Papers

This paper reviews the different information retrieval models that can be used to represent information from digital library. It also describes document retrieval from the digital library, followed by experimental analysis of various traditional IR models like Boolean model, Vector model, Probabilistic model on a sample corpus. We present the Private Digital Library (PDL) project that represents a service of the Corporate Digital Library (CDL) prototype. The main ideas underlying this project are the following. When a user is looking for documents that he already retrieved in the past, he has to repeat the search procedure and solve the same problems he encountered in the past access. On the contrary, consider the possibility for the user to store documents in a private library. In this case, he will have the chance to retrieve the documents of interest more easily and quickly. It may also be the case that the user has not a clear understanding of what he is looking for. Moreover, he might not know exactly the library content and organization. This paper also discusses way of representing index terms in form of indices so that they can be searched later. Thus, he needs for assistance to suggest him what to look for and how to query the system. A method to store and catalogue documents according to personal criteria might help to overcome these problems.

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With the rapid growth of the World Wide Web (www), it becomes a critical issue to design and organize the vast amounts of on-line documents on the web according to their topic. Even for the search engines it is very important to group similar documents in order to improve their performance when a query is submitted to the system. Clustering is useful for taxonomy design and similarity search of documents on such a domain.

Similarity or distance measures play important role in the performance of clustering algorithms. This paper compares three term based similarity measures for web document clustering. The similarity measures used are Euclidean distance, cosine measure and jaccard measure. The clustering algorithm used is the so-called k-means clustering to cluster web documents. These three different similarity measures are used to find the similarity between documents. The clusters are formed based on their similarity measure calculation using k-means clustering algorithm. Overall similarity measure is used to evaluate the clusters formed using different similarity measure. Tested with web data, we observe that the Euclidean measure outperforms the other similarity measures in clustering accuracy.

DNA TRACERS is developed to assist in solving violent crime using forensics method. It is an experimental approach to aid the process of investigating crime by treating cases and subjects of the case as the nodes that might be linked to each other using DNA matching. The relation between the subjects of the cases and cases itself is defined by their DNA frequency and likelihood ratio. DNA TRACERS analyze each cases and subjects using non-linear method to further improve investigation by finding any possible relation between distinct cases, which is not implemented as an automated process in current practice. These relations are depicted in the form of graphical diagram which is easy to interpret by end-user. Statistical graph and score table for convicted offenders is calculated based on several criteria. It is provided in order to support the process of decision making. Mechanism procedure in DNA TRACERS made it possible to separate between forensics unit and investigative unit as different entities. Both entities are working together to solve cases with controlled access to their authorized database. DNA TRACERS support structured data format for casework, case subject and case analysis record. Built in security and strict data transfer control is implemented to ensure the integrity of the system and subsystems. Generated report automation with standardize format will become easier for different investigative unit do collaboration to solve crime investigations