M Tech Dissertations

Permanent URI for this collectionhttp://ir.daiict.ac.in/handle/123456789/3

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Subject: search.filters.subject.Information storage

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Now showing 1 - 4 of 4
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    Biomedical information retrieval
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2018) Purabia, Pooja R.; Majumder, Prasenjit
    It is well known that the volume of biomedical literature is growing exponentially and that scientists are being overwhelmed when they sift through the scope and diversity of this unstructured knowledge to find relevant information. TREC Precision Medicine 2017 is a track focusing on retrieving relevant scientific abstract and clinical trials from PubMed and Clinicaltrails.gov for cancer patients given their medical case. This report describes the system architecture for the TREC 2017 Precision Medicine Track. I explored query expansion techniques using wellknown broad knowledge sources such as Metamap and Entrez database. I used different pseudo relevance feedback technique like TF-IDF, BO1 and Local Context Analysis to retrieve relevant medical abstracts. I have used hidden aspects of topic like precision medicine and treatment aspect to improve the scores. I report infNDCG, R-Prec and P@10 scores.
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    Document representation using extended locality preserving indexing
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2018) Khalpada, Vaidehi S.; Mitra, Suman K.
    The main purpose of web search is to obtain the relevant information pertaining to our need from the documents available on the Internet. Each term (word) in a document contributes to a dimension. It is challenging to process this high dimensional data. Not all terms convey important meaning, some terms are related to each other, some are synonyms. This redundancy in the document collection increases the dimensionality of the document space. Processing this high dimensional document collection to obtain useful information from it requires a lot of storage space and time for computation. Dimensionality reduction plays an important role here to reduce the data dimension so that computation can be done fast and the storage required is also less. These documents are represented as vectors in high dimensional space. Our main aim is to obtain the representation of documents in this reduced subspace so that the relation among the documents in the subspace does not get changed from the one in original vector space. So, the accuracy of the similarity measure of the documents obtained in the subspace is evaluated. Document representation in terms of term document matrix is an important step in document indexing. Document indexing is the process to obtain an index which helps in retrieving relevant documents effectively, analogous to the index of a book. Latent Semantic Indexing (LSI) is a global structure preserving approach while Locality Preserving Indexing (LPI) is a local structure preserving approach. LPI assigns weights to the neighbours to obtain the reduced representation while preserving local structure. However, it does not retain any information about nonneighbours. A new approach Extended Locality Preserving Indexing (ELPI) is proposed which preserves the topology of the document space by modifying the weighing scheme. Experiments for evaluating document similarity and for classification show small but encouraging improvement using ELPI as compared to LPI.
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    SMS query processing for information retrieval
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2012) Shinghal, Khushboo; Majumder, Prasenjit
    SMS text messaging is one of the fast and popular communication mode on mobile phones these days. This study presents a query processing system for information retrieval system when queries are Short-message-Service (SMS). SMS contains various user improvisation and typographical errors. Proposed approach uses approximate string matching techniques and context extraction to normalize SMS queries with minimum linguistic resources. We have tested the system on FIRE 2011 SMS based FAQ retrieval corpus. Results seems encouraging
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    SQL-GQL inter-query translation for Google App engine datastore
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2012) Kotecha, Shyam; Bhise, Minal
    On demand services, usage based pricing, and scalability features of cloud computing has attracted many customers to move their applications into cloud. But different cloud service providers are using different standards & frameworks to host applications & data. Customers have to follow these standards and frameworks. When customer wants to migrate application and/or data to another cloud service provider, application code and database structure must be modified according to the standard of new cloud service provider. This modification is very costly and as a consequence, changing cloud service provider becomes difficult. This situation is called vendor lock-in in cloud. Focusing on database, complete database migration requires migration of data, database schema, and query. This thesis work concentrates on migration of query. Automation in migration process is achieved by translation algorithms. This thesis work introduces inter-query translation algorithms. These algorithms translate SQL (Structured Query Language) query and GQL (Google Query Language) query into each other. The implementation of these algorithms is demonstrated for MySQL Sakila database