AIS Lab: Projects
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Current Projects

Ph.D Thesis

Meisam Nazariani ( )

Title: Quality Engineering of Business Intelligence Systems

 Software Quality Engineering is an emerging discipline which is concerned with improving the quality of software systems. This discipline needs to be firmly rooted in a quality model satisfying its requirements. In order to specify the requirements of this discipline, the meaning of quality is defined by reviewing the related literature. Software Quality Engineering needs to use a quality model throughout the life cycle of software systems. The goal of this research is to propose the characteristics of a quality model to be used as a foundation for Quality Engineering of Business Intelligence Systems.

Reza Gorgan Mohammadi ( )

Title: Inconsistency Detection and Resolution for Multi-Model Structures

 In software development, we are often faced with a collection of interrelated models each of which capturing a special view of the desired system. Such a collection is called a multi-model. One of the main challenges in multi-modeling is conforming to both local and global consistency rules. To get access to this aim, there should be a method for conflict detection and resolution and also providing the required repairs. In this research, a new method of gaining access to this aim and realizing the version of model driven development is envisioned.

Ali Rahnama ( )

Title: Active Ontology Based on Evolutionary Methodology

 Knowledge of a domain is not static and usually changes, so it needs to be kept update. In ontology evolution with the occurrence of a change we do not backtrack to scratch, we try by implementing those changes into the ontology and creating a different and newer version of the ontology. Our goal is simplifying the management of the ontology evolution process which can be achieved with the help of patterns and high level processes. in this research we intend to present a novel methodology and process model for ontology evolution based on our Cognibase model.

MSc Thesis

Malihe Hashemi ( )

Title: Verification and Validation of Data Mining Systems

Ali Kamali ( )

Title: Service-Oriented Architecture for Cloud Environment

Soheil Mohammadi ( )

Title: Design and Implementation of Data Warehouse in Cloud Environment

Masoumeh Nourollahi ( )

Title: A Method for Ontology Validation and Verification for Quality Engineering

 Ontology is the formal specification of a conceptualization. Despite it's many uses in different fields, such as information extraction and machine learning, Ontology's usage is yet to spread to its full potential. One of the most important causes of this, is the lack of a standardized method for its validation and verification. In this research, we'll study validation and verification methods, specially the methods applicable to ontologies, and will present our own method to verify and validate an ontology, based on quality engineering. Quality engineering consists of ontology evaluation factors, and their measurement and evaluation.

Davood Bahadori ( )

Title: SOA-Based Systems Reconfiguration Using Middleware

 In some cases, SOA-based systems may fail to deliver their services to service consumers. So, it is necessary to have some mechanisms to ensure service delivery continuity and whole system availability. One the advanced features and mechanisms in this area is system reconfiguration. By using dynamic system reconfiguration we are able to provide an appropriate level of system stability. Furthermore, it is possible to guarantee continuous access of an application as service consumer to the desired services. We aim to propose an appropriate mechanism for the reconfiguration ability of SOA-based systems in order to provide system stability. In order to make some level of transparency for some operations of system reconfiguration from service consumer, we will use ESB as a Middleware.

Completed Projects

Ph.D Thesis

Abbas Rasoolzadegan ( )

Title: Reliable yet Flexible Software Development Process with Formal and Visual Modeling Methods

 The results of literature review show that the need for both reliability and flexibility is increasingly becoming important among the various classes of software applications. Developing reliable yet flexible software is a hard problem. Although modeling methods enjoy a lot of advantages, the use of just one of them, in many cases, may not guarantee the development of reliable and flexible software. Formal modeling methods ensure reliability. However, lack of knowledge and high cost practically force developers to use semi-formal methods instead. Semi-formal (visual) modeling methods, which are widely used in practical large-scale software development, are not good enough for reliable software development.
This dissertation proposes a new practical process to the development of reliable yet flexible software. In the proposed process, formal (Object-Z) and semi-formal (UML) models are transformed into each other using a set of bidirectional formal rules. Formal modeling and refinement ensure the reliability of software. Visual models facilitate the interactions among stakeholders who are not familiar enough with the complex mathematical concepts of formal methods. Visual models help detect the unexpected behavior and inconsistencies of software. Applying design patterns to visual models improves the flexibility of software. The transformation of formal and visual models into each other through the iterative and evolutionary process, proposed in this dissertation, helps develop the software applications that need to be highly reliable yet flexible. The feasibility and the applicability of the proposed process have been investigated using the multi-lift case study. Moreover, the flexibility and the reliability of the process have been quantitatively measured using the proposed systematic methods.

Shiva Vafadar ( )

Title: Analysis Patterns for Agent's Intelligence Engineering

 Exploiting Artificial Intelligence (AI) techniques to software systems helps software systems to handle the complexity of dynamic and unexpected situations. From software engineering perspective, all the capabilities of software systems (including intelligence) should be developed through a set of pre-defined development processes. Requirements Engineering (RE) is an important process for elicitation, analysis and specification of requirements.
In this thesis, intelligence is introduced as a new requirement of software systems, and a framework is presented for elicitation, specification and analysis of intelligence requirements via designing software pattens. This framework contains the taxonomy of intelligence. It categorizes the sub-characteristics of intelligence via a quantitative analysis on 71 definitions of intelligence in public notion, psychology and AI. These characteristics are considered as the potential requirements of intelligent software systems. The presented categorization is used for elicitation of intelligence requirements. This framework also contains software requirements patterns and analysis patterns, as tools that are used in RE process. Theses patterns are presented for learning and planning, as two candidate intelligence characteristics.
In this thesis, two approaches are used for evaluation: 1- Empirical Study: In this study, requirements patterns are applied for requirements specification. The resulted functional, quality and intelligence requirements are compared for validity, completeness, relation with other requirements, changeability, implementability and testability. The results show that intelligence requirements are similar to the functional and quality requirements based on the measured criteria. 2- Integration with the existing agent-oriented methodologies: In this approach, O-MaSE meta-model and learning analysis meta-models are mapped. Based on this mapping, an O-MaSE based process is presented for developing learning as an intelligence requirement.

Mohammad Karim Sohrabi ( )

Title: Frequent Pattern Mining on Very Large Transaction Databases Using Divide-and-Conquer Approach

Negin Daneshpour ( )

Title: Active View Prediction, to Store Aggregated Data in order to Efficient OLAP System

 On-Line Analytical Processing (OLAP) systems based on Data Warehouses are main working systems for managerial decisions making. Therefore these systems should have quick response time. Several algorithms have been presented to select proper set of data and elicit suitable structured environments to handle the queries submitted to OLAP systems, which are called view selection algorithms to materialize. Some of these algorithms are static and the others are dynamic. Since users’ requirements may change during the run time, we have to consider view materialization dynamically. In this thesis, we propose a new solution for dynamic view selection. We test and verify this solution through a dynamic view management system with new and improved architecture. This system extracts sequential patterns of incoming queries and predicts the next query through three probabilistic reasoning approaches: Conditional probability, Bayes’ rule, and Naïve Bayes’ rule. The proposed system is compared with DynaMat system (a well-known dynamic view management system), Hybrid system (the only dynamic view management system based on probabilistic reasoning approaches and outperforms DynaMat for drill-down queries), and extended Hybrid system (without constraints) through two standard measures which have been used in related works to evaluation. These measures are Detailed Cost Saving Ratio and Cumulative Replacement Count which are the average cost saving to answer input queries and the total number of view replacements in the pool with new selections respectively. Our experimental results show that the proposed dynamic view selection system improves these measures.

MSc Thesis

Sadegh Abeyat ( )

Mahdieh Monzavi ( )

Rezvan Shiravi ( )

Title: New Requirement Process Model for Critical Systems Focusing on Validation and Verification

 Development of critical systems is very important because in these systems any incorrect behavior may lead to catastrophic loss in terms of cost, damage to the environment or even human life. To achieve this goal, requirements should be identified and specified accurately, completely and precisely. For this reason, Verification and Validation (V&V) in Requirement Engineering (RE) must be carried out in order to produce such an errorless system.
Although, some techniques have been presented in this area by some researchers, drawbacks such as using a specific approach, limitation of system’s size, high time consuming and complexity make them inappropriate in many situations. This thesis presents a requirement V&V technique in order to smooth weaknesses of current ones.
Before supposing new technique, for identifying the position of critical systems different kinds of software systems were classified from different viewpoints. A survey of current V&V techniques was conducted and subsequently they were classified in two levels. Then some of them which were useful in RE were extracted. For evaluating of V&V techniques, a framework was constructed in which a set of measurable criteria were suggested.
In suggested technique, Requirements were divided in two categories, critical and non-critical, due to interest in decreasing time consumption as well as complexity. Because of the importance of critical requirements, concentration of technique was lead toward them. Suggested technique is a combination of informal, semi-formal and formal models in which there is an efficient communication between customers and users as well as precise and accurate specification of requirements.
After presenting new technique, phases of RE process were identified and the position of new technique was specified. For investigating new technique, traffic control system was selected as a case study and technique was applied on it successfully. In order to evaluate this technique, first a comparison between suggested technique and its rivals was conducted descriptively.
Because of some ambiguities in a descriptive comparison, a qualitative comparison between suggested technique and two others, theorem proving and goal-oriented approach were carried out by applying presented framework.
Results show that suggested technique meets precise, accurate and valid requirements, and detects errors, defects and inconsistencies. Moreover, time consumption and complexity of this technique are lower than other ones and does not include their limitations. Although required technical skill is at high level, by an automation tool, this deficiency could be compensated.

Ali Abdoli ( )

Title: Duplicate Record Detection in Operational Data Using Semantic Analysis

 Duplicate record detection is main activity in information systems. Detecting approximate duplicate records is a key problem in data integration and data cleaning. The process of duplicate record detection aims at defining whether two records represent the same real word object.
Similarity function is the major element in duplicate record detection. Similarity function assigns a score to pair of data values. Most approaches concentrate on string similarity measures for comparing records. However, they fail to identify records which share semantic information. So in this study we propose a new similarity function which takes into account both semantic and string similarity.
Find the proper similarity function according to data set is key problem in duplicate record detection. In this study the new method proposed to find the most proper similarity functions for data sets.
All proposed methods are experimented on real world data sets and evaluate based on standard metrics. Experimental results indicate the new similarity function outperforms popular similarity functions in standard metrics. Based on results, proposed method in finding proper similarity function, outperform all other combination of similarity functions.

Fatemeh Jabbari ( )

Title: Using Data Mining Techniques in Web Log Analysis for Producing Personalized Web Pages

 Web Mining is the application of Data Mining techniques in World Wide Web to automatically extract knowledge from Web data. There are three main branches in Web mining with respect to the data being mined: Web Content Mining, Web Structure Mining and Web Usage Mining. The purpose of Web usage mining is to automatically extract knowledge from web usage data in a website or a specific Web domain. In Web usage mining, it is aimed to extract meaningful knowledge from user’s navigational behaviour in the website, analysing the Web log files on Web servers. The extracted knowledge can be used in different applications such as Web Personalization. Recently, using Web usage mining in Web personalization has become common as an alternative for classic methods. This Project aims to use data mining techniques in Web log analysis in a way that improves the efficiency of Web personalization. This efficiency is measured by precision and coverage of the personalized system. For this aim, we have chosen sequential pattern mining techniques and have tried to improve algorithms considering web log data qualities. An important challenge in sequential pattern mining algorithms is that they ignore the different nature of items in mining process. The proposed method tries to take into account the different occurrences of the same item in different sessions, by adding weight to item support counts. The proposed method has been tested in a personalization system. The results show improvement in precision and coverage of personalization.

Ani Megerdoumian ( )

Title: Evaluation of Machine Translation Systems Architecture to Improve Hybrid Architecture

 Less attention has been taken to intelligent systems and especially machine translation applications in terms of the production process which is based on software engineering. Machine translation is a kind of natural language processing application, in which a text is being taken as an input from a source language and a text equivalent to the destination language is produced. Machine translation is an open task, which means, we can produce many valid translations from different combinations of words. Because of costs and resource expenses, it is better to evaluate the structural quality of machine translation systems during analysis and design phase. Current machine translation evaluation techniques’ attention is much focused on the quality of produced sentences rather than the structure of the system. Quality attributes and issues connected to non-functional requirements of such systems are ignored because of the above mentioned negligence. Regarding to these facts, we are going to propose a method for evaluating the architecture of machine translation systems.
In this thesis we come up with a new method which is going to evaluate the architecture of machine translation systems. In this method, the non-functional requirements of machine translation systems are going to be assessed by representing quality attributes qualitatively. What is more, by making use of our proposed method, we are going to evaluate architecture of three hybrid machine translation systems. Eventually, we will analyze our method using a framework to choose architecture evaluation methods and we will show that our proposed method is an appropriate approach to evaluate the architecture of hybrid machine translation systems.





Past Projects


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