Maqbool Uddin Shaikh

Intelligent Decision Making Based on Data Mining Using Differential Evolution Algorithms and Framework for ETL Workflow Management

Business decision-making is not a simple task. There are many reasons for that but one of the main reason might be that data comes from different operational sources of an organization and it is difficult to organize and maintain especially if a huge volume of data is involved. A Data warehouse is helpful in this regard as it can assist in business decision-making by collecting data from different operational sources of the organization. To collect the data from different sources and then loading it into a warehouse is also difficult to manage the data on these sources is not in a consistent format. It is usually a three step process that involves extraction, transformation and loading. The extraction of data from these different sources, then transform it into a consistent format and consequently load it into the warehouse, by using ETL (Extract, transform and load) tools. Nowadays, the majority of ETL tools organize workflow. An ETL workflow can be considered as a group of ETL jobs with dependencies between them. In this research different considerations are discussed which are necessary for the proper management of workflow in context of efficient business decision-making process. The purpose of this research paper is to give a novel idea about the possibility of designing an intelligent decision support system by using the techniques of data mining as well as the differential evolution algorithm of artificial neural networks. Artificial neural networks are being used to enhance the capabilities of the intelligent decision support system. A preexisting differential evolution algorithm with slight modification is being used within the DSS environment. It has been assumed that this merger will lead towards more development and advancement within the concept of DSS.

Web warehouse: Towards efficient distributed business management

Distributed Database is a very well known and popular concept that most of the organizations use when they need to store data over the network or in different locations. Now when they have large amount of operational data they want to use that data to support decision making as a mean of gaining competitive advantages. But the problem is that these operational systems were not designed to support any sort of decision making. Organizations need to have a way in which they can use their archive and operational data for decision making. Data warehouse was designed to meet the requirements of the organizations which receive data from different operational sources and support decision making. Organizations have there data on the web and as the organization matures data also increases. The problem for business organizations was to manage that data effectively because there is no global structure and organization of data available on the web. To maintain data effectively on the web we need a Web Warehouse. Web warehouse is the management of data on the web, it is a data warehouse on the net. This paper describes the effectiveness of the web warehouse leading towards business intelligence.

Proposed Text Mining Framework to Explore Issues from Text in a Certain Domain

Every research field (domain) has a lot of data or information in textual format. The main problem is how to optimally arrange and transform the textual data or information to explore research issues. The authors propose a framework that may help in exploration of research issues from text of specific field (domain). First textual data or information is transformed to semantic network by using some transformation functions or algorithms. Then the semantic network is transformed to frames using other transformation functions or algorithms. Knowledge base, semantic networks and frames repositories are updated from new concepts, semantic networks and frames respectively. By doing this, exploration of research issues from text may become quick, efficient and easy.

A framework for generation of rules from decision tree and decision table

Within an organization knowledge is present in various form, may be in minds of workers or in documented form. Knowledge is one the most precious resource of an organization. Every organization wishes to preserve and fully utilize its knowledge has various representation schemes such as frames, scripts, lists, rules, decision trees and decision tables etc. Knowledge in the form of rules is easy to understand and fast to extract and implement as compared to decision tree and decision table. The authors propose a framework to automate the construction of rules from decision tree and decision table. The knowledge in decision table can be converted to set of rules by transformation of decision table to decision tree or directly to set of rules. The knowledge in decision tree can be directly transformed to set of rules. The set of rules are then refined and optimized, and the existing knowledge base is updated through these rules.

The Relationship between Cyber Crime and Mental Illness

In this paper an attempt is made to describe the relationship between cyber crime and mental illness. It will create awareness among people about cyber crimes and criminality due to mental illness. Computers and the internet make many actions easier for us. They also make many unlawful activities easier for criminals such as the sharing of, copy right infringement, stock manipulation, child pornography and a range of scams. In order to understand the criminal activity it is important to understand the main factor behind the criminal activity for e.g. mental illness.

Data Warehousing an Academic Discipline; "Curriculum Development Approach, Methodologies and Issues"

Data warehousing is multidisciplinary novel field of study in computing. It has a demanding role in multinational companies. The multidisciplinary nature of date warehousing may cause the overlapping of the topics and even contradictory definitions in its study. By sense this prospective, the authors of the paper suggested that, data warehousing has to be considered as a standalone academic discipline. For this discipline, a consolidated curriculum is required. Keeping in view the curricula development methodologies of computing discipline, authors present a curriculum for data warehouse. The presented curriculum is modular base. Each module encapsulates the teaching requirement of the discipline for a specific level of expertise. Furthermore in the paper the general issues in the process of curricula development are discussed in light of the data warehousing discipline.

A new traffic route analyzer for commuter’s guidance in developing countries: application study in Islamabad, Pakistan

Growth of population in the capital city of Pakistan—Islamabad—is too high. This growth rate has caused a negative impact on the smooth flow of traffic system. The aim of this paper is to provide a solution to facilitate the car-based commuters to pick the route with minimal bottlenecks and to minimize the distance to reach the destination. The proposed solution is to manage and control the traffic system in the city of Islamabad. The current traffic data collection system in Islamabad and other urban areas does not provide timely and reliable data that can be useful to the Regional Transportation Authority for planning activities. To overcome this problem, our approach is based on collecting the missing data using a custom-built mobile app. Our system analyses and manipulates the collected information based on artificial neural network scheme that can indicate the bottlenecks for each route and predicts the shortest route based on the user’s severity levels. To validate our proposed approach, we tested six different randomly selected routes in Islamabad with different bottlenecks.

Intelligent Data Mining in Autonomous Heterogeneous Distributed Bio Databases

A few years ago there is revolutionary change in data mining and bioinformatics. Data mining techniques and tools play important role in field of bioinformatics. It is very useful to use data mining techniques to evaluate and analyze bio-medical data. In this paper authors purpose a frame work for intelligent data mining system for bio databases. This system allows scientist to apply bio data analysis data mining techniques intelligently on bio data as well as their proprietary data. System first extracts the relevant, useful, valid and actionable data from bio databases. Bio databases is often autonomous heterogeneous and distributed in nature. The extracted data is preprocessed. After preprocessing, data mining techniques are applied on preprocessed data, local data as well as proprietary data. Then the mined knowledge is integrated with expert system knowledge to assist researchers and scientists in their research work and crucial decision making process.