORMATION PLEASE FIND THE BELOW DETAILS:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com
Mobile: 9791938249
Telephone: 0413-2211159
www.nexgenproject.com
Certain Investigation on Dynamic Clustering in Dynamic Dataminingijdmtaiir
Clustering is the process of grouping a set of objects
into classes of similar objects. Dynamic clustering comes in a
new research area that is concerned about dataset with dynamic
aspects. It requires updates of the clusters whenever new data
records are added to the dataset and may result in a change of
clustering over time. When there is a continuous update and
huge amount of dynamic data, rescan the database is not
possible in static data mining. But this is possible in Dynamic
data mining process. This dynamic data mining occurs when
the derived information is present for the purpose of analysis
and the environment is dynamic, i.e. many updates occur.
Since this has now been established by most researchers and
they will move into solving some of the problems and the
research is to concentrate on solving the problem of using data
mining dynamic databases. This paper gives some
investigation of existing work done in some papers related with
dynamic clustering and incremental data clustering
With the development of database, the data volume stored in database increases rapidly and in the large
amounts of data much important information is hidden. If the information can be extracted from the
database they will create a lot of profit for the organization. The question they are asking is how to extract
this value. The answer is data mining. There are many technologies available to data mining practitioners,
including Artificial Neural Networks, Genetics, Fuzzy logic and Decision Trees. Many practitioners are
wary of Neural Networks due to their black box nature, even though they have proven themselves in many
situations. This paper is an overview of artificial neural networks and questions their position as a
preferred tool by data mining practitioners.
Test Data Generation is seen to be a complex problem and though a lot of solutions have come forth most of them are limited to toy programs. DTM Data Generator is a simple, powerful and fully customizable tool that generates data for database testing purposes. The project is a set of generation rules.
It's a free, open source tool written in JavaScript, PHP and MySQL that lets you quickly generate large volumes of custom data in a variety of formats for use in testing software, populating databases, and... so on and so forth.
Classification of data is a data mining technique based on machine learning is used to classification of each item set in as a set of dataset into a set of predefined labelled as classes or groups. Classification is tasks for different application such as text classification, image classification, class’s predictions, data Classification etc. In this paper, we presenting the major classification techniques used for prediction of classes using supervised learning dataset. Several major types of classification method including Random Forest, Naive Bayes, Support Vector Machine (SVM) techniques. The goal of this review paper is to provide a review, accuracy and comparative between different classification techniques in data mining.
Document Classification Using Expectation Maximization with Semi Supervised L...ijsc
As the amount of online document increases, the demand for document classification to aid the analysis and management of document is increasing. Text is cheap, but information, in the form of knowing what classes a document belongs to, is expensive. The main purpose of this paper is to explain the expectation maximization technique of data mining to classify the document and to learn how to improve the accuracy while using semi-supervised approach. Expectation maximization algorithm is applied with both supervised and semi-supervised approach. It is found that semi-supervised approach is more accurate and effective. The main advantage of semi supervised approach is “DYNAMICALLY GENERATION OF NEW CLASS”. The algorithm first trains a classifier using the labeled document and probabilistically classifies the
unlabeled documents. The car dataset for the evaluation purpose is collected from UCI repository dataset in which some changes have been done from our side.
An exploratory analysis on half hourly electricity load patterns leading to h...acijjournal
Accurate prediction of electricity demand can bring
extensive benefits to any country as the forecaste
d
values help the relevant authorities to take decisi
ons regarding electricity generation, transmission
and
distribution appropriately. The literature reveals
that, when compared to conventional time series
techniques, the improved artificial intelligent app
roaches provide better prediction accuracies. Howev
er,
the accuracy of predictions using intelligent appro
aches like neural networks are strongly influenced
by the
correct selection of inputs and the number of neuro
-forecasters used for prediction. Deshani, Hansen,
Attygalle, & Karunarathne (2014) suggested that a c
luster analysis could be performed to group similar
day types, which contribute towards selecting a bet
ter set of neuro-forecasters in neural networks. Th
e
cluster analysis was based on the daily total elect
ricity demands as their target was to predict the d
aily
total demands using neural networks. However, predi
cting half-hourly demand seems more appropriate
due to the considerable changes of electricity dema
nd observed during a particular day. As such cluste
rs
are identified considering half-hourly data within
the daily load distribution curves. Thus, this pape
r is an
improvement to Deshani et. al. (2014), which illust
rates how the half hourly demand distribution withi
n a
day, is incorporated when selecting the inputs for
the neuro-forecasters.
https://www.youtube.com/watch?v=Y_-o-4rKxUk
Machine learning powered metabolomic network analysis
Dmitry Grapov PhD,
Director of Data Science and Bioinformatics,
CDS- Creative Data Solutions
www.createdatasol.com
Metabolomic network analysis can be used to interpret experimental results within a variety of contexts including: biochemical relationships, structural and spectral similarity and empirical correlation. Machine learning is useful for modeling relationships in the context of pattern recognition, clustering, classification and regression based predictive modeling. The combination of developed metabolomic networks and machine learning based predictive models offer a unique method to visualize empirical relationships while testing key experimental hypotheses. The following presentation focuses on data analysis, visualization, machine learning and network mapping approaches used to create richly mapped metabolomic networks. Learn more at www.createdatasol.com
Internet becomes the most popular surfing environment which increases the
service oriented data size. As the data size grows, finding and retrieving the most
similar data from the large volume of data would become more difficult task. This
problem is focused in the various research methods, which attempts to cluster the
large volume of data. In the existing research method Clustering-based Collaborative
Filtering approach (ClubCF) is introduced whose main goal is to cluster the similar
kind of data together, so that retrieval time cost can be reduced considerably.
However, existing research methods cannot find the similar reviews accurately which
needs to be focused more for efficient and accurate recommendation system. This is
ensured in the proposed research method by introducing the novel research technique
namely Modified Collaborative Filtering and Clustering with Regression (MoCFCR).
In this research method, initially k means algorithm is used to cluster the similar
movie reviewer together, so that recommendation process can be done in the easier
way. In order to handle the large volume of data this research work adapts the map
reduce framework which will divide the entire data into subsets which will assigned
on separate nodes with individual key values. After clustering, the clustered outcome
is merged together using inverted index procedure in which similarity between movies
would be calculated. Here collaborative filtering is applied to remove the movies that
are not relevant to input. Finally recommendations of movies are made in the accurate
way by using the logistic regression method. The overall evaluation of the proposed
research method is done in Hadoop from which it can be proved that the proposed
research technique can lead to provide better outcome than the existing research
techniques
Using Classification and Clustering with Azure Machine Learning Models shows how to use classification and clustering algorithms with Azure Machine Learning.
Final year M.E IEEE PROJECTS TITLES 2014-2015 Final year IEEE PROJECTS TITLES 2014-2015 Final year M.TECH IEEE PROJECTS TITLES 2014-2015 Final year B.E IEEE
Final year M.E IEEE PROJECTS TITLES 2014-2015 Final year IEEE PROJECTS TITLES 2014-2015 Final year M.TECH IEEE PROJECTS TITLES 2014-2015 Final year B.E IEEE
Mumbai Academics is Mumbai’s first dedicated Professional Training Center for Training with Spoke and hub model with Multiple verticles . The strong foundation of Mumbai Academics is laid by highly skilled and trained Professionals, carrying mission to provide industry level input to the freshers and highly skilled and trained Software Professionals/other professional to IT companies.
Final year M.E IEEE PROJECTS TITLES 2014-2015 Final year IEEE PROJECTS TITLES 2014-2015 Final year M.TECH IEEE PROJECTS TITLES 2014-2015 Final year B.E IEEE
Elimination of data redundancy before persisting into dbms using svm classifi...nalini manogaran
Elimination of data redundancy before persisting into dbms using svm classification,
Data Base Management System is one of the
growing fields in computing world. Grid computing, internet
sharing, distributed computing, parallel processing and cloud
are the areas store their huge amount of data in a DBMS to
maintain the structure of the data. Memory management is
one of the major portions in DBMS due to edit, delete, recover
and commit operations used on the records. To improve the
memory utilization efficiently, the redundant data should be
eliminated accurately. In this paper, the redundant data is
fetched by the Quick Search Bad Character (QSBC) function
and intimate to the DB admin to remove the redundancy.
QSBC function compares the entire data with patterns taken
from index table created for all the data persisted in the
DBMS to easy comparison of redundant (duplicate) data in
the database. This experiment in examined in SQL server
software on a university student database and performance is
evaluated in terms of time and accuracy. The database is
having 15000 students data involved in various activities.
Keywords—Data redundancy, Data Base Management System,
Support Vector Machine, Data Duplicate.
I. INTRODUCTION
The growing (prenominal) mass of information
present in digital media has become a resistive problem for
data administrators. Usually, shaped on data congregate
from distinct origin, data repositories such as those used by
digital libraries and e-commerce agent based records with
disparate schemata and structures. Also problems regarding
to low response time, availability, security and quality
assurance become more troublesome to manage as the
amount of data grow larger. It is practicable to specimen
that the peculiarity of the data that an association uses in its
systems is relative to its efficiency for offering beneficial
services to their users. In this environment, the
determination of maintenance repositories with “dirty” data
(i.e., with replicas, identification errors, equal patterns,
etc.) goes greatly beyond technical discussion such as the
everywhere quickness or accomplishment of data
administration systems.
Nalini.M, nalini.tptwin@gmail.com, Anbu.S, anomaly detection,
data mining
big data
dbms
intrusion detection
dublicate detection
data cleaning
data redundancy
data replication, redundancy removel, QSBC, Duplicate detection, error correction, de-duplication, Data cleaning, Dbms, Data sets
Query aware determinization of uncertain objects is an ieee project.
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Power Management in Micro grid Using Hybrid Energy Storage Systemijcnes
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CourierInformationSystem
CYBER_SHOPPING
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IntranetChatting
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NetConferening
online order processing system with AJAX enabled
OnLineExam process
web based Manufacturing
WEBREPORTING PROCESS
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Classifieds
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DataMart Management Software
E Procurement System
e-Classifieds
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Elearn
Employee Resource Info sys
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E-TRANSACTION_Totalproj
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global communication
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smartcard
SpeedAge
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👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
2. Abstract:
Usually users are interested in querying data over a relatively
small subset of the entire attribute set at a time. A potential solution is
to use lower dimensional indexes that accurately represent the user
access patterns. If the query pattern change, then the query response
using the physical database design that is developed based on a static
snapshot of the query workload may significantly degrade. To address
these issues, we introduce a parameterizable technique to recommend
indexes based on index types that are frequently used for highdimensional data sets and to dynamically adjust indexes as the
underlying query workload changes. We incorporate a query pattern
change detection mechanism to determine when the access patterns
have changed enough to warrant change in the physical database
design. By adjusting analysis parameters, we trade off analysis speed
against analysis resolution.
3. 2 Introduction:
AN increasing number of database applications such as business data
warehouses and scientific data repositories deal with high-dimensional
data sets. As the number of dimensions/attributes and the overall size
of data sets increase, it becomes essential to efficiently retrieve
specific queried data from the database in order to effectively utilize
the database. Indexing support is needed to effectively prune out
significant portions of the data set that are not relevant for the
queries. Multidimensional indexing, dimensionality reduction, and
Relational Database Management System (RDBMS) index selection
tools all could be applied to the problem. However, for highdimensional data sets, each of these potential solutions has inherent
problems.
To
illustrate
these
problems,
consider
a
uniformly
distributed data set of 1,000,000 data objects with several hundred
attributes. Range queries are consistently executed over five of the
attributes. The query selectivity over each attribute is 0.1, so the
overall query selectivity is 1=105 (that is, the answer set contains
about 10 results). An ideal solution would allow us to read from the
disk only those pages that contain matching answers to the query. We
could build a multidimensional index over the data set so that we can
directly answer any query by only using the index. However, the
performance of multidimensional index structures is subject to
Bellman’s curse of dimensionality and rapidly degrades as the number
of dimensions increases. For the given example, such an index would
perform much worse than a sequential scan. Another possibility would
be to build an index over each single dimension. The effectiveness of
this approach is limited to the amount of search space that can be
pruned by a single dimension (in the example, the search space would
only be pruned to 100,000 objects).
4. High Dimensional Indexing:
A number of techniques have been introduced to address the
high-dimensional indexing problem such as the X-tree [5] and the GCtree [6]. Although these index structures have been shown to increase
the range of effective dimensionality, they still suffer performance
degradation at higher index dimensionality.
Feature Selection
Feature selection techniques are a subset of dimensionality reduction
targeted at finding a set of untransformed attributes that best
represent the overall data set. These techniques are also focused on
maximizing data energy or classification accuracy rather than query
response. As a result, selected features may have no overlap with
queried attributes.
Index Selection
The index selection problem has been identified as a variation of the
Knapsack Problem, and several papers proposed designs for index
recommendations based on optimization rules. These earlier designs
could not take advantage of modern database systems’ query
optimizer. Currently, almost every commercial RDBMS provides the
users with an index
recommendation tool based on a query workload and uses the query
optimizer to obtain cost estimates. A query workload is a set of SQL
data manipulation statements. The
query workload should be a good representative of the types of
queries that an application supports.
5. Automatic Index Selection
The ideas of having a database that can tune itself by automatically
creating new indexes as the queries arrive have been proposed. In a
cost model is used
to identify beneficial indexes and decide when to create or drop an
index at runtime. Costa and Lifschitz propose an agent-based database
architecture to deal with an
automatic index creation. Microsoft Research has proposed a physicaldesign alerter to identify when a modification to the physical design
could result in improved performance.
Literature survey:
Index Selection
Index Selection is a method of artificial selection in which several
useful traits are selected simultaneously. First, each trait that is going
to be selected is assigned a weight, the importance of the trait. I.e., if
you were selecting for both height and the darkness of the coat in
dogs, if height was more important to you, one would assign that a
higher weighting. For instance, heights weighting could be ten and
coat darkness' could be two. This weighting value is then multiplied by
the observed value in each individual animal and then the score for
each of the characteristics is summed for each individual. This result is
the index score and can be used to compare the worth of each
organism being selected. Therefore, only those with the highest index
score are selected for breeding via artificial selection.
This method has advantages over other methods of artificial selection,
such as tandem selection, in that you can select for traits
simultaneously rather than sequentially. Thereby, no useful traits are
6. being excluded from selection at any one time and so none will start to
reverse while you concentrate on improving another property of the
organism. However, its major disadvantage is that the weightings
assigned to each characteristic are inherently quite hard to calculate
precisely and so require some elements of trial and error before they
become optimal to the breeder.
Query Access pattern:
The advantage of using data access objects is the relatively simple and
rigorous separation between two important parts of an application
which can and should know almost nothing of each other, and which
can be expected to evolve frequently and independently. Changing
business logic can rely on the same DAO interface, while changes to
persistence logic do not affect DAO clients as long as the interface
remains correctly implemented.
In the specific context of the Java programming language, Data Access
Objects can be used to insulate an application from the particularly
numerous, complex and varied Java persistence technologies, which
could be JDBC, JDO, EJB CMP, Hibernate, or many others. Using Data
Access Objects means the underlying technology can be upgraded or
swapped without changing other parts of the application.
7. Existing System:
Query response does not perform well if query
patterns change.
Because it uses static query workload.
Its performance may degrade if the database size
gets increased.
Tradition feature selection technique may offer less
or no data pruning capability given query attributes.
Proposed System:
We develop a flexible index selection frame work to
achieve static index selection and dynamic index
selection for high dimensional data.
A control feedback technique is introduced for
measuring the performance.
Through this a database could benefit from an index
change.
The index selection minimizes the cost of the queries
in the work load.
Online index selection is designed in the motivation if
the query pattern changes over time.
By monitoring the query workload and detecting
when there is a change on the query pattern, able to
evolve good performance as query patterns evolve
8. Software requirements:
Hardware:
PROCESSOR
:
PENTIUM IV 2.6 GHz
RAM
:
512 MB DD RAM
MONITOR
:
15” COLOR
HARD DISK
:
20 GB
FLOPPY DRIVE
:
1.44 MB
CDDRIVE
:
LG 52X
Front End
:
J2EE(JSP)
Back End
:
MS SQL 2000
Tools Used
:
JFrameBuilder
Software:
Operating System
:
WindowsXP