G. Anuradha ¹, D. Joel Varma ¹

Affiliations
1 Department of CSE, GMRIT, Rajam-532127, Andhra Pradesh, IN

Abstract

Objectives: To develop an extension for postgresql which will include the fundamental graph operations required for graph algorithms to allow moderately associated datasets to have both(graph and non-graph) concepts. Methods/ Statistical Analysis: The extension in postgresql uses the query optimizations of the SQL, although this can be done manually, so it was avoided. The algorithm used by the extension was a depth first search (DFS) algorithm. It is very simple and was written just to show the speed difference between using SQL and extensions. Findings: Relational databases are hard to work on graphs, as the tabular form cannot represent graphs well which Dedicated Graph databases like Neo4j, Titan and Allegrograph can and also provide performance when it comes to graph related queries which the relational databases deem complex and sometimes not possible.Common Table Expression (CTE) in postgresql perform graph related operations, but unable to perform more optimized and complex operations. To overcome this problem, we are going to develop extensions for postgresql. Application/Improvement: The time and cost i.e. the memory required is very less compared CTE’s.

Keywords

Allegro, Neo4j, Postgresql, Structured Databases, Titan.

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G. Anuradha ¹, N. Deepak Kumar ¹

Affiliations
1 Department of CSE, GMRIT, Rajam - 532127, Andhra Pradesh, IN

Abstract

Objective: The objective is to classify web pages and assign ranking to web pages using feature selection with rough sets and TF_IDF methodology. Proposed Method: Web page ranking is a process to assign position at a particular site appears in the result of web page. A site is said to have a high page ranking when it appear at or near the top of the list of web result. A challenge in web page ranking is to provide relevant information to the user according to query. To finding relevant information from the result set is a tedious process. To obtain a refined result set that contains the URL’s more relevant to the user’s query, so it is essential to rank. For classification purpose, we are using feature reduction method based Rough Set Theory (RST). Application: Feature selection is most essential technique in rough sets as well as the data mining. Attribute selection is a main challenge for expanding the theory and making use of rough set. Findings: The proposed method emphases on the removal of the unnecessary attributes as a way to sort the effective reduct set and framing the core of the attribute set. After successful classification procedure, we have to applying TF_IDF methodology for assign the ranking to the documents.

Keywords

Core, Data Preprocessing, Data Mining, Feature Selection, Rough Sets Theory (RST), Reduct, Tf-IDF, Text Mining

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G. Anuradha ¹, D. Joel Varma ¹

Affiliations
1 Department of Computer Science Engineering, GMRIT, Rajam – 532127, Andra Pradesh, IN

Abstract

Background/Objectives: There is a tremendous growth in the online marketing where customers buy a product and leave a comment on it about their experience. These experiences which are in the form of Reviews help in two ways. Methods/Statistical Analysis: Firstly, the buyer will have a clear idea about the product pros and cons. Secondly; manufacturer will also find them helpful to make the user experience better by improving the product or service in negative areas. This converges at a point where, if the user reviews are thousands in number for a single product, we can propose a system which provides the summary of all user generated reviews. This is what motivated opinion mining systems to summarize the user review. Opinion mining is the current technology which can classify the review documents to summarize them. Findings: This paper implements the opinion mining based on fuzzy logic to improve classification of reviews for generating the concise summary about the product. Application/Improvements: This is a Feature based sentiment classification which is a multistep process which involves pre-processing phase, fuzzy score to classify each review, training the Naive bayes classifier, evaluating each sentence in the test set depending on the trained classifier and ranking the sentences for each feature. Thus sentences evaluated are a fine grained classification to better summarize the reviews.

Keywords

Naive Bayes Classifier, Opinion Mining, Sentiment Analysis, Sentence Ranking.

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