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A Hybrid Approach for the Representation and Visualization of Composition in Recognized Design Patterns

Thesis Info

Access Option

External Link

Author

Kiran Iqbal

Institute

Virtual University of Pakistan

Institute Type

Public

City

Lahore

Province

Punjab

Country

Pakistan

Thesis Completing Year

2013

Thesis Completion Status

Completed

Subject

Software Engineering

Language

English

Link

http://vspace.vu.edu.pk/detail.aspx?id=40

Added

2021-02-17 19:49:13

Modified

2024-03-24 20:25:49

ARI ID

1676720950632

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Design patterns are standard design solutions that can be applied and composed in different applications where pattern related information is generally implicit in the UML diagrams of the applications. Applications of design patterns improve the quality and development productivity of software applications. Most software designs are based on different design patterns which are composed with each other. Therefore visualization of overlapping in recognized design patterns play an important role for the program comprehension. Different representations of design patterns have been proposed but each representation has its strengths and limitations. State of the art design pattern visualization approaches are unable to capture all the aspects of design patterns visualization which is important for the comprehension of any software application e.g. the role that a class, attribute and operation plays in a design pattern. Additionally, there exist multiple instances of a design pattern and different kinds of overlapping in design of different systems. Visualization of overlapping and composition in design patterns is undermining area. This research work is intended to analyze the characteristics, strengths and limitations of key design pattern representations used for visualization and based on the critical analysis of state of the art design pattern visualization approaches propose a hybrid approach which incorporates best features of existing approaches while suppressing their limitations. Our proposed hybrid notation is used to represent design pattern information related to class and to visualize different kinds of overlapping. Stereotypes, their associated tagged values, semantics and constraints are defined to represent the design pattern information related to attributes and/or operations of a class. We developed a prototyping tool named VisCDP to validate the concept of our approach.VisCDP is used to visualize design patterns information related to classes, operations and/or attributes in the composition of Recognized Design patterns. It provides static as well as on demand (dynamic) visualization in any UML class diagram. A real world case study is used to evaluate our approach and to compare the results with the other approaches.
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صفاتِ حسنِ مطلق سے بشر کی آشنائی ہو


صفاتِ حسنِ مطلق سے بشر کی آشنائی ہو
’’پسِ فکر و تعمّل جب جمالِ مُصطفائی ہو‘‘

یہی پُر نور منظر مظہرِ حسنِ حقیقت ہے
رُخِ تاباں کی رعنائی سے حق کی رونمائی ہو

اگر شامل درودوں کی صدائیں اِس میں ہو جائیں
بھلا حرفِ دعا کو کیوں ملالِ نارسائی ہو

گدایانِ درِ آلِ نبیؐ کیسے نہ نازاں ہوں
فزوں تر تختِ شاہی سے جب اِس در کی گدائی ہو

یہ وہ کوچہ ہے جس کوچے پہ جنّت ناز کرتی ہے
یہی وہ در ہے جس در پر دو عالم کی بھلائی ہو

سرِ شاخِ تمنّا غنچہ ہائے شوق رقصاں ہوں
صبا گلزارِ طیبہ سے کوئی پیغام لائی ہو

عطائے حرفِ مدحت ہو پسِ خاموشیِ خلوت
سرِ بزمِ سخن ہم کو عطا پھر لب کشائی ہو

وہاں عرفانؔ سا عاجز جھکائے کیوں نہ سر اپنا
جہاں پر سرنگوں سارے جہاں کی پارسائی ہو

Detection of Metalo-Beta-Lactamase Gene in Carbapenem Resistant Pseudomonas Aeruginosa Isolated From Lahore, Pakistan Detection of Metalo-Beta-Lactamase Gene in Pseudomonas Aeruginosa

Pseudomonas aeruginosa is a widespread organism, caused severe nosocomial infection in human and associated with multiple drug resistance (MDR)Objective: The present study was carried out to observe current antimicrobial resistant pattern of Pseudomonas aeruginosa in Lahore and to detect the Metallo-beta-lactamase (MBL) gene in carbapenem resistantPseudomonas aeruginosaMethods: By screening 360 samples total 123 Pseudomonas aeruginosa was identified by standard microbiology techniques such as microscopy and biochemical testing. The isolated Pseudomonas aeruginosa was evaluated for drug resistance by disc diffusion method and polymerase chain reaction(PCR) was used to identify the carbapenem resistance causing gene (bla-VIM and bla-IMP) Results: Following antibiotic resistant pattern was observed, Gentamycin (59.00%), Ceftazidime(58.7%), Ceftriaxone (58.00%), Cefotazime (57.0%) and Ciprofloxacin (55.00%). Resistance rates to carbapenem group of antibiotics is Doripenem (30.5%) Meropenem(31.0%) and Imipenem (28.0%). Out of 123 samples of Pseudomonas aeruginosa, 28 isolates were found resistant to carbapenem group of antibiotic which was supposed to be highly sensitive for this bacterium. Molecular based identification of resistance genes showed that bla-IMP gene was present in 32.1% (09) and bla-VIM was found positive in 17.8% (04) samples. Metallo-beta-lactamasesproducing genes (bla-VIM and bla-IMP), amongcarbapenem resistant Pseudomonas aeruginosa were detectedin 28.1% of samples. If other carbapenem resistant gene were also included this number might be higherConclusions: PCRbased test should be included in routine laboratory examination for quick detection of the resistancecausing genes.

A Novel Architecture to Integrate Multi-Sourcedatain Distributed Environment

The amount of data has been increasing over the last few years due to the emergence of various end-user applications. These applications utilize cloud computing infrastructure in the data centers. Apart from the increasing volume of data, there are other factors such as variety, velocity, and veracity of the data which result in the problem of big data. Traditional database management systems are not efficient to handle big data. The use of big data platform is necessary to resolve the big data problem. Hadoop is one of the platforms which resolve the problem of big data. Hadoop uses a distributed storage system. Hive and HBase are some of the big data tools for storing big data in Hadoop. They run on top of Hadoop distributed file system (HDFS). Hive is a data warehouse framework for querying and analysis of data that is stored in HDFS.?Hive?is an open-source software that lets programmers analyze large data sets on Hadoop. HBase is a column-oriented, distributed and high fault-tolerant database. It is used to store and manage big data. It can store billions of rows at a time. Both Hive and HBase can be used to store the big data in Hadoop. When the data comes from multiple sources, it is stored into multiple tables in Hive and HBase. As a result, its performance degrades when there is a need to perform join operations. In this thesis, we propose an architecture which stores data from multiple sources into a single HBase table. A new table schema with a unique row key is designed which integrates multi-source data in a table. There is no need to perform join operation in the proposed technique as the data is integrated into a single HBase table. We evaluated the proposed technique using a real testbed by considering a dataset of two publishers. We compare the performance by storing data into Hive and also in the proposed HBase table. Results show improved query performance of the proposed technique as compared to the traditional approach of using join operations in multiple tables in Hive.