Enhancing Privacy of Confidential Data using K AnonymizationIDES Editor
Recent advances in the field of data collection and
related technologies have inaugurated a new era of
research where existing data mining algorithms should be
reconsidered from a different point of view, this of privacy
preservation. Much research has been done recently on
privacy preserving data mining (PPDM) based on
perturbation, randomization and secure multiparty
computations and more recently on anonymity including
k-anonymity and l-diversity.
We use the technique of k-Anonymization to de-associate
sensitive attributes from the corresponding identifiers.
This is done by anonymizing the linking attributes so that
at least k released records match each value combination
of the linking attributes. This paper proposes a k-
Anonymization solution for classification. The proposed
method has been implemented and evaluated using UCI
repository datasets.
After the k-anonymization solution is determined for the
original data, classification, a data mining technique using
the ID3 algorithm, is applied on both the original table and
the compressed table .The accuracy of the both is
compared by determining the entropy and the information
gain values. Experiments show that the quality of
classification can be preserved even for highly restrictive
anonymity requirements
Enhancing Privacy of Confidential Data using K AnonymizationIDES Editor
Recent advances in the field of data collection and
related technologies have inaugurated a new era of
research where existing data mining algorithms should be
reconsidered from a different point of view, this of privacy
preservation. Much research has been done recently on
privacy preserving data mining (PPDM) based on
perturbation, randomization and secure multiparty
computations and more recently on anonymity including
k-anonymity and l-diversity.
We use the technique of k-Anonymization to de-associate
sensitive attributes from the corresponding identifiers.
This is done by anonymizing the linking attributes so that
at least k released records match each value combination
of the linking attributes. This paper proposes a k-
Anonymization solution for classification. The proposed
method has been implemented and evaluated using UCI
repository datasets.
After the k-anonymization solution is determined for the
original data, classification, a data mining technique using
the ID3 algorithm, is applied on both the original table and
the compressed table .The accuracy of the both is
compared by determining the entropy and the information
gain values. Experiments show that the quality of
classification can be preserved even for highly restrictive
anonymity requirements
Data Structure is a way of collecting and organising data in such a way that we can perform operations on these data in an effective way. Data Structures is about rendering data elements in terms of some relationship, for better organization and storage. For example, we have data player's name "Virat" and age 26. Here "Virat" is of String data type and 26 is of integer data type.
We can organize this data as a record like Player record. Now we can collect and store player's records in a file or database as a data structure. For example: "Dhoni" 30, "Gambhir" 31, "Sehwag" 33
In simple language, Data Structures are structures programmed to store ordered data, so that various operations can be performed on it easily.
Similarity-preserving hash for content-based audio retrieval using unsupervis...IJECEIAES
Due to its efficiency in storage and search speed, binary hashing has become an attractive approach for a large audio database search. However, most existing hashing-based methods focus on data-independent scheme where random linear projections or some arithmetic expression are used to construct hash functions. Hence, the binary codes do not preserve the similarity and may degrade the search performance. In this paper, an unsupervised similarity-preserving hashing method for content-based audio retrieval is proposed. Different from data-independent hashing methods, we develop a deep network to learn compact binary codes from multiple hierarchical layers of nonlinear and linear transformations such that the similarity between samples is preserved. The independence and balance properties are included and optimized in the objective function to improve the codes. Experimental results on the Extended Ballroom dataset with 8 genres of 3,000 musical excerpts show that our proposed method significantly outperforms state-ofthe-art data-independent method in both effectiveness and efficiency.
COMPARISON OF WAVELET NETWORK AND LOGISTIC REGRESSION IN PREDICTING ENTERPRIS...ijcsit
Enterprise financial distress or failure includes bankruptcy prediction, financial distress, corporate performance prediction and credit risk estimation. The aim of this paper is that using wavelet networks innon-linear combination prediction to solve ARMA (Auto-Regressive and Moving Average) model problem.ARMA model need estimate the value of all parameters in the model, it has a large amount of computation.Under this aim, the paper provides an extensive review of Wavelet networks and Logistic regression. Itdiscussed the Wavelet neural network structure, Wavelet network model training algorithm, Accuracy rateand error rate (accuracy of classification, Type I error, and Type II error). The main research opportunity exist a proposed of business failure prediction model (wavelet network model and logistic regression
model). The empirical research which is comparison of Wavelet Network and Logistic Regression on training and forecasting sample, the result shows that this wavelet network model is high accurate and the overall prediction accuracy, Type Ⅰerror and Type Ⅱ error, wavelet networks model is better thanlogistic regression model.
http://inarocket.com
Learn BEM fundamentals as fast as possible. What is BEM (Block, element, modifier), BEM syntax, how it works with a real example, etc.
How to Build a Dynamic Social Media PlanPost Planner
Stop guessing and wasting your time on networks and strategies that don’t work!
Join Rebekah Radice and Katie Lance to learn how to optimize your social networks, the best kept secrets for hot content, top time management tools, and much more!
Watch the replay here: bit.ly/socialmedia-plan
Content personalisation is becoming more prevalent. A site, it's content and/or it's products, change dynamically according to the specific needs of the user. SEO needs to ensure we do not fall behind of this trend.
Lightning Talk #9: How UX and Data Storytelling Can Shape Policy by Mika Aldabaux singapore
How can we take UX and Data Storytelling out of the tech context and use them to change the way government behaves?
Showcasing the truth is the highest goal of data storytelling. Because the design of a chart can affect the interpretation of data in a major way, one must wield visual tools with care and deliberation. Using quantitative facts to evoke an emotional response is best achieved with the combination of UX and data storytelling.
Succession “Losers”: What Happens to Executives Passed Over for the CEO Job?
By David F. Larcker, Stephen A. Miles, and Brian Tayan
Stanford Closer Look Series
Overview:
Shareholders pay considerable attention to the choice of executive selected as the new CEO whenever a change in leadership takes place. However, without an inside look at the leading candidates to assume the CEO role, it is difficult for shareholders to tell whether the board has made the correct choice. In this Closer Look, we examine CEO succession events among the largest 100 companies over a ten-year period to determine what happens to the executives who were not selected (i.e., the “succession losers”) and how they perform relative to those who were selected (the “succession winners”).
We ask:
• Are the executives selected for the CEO role really better than those passed over?
• What are the implications for understanding the labor market for executive talent?
• Are differences in performance due to operating conditions or quality of available talent?
• Are boards better at identifying CEO talent than other research generally suggests?
Data Structure is a way of collecting and organising data in such a way that we can perform operations on these data in an effective way. Data Structures is about rendering data elements in terms of some relationship, for better organization and storage. For example, we have data player's name "Virat" and age 26. Here "Virat" is of String data type and 26 is of integer data type.
We can organize this data as a record like Player record. Now we can collect and store player's records in a file or database as a data structure. For example: "Dhoni" 30, "Gambhir" 31, "Sehwag" 33
In simple language, Data Structures are structures programmed to store ordered data, so that various operations can be performed on it easily.
Similarity-preserving hash for content-based audio retrieval using unsupervis...IJECEIAES
Due to its efficiency in storage and search speed, binary hashing has become an attractive approach for a large audio database search. However, most existing hashing-based methods focus on data-independent scheme where random linear projections or some arithmetic expression are used to construct hash functions. Hence, the binary codes do not preserve the similarity and may degrade the search performance. In this paper, an unsupervised similarity-preserving hashing method for content-based audio retrieval is proposed. Different from data-independent hashing methods, we develop a deep network to learn compact binary codes from multiple hierarchical layers of nonlinear and linear transformations such that the similarity between samples is preserved. The independence and balance properties are included and optimized in the objective function to improve the codes. Experimental results on the Extended Ballroom dataset with 8 genres of 3,000 musical excerpts show that our proposed method significantly outperforms state-ofthe-art data-independent method in both effectiveness and efficiency.
COMPARISON OF WAVELET NETWORK AND LOGISTIC REGRESSION IN PREDICTING ENTERPRIS...ijcsit
Enterprise financial distress or failure includes bankruptcy prediction, financial distress, corporate performance prediction and credit risk estimation. The aim of this paper is that using wavelet networks innon-linear combination prediction to solve ARMA (Auto-Regressive and Moving Average) model problem.ARMA model need estimate the value of all parameters in the model, it has a large amount of computation.Under this aim, the paper provides an extensive review of Wavelet networks and Logistic regression. Itdiscussed the Wavelet neural network structure, Wavelet network model training algorithm, Accuracy rateand error rate (accuracy of classification, Type I error, and Type II error). The main research opportunity exist a proposed of business failure prediction model (wavelet network model and logistic regression
model). The empirical research which is comparison of Wavelet Network and Logistic Regression on training and forecasting sample, the result shows that this wavelet network model is high accurate and the overall prediction accuracy, Type Ⅰerror and Type Ⅱ error, wavelet networks model is better thanlogistic regression model.
http://inarocket.com
Learn BEM fundamentals as fast as possible. What is BEM (Block, element, modifier), BEM syntax, how it works with a real example, etc.
How to Build a Dynamic Social Media PlanPost Planner
Stop guessing and wasting your time on networks and strategies that don’t work!
Join Rebekah Radice and Katie Lance to learn how to optimize your social networks, the best kept secrets for hot content, top time management tools, and much more!
Watch the replay here: bit.ly/socialmedia-plan
Content personalisation is becoming more prevalent. A site, it's content and/or it's products, change dynamically according to the specific needs of the user. SEO needs to ensure we do not fall behind of this trend.
Lightning Talk #9: How UX and Data Storytelling Can Shape Policy by Mika Aldabaux singapore
How can we take UX and Data Storytelling out of the tech context and use them to change the way government behaves?
Showcasing the truth is the highest goal of data storytelling. Because the design of a chart can affect the interpretation of data in a major way, one must wield visual tools with care and deliberation. Using quantitative facts to evoke an emotional response is best achieved with the combination of UX and data storytelling.
Succession “Losers”: What Happens to Executives Passed Over for the CEO Job?
By David F. Larcker, Stephen A. Miles, and Brian Tayan
Stanford Closer Look Series
Overview:
Shareholders pay considerable attention to the choice of executive selected as the new CEO whenever a change in leadership takes place. However, without an inside look at the leading candidates to assume the CEO role, it is difficult for shareholders to tell whether the board has made the correct choice. In this Closer Look, we examine CEO succession events among the largest 100 companies over a ten-year period to determine what happens to the executives who were not selected (i.e., the “succession losers”) and how they perform relative to those who were selected (the “succession winners”).
We ask:
• Are the executives selected for the CEO role really better than those passed over?
• What are the implications for understanding the labor market for executive talent?
• Are differences in performance due to operating conditions or quality of available talent?
• Are boards better at identifying CEO talent than other research generally suggests?
Chemicalengineeringthermodynamics I Jntu Btech 2008 Jntu Model Paper{Www.Stud...
Databasemanagementsystems Jntu Model Paper{Www.Studentyogi.Com}
1. www.studentyogi.com www.studentyogi.com
Code No: RR220502
Set No. 1
II B.Tech II Semester Supplimentary Examinations, Apr/May 2008
DATA BASE MANAGEMENT SYSTEMS
( Common to Computer Science & Engineering, Information Technology
and Computer Science & Systems Engineering)
Time: 3 hours Max Marks: 80
Answer any FIVE Questions
All Questions carry equal marks
1. (a) Explain the drawbacks of traditional le processing systems with examples.
(b) Explain the three levels of data abstraction. [7+9]
2. (a) What is a view? Explain the a views in SQL?
(b) Explain nested queries with example in SQL ? [8+8]
3. (a) Which of the three basic le organizations would you cho ose for a le where
the most frequent operations are as follows,
i. Search for records based on a range of eld values.
ii. Perform insert and scans where the order of records does not matter.
iii. Search for a record based on a particular eld value.
(b) De ne dense index.
(c) How does multi level indexing improve the performance of searching an index
le. [6+4+6]
4. (a) Explain about pro jection based on sorting.
(b) Explain about pro jection based on hashing. [8+8]
5. (a) What is indexing ? Explain with an example.
(b) Explain about query processing. [8+8]
6. (a) Explain functional dependencies and multivalued dependencies with examples.
(b) Consider the relation R(A,B,C,D,E,F) and FD’s
A BC F A
C A
D EE D
is the decomposition of R into 1 (A,C,D), 2 (B,C,D) and 3 (E,F,D) loss
less? Explain the requirement of loss less decomposition. [8+8]
7. (a) De ne the concept of a schedule for a set of concurrent transactions. Give a
suitable example.
(b) Explain how does granularity of locking a ect the performance of concurrency
2. www.studentyogi.com www.studentyogi.com
Code No: RR220502
Set No. 1
8. Explain WAL Proto col, UNDO algorithm, Check pointing and Media Recovery?
[16]
3. www.studentyogi.com www.studentyogi.com
Code No: RR220502
Set No. 2
II B.Tech II Semester Supplimentary Examinations, Apr/May 2008
DATA BASE MANAGEMENT SYSTEMS
( Common to Computer Science & Engineering, Information Technology
and Computer Science & Systems Engineering)
Time: 3 hours Max Marks: 80
Answer any FIVE Questions
All Questions carry equal marks
1. Write short notes on:
(a) Key constraints
(b) General constraints
(c) Relational calculus. [6+5+5]
2. (a) What is a view? Explain the a views in SQL?
(b) Explain nested queries with example in SQL ? [8+8]
3. (a) Explain the limitations of static hashing. Explain how this is overcome in
dynamic hashing.
(b) Write a note on indexed sequential les. [10+6]
4. (a) Consider the following SQL query for a bank database
-
¿ -=”
Write an e cient relational algebra expression that is equivalent to the query.
(b) De ne query optimization and at what point during query processing does
optimization o ccur? [8+8]
5. (a) What is indexing ? Explain with an example.
(b) Explain about query processing. [8+8]
6. (a) Let R=(A,B,C,D,E) and let M be the following set of multivalued dependencies
A- BC
B- CD
E- AD
List the nontrivial dependencies in M+
(b) Describe the properties of normalized and unnormalized relations. [10+6]
7. (a) Explain the concept of transaction atomicity.
(b) How does the two phase locking proto col ensures serializability? [6+10]
8. Explain in detail the ARIES recovery method. [16]
4. www.studentyogi.com www.studentyogi.com
Code No: RR220502
Set No. 3
II B.Tech II Semester Supplimentary Examinations, Apr/May 2008
DATA BASE MANAGEMENT SYSTEMS
( Common to Computer Science & Engineering, Information Technology
and Computer Science & Systems Engineering)
Time: 3 hours Max Marks: 80
Answer any FIVE Questions
All Questions carry equal marks
1. (a) What is DBMS? Explain the advantages of DBMS. [2+5=7]
(b) What is a data model? Explain the relational data model. [3+6=9]
2. (a) Give the various methods of managing data security.
(b) Describe the “dynamic SQL”. [8+8]
3. Discuss the di erence between index sequential and hashed le organizations. Com-
pare their storage and access e ciencies. List the applications where each of the
le organization is suitable. [16]
4. (a) Consider the following SQL query for a bank database
-
¿ -=”
Write an e cient relational algebra expression that is equivalent to the query.
(b) De ne query optimization and at what point during query processing does
optimization o ccur? [8+8]
5. Show that the following equivalences hold and explain how they can be applied to
improve the e ciency of certain updates.
(a) ( 1 2) 3 = 1 ( 2 3)
(b) 1 2 = 2 = 2 3
(c) p( 1 - 2) = p(r1) - p( 2) [5+4+7]
6. (a) List the three design goals for relational database and explain why they are
desirable.
(b) Consider the relation scheme Emp Dept( Ename, SSN, Bdate, Address, Dnum-
ber, Dname, DMGRSSN) and the following set of FD’s
F={ SSN- Ename,Bdate, Address, Dnumber
Dnumber- Dname, DMGRSSN}
Calculate the closer {SSN}+ and {Dnumber}+ with respect to F. [6+10]
7. (a) What information does the dirty page table and transaction table contain?
(b) Give a short notes on recovery from deadlock. [6+10]
5. www.studentyogi.com www.studentyogi.com
Code No: RR220502
Set No. 3
8. Describe the shadow paging recovery technique. Under what circumstances does it
not require a log. [16]
6. www.studentyogi.com www.studentyogi.com
Code No: RR220502
Set No. 4
II B.Tech II Semester Supplimentary Examinations, Apr/May 2008
DATA BASE MANAGEMENT SYSTEMS
( Common to Computer Science & Engineering, Information Technology
and Computer Science & Systems Engineering)
Time: 3 hours Max Marks: 80
Answer any FIVE Questions
All Questions carry equal marks
1. (a) What is a Data model ? List the important data models [8]
(b) Explain
i. DDL
ii. DML
iii. Data sublanguage
iv. Host language [2+2+2+2]
2. (a) Discuss the various DDL, DML commands with illustrations in SQL.
(b) Why are null values not preferred in a relation? [12+4]
3. Give algorithms for inserting a new key into a B-tree [16]
4. (a) Discuss about cost based optimization.
(b) Give a detailed account of heuristic optimization. [8+8]
5. (a) Discuss the reasons for converting SQL queries into relational algebra queries
before optimization is done.
(b) What is meant by query execution plan? Explain its signi cance. [10+6]
6. (a) Explain the functional dependencies and multi valued dependencies with ex-
amples.
(b) What is normalization? Discuss the 1NF,2NF, and 3NF Normal forms with
examples. [8+8]
7. (a) Explain timestamp ordering with an algorithm.
(b) Explain di erent locking Techniques for concurrency control. [8+8]
8. (a) When a system recovers from a crash ? In what order must transaction be
Undone and Redone? Why is this order important?
(b) What is a log in the content of DBMS? How does check pointing eliminate
some of the problems associated with log based recovery? [8+8]