Ranjana Vyas

Temporal approach to association rule mining using t-tree and p-tree

The real transactional databases often exhibit temporal characteristic and time varying behavior. Temporal association rule has thus become an active area of research. A calendar unit such as months and days, clock units such as hours and seconds and specialized units such as business days and academic years, play a major role in a wide range of information system applications. The calendar-based pattern has already been proposed by researchers to restrict the time-based associationships. This paper proposes a novel algorithm to find association rule on time dependent data using efficient T tree and P-tree data structures. The algorithm elaborates the significant advantage in terms of time and memory while incorporating time dimension. Our approach of scanning based on time-intervals yields smaller dataset for a given valid interval thus reducing the processing time. This approach is implemented on a synthetic dataset and result shows that temporal TFP tree gives better performance over a TFP tree approach.

Associative Classifiers for Predictive Analytics: Comparative Performance Study

A new predictive modelling approach known as associative classification, integrating association mining and classification into single system is being discussed as a better alternative for predictive analytics. Our paper investigates the performance issues of significant associative classifiers likes CMAR and CPAR. Performance comparisons observe that CPAR achieves improved performance as compared to CMAR. We have proposed the modification in these approaches by incorporating temporal dimension. The new approach was compared with their non-temporal counterparts and the results were analyzed for classifier accuracy and execution time. The study concludes that temporal CPAR achieves better performance than temporal CBA and temporal CMAR. The three temporal associative classifiers (TACs) were compared on ten different datasets for classifier accuracy and significant conclusion was drawn as temporal associative classifiers performed better than their non-temporal counterparts, while temporal CPAR being the best among the three TACs.

Cloud computing in optimizing supply chain management of automotive component industry

Cloud Computing is the trending technology that is relocating the infrastructure practically on cloud. The study tries to streamline the supply chain management of Automotive component industry with the help of cloud computing. It also highlights the effect of using cloud computing for optimizing the resources, reducing cost, increase in revenue, better CRM strategies and making more efficient service delivery in the automobile industry. Cloud Computing has contributed significantly in optimizing the IT industry and other industry also. Optimizing resource significantly contributes in the cost reduction and directly affects the revenue generation of the company. Incorporating cloud computing in optimizing the supply chain of the automotive component industry will help in preparing the industry more agile.

Towards an Ontology development for automated applications in Smart City environment of SmartME Project

Cities are becoming everyday smarter, with an increasing multitude of electronic nodes distributed throughout the territory. These range from rather simple ones, such as sensors/actuators, but also smartphones, to more complex ones, such as data centers and workstations. Citizens may have a central role in consuming, but also producing the data. The consequence of all this is that the amount of data collected is enormous and these data need to be properly processed in order to make the most of them. Unfortunately, there are several challenges in data processing, but exploiting the Semantic Web technologies and linking data among them is the right way to face them. This paper introduces the SmartME project developed by the University of Messina, Italy, and discusses the technologies and approaches that can be utilized to properly manage the collected data. In this paper, we are working towards the incorporation of semantic layer with the SmartMe project of University of Messina. In this, our contribution is to build logic for maintaining sensors and their collected information and query them in more meaningful way for getting accurate results. Also, we have presented a way of modifying a previously developed ontology, SSN, and customized it for our purpose.

A Study on IDS (Intrusion Detection System) and Introduction of IFS (Intrusion Filtration System)

Network security in organizations is not limited to tangible systems but beyond the physical existence, its focusing on security of non-tangible data flowing in network inside and outside of organization while communicating through Internet. In this paper, we will discuss about different types of intrusion detection system (IDS) available and comparison of their various aspects. Finally, I propose my research work as intrusion filtration system (IFS), which will be a new methodology for network security.