Thesis Title:
Semantic Based Data Cleaning to Detect Duplication via ontology
Abstract:
Today, in the age of digital information, data plays a very key role in business and in everyday human life in general. Nowadays, the expansion of the use of the Internet has confronted us with huge data sources such as IoT devices, social networks, medical systems, etc., which are constantly producing a huge flood of Are data. Collecting and maintaining this data is valuable when it can be used as useful information to improve the current trend. For this purpose, data analysis technologies are introduced. In addition, an efficient and, more importantly, reliable data analysis can be performed when we have quality data. This is why the issue of data quality is so important. Data quality is one of the key factors in the success or failure of data-based systems.
In order to improve the quality of data, various processes must be performed for each of the data problems in order to obtain quality data. Duplicate data considered as one of the main quality problems. Semantic duplicate data as the most complex type of duplicate data that requires the machine to understand the meaning of the data, our main goal. For this purpose, we will try to use the ontology.
Thesis Title:
Data Science Technology Impact on Business Intelligence Quality
Abstract:
Business intelligence is defined as a set of methods, processes, architectures and technologies that turn raw data into meaningful and useful information. Today, different types of data are generated in large volumes and quickly and requires organizations to make decisions and respond in a timely manner. But existing analytical techniques can hardly extract useful information in real time from huge amounts of data.
Today, timely results are very important for business organizations. If the results are not produced accurately and in a timely manner, they will have the least value and use. Big data is generated from a variety of high-speed sources that, in order to gain a competitive edge in the market, must be processed in real time by commercial organizations. The main challenge of this research is to improve the immediate and timely implementation of business intelligence so that organizations can make quick, accurate and effective decisions.
Business intelligence is delayed in the stages of data storage, data analysis and decision making. Therefore, we intend to minimize the existing delays and improve the immediate implementation of business intelligence through the data analysis methods discussed in the topic of data science technology, and thus increase the quality of business intelligence
Thesis Title:
User Experience Evaluation and its Impact on User Interface Quality
Abstract:
Today, the user interface designed for a system does not meet the needs of users alone. For this reason, user experiences in interacting with software and intelligent systems have a significant impact and have become a controversial topic in the field of human-computer interaction. UI specialists can design the user interface according to users’ emotions and using intelligent techniques to increase its quality. In this project, the impact of users’ feelings and experiences in interacting with intelligent systems has been investigated. Using web browser recordings, user movement and mouse clicks when interacting with the system, user behavior is recorded and analyzed.
Thesis Title:
Semantic Based Data Analysis to Detecting Contradiction Data in Social Networks Based on Ontology
Abstract:
Business intelligence is defined as a set of methods, processes, architectures and technologies that turn raw data into meaningful and useful information. Today, different types of data are generated in large volumes and quickly and requires organizations to make decisions and respond in a timely manner. But existing analytical techniques can hardly extract useful information in real time from huge amounts of data.
Today, timely results are very important for business organizations. If the results are not produced accurately and in a timely manner, they will have the least value and use. Big data is generated from a variety of high-speed sources that, in order to gain a competitive edge in the market, must be processed in real time by commercial organizations. The main challenge of this research is to improve the immediate and timely implementation of business intelligence so that organizations can make quick, accurate and effective decisions.
Business intelligence is delayed in the stages of data storage, data analysis and decision making. Therefore, we intend to minimize the existing delays and improve the immediate implementation of business intelligence through the data analysis methods discussed in the topic of data science technology, and thus increase the quality of business intelligence
Thesis Title:
Big Data Storage and Retrieval Optimization based on a Quality Model
Abstract:
By the spread of using internet, social networks and different data sources, the importance of “Big Data” is increasing. One of the most important challenges in dealing with Big Data is “Storage and Retrieval”. So it is necessary to provide an efficient algorithm and evaluate it, for Big Data storage and retrieval optimization. In this thesis, we will select a quality model, then we will propose an algorithm to optimize big data storage and retrieval based on the selected quality model.
Thesis Title:
Design and Implementation Tool for Text Analysis of Social Networks to Elicitate Requirement
Abstract:
Spread of social networks in recent year’s leads to usage of those by software organizations in order to communicate with their software users. Researches have shown that messages have been sent by users contain opinions, ideas, bug reports and new requirements related to improvement of software systems. Therefore, elicitation of those messages will have a great impact on software maintenance and release planning tasks. On the other hand, due to the high percentage of irrelevant messages, diversity of writing styles and unstructured nature of messages, elicitation of related messages would be challengeable task in this area. In this thesis, we will propose an approach so as to Elicitate requirements, and then we will propose a quality model to assess and analyses quality of the requirements.
Thesis Title:
Business Analysis using predictive analytics methods in data analysis
Abstract:
To do a successful scientific work, we are dependent on: 1) providing appropriate information and data in management information systems; 2) decision making in decision support systems; 3) automating processes.
Business analysis is in the field of the following activities: 1) Online analytical processing 2) creation of dashboards, 3) data mining 4) and in recent years, Predictive analysis.
The purpose of this thesis is to use the prediction analytics methods for data analysis. In this project, we want to propose a method based on prediction analytics in business organizations and examine the problems and issues related to it using techniques related to the proposed method.
Thesis Title:
Requirements engineering in internet of things systems
Thesis Abstract:
One of the most important steps of software development is defining its functional and qualitative properties that introduced as requirements. So Requirements Engineering is a very important stage in life cycle of system development that determines the scope of system and all information about development process. Generally requirements engineering include the following steps: requirements elicitation, modelling and requirements analysis, validation and verification, definition of requirement whit RDL, change requirements management, updating requirements. In general, Requirements Engineering addresses the discovery, development, tracking, analysis, description, creation and management of requirements, and ultimately defines the system at various levels of abstraction.
Internet of Things consists of sensor devices, remote control services, communication networks and free text processes. Internet of Things attempts to create an integrated network that human and things can communicate everywhere. Each of the components of Internet of Things must have unique properties to meet the expectations of users. Things to carry out operations and communicate with the Internet, human, other objects and, in general, their environment need a set of features (requirements) such as Internet connectivity, sensors and communications software. Failure to review and engineer each of these requirements generates software that is different from the needs of users. So, with the modeling, validation and verification of the requirements, we must try to create software for Internet of Things.
Seminar Title:
A Comparison Between Business Intelligence Tools and How to Choose the Right One
Abstract:
Business Intelligence (BI) is set of tools, technologies, methodologies and solutions for collecting, integrating and analyzing data to provide knowledge and strategic insight of businesses and helps executives and managers to make informed business decisions. Nowadays, the demand for using BI tools is increasing. Organizations have different needs and requirements and BI tools are various. So, choosing an appropriate tool could be challenging. The purpose of this seminar is to conduct a comparison between BI tools and provide a way for selecting the right one.
Seminar Title:
A Survey On The Data Layer of IoT-based Systems
Abstract:
The Internet of Things (IoT) is defined as a paradigm in which objects equipped with sensors, actuators, and processors communicate with each other to serve a meaningful purpose. The increasing popularity of IoT services causes tremendous growth of the amount of produced data. These data must be effectively stored and retrieved by the IoT service providers. This seminar paper investigates the state-of-the-art research efforts directed toward data-related challenges in IoT-based software systems. We first present an overview of IoT and its related concepts and then introduce a number of applications that have been made feasible by the emergence of IoT. Then we compare the proposed architectures for IoT and discuss how the data-layer will fit into these architectures. Finally, open research challenges are presented as future research directions.
Seminar Title:
Machine Learning Micro-service Specification to Facilitate Implementation of Intelligent Systems
Abstract:
Nowadays, Microservices Architecture is taking the industry by storm as well as Service-Oriented Architecture did in mid-2000s. As both of them are service-based architecture, they somehow share same software style presented through communication protocols over network. Main purpose driving technology to focus on microservices is enhancement of product and process quality. Ambitions including improvement in modularity, fast and parallelized development, loosely couples services, efficient resource usage and etc. are reasons to lean toward Microservices. Machine Learning tools and techniques with study on statistical and mathematical models can progressively improve microservices performance and help specifying them.
Seminar Title :
IoT-Based Software Systems Problems
Abstract:
IoT (Internet of Things) is all about connecting sensors, actuators, and devices to a network and enabling the collection, exchange, and analysis of generated information. As much as IoT networks grows, the need of a qualified software to manage these networks become more important. In the seminar, we intend to find the problems and challenges of producing IoT software and present a qualified solution.
Seminar Title:
Analysis Methods and Tools for Developing BI Systems
Abstract:
Data Science, as used in business, is intrinsically data-driven, where many interdisciplinary sciences are applied together to extract meaning and insights from available business data, which is typically large and complex. On the other hand, Business Intelligence or BI helps monitor the current state of business data to understand the historical performance of a business. In fact, if BI experts and Data Scientists work together, then BI analysts can prepare the data for Data Scientists to feed into their algorithmic models. BI experts can offer their current understanding and knowledge of Analytics requirements of a business and help the Data Scientists build powerful models to forecast future trends and patterns.
Thesis Title:
Use Data Lake to improve generation of Business Intelligence
Abstract:
Today’s business world is always evolving as a result of rapid and intricate advances in their operative context and costumers’ disparate requirements, market and technology strains. Due to the very fast and intricate changes that are taking place around them, some refinements seem necessary to the contemporary business architecture and Business Intelligence (BI) is the answer. The issue in this area that promotes business value further is the quality of output information generated by this architecture. The goal of this thesis is to improve the quality of this category of information.
Seminar Title:
Analysis Methods and Tools for Developing BI Systems
Abstract:
Data Science, as used in business, is intrinsically data-driven, where many interdisciplinary sciences are applied together to extract meaning and insights from available business data, which is typically large and complex. On the other hand, Business Intelligence or BI helps monitor the current state of business data to understand the historical performance of a business. In fact, if BI experts and Data Scientists work together, then BI analysts can prepare the data for Data Scientists to feed into their algorithmic models. BI experts can offer their current understanding and knowledge of Analytics requirements of a business and help the Data Scientists build powerful models to forecast future trends and patterns.
Seminar Title:
Analysis Methods and Tools for Developing BI Systems
Abstract:
Data Science, as used in business, is intrinsically data-driven, where many interdisciplinary sciences are applied together to extract meaning and insights from available business data, which is typically large and complex. On the other hand, Business Intelligence or BI helps monitor the current state of business data to understand the historical performance of a business. In fact, if BI experts and Data Scientists work together, then BI analysts can prepare the data for Data Scientists to feed into their algorithmic models. BI experts can offer their current understanding and knowledge of Analytics requirements of a business and help the Data Scientists build powerful models to forecast future trends and patterns.
Seminar Title:
Making Intelligent Enterprise Organization Focusing On Digital Banking
