Trust relationships occur naturally in many diverse contexts such as ecommerce, interpersonal interactions, social networks, sensor web, etc. As agents providing content and services become increasingly removed from t...
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ISBN:
(纸本)9781612846385
Trust relationships occur naturally in many diverse contexts such as ecommerce, interpersonal interactions, social networks, sensor web, etc. As agents providing content and services become increasingly removed from the agents that consume them, the issue of robust trust inference and update become critical. Unfortunately, there is neither a universal notion of trust that is applicable to all domains nor a clear explication of its semantics or computation in many situations. In this beginner's level tutorial, we motivate the trust problem, explain the relevant concepts, summarize research in modeling trust and gleaning trustworthiness, and discuss challenges confronting us in this process.
Running MapReduce programs in the public cloud introduces the important problem: how to optimize resource provisioning to minimize the financial charge for a specific job? In this paper, we study the whole process of ...
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Running MapReduce programs in the public cloud introduces the important problem: how to optimize resource provisioning to minimize the financial charge for a specific job? In this paper, we study the whole process of MapReduce processing and build up a cost function that explicitly models the relationship between the amount of input data, the available system resources (Map and Reduce slots), and the complexity of the Reduce function for the target MapReduce job. The model parameters can be learned from test runs with a small number of nodes. Based on this cost model, we can solve a number of decision problems, such as the optimal amount of resources that can minimize the financial cost with a time deadline or minimize the time under certain financial budget. Experimental results show that this cost model performs well on tested MapReduce programs.
Document management has become an important consideration for the scientific community over the last decade. Human knowledge is central to many scientific domains, thus it is not possible to completely automate the do...
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Document management has become an important consideration for the scientific community over the last decade. Human knowledge is central to many scientific domains, thus it is not possible to completely automate the document management process. Managing scientific documents require a semi-automatic approach to overcome issues of large volume, yet support the human participation in the process. In this paper we present Kino, a set of tools that streamline the document management process in life science domains. Kino is integrated with National center for Biomedical On-to logy (NCBO), providing scientists access to quality domain models. Annotated documents are indexed using a faceted indexing and search engine that provides fine grained search capabilities to the scientists. We present two use cases that highlight the pain points in managing scientific literature and also include an empirical evaluation.
Trust is an amorphous concept that is becoming Increasingly important in many domains, such as P2P networks, E-commerce, social networks, and sensor networks. While we all have an intuitive notion of trust, the litera...
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Trust is an amorphous concept that is becoming Increasingly important in many domains, such as P2P networks, E-commerce, social networks, and sensor networks. While we all have an intuitive notion of trust, the literature is scattered with a wide assortment of differing definitions and descriptions; often these descriptions are highly dependent on a single domain or application of interest. In addition, they often discuss orthogonal aspects of trust while continuing to use the general term “trust”. In order to make sense of the situation, we have developed an ontology of trust that integrates and relates its various aspects into a single model.
Cloud computing has become one of the key considerations both in academia and industry. Cheap, seemingly unlimited computing resources that can be allocated almost instantaneously and pay-as-you-go pricing schemes are...
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Cloud computing has become one of the key considerations both in academia and industry. Cheap, seemingly unlimited computing resources that can be allocated almost instantaneously and pay-as-you-go pricing schemes are some of the reasons for the success of Cloud computing. The Cloud computing landscape, however, is plagued by many issues hindering adoption. One such issue is vendor lock-in, forcing the Cloud users to adhere to one service provider in terms of data and application logic. Semantic Web has been an important research area that has seen significant attention from both academic and industrial researchers. One key property of Semantic Web is the notion of interoperability and portability through high level models. Significant work has been done in the areas of data modeling, matching, and transformations. The issues the Cloud computing community is facing now with respect to portability of data and application logic are exactly the same issue the Semantic Web community has been trying to address for some time. In this paper we present an outline of the use of well established semantic technologies to overcome the vendor lock-in issues in Cloud computing. We present a semantics-centric programming paradigm to create portable Cloud applications and discuss MobiCloud, our early attempt to implement the proposed approach.
The advancements in computing have resulted in a boom of cheap, ubiquitous, connected mobile devices as well as seemingly unlimited, utility style, pay as you go computing resources, commonly referred to as Cloud comp...
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The advancements in computing have resulted in a boom of cheap, ubiquitous, connected mobile devices as well as seemingly unlimited, utility style, pay as you go computing resources, commonly referred to as Cloud computing. However, taking full advantage of this mobile and cloud computing landscape, especially for the data intensive domains has been hampered by the many heterogeneities that exist in the mobile space as well as the Cloud space. Our research focuses on exploiting the capabilities of the mobile and cloud landscape by defining a new class of applications called cloud mobile hybrid (CMH) applications and a Domain Specific Language (DSL) based methodology to develop these applications. We define Cloud-mobile hybrid as a collective application that has a Cloud based back-end and a mobile device front-end. Using a single DSL script, our toolkit is capable of generating a variety of CMH applications. These applications are composed of multiple combinations of native Cloud and mobile applications. Our approach not only reduces the learning curve but also shields developers from the complexities of the target platforms. We provide a detailed description of our language and present the results obtained using our prototype generator implementation. We also present a list of extensions that will enhance the various aspects of this platform.
Applying Cloud computing techniques for analyzing large data sets has shown promise in many data-driven scientific applications. Our approach presented here is to use Cloud computing for Nuclear Magnetic Resonance (NM...
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Applying Cloud computing techniques for analyzing large data sets has shown promise in many data-driven scientific applications. Our approach presented here is to use Cloud computing for Nuclear Magnetic Resonance (NMR)data analysis which normally consists of large amounts of data. Biologists often use third party or commercial software for ease of use. Enabling the capability to use this kind of software in a Cloud will be highly advantageous in many ways. Scripting languages especially designed for clouds may not have the flexibility biologists need for their purposes. Although this is true, they are familiar with special software packages that allow them to write complex calculations with minimum effort, but are often not compatible with a Cloud environment. Therefore, biologists who are trying to perform analysis on NMR data, acquire many advantages due to our proposed solution. Our solution gives them the flexibility to Cloud-enable their familiar software and it also enables them to perform calculations on a significant amount of data that was not previously possible. Our study is also applicable to any other environment in need of similar flexibility. We are currently in the initial stage of developing a framework for NMR data analysis.
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