This document outlines exam questions for an Advanced Database Management Systems exam. It covers topics such as data mining vs knowledge discovery in databases, index implementations in PostgreSQL, database normalization, granularity and security/performance, semantic databases, shadow paging, clustering vs classification, database queries, views, XML documents, data dictionaries, multi-version schemes, and the Apriori algorithm for association rule mining. The exam questions require students to define concepts, provide examples, and demonstrate techniques like database normalization and joining normalized relations.
Day by day data is increasing, and most of the data stored in a database after manual transformations and derivations. Scientists can facilitate data intensive applications to study and understand the behaviour of a complex system. In a data intensive application, a scientific model facilitates raw data products to produce new data products and that data is collected from various sources such as physical, geological, environmental, chemical and biological etc. Based on the generated output, it is important to have the ability of tracing an output data product back to its source values if that particular output seems to have an unexpected value. Data provenance helps scientists to investigate the origin of an unexpected value. In this paper our aim is to find a reason behind the unexpected value from a database using query inversion and we are going to propose some hypothesis to make an inverse query for complex aggregation function and multiple relationship (join, set operation) function.
EFFICIENT SCHEMA BASED KEYWORD SEARCH IN RELATIONAL DATABASESIJCSEIT Journal
Keyword search in relational databases allows user to search information without knowing database
schema and using structural query language (SQL). In this paper, we address the problem of generating
and evaluating candidate networks. In candidate network generation, the overhead is caused by raising the
number of joining tuples for the size of minimal candidate network. To reduce overhead, we propose
candidate network generation algorithms to generate a minimum number of joining tuples according to the
maximum number of tuple set. We first generate a set of joining tuples, candidate networks (CNs). It is
difficult to obtain an optimal query processing plan during generating a number of joins. We also develop a
dynamic CN evaluation algorithm (D_CNEval) to generate connected tuple trees (CTTs) by reducing the
size of intermediate joining results. The performance evaluation of the proposed algorithms is conducted
on IMDB and DBLP datasets and also compared with existing algorithms.
CONTEXT-AWARE CLUSTERING USING GLOVE AND K-MEANSijseajournal
ABSTRACT
In this paper we propose a novel method to cluster categorical data while retaining their context. Typically, clustering is performed on numerical data. However it is often useful to cluster categorical data as well, especially when dealing with data in real-world contexts. Several methods exist which can cluster categorical data, but our approach is unique in that we use recent text-processing and machine learning advancements like GloVe and t- SNE to develop a a context-aware clustering approach (using pre-trained
word embeddings). We encode words or categorical data into numerical, context-aware, vectors that we use to cluster the data points using common clustering algorithms like K-means.
Clustering the results of a search helps the user to overview the information returned. In this paper, we
look upon the clustering task as cataloguing the search results. By catalogue we mean a structured label
list that can help the user to realize the labels and search results. Labelling Cluster is crucial because
meaningless or confusing labels may mislead users to check wrong clusters for the query and lose extra
time. Additionally, labels should reflect the contents of documents within the cluster accurately. To be able
to label clusters effectively, a new cluster labelling method is introduced. More emphasis was given to
/produce comprehensible and accurate cluster labels in addition to the discovery of document clusters. We
also present a new metric that employs to assess the success of cluster labelling. We adopt a comparative
evaluation strategy to derive the relative performance of the proposed method with respect to the two
prominent search result clustering methods: Suffix Tree Clustering and Lingo.
we perform the experiments using the publicly available Datasets Ambient and ODP-239
With the rapid development in Geographic Information Systems (GISs) and their applications, more and
more geo-graphical databases have been developed by different vendors. However, data integration and
accessing is still a big problem for the development of GIS applications as no interoperability exists among
different spatial databases. In this paper we propose a unified approach for spatial data query. The paper
describes a framework for integrating information from repositories containing different vector data sets
formats and repositories containing raster datasets. The presented approach converts different vector data
formats into a single unified format (File Geo-Database “GDB”). In addition, we employ “metadata” to
support a wide range of users’ queries to retrieve relevant geographic information from heterogeneous and
distributed repositories. Such an employment enhances both query processing and performance.
Day by day data is increasing, and most of the data stored in a database after manual transformations and derivations. Scientists can facilitate data intensive applications to study and understand the behaviour of a complex system. In a data intensive application, a scientific model facilitates raw data products to produce new data products and that data is collected from various sources such as physical, geological, environmental, chemical and biological etc. Based on the generated output, it is important to have the ability of tracing an output data product back to its source values if that particular output seems to have an unexpected value. Data provenance helps scientists to investigate the origin of an unexpected value. In this paper our aim is to find a reason behind the unexpected value from a database using query inversion and we are going to propose some hypothesis to make an inverse query for complex aggregation function and multiple relationship (join, set operation) function.
EFFICIENT SCHEMA BASED KEYWORD SEARCH IN RELATIONAL DATABASESIJCSEIT Journal
Keyword search in relational databases allows user to search information without knowing database
schema and using structural query language (SQL). In this paper, we address the problem of generating
and evaluating candidate networks. In candidate network generation, the overhead is caused by raising the
number of joining tuples for the size of minimal candidate network. To reduce overhead, we propose
candidate network generation algorithms to generate a minimum number of joining tuples according to the
maximum number of tuple set. We first generate a set of joining tuples, candidate networks (CNs). It is
difficult to obtain an optimal query processing plan during generating a number of joins. We also develop a
dynamic CN evaluation algorithm (D_CNEval) to generate connected tuple trees (CTTs) by reducing the
size of intermediate joining results. The performance evaluation of the proposed algorithms is conducted
on IMDB and DBLP datasets and also compared with existing algorithms.
CONTEXT-AWARE CLUSTERING USING GLOVE AND K-MEANSijseajournal
ABSTRACT
In this paper we propose a novel method to cluster categorical data while retaining their context. Typically, clustering is performed on numerical data. However it is often useful to cluster categorical data as well, especially when dealing with data in real-world contexts. Several methods exist which can cluster categorical data, but our approach is unique in that we use recent text-processing and machine learning advancements like GloVe and t- SNE to develop a a context-aware clustering approach (using pre-trained
word embeddings). We encode words or categorical data into numerical, context-aware, vectors that we use to cluster the data points using common clustering algorithms like K-means.
Clustering the results of a search helps the user to overview the information returned. In this paper, we
look upon the clustering task as cataloguing the search results. By catalogue we mean a structured label
list that can help the user to realize the labels and search results. Labelling Cluster is crucial because
meaningless or confusing labels may mislead users to check wrong clusters for the query and lose extra
time. Additionally, labels should reflect the contents of documents within the cluster accurately. To be able
to label clusters effectively, a new cluster labelling method is introduced. More emphasis was given to
/produce comprehensible and accurate cluster labels in addition to the discovery of document clusters. We
also present a new metric that employs to assess the success of cluster labelling. We adopt a comparative
evaluation strategy to derive the relative performance of the proposed method with respect to the two
prominent search result clustering methods: Suffix Tree Clustering and Lingo.
we perform the experiments using the publicly available Datasets Ambient and ODP-239
With the rapid development in Geographic Information Systems (GISs) and their applications, more and
more geo-graphical databases have been developed by different vendors. However, data integration and
accessing is still a big problem for the development of GIS applications as no interoperability exists among
different spatial databases. In this paper we propose a unified approach for spatial data query. The paper
describes a framework for integrating information from repositories containing different vector data sets
formats and repositories containing raster datasets. The presented approach converts different vector data
formats into a single unified format (File Geo-Database “GDB”). In addition, we employ “metadata” to
support a wide range of users’ queries to retrieve relevant geographic information from heterogeneous and
distributed repositories. Such an employment enhances both query processing and performance.
USING ONTOLOGIES TO IMPROVE DOCUMENT CLASSIFICATION WITH TRANSDUCTIVE SUPPORT...IJDKP
Many applications of automatic document classification require learning accurately with little training
data. The semi-supervised classification technique uses labeled and unlabeled data for training. This
technique has shown to be effective in some cases; however, the use of unlabeled data is not always
beneficial.
On the other hand, the emergence of web technologies has originated the collaborative development of
ontologies. In this paper, we propose the use of ontologies in order to improve the accuracy and efficiency
of the semi-supervised document classification.
We used support vector machines, which is one of the most effective algorithms that have been studied for
text. Our algorithm enhances the performance of transductive support vector machines through the use of
ontologies. We report experimental results applying our algorithm to three different datasets. Our
experiments show an increment of accuracy of 4% on average and up to 20%, in comparison with the
traditional semi-supervised model.
ModelDR - the tool that untangles complex informationSimon Roberts
'Whether upgrading data aggregation processes or improving system governance architecture, ModelDR solves the major problem that prevents financial institutions from earning earning more than their cost of capital; the increasing burden of regulation.’
This report discusses three submissions based on the Duet architecture to the Deep Learning track at TREC 2019. For the document retrieval task, we adapt the Duet model to ingest a "multiple field" view of documents—we refer to the new architecture as Duet with Multiple Fields (DuetMF). A second submission combines the DuetMF model with other neural and traditional relevance estimators in a learning-to-rank framework and achieves improved performance over the DuetMF baseline. For the passage retrieval task, we submit a single run based on an ensemble of eight Duet models.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Theoretical work submitted to the Journal should be original in its motivation or modeling structure. Empirical analysis should be based on a theoretical framework and should be capable of replication. It is expected that all materials required for replication (including computer programs and data sets) should be available upon request to the authors.
Conformer-Kernel with Query Term Independence @ TREC 2020 Deep Learning TrackBhaskar Mitra
We benchmark Conformer-Kernel models under the strict blind evaluation setting of the TREC 2020 Deep Learning track. In particular, we study the impact of incorporating: (i) Explicit term matching to complement matching based on learned representations (i.e., the “Duet principle”), (ii) query term independence (i.e., the “QTI assumption”) to scale the model to the full retrieval setting, and (iii) the ORCAS click data as an additional document description field. We find evidence which supports that all three aforementioned strategies can lead to improved retrieval quality.
Very few research works have been done on XML security over relational databases despite that XML became the de facto standard for the data representation and exchange on the internet and a lot of XML documents are stored in RDBMS. In [14], the author proposed an access control model for schema-based storage of XML documents in relational storage and translating XML access control rules to relational access control rules. However, the proposed algorithms had performance drawbacks. In this paper, we will use the same access control model of [14] and try to overcome the drawbacks of [14] by proposing an efficient technique to store the XML access control rules in a relational storage of XML DTD. The mapping of the XML DTD to relational schema is proposed in [7]. We also propose an algorithm to translate XPath queries to SQL queries based on the mapping algorithm in [7].
Analysis of different similarity measures: SimrankAbhishek Mungoli
SimRank exploits object-to-object relationships very well and finds out the similarity between two objects.
We have used it in our project to find similar reasearch papers from DBLP dataset (DBLP Dataset provides a comprehensive list of research papers in computer science domain).
SimRank is a generic approach and its basic idea can also be applied to other domain of interests as well.
COLOCATION MINING IN UNCERTAIN DATA SETS: A PROBABILISTIC APPROACHIJCI JOURNAL
In this paper we investigate colocation mining problem in the context of uncertain data. Uncertain data is a
partially complete data. Many of the real world data is Uncertain, for example, Demographic data, Sensor
networks data, GIS data etc.,. Handling such data is a challenge for knowledge discovery particularly in
colocation mining. One straightforward method is to find the Probabilistic Prevalent colocations (PPCs).
This method tries to find all colocations that are to be generated from a random world. For this we first
apply an approximation error to find all the PPCs which reduce the computations. Next find all the
possible worlds and split them into two different worlds and compute the prevalence probability. These
worlds are used to compare with a minimum probability threshold to decide whether it is Probabilistic
Prevalent colocation (PPCs) or not. The experimental results on the selected data set show the significant
improvement in computational time in comparison to some of the existing methods used in colocation
mining.
USING ONTOLOGIES TO IMPROVE DOCUMENT CLASSIFICATION WITH TRANSDUCTIVE SUPPORT...IJDKP
Many applications of automatic document classification require learning accurately with little training
data. The semi-supervised classification technique uses labeled and unlabeled data for training. This
technique has shown to be effective in some cases; however, the use of unlabeled data is not always
beneficial.
On the other hand, the emergence of web technologies has originated the collaborative development of
ontologies. In this paper, we propose the use of ontologies in order to improve the accuracy and efficiency
of the semi-supervised document classification.
We used support vector machines, which is one of the most effective algorithms that have been studied for
text. Our algorithm enhances the performance of transductive support vector machines through the use of
ontologies. We report experimental results applying our algorithm to three different datasets. Our
experiments show an increment of accuracy of 4% on average and up to 20%, in comparison with the
traditional semi-supervised model.
ModelDR - the tool that untangles complex informationSimon Roberts
'Whether upgrading data aggregation processes or improving system governance architecture, ModelDR solves the major problem that prevents financial institutions from earning earning more than their cost of capital; the increasing burden of regulation.’
This report discusses three submissions based on the Duet architecture to the Deep Learning track at TREC 2019. For the document retrieval task, we adapt the Duet model to ingest a "multiple field" view of documents—we refer to the new architecture as Duet with Multiple Fields (DuetMF). A second submission combines the DuetMF model with other neural and traditional relevance estimators in a learning-to-rank framework and achieves improved performance over the DuetMF baseline. For the passage retrieval task, we submit a single run based on an ensemble of eight Duet models.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Theoretical work submitted to the Journal should be original in its motivation or modeling structure. Empirical analysis should be based on a theoretical framework and should be capable of replication. It is expected that all materials required for replication (including computer programs and data sets) should be available upon request to the authors.
Conformer-Kernel with Query Term Independence @ TREC 2020 Deep Learning TrackBhaskar Mitra
We benchmark Conformer-Kernel models under the strict blind evaluation setting of the TREC 2020 Deep Learning track. In particular, we study the impact of incorporating: (i) Explicit term matching to complement matching based on learned representations (i.e., the “Duet principle”), (ii) query term independence (i.e., the “QTI assumption”) to scale the model to the full retrieval setting, and (iii) the ORCAS click data as an additional document description field. We find evidence which supports that all three aforementioned strategies can lead to improved retrieval quality.
Very few research works have been done on XML security over relational databases despite that XML became the de facto standard for the data representation and exchange on the internet and a lot of XML documents are stored in RDBMS. In [14], the author proposed an access control model for schema-based storage of XML documents in relational storage and translating XML access control rules to relational access control rules. However, the proposed algorithms had performance drawbacks. In this paper, we will use the same access control model of [14] and try to overcome the drawbacks of [14] by proposing an efficient technique to store the XML access control rules in a relational storage of XML DTD. The mapping of the XML DTD to relational schema is proposed in [7]. We also propose an algorithm to translate XPath queries to SQL queries based on the mapping algorithm in [7].
Analysis of different similarity measures: SimrankAbhishek Mungoli
SimRank exploits object-to-object relationships very well and finds out the similarity between two objects.
We have used it in our project to find similar reasearch papers from DBLP dataset (DBLP Dataset provides a comprehensive list of research papers in computer science domain).
SimRank is a generic approach and its basic idea can also be applied to other domain of interests as well.
COLOCATION MINING IN UNCERTAIN DATA SETS: A PROBABILISTIC APPROACHIJCI JOURNAL
In this paper we investigate colocation mining problem in the context of uncertain data. Uncertain data is a
partially complete data. Many of the real world data is Uncertain, for example, Demographic data, Sensor
networks data, GIS data etc.,. Handling such data is a challenge for knowledge discovery particularly in
colocation mining. One straightforward method is to find the Probabilistic Prevalent colocations (PPCs).
This method tries to find all colocations that are to be generated from a random world. For this we first
apply an approximation error to find all the PPCs which reduce the computations. Next find all the
possible worlds and split them into two different worlds and compute the prevalence probability. These
worlds are used to compare with a minimum probability threshold to decide whether it is Probabilistic
Prevalent colocation (PPCs) or not. The experimental results on the selected data set show the significant
improvement in computational time in comparison to some of the existing methods used in colocation
mining.
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This is the Continuous Assessment Test for SS2 DP with Test of Practicals included. It is accurately measured so that more than one students can get a copy from a single print out.
For each question provide a one to two sentences per questi.pdfablazeaepl
For each question, provide a one to two sentences per question.
Assignments must be submitted as a Microsoft Word or PDF document. Other file formats may not
be accepted.
The file name of the assignment should include the student's name, the assignment number, and
the course name (e.g., John_Smith_Assignment1_Networking101).
All written responses must be typed, double-spaced, and use a standard font (e.g., Times New
Roman or Arial) with 12-point font size.
Assessment questions for the topics:
What is the internet?
a) Define the internet in your own words.
b) What are some of the key components that make up the internet?
Network Edge and Network Core:
a) What is the difference between the network edge and the network core?
b) What types of devices are typically found at the network edge? What types of devices are
typically found in the network core?
Delay, Loss, and Throughput in Packet Switched Networks:
a) What is packet loss? How can it impact network performance?
b) Explain the difference between latency and throughput in the context of packet-switched
networks.
Protocol Layers:
a) What is the OSI model? What are its seven layers?
b) Why is it useful to organize protocols into distinct layers?
Service Models:
a) What is Infrastructure as a Service (IaaS)? How does it differ from Platform as a Service (PaaS)
and Software as a Service (SaaS)?
b) What are some advantages of using a service model for providing computing resources?
Network Security Under Attack:
a) What is a denial-of-service (DoS) attack? How can it be prevented?
b) Describe the difference between phishing and malware attacks.
Is infrastructure network security enough to protect against all cyber threats?
a) Explain why or why not.
b) What are some common vulnerabilities in network infrastructure that can be exploited by
cybercriminals?
What is application security?
a) How does application security differ from infrastructure security?
b) Give an example of an application security vulnerability.
How can a security culture be developed within an organization?
a) What are some ways to promote security awareness and best practices among employees?
b) What are some benefits of having a strong security culture?
What is the role of risk management in network security?
a) Explain why risk management is important for network security.
b) What are some common risk assessment methodologies used in network security?.
A database management system (or DBMS) is essentially nothing more than a computerized data-keeping system. Users of the system are given facilities to perform several kinds of operations on such a system for either manipulation of the data in the database or the management of the database structure itself.
USING ONTOLOGIES TO OVERCOMING DRAWBACKS OF DATABASES AND VICE VERSA: A SURVEYcseij
For a same domain, several databases (DBs) exist. The emergence of classical web to the semantic web has
contributed to the appearance of the notion of ontology that have shared and consensual vocabulary. For a
given, it is more interesting to take advantage of existing databases, to build an ontology. Most of the data
are already stored in these databases. So many DBs can be integrated to enable reuse of existing data for
the semantic web. Even for existing ontologies, the relevance of the information they contain requires
regular updating. These databases can be useful sources to enrich these ontologies. In the other hand, for
these ontologies more than the ratio ‘size of the instances on the size of working memory’ is large more
than the management of these instances, in memory, is difficult. Finding a way to store these instances in a
structured manner to satisfy the needs of performance and reliability required for many applications
becomes an obligation. As a consequence, defining query languages to support these structures becomes a
challenge for SW community. We will show through this paper how ontologies can benefit from DBs to
increase system performance and facilitate their design cycle. The DBs in their turn suffers from several
drawbacks namely complexity of the design cycle and lack of semantics. Since ontologies are rich in
semantic, DBs can profit from this advantage to overcoming their drawbacks.
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
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Vaccine management system project report documentation..pdfKamal Acharya
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
TECHNICAL TRAINING MANUAL GENERAL FAMILIARIZATION COURSEDuvanRamosGarzon1
AIRCRAFT GENERAL
The Single Aisle is the most advanced family aircraft in service today, with fly-by-wire flight controls.
The A318, A319, A320 and A321 are twin-engine subsonic medium range aircraft.
The family offers a choice of engines
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Forklift Classes Overview by Intella PartsIntella Parts
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1. No. of Printed Pages : 5 MCS-043 I
MCA (Revised)
Term-End Examination
0.473C1
December, 2017
MCS-043 : ADVANCED DATABASE MANAGEMENT
SYSTEMS
Time : 3" hours Maximum Marks : 100
Note : Question number 1 is compulsory. Answer any
three questions from the rest.
1. (a) How does Data-mining differ from the
technique of Knowledge Discovery in
Databases (KDD) ? Can we use these two
techniques interchangeably or
alternatively ? Justify. 5
(b) Explain the index implementations
available in PostgreSQL, with suitable
examples.
(c) Consider the following data :
Employee
Code (EC)
Project Code on
which employee
works (PC) ,
Tools that
employee can
work on (TC)
El
P1
P2
T1
T2
E2
P3
P4
T2
T3
MCS-043 1 P.T.O.
2. Employee with code El works on two
projects, P1 and P2, and can use two tools
T1 and T2. Likewise, E2 works on projects
P3 and P4 and can use tools T2 and T3.
You may assume that projects and tools
are independent of each other, but depend
only on employee.
Perform the following tasks for the data
(i) Represent the data as above in a
relation, namely,
Employee_Project_Tool (EC, PC, TC). 2
(ii) List all the MVDs in the relation
created in part (i) above. Also identify
the primary key of the relation.
(iii) Normalise the relation created in
part (i) into 4NF. 3
(iv) Join the relations created in part (iii)
above and show that they will
produce the same data on joining as
per the relation created in part (i).
(d) What is Granularity in databases ? How
does granularity relate to the security of
databases ? In a concurrent environment,
how does granularity affect the
performance ? 5
MCS-043 2
3. (e) What are Semantic databases ? Give the
features of semantic databases. Discuss the
process of searching the knowledge in
these databases.
(f) Illustrate the concept of Shadow Paging
with suitable example. Give the advantages
and disadvantages of shadow paging.
(g) How does Clustering differ from
Classification ? Briefly discuss one
approach for both, i.e., clustering and
classification.
2. (a) What are Alerts, Cursors, Stored
Procedures and Triggers ? Give the utility
of each. Explain each with suitable code of
your choice. 10
(b) What is Simple Hash Join ? Discuss the
algorithm and cost calculation process for
simple hash join. Explain how Hash join is
applicable to Equi join and Natural join. 10
3. (a) Differentiate between the following :
(i) Database Queries and Data-mining
Queries
(ii) Star Schema and Snowflake Schema
Give example for each while differentiating. 10
MCS-043 3 P.T.O.
4. (b) What are Deadlocks ? How are they
detected ? Explain with the help of an
example.
(c) What are Mobile Databases ? Explain the
characteristics of mobile databases. Give an
application of mobile databases. 5
4. (a) What are Views in SQL ? What is the
significance of views ? Give an example
code in SQL to create a view. 5
(b) (i) Create an XML document that stores
information about students of a class.
The document should use appropriate
tags/attributes to store information
like student_id (unique), name
(consisting of first name and last
name), class (consisting of class no.
and section) and books issued to the
student (minimum 0, maximum 2).
Create at least two such student
records. 6
(ii) Create the DTD for verification of the
above student data. 4
(c) Explain the features and challenges of
multimedia databases. 5
5. (a) What is Data Dictionary ? Give the
features and benefits of a data dictionary.
What are the disadvantages of a data
dictionary ? 8
MCS-043 4
5. (b) What is the utility of Multi-Version
schemes in databases ? Discuss any one
multi-version scheme, with suitable
example. 5
(c) What is Association Rule Mining ? Write
Apriori Algorithm for finding frequent
itemset. Discuss it with suitable example. 7
MCS-043 5 6,500
