Data Mining and Knowledge Engineering

K. Venkatesh Sharma ¹, K. Satish Kumar ²

Affiliations
1 Department of CSE at Sri Indu College of Engineering & Technology, IN
2 Computer Science and Engineering from Sri Indu College of Engineering & Technology, JNTU, Hyderabad, IN

Abstract

Adoption of web applications is increasing for multipurpose services. However, their correct functioning is mission critical for many businesses. At the same time, Web applications tend to be error prone and implementation vulnerabilities are readily and commonly exploited by attackers. The design of countermeasures that detect or prevent such vulnerabilities or protect against their exploitation is an important research challenge for the fields of software engineering and security engineering. In this paper we introduce a single J2EE based web application which can able to handle several vulnerabilities at application level, mainly these are related to injection types, cross site scripting, browser caching and also protecting the session data dependency via changing session identifier at runtime, sequential access and session expiration. By handling all these things together in an application we can protect our web application successfully from the common vulnerabilities.

Keywords

Web Application, Vulnerabilities, Session Data, Security, Injection Flaw, Cross Site Scripting, Web Application Firewall (WAF).

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K. Venkatesh Sharma ¹, K. Hanumanth Rao ², P. Nagaraj ³

Affiliations
1 Department of computer science and Engineering, Sri Indu College of Engineering & Technology, IN
2 CSE Dept. at Sri Indu College of Engineering & Technology, IN
3 Sri Indu College of Engineering & Technology, IN

Abstract

In recent years, there has been significant interest in the development of ranking functions and efficient top-k retrieval algorithms to help users in ad hoc search and retrieval in databases (e.g., buyers searching for products in a catalog). We introduce a complementary problem: How to guide a seller in selecting the best attributes of a new tuple (e.g., a new product) to highlight so that it stands out in the crowd of existing competitive products and is widely visible to the pool of potential buyers. We develop several formulations of this problem. Although the problems are NP-complete, we give several exact and approximation algorithms that work well in practice. One type of exact algorithms is based on Integer Programming (IP) formulations of the problems. Another class of exact methods is based on maximal frequent item set mining algorithms. The approximation algorithms are based on greedy heuristics. A detailed performance study illustrates the benefits of our methods on real and synthetic data.

Keywords

Data Mining, Knowledge and Data Engineering Tools and Techniques, Marketing, Mining Methods and Algorithms, Retrieval Models.

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K. Venkatesh Sharma ¹, Arif Mohammad Abdul ¹

Affiliations
1 Sri Indu College of Engineering and Technology, IN

Abstract

Medical text mining has gained increasing interest in recent years. Radiology reports contain rich information de- scribing radiologist's observations on the patient's medical conditions in the associated medical images. However, as most reports are in free text format, the valuable information contained in those reports cannot be easily accessed and used, unless proper text mining has been applied. In this paper, we propose a text mining system to extract and use the information in radiology reports. The system consists of three main modules: a medical finding extractor, a report and image retriever, and a text-assisted image feature extractor. In evaluation, the overall precision and re- call for medical finding extraction are 95.5% and 87.9% respectively, and for all modifiers of the medical findings 88.2% and 82.8% respectively. The overall result of report and image retrieval module and text-assisted image feature extraction module is satisfactory to   radiologists.

Keywords

Text Mining, Medical Finding Extractor, Report and Image Retriever, and Text-Assisted Image Feature Extractor.

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K. Venkatesh Sharma ¹, P. Mallesham ², K. Ashok Babu ³, S. Megha Chandrika ⁴

Affiliations
1 CSE Dept., Sri Indu College of Engg. & Tech., IN
2 Sri Indu College of Engg. & Tech., IN
3 ECE Dept., Sri Indu College of Engg. & Tech., IN
4 CSE Dept., Sri Indu Collg. of Engg. & Tech., IN

Abstract

We show that the e-commerce domain can provide all the right ingredients for successful data mining and claim that it is killer domain for data mining. We describe an integrated architecture based on our experience for supporting this integration. The architecture can dramatically reduce the pre-processing, cleaning and data understanding effort often documented to take 80% of the time in knowledge discovery projects. We emphasize the need for data collection at the application server layer (not the web server) in order to support logging of data and metadata that is essential to the discovery process. We describe the data transformation bridges required from the transaction processing systems and customer event streams (e. g. click streams) to the data warehouse. We detail the mining workbench, which needs to provide multiple views of the data through reporting, data mining algorithms, visualization and OLAP, We conclude with a set of challenges.

Keywords

Data Mining, E-Commerce, Session Zing, OLAP, Sniffers.

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