M Tech Dissertations

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

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Subject: search.filters.subject.Information technology

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    ItemOpen Access
    Schema based indexing for namespace mapping of raw sparql and summarization of lod
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2015) Hapani, Hitesh; Jat, P. M.
    Linked open data(LOD) in Semantic Web is growing day by day. There are datasets available that can be used in different application. However, identifying useful dataset from cloud, determining the quality and obtaining inductive information from dataset are all tasks that require to be addressed. The more traffic on LOD increases, the more difficult it will become to identify useful dataset. The reason behind this problem is that there is no useful summary available about datasets. While querying any dataset through endpoint, The most cumbersome part is remembering URIs for resources. There is no known interface that provides URIs for the user terms. There are some standard available for providing summary and metadata about datasets. But till now no standard is available that is universally accepted. Index structure proposed in this thesis gives a schema level information about any dataset and provides URI information for dataset. This index structure has been successfully implemented on local dataset server and remote dataset server in this thesis.
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    ItemOpen Access
    Negotiation for resource allocation on infrastructure as a service cloud
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2010) Akhani, Janki; Divakaran, Srikrishnan
    The Cloud is a computing platform that provides dynamic resource pools, virtualization, and high availability. Cloud computing infrastructures can allow enterprises to achieve more efficient use of their IT hardware and software investments. Infrastructure As A Service (IAAS) cloud providers manage a large set of computing resources. These resources can be provided to cloud consumers on demand in the form of virtual machines. Cloud consumers do not need to manage resources and be worried about the performance issues because they are handled by cloud providers. Open Nebula is an open source cloud toolkit which can be used to setup an IAAS cloud. It has three components: Open Nebula Core, Virtual Machine Scheduler and Cloud Drivers. Haizea is an open-source resource lease manager, and can act as a virtual machine scheduler for Open Nebula or used on its own as a simulator to evaluate the performance of different scheduling strategies. Haizea supports four kinds of resource allocation policies: immediate, best-effort, advance reservation and deadline sensitive. To reserve resources in advance using Haizea, consumer submits parameters like amount of resources, start time and duration of a reservation as a request. If one or more parameters can not be satisfied, then Haizea will reject the request. This method is very rigid method because it does not allow negotiation of any parameter. Consumer can resubmit new requests by modifying previously submitted request parameters. Consumer will not be aware of the current resource allocation on provider side so, the chances of new requests getting rejected are more. Thus, it will increase communication overhead between cloud provider and consumer as well as it will decrease resource utilization on provider’s side. It will also degrade the performance of a provider in managing many incoming requests due to previously rejected ones. To overcome the above problems, negotiation can be provided. Negotiation process consists of three components which are negotiation protocol, negotiation objectives and agents’ decision making algorithm. The proposed algorithm to generate set of counter offers is a part of decision making model at provider side. It provides set of counter offers to consumer when his advance reservation request gets rejected. It provides set of counter offers considering parameters’ flexibilities to maximize the chances of their acceptance. The proposed algorithm for User selection policy is a part of decision making model at consumer side. Consumer can get best suitable offer from set of counter offers using the algorithm of user selection policy. Ranking algorithm is a partof algorithm for user selection policy. Using this ranking algorithm, consumers will get suitable offers sorted according to their needs. It will reduce consumers’ efforts to go through all the provided counter offers and choose best suitable one. These algorithms are implemented in Haizea. Experiments are performed to demonstrate the effectiveness of algorithms. The results show that the proposed algorithm to generate counter offers maximizes resource utilization and acceptance of requests compared to rigid and exact methods.
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    ItemOpen Access
    Use of collaborative filtering for targeted advertising
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2008) Upadhyay, Ankur; Jotwani, Naresh D.
    Often in our daily life, we come across situations when we have many options available and are expected to choose one of them. May it be a bookstore, CD shop or a shopping store, even on the internet, the availability of so many genres and a wide variety among every genre poses difficulty in selection of the item. Recommender systems have been providing suggestions but they are not able to provide us options when we are walking through the aisles of a bookstore or a CD shop. Ideally, recommendations should be made available to the customer without giving explicit command. To provide ease while walking down for shopping in selecting the items based on the item chosen by the customer, the topic focuses on deriving a general model for recommending a product that might save customers money and time along with fulfilling the need. Selection of the product to be advertised by the model is a dynamic decision as it depends on the products kept in the basket. Bayesian approach is used to find the dependencies between items which implements Collaborative filtering and provides real time recommendations on the basis of preferences of earlier customers. The model uses Clustering to limit the complexity of the model that will be built and to aggregate similar items, by grouping customers those who bought items of similar genre. The assumption made is that the selection of the customer is made known to the model in order to process it, to give recommendations; and the recommendations are made known to customers using suitable advertising mechanism.