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Datamining

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    An Iterative Classification Scheme for Sanitizing Large-Scale Datasets

    • B.TECH / M.TECH / M.SC / PH.D

    •  GANGADHAR.T

    • 13 Months ago

    Cheap ubiquitous computing enables the collection of massive amounts of personal data in a wide variety of domains. Many organizations aim to share such data while obscuring features that could disclose personally identifiable information.

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    Analyzing Sentiments in One Go A Supervised Joint Topic Modeling Approach

    • B.TECH / M.TECH / M.SC / PH.D

    •  GANGADHAR.T

    • 12 Months ago

    n this work, we focus on modeling user-generated review and overall rating pairs, and aim to identify semantic aspects and aspect-level sentiments from review data as well as to predict overall sentiments of reviews. We propose a novel probabilistic supervised joint aspect and sentiment model (SJASM) to deal with the problems in one go under a unified framework.

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    Collaborative Filtering-Based Recommendation of Online Social Voting

    • B.TECH / M.TECH / M.SC / PH.D

    •  GANGADHAR.T

    • 15 Months ago

    Social voting is an emerging new feature in online social networks. It poses unique challenges and opportunities for recommendation. In this paper, we develop a set of matrix-factorization (MF) and nearest-neighbor (NN)-based recommender systems (RSs) that explore user social network and group affiliation information for social voting recommendation.

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    Computing Semantic Similarity of Concepts in Knowledge Graphs

    • B.TECH / M.TECH / M.SC / PH.D

    •  GANGADHAR.T

    • 15 Months ago

    This paper presents a method for measuring the semantic similarity between concepts in Knowledge Graphs (KGs) such as WordNet and DBpedia. Previous work on semantic similarity methods have focused on either the structure of the semantic network between concepts (e.g., path length and depth), or only on the Information Content (IC) of concepts.

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    Detecting Stress Based on Social Interactions in Social Networks

    • B.Tech / M.Tech / M.Sc / Ph.D

    •  GANGADHAR.T

    • 12 Months ago

    Psychological stress is threatening people’s health. It is non-trivial to detect stress timely for proactive care. With the popularity of social media, people are used to sharing their daily activities and interacting with friends on social media platforms, making it feasible to leverage online social network data for stress detection

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    Dynamic Facet Ordering for Faceted Product Search Engines

    • B.Tech / M.Tech / M.Sc / Ph.D

    •  GANGADHAR.T

    • 13 Months ago

    Faceted browsing is widely used in Web shops and product comparison sites. In these cases, a fixed ordered list of facets is often employed. This approach suffers from two main issues. First, one needs to invest a significant amount of time to devise an effective list.

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    Efficient Keyword-Aware Representative Travel Route Recommendation

    • B.Tech / M.Tech / M.Sc / Ph.D

    •  GANGADHAR.T

    • 12 Months ago

    With the popularity of social media (e.g., Facebook and Flicker), users can easily share their check-in records and photos during their trips. In view of the huge number of user historical mobility records in social media, we aim to discover travel experiences to facilitate trip planning.

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    Mining Competitors from Large Unstructured Datasets

    • B.Tech / M.Tech / M.Sc / Ph.D

    •  GANGADHAR.T

    • 0 Months ago

    In any competitive business, success is based on the ability to make an item more appealing to customers than the competition. A number of questions arise in the context of this task: how do we formalize and quantify the competitiveness between two items Who are the main competitors of a given item

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    Continuous Top-k Monitoring on Document Streams

    • B.Tech / M.Tech / M.Sc / Ph.D

    •  GANGADHAR.T

    • 13 Months ago

    The efficient processing of document streams plays an important role in many information filtering systems. Emerging applications, such as news update filtering and social network notifications, demand presenting end-users with the most relevant content to their preferences

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    Topological Approach to Symbolic Pole-Zero Extraction Incorporating Design Knowledge

    • B.Tech / M.Tech / M.Sc / Ph.D

    •  GANGADHAR.T

    • 12 Months ago

    This paper addresses the problem of automatic analytical pole-zero extraction for multi-stage operational amplifiers with frequency compensation. Traditional methods mainly rely on numerical reference to derive approximate pole-zero expressions without incorporating any design knowledge.

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    Personal Web Revisitation by Context and Content Keywords with Relevance Feedback

    • B.Tech / M.Tech / M.Sc / Ph.D

    •  GANGADHAR.T

    • 12 Months ago

    Getting back to previously viewed web pages is a common yet uneasy task for users due to the large volume of personally accessed information on the web. This paper leverages human’s natural recall process of using episodic and semantic memory cues to facilitate recall, and presents a personal web revisitation technique.

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    Novelty Indicator for Enhanced Prioritization of Predicted Gene Ontology Annotations

    • B.Tech / M.Tech / M.Sc / Ph.D

    •  GANGADHAR.T

    • 12 Months ago

    Biomolecular controlled annotations have become pivotal in computational biology, because they allow scientists to analyze large amounts of biological data to better understand test results, and to infer new knowledge. Yet, biomolecular annotation databases are incomplete by definition, like our knowledge of biology, and might contain errors and inconsistent information.

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    Energy-efficient Query Processing in Web Search Engines

    • B.Tech / M.Tech / M.Sc / Ph.D

    •  GANGADHAR.T

    • 13 Months ago

    Web search engines are composed by thousands of query processing nodes, i.e., servers dedicated to process user queries. Such many servers consume a significant amount of energy, mostly accountable to their CPUs.

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    Clustering Data Streams Based on Shared Density between Micro-Clusters

    • B.Tech / M.Tech / M.Sc / Ph.D

    •  GANGADHAR.T

    • 12 Months ago

    As more and more applications produce streaming data, clustering data streams has become an important technique for data and knowledge engineering. A typical approach is to summarize the data stream in real-time with an online process into a large number of so called micro-clusters.

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    Association Rule – Extracting Knowledge Using Market Basket Analysis

    • B.TECH / M.TECH / M.SC / PH.D

    •  GANGADHAR.T

    • 12 Months ago

    Decision making and understanding the behavior of the customer has become vital and challenging problem for organizations to sustain their position in the competitive markets. Technological innovations have paved breakthrough in faster processing of queries and sub-second response time. Data mining tools have become surest weapon for analyzing huge amount of data and breakthrough in making correct decisions. The objective of this paper is to analyze the huge amount of data thereby exploiting the consumer behavior and make the correct decision leading to competitive edge over rivals. Experimental analysis has been done employing association rules using Market Basket Analysis to prove its worth over the conventional methodologies.

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    Dynamic Query Forms for Database Queries-real-world databases contain

    • B.TECH / M.TECH / M.SC / PH.D

    •  GANGADHAR.T

    • 12 Months ago

    Modern scientific databases and web databases maintain large and heterogeneous data. These real-world databases contain over hundreds or even thousands of relations and attributes. Traditional predefined query forms are not able to satisfy various ad-hoc queries from users on those databases. This paper proposes DQF, a novel database query form interface, which is able to dynamically generate query forms. The essence of DQF is to capture a user’s preference and rank query form components, assisting him/her to make decisions. The generation of a query form is an iterative process and is guided by the user. At each iteration, the system automatically

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    Privacy Preserving Delegated Access Control in Public Clouds

    • B.TECH / M.TECH / M.SC / PH.D

    •  GANGADHAR.T

    • 12 Months ago

    Current approaches to enforce fine-grained access control on confidential data hosted in the cloud are based on fine-grained encryption of the data. Under such approaches, data owners are in charge of encrypting the data before uploading them on the cloud and re-encrypting the data whenever user credentials or authorization policies change. Data owners thus incur high communication and computation costs. A better approach should delegate the enforcement of fine-grained access control to the cloud, so to minimize the overhead at the data owners, while assuring data confidentiality from the cloud. We propose an approach, based on two layers of

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    Crowd sourcing Predictors of Behavioral Outcomes

    • B.TECH / M.TECH / M.SC / PH.D

    •  GANGADHAR.T

    • 12 Months ago

    Generating models from large data sets—and determining which subsets of data to mine—is becoming increasingly automated. However, choosing what data to collect in the first place requires human intuition or experience, usually supplied by a domain expert. This paper describes a new approach to machine science which demonstrates for the first time that nondomain experts can collectively formulate features and provide values for those features such that they are predictive of some behavioral outcome of interest. This was accomplished by

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    Spatial Approximate String Search

    • B.TECH / M.TECH / M.SC / PH.D

    •  GANGADHAR.T

    • 12 Months ago

    This work deals with the approximate string search in large spatial databases. Specifically, we investigate range queries augmented with a string similarity search predicate in both Euclidean space and road networks. We dub this query the spatial approximate string (SAS) query. In Euclidean space, we propose an approximate solution, the MHR-tree, which embeds min-wise signatures into an R-tree. The min-wise signature for an index node u keeps a concise representation of the union of q-grams from strings under the sub tree of u. We analyze the

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    Privacy-Preserving and Content-Protecting Location Based Queries

    • B.TECH / M.TECH / M.SC / PH.D

    •  GANGADHAR.T

    • 12 Months ago

    In this paper we present a solution to one of the location-based query problems. This problem is defined as follows: (i) a user wants to query a database of location data, known as Points Of Interest (POIs), and does not want to reveal his/her location to the server due to privacy concerns; (ii) the owner of the location data, that is, the location server, does not want to simply distribute its data to all users. The location server desires to have some control over its data,

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    Fast Nearest Neighbor Search with Keywords

    • B.TECH / M.TECH / M.SC / PH.D

    •  GANGADHAR.T

    • 12 Months ago

    Conventional spatial queries, such as range search and nearest neighbour retrieval, involve only conditions on objects’ geometric properties. Today, many modern applications call for novel forms of queries that aim to find objects satisfying both a spatial predicate, and a predicate on their associated texts. For example, instead of considering all the restaurants, a nearest neighbour query would instead ask for

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    A Cocktail Approach for Travel Package Recommendation

    • B.TECH / M.TECH / M.SC / PH.D

    •  GANGADHAR.T

    • 12 Months ago

    Recent years have witnessed an increased interest in recommender systems. Despite significant progress in this field, there still remain numerous avenues to explore. Indeed, this paper provides a study of exploiting online travel information for personalized travel package recommendation. A critical challenge along this line is to address the unique characteristics of travel data, which distinguish travel packages from traditional items for recommendation. To that end, in this paper, we

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    Rank-Based Similarity Search: Reducing the Dimensional Dependence

    • B.TECH / M.TECH / M.SC / PH.D

    •  GANGADHAR.T

    • 12 Months ago

    This paper introduces a data structure for k-NN search, the Rank Cover Tree (RCT), whose pruning tests rely solely on the comparison of similarity values; other properties of the underlying space, such as the triangle inequality, are not employed. Objects are selected according to their ranks with respect to the query object, allowing much tighter control on the overall execution costs. A formal theoretical analysis shows that with very high probability, the

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    k-Nearest Neighbor Classification over Semantically Secure Encrypted Relational Data

    • B.TECH / M.TECH / M.SC / PH.D

    •  GANGADHAR.T

    • 12 Months ago

    Data Mining has wide applications in many areas such as banking, medicine, scientific research and among government agencies. Classification is one of the commonly used tasks in data mining applications. For the past decade, due to the rise of various privacy issues, many theoretical and practical solutions to the classification problem have been proposed under different security models. However, with the recent popularity of cloud computing, users now have the opportunity to outsource their data, in encrypted form, as well as the data mining tasks

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    FastGeo: Efficient Geometric Range Queries on Encrypted Spatial Data

    • B.TECH / M.TECH / M.SC / PH.D

    •  GANGADHAR.T

    • 12 Months ago

    Spatial data have wide applications, e.g., location-based services, and geometric range queries (i.e., finding points inside geometric areas, e.g., circles or polygons) are one of the fundamental search functions over spatial data. The rising demand of outsourcing data is moving large-scale datasets, including large-scale spatial datasets, to public clouds. Meanwhile, due to the concern of insider attackers and hackers on public clouds, the privacy of spatial datasets should be

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    An Internal Intrusion Detection and Protection System by Using Data Mining and Forensic Techniques

    • B.TECH / M.TECH / M.SC / PH.D

    •  GANGADHAR.T

    • 12 Months ago

    Currently, most computer systems use user IDs and passwords as the login patterns to authenticate users. However, many people share their login patterns with coworkers and request these coworkers to assist co-tasks, thereby making the pattern as one of the weakest points of computer security. Insider attackers, the valid users of a system who attack the system internally, are hard to detect since most intrusion detection systems and firewalls identify and isolate

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    Analyzing Sentiments in One Go: A Supervised Joint Topic Modeling Approach

    • B.TECH / M.TECH / M.SC / PH.D

    •  GANGADHAR.T

    • 12 Months ago

    In this work, we focus on modeling user-generated review and overall rating pairs, and aim to identify semantic aspects and aspect-level sentiments from review data as well as to predict overall sentiments of reviews. We propose a novel probabilistic supervised joint aspect and sentiment model (SJASM) to deal with the problems in one go under a unified framework. SJASM represents each review document in the form of opinion pairs, and can simultaneously model aspect terms and corresponding opinion words of the review for hidden aspect and

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